From afb700e2824788b10ebb8a621f30b47efee4fdba Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Thu, 13 Apr 2023 18:36:39 +0700 Subject: [PATCH 01/55] Add model 2023-04-13-CyberbullyingDetection_ClassifierDL_tfhub_en (#13757) Co-authored-by: Naveen-004 --- ...bullyingDetection_ClassifierDL_tfhub_en.md | 97 +++++++++++++++++++ 1 file changed, 97 insertions(+) create mode 100644 docs/_posts/Naveen-004/2023-04-13-CyberbullyingDetection_ClassifierDL_tfhub_en.md diff --git a/docs/_posts/Naveen-004/2023-04-13-CyberbullyingDetection_ClassifierDL_tfhub_en.md b/docs/_posts/Naveen-004/2023-04-13-CyberbullyingDetection_ClassifierDL_tfhub_en.md new file mode 100644 index 00000000000000..16c67d504a8ff3 --- /dev/null +++ b/docs/_posts/Naveen-004/2023-04-13-CyberbullyingDetection_ClassifierDL_tfhub_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Cyberbullying Detection +author: Naveen-004 +name: CyberbullyingDetection_ClassifierDL_tfhub +date: 2023-04-13 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.0 +spark_version: 3.0 +supported: false +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Identify cyberbullying using a multi-class classification framework that distinguishes six different types of cyberbullying. We have used a Twitter dataset from Kaggle and applied various techniques such as text cleaning, data augmentation, document assembling, universal sentence encoding and tensorflow classification model to process and analyze the data. We have also used snscrape to retrieve tweet data for validating our model’s performance. Our results show that our model achieved an accuracy of 85% for testing data and 89% for training data. + +{:.btn-box} + +[Open in Colab](https://colab.research.google.com/drive/1xaIlDtpiGzf14EA1umhJoOXI0FZaYtRc?authuser=4#scrollTo=os2C1v2WW1Hi){:.button.button-orange.button-orange-trans.co.button-icon} +[Download](https://s3.amazonaws.com/community.johnsnowlabs.com/Naveen-004/CyberbullyingDetection_ClassifierDL_tfhub_en_4.4.0_3.0_1681363209630.zip){:.button.button-orange} +[Copy S3 URI](s3://community.johnsnowlabs.com/Naveen-004/CyberbullyingDetection_ClassifierDL_tfhub_en_4.4.0_3.0_1681363209630.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler()\ + .setInputCol("cleaned_text")\ + .setOutputCol("document") + +use = UniversalSentenceEncoder.pretrained(name="tfhub_use_lg", lang="en")\ + .setInputCols("document")\ + .setOutputCol("sentence_embeddings")\ + .setDimension(768) + +classifierdl = ClassifierDLApproach()\ + .setInputCols(["sentence_embeddings"])\ + .setOutputCol("class")\ + .setLabelColumn("cyberbullying_type")\ + .setBatchSize(16)\ + .setMaxEpochs(42)\ + .setDropout(0.4) \ + .setEnableOutputLogs(True)\ + .setLr(4e-3) +use_clf_pipeline = Pipeline( + stages = [documentAssembler, + use, + classifierdl]) +``` + +
+ +## Results + +```bash + precision recall f1-score support + + age 0.94 0.96 0.95 796 + ethnicity 0.94 0.94 0.94 810 + gender 0.87 0.86 0.86 816 + not_cyberbullying 0.74 0.67 0.70 766 +other_cyberbullying 0.67 0.71 0.69 775 + religion 0.94 0.96 0.95 731 + + accuracy 0.85 4694 + macro avg 0.85 0.85 0.85 4694 + weighted avg 0.85 0.85 0.85 4694 + +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|CyberbullyingDetection_ClassifierDL_tfhub| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.0+| +|License:|Open Source| +|Edition:|Community| +|Language:|en| +|Size:|811.9 MB| + +## Included Models + +- DocumentAssembler +- UniversalSentenceEncoder +- ClassifierDLModel \ No newline at end of file From bb9a155e4b147af1981a9ce428af8a3a38f0ea11 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Thu, 20 Apr 2023 18:07:47 +0700 Subject: [PATCH 02/55] 2023-04-20-distilbert_base_uncased_mnli_en (#13761) * Add model 2023-04-20-distilbert_base_uncased_mnli_en * Add model 2023-04-20-distilbert_base_turkish_cased_allnli_tr * Add model 2023-04-20-distilbert_base_turkish_cased_snli_tr * Add model 2023-04-20-distilbert_base_turkish_cased_multinli_tr * Update and rename 2023-04-20-distilbert_base_turkish_cased_allnli_tr.md to 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr.md * Update and rename 2023-04-20-distilbert_base_turkish_cased_multinli_tr.md to 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md * Update and rename 2023-04-20-distilbert_base_turkish_cased_snli_tr.md to 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md * Update and rename 2023-04-20-distilbert_base_uncased_mnli_en.md to distilbert_base_zero_shot_classifier_turkish_cased_snli * Rename distilbert_base_zero_shot_classifier_turkish_cased_snli to distilbert_base_zero_shot_classifier_turkish_cased_snli_en.md * Update 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md * Update 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md * Update 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr.md --------- Co-authored-by: ahmedlone127 --- ...shot_classifier_turkish_cased_allnli_tr.md | 107 +++++++++++++++++ ...ot_classifier_turkish_cased_multinli_tr.md | 108 ++++++++++++++++++ ...o_shot_classifier_turkish_cased_snli_tr.md | 107 +++++++++++++++++ ...o_shot_classifier_turkish_cased_snli_en.md | 107 +++++++++++++++++ 4 files changed, 429 insertions(+) create mode 100644 docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr.md create mode 100644 docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md create mode 100644 docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md create mode 100644 docs/_posts/ahmedlone127/distilbert_base_zero_shot_classifier_turkish_cased_snli_en.md diff --git a/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr.md b/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr.md new file mode 100644 index 00000000000000..6d378879613f22 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr.md @@ -0,0 +1,107 @@ +--- +layout: model +title: DistilBERTZero-Shot Classification Base - distilbert_base_zero_shot_classifier_turkish_cased_allnli +author: John Snow Labs +name: distilbert_base_zero_shot_classifier_turkish_cased_allnli +date: 2023-04-20 +tags: [zero_shot, distilbert, base, tr, turkish, cased, open_source, tensorflow] +task: Zero-Shot Classification +language: tr +edition: Spark NLP 4.4.1 +spark_version: [3.2, 3.0] +supported: true +engine: tensorflow +annotator: DistilBertForZeroShotClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is intended to be used for zero-shot text classification, especially in Trukish. It is fine-tuned on MNLI by using DistilBERT Base Uncased model. + +DistilBertForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of DistilBertForSequenceClassification models, but these models don’t require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it’s slower but it is much more flexible. + +We used TFDistilBertForSequenceClassification to train this model and used DistilBertForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale! + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_allnli_4.4.1_3.2_1681950583033.zip){:.button.button-orange} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr_4.4.1_3.2_1681950583033.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +document_assembler = DocumentAssembler() \ +.setInputCol('text') \ +.setOutputCol('document') + +tokenizer = Tokenizer() \ +.setInputCols(['document']) \ +.setOutputCol('token') + +zeroShotClassifier = DistilBertForZeroShotClassification \ +.pretrained('distilbert_base_zero_shot_classifier_turkish_cased_allnli', 'en') \ +.setInputCols(['token', 'document']) \ +.setOutputCol('class') \ +.setCaseSensitive(True) \ +.setMaxSentenceLength(512) \ +.setCandidateLabels(["olumsuz", "olumlu"]) + +pipeline = Pipeline(stages=[ +document_assembler, +tokenizer, +zeroShotClassifier +]) + +example = spark.createDataFrame([['Senaryo çok saçmaydı, beğendim diyemem.']]).toDF("text") +result = pipeline.fit(example).transform(example) +``` +```scala +val document_assembler = DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = Tokenizer() +.setInputCols("document") +.setOutputCol("token") + +val zeroShotClassifier = DistilBertForZeroShotClassification.pretrained("distilbert_base_zero_shot_classifier_turkish_cased_allnli", "en") +.setInputCols("document", "token") +.setOutputCol("class") +.setCaseSensitive(true) +.setMaxSentenceLength(512) +.setCandidateLabels(Array("olumsuz", "olumlu")) + +val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier)) + +val example = Seq("Senaryo çok saçmaydı, beğendim diyemem.").toDS.toDF("text") + +val result = pipeline.fit(example).transform(example) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_zero_shot_classifier_turkish_cased_allnli| +|Compatibility:|Spark NLP 4.4.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, document]| +|Output Labels:|[multi_class]| +|Language:|tr| +|Size:|254.3 MB| +|Case sensitive:|true| diff --git a/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md b/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md new file mode 100644 index 00000000000000..eb05ea476bc5a4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md @@ -0,0 +1,108 @@ +--- +layout: model +title: DistilBERTZero-Shot Classification Base - distilbert_base_zero_shot_classifier_turkish_cased_multinli +author: John Snow Labs +name: distilbert_base_zero_shot_classifier_turkish_cased_multinli +date: 2023-04-20 +tags: [zero_shot, tr, turkish, distilbert, base, cased, open_source, tensorflow] +task: Zero-Shot Classification +language: tr +edition: Spark NLP 4.4.1 +spark_version: [3.2, 3.0] +supported: true +engine: tensorflow +annotator: DistilBertForZeroShotClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is intended to be used for zero-shot text classification, especially in Trukish. It is fine-tuned on MNLI by using DistilBERT Base Uncased model. + +DistilBertForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of DistilBertForSequenceClassification models, but these models don’t require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it’s slower but it is much more flexible. + +We used TFDistilBertForSequenceClassification to train this model and used DistilBertForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale! + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr_4.4.1_3.2_1681952299918.zip){:.button.button-orange} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr_4.4.1_3.2_1681952299918.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +document_assembler = DocumentAssembler() \ +.setInputCol('text') \ +.setOutputCol('document') + +tokenizer = Tokenizer() \ +.setInputCols(['document']) \ +.setOutputCol('token') + +zeroShotClassifier = DistilBertForZeroShotClassification \ +.pretrained('distilbert_base_zero_shot_classifier_turkish_cased_multinli', 'en') \ +.setInputCols(['token', 'document']) \ +.setOutputCol('class') \ +.setCaseSensitive(True) \ +.setMaxSentenceLength(512) \ +.setCandidateLabels(["ekonomi", "siyaset","spor"]) + +pipeline = Pipeline(stages=[ +document_assembler, +tokenizer, +zeroShotClassifier +]) + +example = spark.createDataFrame([['Dolar yükselmeye devam ediyor.']]).toDF("text") +result = pipeline.fit(example).transform(example) + +``` +```scala +val document_assembler = DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = Tokenizer() +.setInputCols("document") +.setOutputCol("token") + +val zeroShotClassifier = DistilBertForZeroShotClassification.pretrained("distilbert_base_zero_shot_classifier_turkish_cased_multinli", "en") +.setInputCols("document", "token") +.setOutputCol("class") +.setCaseSensitive(true) +.setMaxSentenceLength(512) +.setCandidateLabels(Array("ekonomi", "siyaset","spor")) + +val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier)) + +val example = Seq("Dolar yükselmeye devam ediyor.").toDS.toDF("text") + +val result = pipeline.fit(example).transform(example) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_zero_shot_classifier_turkish_cased_multinli| +|Compatibility:|Spark NLP 4.4.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, document]| +|Output Labels:|[multi_class]| +|Language:|tr| +|Size:|254.3 MB| +|Case sensitive:|true| diff --git a/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md b/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md new file mode 100644 index 00000000000000..63840286509e53 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md @@ -0,0 +1,107 @@ +--- +layout: model +title: DistilBERTZero-Shot Classification Base - distilbert_base_zero_shot_classifier_turkish_cased_snli +author: John Snow Labs +name: distilbert_base_zero_shot_classifier_turkish_cased_snli +date: 2023-04-20 +tags: [zero_shot, tr, turkish, distilbert, base, cased, open_source, tensorflow] +task: Zero-Shot Classification +language: tr +edition: Spark NLP 4.4.1 +spark_version: [3.2, 3.0] +supported: true +engine: tensorflow +annotator: DistilBertForZeroShotClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is intended to be used for zero-shot text classification, especially in Trukish. It is fine-tuned on MNLI by using DistilBERT Base Uncased model. + +DistilBertForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of DistilBertForSequenceClassification models, but these models don’t require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it’s slower but it is much more flexible. + +We used TFDistilBertForSequenceClassification to train this model and used DistilBertForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale! + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_snli_tr_4.4.1_3.2_1681951486863.zip){:.button.button-orange} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_snli_tr_4.4.1_3.2_1681951486863.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +document_assembler = DocumentAssembler() \ +.setInputCol('text') \ +.setOutputCol('document') + +tokenizer = Tokenizer() \ +.setInputCols(['document']) \ +.setOutputCol('token') + +zeroShotClassifier = DistilBertForZeroShotClassification \ +.pretrained('distilbert_base_zero_shot_classifier_turkish_cased_snli', 'en') \ +.setInputCols(['token', 'document']) \ +.setOutputCol('class') \ +.setCaseSensitive(True) \ +.setMaxSentenceLength(512) \ +.setCandidateLabels(["olumsuz", "olumlu"]) + +pipeline = Pipeline(stages=[ +document_assembler, +tokenizer, +zeroShotClassifier +]) + +example = spark.createDataFrame([['Senaryo çok saçmaydı, beğendim diyemem.']]).toDF("text") +result = pipeline.fit(example).transform(example) +``` +```scala +val document_assembler = DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = Tokenizer() +.setInputCols("document") +.setOutputCol("token") + +val zeroShotClassifier = DistilBertForZeroShotClassification.pretrained("distilbert_base_zero_shot_classifier_turkish_cased_snli", "en") +.setInputCols("document", "token") +.setOutputCol("class") +.setCaseSensitive(true) +.setMaxSentenceLength(512) +.setCandidateLabels(Array("olumsuz", "olumlu")) + +val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier)) + +val example = Seq("Senaryo çok saçmaydı, beğendim diyemem.").toDS.toDF("text") + +val result = pipeline.fit(example).transform(example) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_zero_shot_classifier_turkish_cased_snli| +|Compatibility:|Spark NLP 4.4.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, document]| +|Output Labels:|[multi_class]| +|Language:|tr| +|Size:|254.3 MB| +|Case sensitive:|true| diff --git a/docs/_posts/ahmedlone127/distilbert_base_zero_shot_classifier_turkish_cased_snli_en.md b/docs/_posts/ahmedlone127/distilbert_base_zero_shot_classifier_turkish_cased_snli_en.md new file mode 100644 index 00000000000000..10122f3d1afd6d --- /dev/null +++ b/docs/_posts/ahmedlone127/distilbert_base_zero_shot_classifier_turkish_cased_snli_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: DistilBERTZero-Shot Classification Base - MNLI(distilbert_base_zero_shot_classifier_uncased_mnli) +author: John Snow Labs +name: distilbert_base_zero_shot_classifier_uncased_mnli +date: 2023-04-20 +tags: [zero_shot, mnli, distilbert, base, english, en, oepn_source, open_source, tensorflow] +task: Zero-Shot Classification +language: en +edition: Spark NLP 4.4.1 +spark_version: [3.2, 3.0] +supported: true +engine: tensorflow +annotator: DistilBertForZeroShotClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is intended to be used for zero-shot text classification, especially in English. It is fine-tuned on MNLI by using DistilBERT Base Uncased model. + +DistilBertForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of DistilBertForSequenceClassification models, but these models don’t require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it’s slower but it is much more flexible. + +We used TFDistilBertForSequenceClassification to train this model and used DistilBertForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale! + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_uncased_mnli_en_4.4.1_3.2_1681949033641.zip){:.button.button-orange} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_uncased_mnli_en_4.4.1_3.2_1681949033641.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +document_assembler = DocumentAssembler() \ +.setInputCol('text') \ +.setOutputCol('document') + +tokenizer = Tokenizer() \ +.setInputCols(['document']) \ +.setOutputCol('token') + +zeroShotClassifier = DistilBertForZeroShotClassification \ +.pretrained('distilbert_base_zero_shot_classifier_uncased_mnli', 'en') \ +.setInputCols(['token', 'document']) \ +.setOutputCol('class') \ +.setCaseSensitive(True) \ +.setMaxSentenceLength(512) \ +.setCandidateLabels(["urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"]) + +pipeline = Pipeline(stages=[ +document_assembler, +tokenizer, +zeroShotClassifier +]) + +example = spark.createDataFrame([['I have a problem with my iphone that needs to be resolved asap!!']]).toDF("text") +result = pipeline.fit(example).transform(example) +``` +```scala +val document_assembler = DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = Tokenizer() +.setInputCols("document") +.setOutputCol("token") + +val zeroShotClassifier = DistilBertForZeroShotClassification.pretrained("distilbert_base_zero_shot_classifier_uncased_mnli", "en") +.setInputCols("document", "token") +.setOutputCol("class") +.setCaseSensitive(true) +.setMaxSentenceLength(512) +.setCandidateLabels(Array("urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology")) + +val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier)) + +val example = Seq("I have a problem with my iphone that needs to be resolved asap!!").toDS.toDF("text") + +val result = pipeline.fit(example).transform(example) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_zero_shot_classifier_uncased_mnli| +|Compatibility:|Spark NLP 4.4.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, document]| +|Output Labels:|[multi_class]| +|Language:|en| +|Size:|249.7 MB| +|Case sensitive:|true| From ea0ba05d84b2079ccf79f9f6a79596f2f5358429 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Fri, 21 Apr 2023 14:35:34 +0700 Subject: [PATCH 03/55] 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr (#13763) * Add model 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr * Add model 2023-04-20-distilbert_base_zero_shot_classifier_uncased_mnli_en * Add model 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr * Add model 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr * Update 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md * Update 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md --------- Co-authored-by: ahmedlone127 --- ...shot_classifier_turkish_cased_allnli_tr.md | 11 +- ...ot_classifier_turkish_cased_multinli_tr.md | 9 +- ...o_shot_classifier_turkish_cased_snli_tr.md | 10 +- ...se_zero_shot_classifier_uncased_mnli_en.md | 105 ++++++++++++++++++ 4 files changed, 115 insertions(+), 20 deletions(-) create mode 100644 docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_uncased_mnli_en.md diff --git a/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr.md b/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr.md index 6d378879613f22..a9f97daf26b748 100644 --- a/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr.md +++ b/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr.md @@ -4,7 +4,7 @@ title: DistilBERTZero-Shot Classification Base - distilbert_base_zero_shot_class author: John Snow Labs name: distilbert_base_zero_shot_classifier_turkish_cased_allnli date: 2023-04-20 -tags: [zero_shot, distilbert, base, tr, turkish, cased, open_source, tensorflow] +tags: [distilbert, zero_shot, turkish, tr, base, open_source, tensorflow] task: Zero-Shot Classification language: tr edition: Spark NLP 4.4.1 @@ -32,8 +32,8 @@ We used TFDistilBertForSequenceClassification to train this model and used Disti {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_allnli_4.4.1_3.2_1681950583033.zip){:.button.button-orange} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr_4.4.1_3.2_1681950583033.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr_4.4.1_3.2_1682016415236.zip){:.button.button-orange} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr_4.4.1_3.2_1682016415236.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use @@ -63,7 +63,6 @@ document_assembler, tokenizer, zeroShotClassifier ]) - example = spark.createDataFrame([['Senaryo çok saçmaydı, beğendim diyemem.']]).toDF("text") result = pipeline.fit(example).transform(example) ``` @@ -84,9 +83,7 @@ val zeroShotClassifier = DistilBertForZeroShotClassification.pretrained("distilb .setCandidateLabels(Array("olumsuz", "olumlu")) val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier)) - val example = Seq("Senaryo çok saçmaydı, beğendim diyemem.").toDS.toDF("text") - val result = pipeline.fit(example).transform(example) ``` @@ -104,4 +101,4 @@ val result = pipeline.fit(example).transform(example) |Output Labels:|[multi_class]| |Language:|tr| |Size:|254.3 MB| -|Case sensitive:|true| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md b/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md index eb05ea476bc5a4..2395f728406d60 100644 --- a/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md +++ b/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md @@ -32,8 +32,8 @@ We used TFDistilBertForSequenceClassification to train this model and used Disti {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr_4.4.1_3.2_1681952299918.zip){:.button.button-orange} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr_4.4.1_3.2_1681952299918.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr_4.4.1_3.2_1682014879417.zip){:.button.button-orange} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr_4.4.1_3.2_1682014879417.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use @@ -45,7 +45,6 @@ We used TFDistilBertForSequenceClassification to train this model and used Disti document_assembler = DocumentAssembler() \ .setInputCol('text') \ .setOutputCol('document') - tokenizer = Tokenizer() \ .setInputCols(['document']) \ .setOutputCol('token') @@ -63,10 +62,8 @@ document_assembler, tokenizer, zeroShotClassifier ]) - example = spark.createDataFrame([['Dolar yükselmeye devam ediyor.']]).toDF("text") result = pipeline.fit(example).transform(example) - ``` ```scala val document_assembler = DocumentAssembler() @@ -85,9 +82,7 @@ val zeroShotClassifier = DistilBertForZeroShotClassification.pretrained("distilb .setCandidateLabels(Array("ekonomi", "siyaset","spor")) val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier)) - val example = Seq("Dolar yükselmeye devam ediyor.").toDS.toDF("text") - val result = pipeline.fit(example).transform(example) ``` diff --git a/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md b/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md index 63840286509e53..4e98ec4735f69a 100644 --- a/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md +++ b/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md @@ -32,8 +32,8 @@ We used TFDistilBertForSequenceClassification to train this model and used Disti {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_snli_tr_4.4.1_3.2_1681951486863.zip){:.button.button-orange} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_snli_tr_4.4.1_3.2_1681951486863.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_snli_tr_4.4.1_3.2_1682015986268.zip){:.button.button-orange} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_snli_tr_4.4.1_3.2_1682015986268.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use @@ -63,7 +63,6 @@ document_assembler, tokenizer, zeroShotClassifier ]) - example = spark.createDataFrame([['Senaryo çok saçmaydı, beğendim diyemem.']]).toDF("text") result = pipeline.fit(example).transform(example) ``` @@ -75,8 +74,9 @@ val document_assembler = DocumentAssembler() val tokenizer = Tokenizer() .setInputCols("document") .setOutputCol("token") +val zeroShotClassifier = -val zeroShotClassifier = DistilBertForZeroShotClassification.pretrained("distilbert_base_zero_shot_classifier_turkish_cased_snli", "en") +DistilBertForZeroShotClassification.pretrained("distilbert_base_zero_shot_classifier_turkish_cased_snli", "en") .setInputCols("document", "token") .setOutputCol("class") .setCaseSensitive(true) @@ -84,9 +84,7 @@ val zeroShotClassifier = DistilBertForZeroShotClassification.pretrained("distilb .setCandidateLabels(Array("olumsuz", "olumlu")) val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier)) - val example = Seq("Senaryo çok saçmaydı, beğendim diyemem.").toDS.toDF("text") - val result = pipeline.fit(example).transform(example) ``` diff --git a/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_uncased_mnli_en.md b/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_uncased_mnli_en.md new file mode 100644 index 00000000000000..5caaaf1e7bdd2c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_uncased_mnli_en.md @@ -0,0 +1,105 @@ +--- +layout: model +title: DistilBERTZero-Shot Classification Base - MNLI(distilbert_base_zero_shot_classifier_uncased_mnli) +author: John Snow Labs +name: distilbert_base_zero_shot_classifier_uncased_mnli +date: 2023-04-20 +tags: [zero_shot, en, mnli, distilbert, english, base, open_source, tensorflow] +task: Zero-Shot Classification +language: en +edition: Spark NLP 4.4.1 +spark_version: [3.2, 3.0] +supported: true +engine: tensorflow +annotator: DistilBertForZeroShotClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is intended to be used for zero-shot text classification, especially in English. It is fine-tuned on MNLI by using DistilBERT Base Uncased model. + +DistilBertForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of DistilBertForSequenceClassification models, but these models don’t require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it’s slower but it is much more flexible. + +We used TFDistilBertForSequenceClassification to train this model and used DistilBertForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale! + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_uncased_mnli_en_4.4.1_3.2_1682015669457.zip){:.button.button-orange} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_uncased_mnli_en_4.4.1_3.2_1682015669457.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +document_assembler = DocumentAssembler() \ +.setInputCol('text') \ +.setOutputCol('document') + +tokenizer = Tokenizer() \ +.setInputCols(['document']) \ +.setOutputCol('token') + +zeroShotClassifier = DistilBertForZeroShotClassification \ +.pretrained('distilbert_base_zero_shot_classifier_uncased_mnli', 'en') \ +.setInputCols(['token', 'document']) \ +.setOutputCol('class') \ +.setCaseSensitive(True) \ +.setMaxSentenceLength(512) \ +.setCandidateLabels(["urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"]) + +pipeline = Pipeline(stages=[ +document_assembler, +tokenizer, +zeroShotClassifier +]) + +example = spark.createDataFrame([['I have a problem with my iphone that needs to be resolved asap!!']]).toDF("text") +result = pipeline.fit(example).transform(example) +``` +```scala +val document_assembler = DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = Tokenizer() +.setInputCols("document") +.setOutputCol("token") + +val zeroShotClassifier = DistilBertForZeroShotClassification.pretrained("distilbert_base_zero_shot_classifier_uncased_mnli", "en") +.setInputCols("document", "token") +.setOutputCol("class") +.setCaseSensitive(true) +.setMaxSentenceLength(512) +.setCandidateLabels(Array("urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology")) + +val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier)) +val example = Seq("I have a problem with my iphone that needs to be resolved asap!!").toDS.toDF("text") +val result = pipeline.fit(example).transform(example) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_zero_shot_classifier_uncased_mnli| +|Compatibility:|Spark NLP 4.4.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, document]| +|Output Labels:|[multi_class]| +|Language:|en| +|Size:|249.7 MB| +|Case sensitive:|true| \ No newline at end of file From 9afffb17cd7f3e2574b254390970fc781b0c5cf3 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Fri, 5 May 2023 02:52:46 +0700 Subject: [PATCH 04/55] 2023-05-04-roberta_base_zero_shot_classifier_nli_en (#13781) * Add model 2023-05-04-roberta_base_zero_shot_classifier_nli_en * Fix Spark version to 3.0 --------- Co-authored-by: ahmedlone127 Co-authored-by: Maziyar Panahi --- ...oberta_base_zero_shot_classifier_nli_en.md | 105 ++++++++++++++++++ 1 file changed, 105 insertions(+) create mode 100644 docs/_posts/ahmedlone127/2023-05-04-roberta_base_zero_shot_classifier_nli_en.md diff --git a/docs/_posts/ahmedlone127/2023-05-04-roberta_base_zero_shot_classifier_nli_en.md b/docs/_posts/ahmedlone127/2023-05-04-roberta_base_zero_shot_classifier_nli_en.md new file mode 100644 index 00000000000000..ecaf168689e32a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-04-roberta_base_zero_shot_classifier_nli_en.md @@ -0,0 +1,105 @@ +--- +layout: model +title: RoBertaZero-Shot Classification Base roberta_base_zero_shot_classifier_nli +author: John Snow Labs +name: roberta_base_zero_shot_classifier_nli +date: 2023-05-04 +tags: [en, open_source, tensorflow] +task: Zero-Shot Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: [3.0] +supported: true +engine: tensorflow +annotator: RoBertaForZeroShotClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is intended to be used for zero-shot text classification, especially in English. It is fine-tuned on NLI by using Roberta Base model. + +RoBertaForZeroShotClassificationusing a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of RoBertaForZeroShotClassification models, but these models don’t require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it’s slower but it is much more flexible. + +We used TFRobertaForSequenceClassification to train this model and used RoBertaForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale! + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_base_zero_shot_classifier_nli_en_4.4.2_3.0_1683228241365.zip){:.button.button-orange} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_base_zero_shot_classifier_nli_en_4.4.2_3.0_1683228241365.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +document_assembler = DocumentAssembler() \ +.setInputCol('text') \ +.setOutputCol('document') + +tokenizer = Tokenizer() \ +.setInputCols(['document']) \ +.setOutputCol('token') + +zeroShotClassifier = RobertaForSequenceClassification \ +.pretrained('roberta_base_zero_shot_classifier_nli', 'en') \ +.setInputCols(['token', 'document']) \ +.setOutputCol('class') \ +.setCaseSensitive(True) \ +.setMaxSentenceLength(512) \ +.setCandidateLabels(["urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"]) + +pipeline = Pipeline(stages=[ +document_assembler, +tokenizer, +zeroShotClassifier +]) + +example = spark.createDataFrame([['I have a problem with my iphone that needs to be resolved asap!!']]).toDF("text") +result = pipeline.fit(example).transform(example) +``` +```scala +val document_assembler = DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = Tokenizer() +.setInputCols("document") +.setOutputCol("token") + +val zeroShotClassifier = RobertaForSequenceClassification.pretrained("roberta_base_zero_shot_classifier_nli", "en") +.setInputCols("document", "token") +.setOutputCol("class") +.setCaseSensitive(true) +.setMaxSentenceLength(512) +.setCandidateLabels(Array("urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology")) + +val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier)) +val example = Seq("I have a problem with my iphone that needs to be resolved asap!!").toDS.toDF("text") +val result = pipeline.fit(example).transform(example) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_base_zero_shot_classifier_nli| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, document]| +|Output Labels:|[multi_class]| +|Language:|en| +|Size:|466.4 MB| +|Case sensitive:|true| From f4356e55313f4152c91ce887e3ba381bb6885a6c Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Wed, 10 May 2023 16:11:39 +0700 Subject: [PATCH 05/55] 2023-05-09-distilbart_xsum_6_6_en (#13788) * Add model 2023-05-09-distilbart_xsum_6_6_en * Add model 2023-05-09-distilbart_xsum_12_6_en * Add model 2023-05-09-distilbart_cnn_12_6_en * Add model 2023-05-09-distilbart_cnn_6_6_en * Add model 2023-05-09-bart_large_cnn_en * Update 2023-05-09-bart_large_cnn_en.md * Update 2023-05-09-distilbart_cnn_12_6_en.md * Update 2023-05-09-distilbart_cnn_6_6_en.md * Update 2023-05-09-distilbart_xsum_12_6_en.md * Update 2023-05-09-distilbart_xsum_6_6_en.md --------- Co-authored-by: prabod Co-authored-by: Maziyar Panahi --- .../prabod/2023-05-09-bart_large_cnn_en.md | 74 +++++++++++++++ .../2023-05-09-distilbart_cnn_12_6_en.md | 86 ++++++++++++++++++ .../2023-05-09-distilbart_cnn_6_6_en.md | 86 ++++++++++++++++++ .../2023-05-09-distilbart_xsum_12_6_en.md | 91 +++++++++++++++++++ .../2023-05-09-distilbart_xsum_6_6_en.md | 91 +++++++++++++++++++ 5 files changed, 428 insertions(+) create mode 100644 docs/_posts/prabod/2023-05-09-bart_large_cnn_en.md create mode 100644 docs/_posts/prabod/2023-05-09-distilbart_cnn_12_6_en.md create mode 100644 docs/_posts/prabod/2023-05-09-distilbart_cnn_6_6_en.md create mode 100644 docs/_posts/prabod/2023-05-09-distilbart_xsum_12_6_en.md create mode 100644 docs/_posts/prabod/2023-05-09-distilbart_xsum_6_6_en.md diff --git a/docs/_posts/prabod/2023-05-09-bart_large_cnn_en.md b/docs/_posts/prabod/2023-05-09-bart_large_cnn_en.md new file mode 100644 index 00000000000000..f9149776064957 --- /dev/null +++ b/docs/_posts/prabod/2023-05-09-bart_large_cnn_en.md @@ -0,0 +1,74 @@ +--- +layout: model +title: BART (large-sized model), fine-tuned on CNN Daily Mail +author: John Snow Labs +name: bart_large_cnn +date: 2023-05-09 +tags: [bart, summarization, cnn, text_to_text, en, open_source, tensorflow] +task: Summarization +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: BartTransformer +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +BART model pre-trained on English language, and fine-tuned on [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail). It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository (https://github.com/pytorch/fairseq/tree/master/examples/bart). + +Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team. + +### Model description + +BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. + +BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). This particular checkpoint has been fine-tuned on CNN Daily Mail, a large collection of text-summary pairs. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bart_large_cnn_en_4.4.2_3.0_1683645394389.zip){:.button.button-orange} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bart_large_cnn_en_4.4.2_3.0_1683645394389.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +You can use this model for text summarization. + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +bart = BartTransformer.pretrained("bart_large_cnn") \ + .setTask("summarize:") \ + .setMaxOutputLength(200) \ + .setInputCols(["documents"]) \ + .setOutputCol("summaries") +``` +```scala +val bart = BartTransformer.pretrained("bart_large_cnn") + .setTask("summarize:") + .setMaxOutputLength(200) + .setInputCols("documents") + .setOutputCol("summaries") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bart_large_cnn| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|975.3 MB| diff --git a/docs/_posts/prabod/2023-05-09-distilbart_cnn_12_6_en.md b/docs/_posts/prabod/2023-05-09-distilbart_cnn_12_6_en.md new file mode 100644 index 00000000000000..0a599fc1641ce0 --- /dev/null +++ b/docs/_posts/prabod/2023-05-09-distilbart_cnn_12_6_en.md @@ -0,0 +1,86 @@ +--- +layout: model +title: Abstractive Summarization by BART - DistilBART CNN +author: John Snow Labs +name: distilbart_cnn_12_6 +date: 2023-05-09 +tags: [bart, summarization, cnn, distill, text_to_text, en, open_source, tensorflow] +task: Summarization +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: BartTransformer +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension Transformer" The Facebook BART (Bidirectional and Auto-Regressive Transformer) model is a state-of-the-art language generation model that was introduced by Facebook AI in 2019. It is based on the transformer architecture and is designed to handle a wide range of natural language processing tasks such as text generation, summarization, and machine translation. + +This pre-trained model is DistilBART fine-tuned on the Extreme Summarization (CNN) Dataset. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbart_cnn_12_6_en_4.4.2_3.0_1683644937231.zip){:.button.button-orange} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbart_cnn_12_6_en_4.4.2_3.0_1683644937231.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +bart = BartTransformer.pretrained("distilbart_cnn_12_6") \ + .setTask("summarize:") \ + .setMaxOutputLength(200) \ + .setInputCols(["documents"]) \ + .setOutputCol("summaries") +``` +```scala +val bart = BartTransformer.pretrained("distilbart_cnn_12_6") + .setTask("summarize:") + .setMaxOutputLength(200) + .setInputCols("documents") + .setOutputCol("summaries") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbart_cnn_12_6| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|870.4 MB| + +## Benchmarking + +```bash +### Metrics for DistilBART models +| Model Name | MM Params | Inference Time (MS) | Speedup | Rouge 2 | Rouge-L | +|:---------------------------|------------:|----------------------:|----------:|----------:|----------:| +| distilbart-xsum-12-1 | 222 | 90 | 2.54 | 18.31 | 33.37 | +| distilbart-xsum-6-6 | 230 | 132 | 1.73 | 20.92 | 35.73 | +| distilbart-xsum-12-3 | 255 | 106 | 2.16 | 21.37 | 36.39 | +| distilbart-xsum-9-6 | 268 | 136 | 1.68 | 21.72 | 36.61 | +| bart-large-xsum (baseline) | 406 | 229 | 1 | 21.85 | 36.50 | +| distilbart-xsum-12-6 | 306 | 137 | 1.68 | 22.12 | 36.99 | +| bart-large-cnn (baseline) | 406 | 381 | 1 | 21.06 | 30.63 | +| distilbart-12-3-cnn | 255 | 214 | 1.78 | 20.57 | 30.00 | +| distilbart-12-6-cnn | 306 | 307 | 1.24 | 21.26 | 30.59 | +| distilbart-6-6-cnn | 230 | 182 | 2.09 | 20.17 | 29.70 | +``` diff --git a/docs/_posts/prabod/2023-05-09-distilbart_cnn_6_6_en.md b/docs/_posts/prabod/2023-05-09-distilbart_cnn_6_6_en.md new file mode 100644 index 00000000000000..ae0615adc6fde6 --- /dev/null +++ b/docs/_posts/prabod/2023-05-09-distilbart_cnn_6_6_en.md @@ -0,0 +1,86 @@ +--- +layout: model +title: Abstractive Summarization by BART - DistilBART CNN +author: John Snow Labs +name: distilbart_cnn_6_6 +date: 2023-05-09 +tags: [bart, summarization, cnn, distil, text_to_text, en, open_source, tensorflow] +task: Summarization +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: BartTransformer +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension Transformer" The Facebook BART (Bidirectional and Auto-Regressive Transformer) model is a state-of-the-art language generation model that was introduced by Facebook AI in 2019. It is based on the transformer architecture and is designed to handle a wide range of natural language processing tasks such as text generation, summarization, and machine translation. + +This pre-trained model is DistilBART fine-tuned on the Extreme Summarization (CNN) Dataset. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbart_cnn_6_6_en_4.4.2_3.0_1683645206157.zip){:.button.button-orange} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbart_cnn_6_6_en_4.4.2_3.0_1683645206157.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +bart = BartTransformer.pretrained("distilbart_cnn_6_6") \ + .setTask("summarize:") \ + .setMaxOutputLength(200) \ + .setInputCols(["documents"]) \ + .setOutputCol("summaries") +``` +```scala +val bart = BartTransformer.pretrained("distilbart_cnn_6_6") + .setTask("summarize:") + .setMaxOutputLength(200) + .setInputCols("documents") + .setOutputCol("summaries") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbart_cnn_6_6| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|551.9 MB| + +## Benchmarking + +```bash +### Metrics for DistilBART models +| Model Name | MM Params | Inference Time (MS) | Speedup | Rouge 2 | Rouge-L | +|:---------------------------|------------:|----------------------:|----------:|----------:|----------:| +| distilbart-xsum-12-1 | 222 | 90 | 2.54 | 18.31 | 33.37 | +| distilbart-xsum-6-6 | 230 | 132 | 1.73 | 20.92 | 35.73 | +| distilbart-xsum-12-3 | 255 | 106 | 2.16 | 21.37 | 36.39 | +| distilbart-xsum-9-6 | 268 | 136 | 1.68 | 21.72 | 36.61 | +| bart-large-xsum (baseline) | 406 | 229 | 1 | 21.85 | 36.50 | +| distilbart-xsum-12-6 | 306 | 137 | 1.68 | 22.12 | 36.99 | +| bart-large-cnn (baseline) | 406 | 381 | 1 | 21.06 | 30.63 | +| distilbart-12-3-cnn | 255 | 214 | 1.78 | 20.57 | 30.00 | +| distilbart-12-6-cnn | 306 | 307 | 1.24 | 21.26 | 30.59 | +| distilbart-6-6-cnn | 230 | 182 | 2.09 | 20.17 | 29.70 | +``` diff --git a/docs/_posts/prabod/2023-05-09-distilbart_xsum_12_6_en.md b/docs/_posts/prabod/2023-05-09-distilbart_xsum_12_6_en.md new file mode 100644 index 00000000000000..be9d7160d34993 --- /dev/null +++ b/docs/_posts/prabod/2023-05-09-distilbart_xsum_12_6_en.md @@ -0,0 +1,91 @@ +--- +layout: model +title: Abstractive Summarization by BART - DistilBART XSUM +author: John Snow Labs +name: distilbart_xsum_12_6 +date: 2023-05-09 +tags: [bart, summarization, text_to_text, xsum, distil, en, open_source, tensorflow] +task: Summarization +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: BartTransformer +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension Transformer" The Facebook BART (Bidirectional and Auto-Regressive Transformer) model is a state-of-the-art language generation model that was introduced by Facebook AI in 2019. It is based on the transformer architecture and is designed to handle a wide range of natural language processing tasks such as text generation, summarization, and machine translation. + +This pre-trained model is DistilBART fine-tuned on the Extreme Summarization (XSum) Dataset. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbart_xsum_12_6_en_4.4.2_3.0_1683644681912.zip){:.button.button-orange} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbart_xsum_12_6_en_4.4.2_3.0_1683644681912.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +bart = BartTransformer.pretrained("distilbart_xsum_12_6") \ + .setTask("summarize:") \ + .setMaxOutputLength(200) \ + .setInputCols(["documents"]) \ + .setOutputCol("summaries") +``` +```scala +val bart = BartTransformer.pretrained("distilbart_xsum_12_6") + .setTask("summarize:") + .setMaxOutputLength(200) + .setInputCols("documents") + .setOutputCol("summaries") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbart_xsum_12_6| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|733.7 MB| + +## References + +https://huggingface.co/sshleifer/distilbart-xsum-12-6 + +## Benchmarking + +```bash +### Metrics for DistilBART models + +| Model Name | MM Params | Inference Time (MS) | Speedup | Rouge 2 | Rouge-L | +|:---------------------------|------------:|----------------------:|----------:|----------:|----------:| +| distilbart-xsum-12-1 | 222 | 90 | 2.54 | 18.31 | 33.37 | +| distilbart-xsum-6-6 | 230 | 132 | 1.73 | 20.92 | 35.73 | +| distilbart-xsum-12-3 | 255 | 106 | 2.16 | 21.37 | 36.39 | +| distilbart-xsum-9-6 | 268 | 136 | 1.68 | 21.72 | 36.61 | +| bart-large-xsum (baseline) | 406 | 229 | 1 | 21.85 | 36.50 | +| distilbart-xsum-12-6 | 306 | 137 | 1.68 | 22.12 | 36.99 | +| bart-large-cnn (baseline) | 406 | 381 | 1 | 21.06 | 30.63 | +| distilbart-12-3-cnn | 255 | 214 | 1.78 | 20.57 | 30.00 | +| distilbart-12-6-cnn | 306 | 307 | 1.24 | 21.26 | 30.59 | +| distilbart-6-6-cnn | 230 | 182 | 2.09 | 20.17 | 29.70 | +``` diff --git a/docs/_posts/prabod/2023-05-09-distilbart_xsum_6_6_en.md b/docs/_posts/prabod/2023-05-09-distilbart_xsum_6_6_en.md new file mode 100644 index 00000000000000..c74568eb8462d5 --- /dev/null +++ b/docs/_posts/prabod/2023-05-09-distilbart_xsum_6_6_en.md @@ -0,0 +1,91 @@ +--- +layout: model +title: Abstractive Summarization by BART - DistilBART XSUM +author: John Snow Labs +name: distilbart_xsum_6_6 +date: 2023-05-09 +tags: [bart, summarization, xsum, distil, text_to_text, en, open_source, tensorflow] +task: Summarization +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: BartTransformer +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension Transformer" The Facebook BART (Bidirectional and Auto-Regressive Transformer) model is a state-of-the-art language generation model that was introduced by Facebook AI in 2019. It is based on the transformer architecture and is designed to handle a wide range of natural language processing tasks such as text generation, summarization, and machine translation. + +This pre-trained model is DistilBART fine-tuned on the Extreme Summarization (XSum) Dataset. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbart_xsum_6_6_en_4.4.2_3.0_1683644259136.zip){:.button.button-orange} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbart_xsum_6_6_en_4.4.2_3.0_1683644259136.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +bart = BartTransformer.pretrained("distilbart_xsum_6_6") \ + .setTask("summarize:") \ + .setMaxOutputLength(200) \ + .setInputCols(["documents"]) \ + .setOutputCol("summaries") +``` +```scala +val bart = BartTransformer.pretrained("distilbart_xsum_6_6") + .setTask("summarize:") + .setMaxOutputLength(200) + .setInputCols("documents") + .setOutputCol("summaries") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbart_xsum_6_6| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|551.7 MB| + +## References + +https://huggingface.co/sshleifer/distilbart-xsum-6-6 + +## Benchmarking + +```bash +### Metrics for DistilBART models + +| Model Name | MM Params | Inference Time (MS) | Speedup | Rouge 2 | Rouge-L | +|:---------------------------|------------:|----------------------:|----------:|----------:|----------:| +| distilbart-xsum-12-1 | 222 | 90 | 2.54 | 18.31 | 33.37 | +| distilbart-xsum-6-6 | 230 | 132 | 1.73 | 20.92 | 35.73 | +| distilbart-xsum-12-3 | 255 | 106 | 2.16 | 21.37 | 36.39 | +| distilbart-xsum-9-6 | 268 | 136 | 1.68 | 21.72 | 36.61 | +| bart-large-xsum (baseline) | 406 | 229 | 1 | 21.85 | 36.50 | +| distilbart-xsum-12-6 | 306 | 137 | 1.68 | 22.12 | 36.99 | +| bart-large-cnn (baseline) | 406 | 381 | 1 | 21.06 | 30.63 | +| distilbart-12-3-cnn | 255 | 214 | 1.78 | 20.57 | 30.00 | +| distilbart-12-6-cnn | 306 | 307 | 1.24 | 21.26 | 30.59 | +| distilbart-6-6-cnn | 230 | 182 | 2.09 | 20.17 | 29.70 | +``` From de3e19e1a7ae58d8f959a9f2658ffeb9b3e275df Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Thu, 11 May 2023 20:09:02 +0700 Subject: [PATCH 06/55] 2023-05-11-distilbart_cnn_12_6_en (#13795) * Add model 2023-05-11-distilbart_cnn_12_6_en * Add model 2023-05-11-distilbart_cnn_6_6_en * Add model 2023-05-11-distilbart_xsum_12_6_en * Add model 2023-05-11-distilbart_xsum_6_6_en * Add model 2023-05-11-bart_large_cnn_en * Update 2023-05-11-bart_large_cnn_en.md * Update 2023-05-11-distilbart_cnn_12_6_en.md * Update 2023-05-11-distilbart_cnn_6_6_en.md * Update 2023-05-11-distilbart_xsum_12_6_en.md * Update 2023-05-11-distilbart_xsum_6_6_en.md --------- Co-authored-by: prabod Co-authored-by: Maziyar Panahi --- .../prabod/2023-05-11-bart_large_cnn_en.md | 74 +++++++++++++++ .../2023-05-11-distilbart_cnn_12_6_en.md | 86 ++++++++++++++++++ .../2023-05-11-distilbart_cnn_6_6_en.md | 86 ++++++++++++++++++ .../2023-05-11-distilbart_xsum_12_6_en.md | 90 +++++++++++++++++++ .../2023-05-11-distilbart_xsum_6_6_en.md | 90 +++++++++++++++++++ 5 files changed, 426 insertions(+) create mode 100644 docs/_posts/prabod/2023-05-11-bart_large_cnn_en.md create mode 100644 docs/_posts/prabod/2023-05-11-distilbart_cnn_12_6_en.md create mode 100644 docs/_posts/prabod/2023-05-11-distilbart_cnn_6_6_en.md create mode 100644 docs/_posts/prabod/2023-05-11-distilbart_xsum_12_6_en.md create mode 100644 docs/_posts/prabod/2023-05-11-distilbart_xsum_6_6_en.md diff --git a/docs/_posts/prabod/2023-05-11-bart_large_cnn_en.md b/docs/_posts/prabod/2023-05-11-bart_large_cnn_en.md new file mode 100644 index 00000000000000..bbb4f79f416e91 --- /dev/null +++ b/docs/_posts/prabod/2023-05-11-bart_large_cnn_en.md @@ -0,0 +1,74 @@ +--- +layout: model +title: BART (large-sized model), fine-tuned on CNN Daily Mail +author: John Snow Labs +name: bart_large_cnn +date: 2023-05-11 +tags: [bart, summarization, cnn, text_to_text, en, open_source, tensorflow] +task: Summarization +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: BartTransformer +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +BART model pre-trained on English language, and fine-tuned on [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail). It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository (https://github.com/pytorch/fairseq/tree/master/examples/bart). + +Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team. + +### Model description + +BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. + +BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). This particular checkpoint has been fine-tuned on CNN Daily Mail, a large collection of text-summary pairs. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bart_large_cnn_en_4.4.2_3.0_1683808096812.zip){:.button.button-orange} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bart_large_cnn_en_4.4.2_3.0_1683808096812.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +You can use this model for text summarization. + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +bart = BartTransformer.pretrained("bart_large_cnn") \ + .setTask("summarize:") \ + .setMaxOutputLength(200) \ + .setInputCols(["documents"]) \ + .setOutputCol("summaries") +``` +```scala +val bart = BartTransformer.pretrained("bart_large_cnn") + .setTask("summarize:") + .setMaxOutputLength(200) + .setInputCols("documents") + .setOutputCol("summaries") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bart_large_cnn| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|975.3 MB| diff --git a/docs/_posts/prabod/2023-05-11-distilbart_cnn_12_6_en.md b/docs/_posts/prabod/2023-05-11-distilbart_cnn_12_6_en.md new file mode 100644 index 00000000000000..3bc524718d97a6 --- /dev/null +++ b/docs/_posts/prabod/2023-05-11-distilbart_cnn_12_6_en.md @@ -0,0 +1,86 @@ +--- +layout: model +title: Abstractive Summarization by BART - DistilBART CNN +author: John Snow Labs +name: distilbart_cnn_12_6 +date: 2023-05-11 +tags: [bart, summarization, cnn, distil, text_to_text, en, open_source, tensorflow] +task: Summarization +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: BartTransformer +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension Transformer" The Facebook BART (Bidirectional and Auto-Regressive Transformer) model is a state-of-the-art language generation model that was introduced by Facebook AI in 2019. It is based on the transformer architecture and is designed to handle a wide range of natural language processing tasks such as text generation, summarization, and machine translation. + +This pre-trained model is DistilBART fine-tuned on the Extreme Summarization (CNN) Dataset. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbart_cnn_12_6_en_4.4.2_3.0_1683807053526.zip){:.button.button-orange} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbart_cnn_12_6_en_4.4.2_3.0_1683807053526.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +bart = BartTransformer.pretrained("distilbart_cnn_12_6") \ + .setTask("summarize:") \ + .setMaxOutputLength(200) \ + .setInputCols(["documents"]) \ + .setOutputCol("summaries") +``` +```scala +val bart = BartTransformer.pretrained("distilbart_cnn_12_6") + .setTask("summarize:") + .setMaxOutputLength(200) + .setInputCols("documents") + .setOutputCol("summaries") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbart_cnn_12_6| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|870.4 MB| + +## Benchmarking + +```bash +### Metrics for DistilBART models +| Model Name | MM Params | Inference Time (MS) | Speedup | Rouge 2 | Rouge-L | +|:---------------------------|------------:|----------------------:|----------:|----------:|----------:| +| distilbart-xsum-12-1 | 222 | 90 | 2.54 | 18.31 | 33.37 | +| distilbart-xsum-6-6 | 230 | 132 | 1.73 | 20.92 | 35.73 | +| distilbart-xsum-12-3 | 255 | 106 | 2.16 | 21.37 | 36.39 | +| distilbart-xsum-9-6 | 268 | 136 | 1.68 | 21.72 | 36.61 | +| bart-large-xsum (baseline) | 406 | 229 | 1 | 21.85 | 36.50 | +| distilbart-xsum-12-6 | 306 | 137 | 1.68 | 22.12 | 36.99 | +| bart-large-cnn (baseline) | 406 | 381 | 1 | 21.06 | 30.63 | +| distilbart-12-3-cnn | 255 | 214 | 1.78 | 20.57 | 30.00 | +| distilbart-12-6-cnn | 306 | 307 | 1.24 | 21.26 | 30.59 | +| distilbart-6-6-cnn | 230 | 182 | 2.09 | 20.17 | 29.70 | +``` diff --git a/docs/_posts/prabod/2023-05-11-distilbart_cnn_6_6_en.md b/docs/_posts/prabod/2023-05-11-distilbart_cnn_6_6_en.md new file mode 100644 index 00000000000000..72730f4e25c11a --- /dev/null +++ b/docs/_posts/prabod/2023-05-11-distilbart_cnn_6_6_en.md @@ -0,0 +1,86 @@ +--- +layout: model +title: Abstractive Summarization by BART - DistilBART CNN +author: John Snow Labs +name: distilbart_cnn_6_6 +date: 2023-05-11 +tags: [bart, summarization, cnn, distil, text_to_text, en, open_source, tensorflow] +task: Summarization +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: BartTransformer +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension Transformer" The Facebook BART (Bidirectional and Auto-Regressive Transformer) model is a state-of-the-art language generation model that was introduced by Facebook AI in 2019. It is based on the transformer architecture and is designed to handle a wide range of natural language processing tasks such as text generation, summarization, and machine translation. + +This pre-trained model is DistilBART fine-tuned on the Extreme Summarization (CNN) Dataset. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbart_cnn_6_6_en_4.4.2_3.0_1683807295608.zip){:.button.button-orange} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbart_cnn_6_6_en_4.4.2_3.0_1683807295608.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +bart = BartTransformer.pretrained("distilbart_cnn_6_6") \ + .setTask("summarize:") \ + .setMaxOutputLength(200) \ + .setInputCols(["documents"]) \ + .setOutputCol("summaries") +``` +```scala +val bart = BartTransformer.pretrained("distilbart_cnn_6_6") + .setTask("summarize:") + .setMaxOutputLength(200) + .setInputCols("documents") + .setOutputCol("summaries") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbart_cnn_6_6| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|551.9 MB| + +## Benchmarking + +```bash +### Metrics for DistilBART models +| Model Name | MM Params | Inference Time (MS) | Speedup | Rouge 2 | Rouge-L | +|:---------------------------|------------:|----------------------:|----------:|----------:|----------:| +| distilbart-xsum-12-1 | 222 | 90 | 2.54 | 18.31 | 33.37 | +| distilbart-xsum-6-6 | 230 | 132 | 1.73 | 20.92 | 35.73 | +| distilbart-xsum-12-3 | 255 | 106 | 2.16 | 21.37 | 36.39 | +| distilbart-xsum-9-6 | 268 | 136 | 1.68 | 21.72 | 36.61 | +| bart-large-xsum (baseline) | 406 | 229 | 1 | 21.85 | 36.50 | +| distilbart-xsum-12-6 | 306 | 137 | 1.68 | 22.12 | 36.99 | +| bart-large-cnn (baseline) | 406 | 381 | 1 | 21.06 | 30.63 | +| distilbart-12-3-cnn | 255 | 214 | 1.78 | 20.57 | 30.00 | +| distilbart-12-6-cnn | 306 | 307 | 1.24 | 21.26 | 30.59 | +| distilbart-6-6-cnn | 230 | 182 | 2.09 | 20.17 | 29.70 | +``` diff --git a/docs/_posts/prabod/2023-05-11-distilbart_xsum_12_6_en.md b/docs/_posts/prabod/2023-05-11-distilbart_xsum_12_6_en.md new file mode 100644 index 00000000000000..45d5ac6fc19a34 --- /dev/null +++ b/docs/_posts/prabod/2023-05-11-distilbart_xsum_12_6_en.md @@ -0,0 +1,90 @@ +--- +layout: model +title: Abstractive Summarization by BART - DistilBART XSUM +author: John Snow Labs +name: distilbart_xsum_12_6 +date: 2023-05-11 +tags: [bart, summarization, text_to_text, xsum, distil, en, open_source, tensorflow] +task: Summarization +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: BartTransformer +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension Transformer" The Facebook BART (Bidirectional and Auto-Regressive Transformer) model is a state-of-the-art language generation model that was introduced by Facebook AI in 2019. It is based on the transformer architecture and is designed to handle a wide range of natural language processing tasks such as text generation, summarization, and machine translation. + +This pre-trained model is DistilBART fine-tuned on the Extreme Summarization (XSum) Dataset. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbart_xsum_12_6_en_4.4.2_3.0_1683807498835.zip){:.button.button-orange} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbart_xsum_12_6_en_4.4.2_3.0_1683807498835.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +bart = BartTransformer.pretrained("distilbart_xsum_12_6") \ + .setTask("summarize:") \ + .setMaxOutputLength(200) \ + .setInputCols(["documents"]) \ + .setOutputCol("summaries") +``` +```scala +val bart = BartTransformer.pretrained("distilbart_xsum_12_6") + .setTask("summarize:") + .setMaxOutputLength(200) + .setInputCols("documents") + .setOutputCol("summaries") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbart_xsum_12_6| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|733.7 MB| + +## References + +https://huggingface.co/sshleifer/distilbart-xsum-12-6 + +## Benchmarking + +```bash +### Metrics for DistilBART models +| Model Name | MM Params | Inference Time (MS) | Speedup | Rouge 2 | Rouge-L | +|:---------------------------|------------:|----------------------:|----------:|----------:|----------:| +| distilbart-xsum-12-1 | 222 | 90 | 2.54 | 18.31 | 33.37 | +| distilbart-xsum-6-6 | 230 | 132 | 1.73 | 20.92 | 35.73 | +| distilbart-xsum-12-3 | 255 | 106 | 2.16 | 21.37 | 36.39 | +| distilbart-xsum-9-6 | 268 | 136 | 1.68 | 21.72 | 36.61 | +| bart-large-xsum (baseline) | 406 | 229 | 1 | 21.85 | 36.50 | +| distilbart-xsum-12-6 | 306 | 137 | 1.68 | 22.12 | 36.99 | +| bart-large-cnn (baseline) | 406 | 381 | 1 | 21.06 | 30.63 | +| distilbart-12-3-cnn | 255 | 214 | 1.78 | 20.57 | 30.00 | +| distilbart-12-6-cnn | 306 | 307 | 1.24 | 21.26 | 30.59 | +| distilbart-6-6-cnn | 230 | 182 | 2.09 | 20.17 | 29.70 | +``` diff --git a/docs/_posts/prabod/2023-05-11-distilbart_xsum_6_6_en.md b/docs/_posts/prabod/2023-05-11-distilbart_xsum_6_6_en.md new file mode 100644 index 00000000000000..a18c746a5cf4ae --- /dev/null +++ b/docs/_posts/prabod/2023-05-11-distilbart_xsum_6_6_en.md @@ -0,0 +1,90 @@ +--- +layout: model +title: Abstractive Summarization by BART - DistilBART XSUM +author: John Snow Labs +name: distilbart_xsum_6_6 +date: 2023-05-11 +tags: [bart, summarization, xsum, distil, text_to_text, en, open_source, tensorflow] +task: Summarization +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: BartTransformer +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension Transformer" The Facebook BART (Bidirectional and Auto-Regressive Transformer) model is a state-of-the-art language generation model that was introduced by Facebook AI in 2019. It is based on the transformer architecture and is designed to handle a wide range of natural language processing tasks such as text generation, summarization, and machine translation. + +This pre-trained model is DistilBART fine-tuned on the Extreme Summarization (XSum) Dataset. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbart_xsum_6_6_en_4.4.2_3.0_1683807832345.zip){:.button.button-orange} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbart_xsum_6_6_en_4.4.2_3.0_1683807832345.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +bart = BartTransformer.pretrained("distilbart_xsum_6_6") \ + .setTask("summarize:") \ + .setMaxOutputLength(200) \ + .setInputCols(["documents"]) \ + .setOutputCol("summaries") +``` +```scala +val bart = BartTransformer.pretrained("distilbart_xsum_6_6") + .setTask("summarize:") + .setMaxOutputLength(200) + .setInputCols("documents") + .setOutputCol("summaries") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbart_xsum_6_6| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|551.7 MB| + +## References + +https://huggingface.co/sshleifer/distilbart-xsum-6-6 + +## Benchmarking + +```bash +### Metrics for DistilBART models +| Model Name | MM Params | Inference Time (MS) | Speedup | Rouge 2 | Rouge-L | +|:---------------------------|------------:|----------------------:|----------:|----------:|----------:| +| distilbart-xsum-12-1 | 222 | 90 | 2.54 | 18.31 | 33.37 | +| distilbart-xsum-6-6 | 230 | 132 | 1.73 | 20.92 | 35.73 | +| distilbart-xsum-12-3 | 255 | 106 | 2.16 | 21.37 | 36.39 | +| distilbart-xsum-9-6 | 268 | 136 | 1.68 | 21.72 | 36.61 | +| bart-large-xsum (baseline) | 406 | 229 | 1 | 21.85 | 36.50 | +| distilbart-xsum-12-6 | 306 | 137 | 1.68 | 22.12 | 36.99 | +| bart-large-cnn (baseline) | 406 | 381 | 1 | 21.06 | 30.63 | +| distilbart-12-3-cnn | 255 | 214 | 1.78 | 20.57 | 30.00 | +| distilbart-12-6-cnn | 306 | 307 | 1.24 | 21.26 | 30.59 | +| distilbart-6-6-cnn | 230 | 182 | 2.09 | 20.17 | 29.70 | +``` From 71de0f7ab6a2d2a1ec45c5687f04881e6b18589e Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Sun, 21 May 2023 15:44:15 +0700 Subject: [PATCH 07/55] 2023-05-19-match_pattern_en (#13805) * Add model 2023-05-19-match_pattern_en * Add model 2023-05-19-dependency_parse_en * Add model 2023-05-20-explain_document_md_fr * Add model 2023-05-20-dependency_parse_en * Add model 2023-05-20-explain_document_md_it * Add model 2023-05-20-entity_recognizer_lg_fr * Add model 2023-05-20-entity_recognizer_md_fr * Add model 2023-05-20-entity_recognizer_lg_it * Add model 2023-05-20-entity_recognizer_md_it * Add model 2023-05-20-check_spelling_en * Add model 2023-05-20-match_datetime_en * Add model 2023-05-20-match_pattern_en * Add model 2023-05-20-clean_pattern_en * Add model 2023-05-20-clean_stop_en * Add model 2023-05-20-movies_sentiment_analysis_en * Add model 2023-05-20-explain_document_ml_en * Add model 2023-05-20-analyze_sentiment_en * Add model 2023-05-20-explain_document_dl_en * Add model 2023-05-20-recognize_entities_dl_en * Add model 2023-05-20-recognize_entities_bert_en * Add model 2023-05-20-explain_document_md_de * Add model 2023-05-21-entity_recognizer_lg_de * Add model 2023-05-21-entity_recognizer_md_de * Add model 2023-05-21-onto_recognize_entities_sm_en * Add model 2023-05-21-onto_recognize_entities_lg_en * Add model 2023-05-21-match_chunks_en * Add model 2023-05-21-explain_document_lg_es * Add model 2023-05-21-explain_document_md_es * Add model 2023-05-21-explain_document_sm_es * Add model 2023-05-21-entity_recognizer_lg_es * Add model 2023-05-21-entity_recognizer_md_es * Add model 2023-05-21-entity_recognizer_sm_es * Add model 2023-05-21-explain_document_lg_ru * Add model 2023-05-21-explain_document_md_ru * Add model 2023-05-21-explain_document_sm_ru * Add model 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2023-05-21-distilbert_token_classifier_typo_detector_pipeline_en --------- Co-authored-by: ahmedlone127 --- .../2023-05-19-dependency_parse_en.md | 119 ++++++++++++ .../2023-05-19-match_pattern_en.md | 77 ++++++++ .../2023-05-20-analyze_sentiment_en.md | 130 ++++++++++++++ .../2023-05-20-check_spelling_en.md | 120 +++++++++++++ .../2023-05-20-clean_pattern_en.md | 120 +++++++++++++ .../ahmedlone127/2023-05-20-clean_stop_en.md | 120 +++++++++++++ .../2023-05-20-dependency_parse_en.md | 119 ++++++++++++ .../2023-05-20-entity_recognizer_lg_fr.md | 122 +++++++++++++ .../2023-05-20-entity_recognizer_lg_it.md | 122 +++++++++++++ .../2023-05-20-entity_recognizer_md_fr.md | 121 +++++++++++++ .../2023-05-20-entity_recognizer_md_it.md | 121 +++++++++++++ .../2023-05-20-explain_document_dl_en.md | 128 +++++++++++++ .../2023-05-20-explain_document_md_de.md | 123 +++++++++++++ .../2023-05-20-explain_document_md_fr.md | 123 +++++++++++++ .../2023-05-20-explain_document_md_it.md | 123 +++++++++++++ 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docs/_posts/ahmedlone127/2023-05-21-xlnet_base_token_classifier_conll03_pipeline_en.md create mode 100644 docs/_posts/ahmedlone127/2023-05-21-xlnet_large_token_classifier_conll03_pipeline_en.md diff --git a/docs/_posts/ahmedlone127/2023-05-19-dependency_parse_en.md b/docs/_posts/ahmedlone127/2023-05-19-dependency_parse_en.md new file mode 100644 index 00000000000000..9ac11bf27b47f9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-19-dependency_parse_en.md @@ -0,0 +1,119 @@ +--- +layout: model +title: Typed Dependency Parsing pipeline for English +author: John Snow Labs +name: dependency_parse +date: 2023-05-19 +tags: [pipeline, dependency_parsing, untyped_dependency_parsing, typed_dependency_parsing, laballed_depdency_parsing, unlaballed_depdency_parsing, en, open_source] +task: Dependency Parser +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Typed Dependency parser, trained on the on the CONLL dataset. + +Dependency parsing is the task of extracting a dependency parse of a sentence that represents its grammatical structure and defines the relationships between “head” words and words, which modify those heads. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dependency_parse_en_4.4.2_3.0_1684522392175.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dependency_parse_en_4.4.2_3.0_1684522392175.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('dependency_parse', lang = 'en') +annotations = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("dependency_parse", lang = "en") +val result = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence")(0) + +``` + +{:.nlu-block} +```python + +nlu.load("dep.typed").predict("Dependencies represents relationships betweens words in a Sentence") + + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('dependency_parse', lang = 'en') +annotations = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("dependency_parse", lang = "en") +val result = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence")(0) +``` + +{:.nlu-block} +```python +nlu.load("dep.typed").predict("Dependencies represents relationships betweens words in a Sentence") +``` +
+ +## Results + +```bash +Results + + ++---------------------------------------------------------------------------------+--------------------------------------------------------+ +|result |result | ++---------------------------------------------------------------------------------+--------------------------------------------------------+ +|[ROOT, Dependencies, represents, words, relationships, Sentence, Sentence, words]|[root, parataxis, nsubj, amod, nsubj, case, nsubj, flat]| ++---------------------------------------------------------------------------------+--------------------------------------------------------+ + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dependency_parse| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|23.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel +- DependencyParserModel +- TypedDependencyParserModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-19-match_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-19-match_pattern_en.md new file mode 100644 index 00000000000000..4865e422088735 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-19-match_pattern_en.md @@ -0,0 +1,77 @@ +--- +layout: model +title: Match Pattern +author: John Snow Labs +name: match_pattern +date: 2023-05-19 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The match_pattern is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and matches pattrens . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_pattern_en_4.4.2_3.0_1684521353408.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_pattern_en_4.4.2_3.0_1684521353408.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models") + result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models") + result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|match_pattern| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|29.1 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- RegexMatcherModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-20-analyze_sentiment_en.md b/docs/_posts/ahmedlone127/2023-05-20-analyze_sentiment_en.md new file mode 100644 index 00000000000000..16f8004a7107ae --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-20-analyze_sentiment_en.md @@ -0,0 +1,130 @@ +--- +layout: model +title: Sentiment Analysis pipeline for English +author: John Snow Labs +name: analyze_sentiment +date: 2023-05-20 +tags: [open_source, english, analyze_sentiment, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The analyze_sentiment is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/analyze_sentiment_en_4.4.2_3.0_1684625826708.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/analyze_sentiment_en_4.4.2_3.0_1684625826708.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python + +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en') + +result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") + + +``` +```scala + +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en") + +val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") + +``` + +{:.nlu-block} +```python + +import nlu +text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""] +result_df = nlu.load('en.classify').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en') + +result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en") + +val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") +``` + +{:.nlu-block} +```python +import nlu +text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""] +result_df = nlu.load('en.classify').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | text | sentiment | +|---:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------| +| 0 | Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now! | positive | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|analyze_sentiment| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|5.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel +- ViveknSentimentModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-20-check_spelling_en.md b/docs/_posts/ahmedlone127/2023-05-20-check_spelling_en.md new file mode 100644 index 00000000000000..c0d036659a739e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-20-check_spelling_en.md @@ -0,0 +1,120 @@ +--- +layout: model +title: Spell Checking Pipeline for English +author: John Snow Labs +name: check_spelling +date: 2023-05-20 +tags: [open_source, english, check_spelling, pipeline, en] +task: Spell Check +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The check_spelling is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/check_spelling_en_4.4.2_3.0_1684624849889.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/check_spelling_en_4.4.2_3.0_1684624849889.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('check_spelling', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("check_spelling", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('check_spelling', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("check_spelling", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | checked | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------------------------| +| 0 | ['I liek to live dangertus ! '] | ['I liek to live dangertus !'] | ['I', 'liek', 'to', 'live', 'dangertus', '!'] | ['I', 'like', 'to', 'live', 'dangerous', '!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|check_spelling| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|906.3 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-20-clean_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-20-clean_pattern_en.md new file mode 100644 index 00000000000000..87defa5ce1ce92 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-20-clean_pattern_en.md @@ -0,0 +1,120 @@ +--- +layout: model +title: Clean patterns pipeline for English +author: John Snow Labs +name: clean_pattern +date: 2023-05-20 +tags: [open_source, english, clean_pattern, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The clean_pattern is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clean_pattern_en_4.4.2_3.0_1684625071729.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clean_pattern_en_4.4.2_3.0_1684625071729.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_pattern', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala + + +val pipeline = new PretrainedPipeline("clean_pattern", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python + + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.pattern').predict(text) +result_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_pattern', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("clean_pattern", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.pattern').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + + +| | document | sentence | token | normal | +|---:|:-----------|:-----------|:----------|:----------| +| 0 | ['Hello'] | ['Hello'] | ['Hello'] | ['Hello'] || | document | sentence | token | normal | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clean_pattern| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|17.2 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NormalizerModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-20-clean_stop_en.md b/docs/_posts/ahmedlone127/2023-05-20-clean_stop_en.md new file mode 100644 index 00000000000000..2cafdec0c1da18 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-20-clean_stop_en.md @@ -0,0 +1,120 @@ +--- +layout: model +title: Clean documents pipeline for English +author: John Snow Labs +name: clean_stop +date: 2023-05-20 +tags: [open_source, english, clean_stop, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The clean_stop is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clean_stop_en_4.4.2_3.0_1684625150387.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clean_stop_en_4.4.2_3.0_1684625150387.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_stop', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("clean_stop", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.stop').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_stop', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("clean_stop", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.stop').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | cleanTokens | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:---------------------------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | ['Hello', 'John', 'Snow', 'Labs', '!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clean_stop| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|14.2 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- StopWordsCleaner \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-20-dependency_parse_en.md b/docs/_posts/ahmedlone127/2023-05-20-dependency_parse_en.md new file mode 100644 index 00000000000000..44b94af25c75bb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-20-dependency_parse_en.md @@ -0,0 +1,119 @@ +--- +layout: model +title: Typed Dependency Parsing pipeline for English +author: John Snow Labs +name: dependency_parse +date: 2023-05-20 +tags: [pipeline, dependency_parsing, untyped_dependency_parsing, typed_dependency_parsing, laballed_depdency_parsing, unlaballed_depdency_parsing, en, open_source] +task: Dependency Parser +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Typed Dependency parser, trained on the on the CONLL dataset. + +Dependency parsing is the task of extracting a dependency parse of a sentence that represents its grammatical structure and defines the relationships between “head” words and words, which modify those heads. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dependency_parse_en_4.4.2_3.0_1684622934996.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dependency_parse_en_4.4.2_3.0_1684622934996.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('dependency_parse', lang = 'en') +annotations = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("dependency_parse", lang = "en") +val result = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence")(0) + +``` + +{:.nlu-block} +```python + +nlu.load("dep.typed").predict("Dependencies represents relationships betweens words in a Sentence") + + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('dependency_parse', lang = 'en') +annotations = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("dependency_parse", lang = "en") +val result = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence")(0) +``` + +{:.nlu-block} +```python +nlu.load("dep.typed").predict("Dependencies represents relationships betweens words in a Sentence") +``` +
+ +## Results + +```bash +Results + + ++---------------------------------------------------------------------------------+--------------------------------------------------------+ +|result |result | ++---------------------------------------------------------------------------------+--------------------------------------------------------+ +|[ROOT, Dependencies, represents, words, relationships, Sentence, Sentence, words]|[root, parataxis, nsubj, amod, nsubj, case, nsubj, flat]| ++---------------------------------------------------------------------------------+--------------------------------------------------------+ + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dependency_parse| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|23.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel +- DependencyParserModel +- TypedDependencyParserModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_lg_fr.md b/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_lg_fr.md new file mode 100644 index 00000000000000..5786fc442e5e44 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_lg_fr.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL pipeline for French - Large +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-20 +tags: [open_source, french, entity_recognizer_lg, pipeline, fr] +task: Named Entity Recognition +language: fr +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fr_4.4.2_3.0_1684623798659.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fr_4.4.2_3.0_1684623798659.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:--------------------------------|:-------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Bonjour de John Snow Labs! '] | ['Bonjour de John Snow Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | [[-0.010997000150382,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fr| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_lg_it.md b/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_lg_it.md new file mode 100644 index 00000000000000..f38400fce314b6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_lg_it.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL pipeline for Italian - Large +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-20 +tags: [open_source, italian, entity_recognizer_lg, pipeline, it] +task: Named Entity Recognition +language: it +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_it_4.4.2_3.0_1684624524205.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_it_4.4.2_3.0_1684624524205.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Ciao da John Snow Labs! '] | ['Ciao da John Snow Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | [[-0.238279998302459,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|it| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_md_fr.md b/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_md_fr.md new file mode 100644 index 00000000000000..1ddb38d5994928 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_md_fr.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for French - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-20 +tags: [open_source, french, entity_recognizer_md, pipeline, fr] +task: Named Entity Recognition +language: fr +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fr_4.4.2_3.0_1684624035716.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fr_4.4.2_3.0_1684624035716.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:--------------------------------|:-------------------------------|:-------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------------------| +| 0 | ['Bonjour de John Snow Labs! '] | ['Bonjour de John Snow Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | [[0.0783179998397827,.,...]] | ['I-MISC', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['Bonjour', 'John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fr| +|Size:|462.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_md_it.md b/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_md_it.md new file mode 100644 index 00000000000000..5e6bcb2b483494 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_md_it.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Italian - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-20 +tags: [open_source, italian, entity_recognizer_md, pipeline, it] +task: Named Entity Recognition +language: it +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_it_4.4.2_3.0_1684624759913.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_it_4.4.2_3.0_1684624759913.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Ciao da John Snow Labs! '] | ['Ciao da John Snow Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | [[-0.146050006151199,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|it| +|Size:|462.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-20-explain_document_dl_en.md b/docs/_posts/ahmedlone127/2023-05-20-explain_document_dl_en.md new file mode 100644 index 00000000000000..2cb06faa2f8753 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-20-explain_document_dl_en.md @@ -0,0 +1,128 @@ +--- +layout: model +title: Explain Document DL Pipeline for English +author: John Snow Labs +name: explain_document_dl +date: 2023-05-20 +tags: [open_source, english, explain_document_dl, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_dl is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_dl_en_4.4.2_3.0_1684626017355.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_dl_en_4.4.2_3.0_1684626017355.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_dl', lang = 'en') +annotations = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_dl", lang = "en") +val result = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = ["The Mona Lisa is an oil painting from the 16th century."] +result_df = nlu.load('en.explain.dl').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_dl', lang = 'en') +annotations = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_dl", lang = "en") +val result = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")(0) +``` + +{:.nlu-block} +```python +import nlu +text = ["The Mona Lisa is an oil painting from the 16th century."] +result_df = nlu.load('en.explain.dl').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + ++--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------+-----------+ +| text| document| sentence| token| checked| lemma| stem| pos| embeddings| ner| entities| ++--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------+-----------+ +|The Mona Lisa is an oil painting from the 16th ...|[The Mona Lisa is an oil painting from the 16th...|[The Mona Lisa is an oil painting from the 16th...|[The, Mona, Lisa, is, an, oil, painting, from, ...|[The, Mona, Lisa, is, an, oil, painting, from, ...|[The, Mona, Lisa, be, an, oil, painting, from, ...|[the, mona, lisa, i, an, oil, paint, from, the,...|[DT, NNP, NNP, VBZ, DT, NN, NN, IN, DT, JJ, NN, .]|[[-0.038194, -0.24487, 0.72812, -0.39961, 0.083...|[O, B-PER, I-PER, O, O, O, O, O, O, O, O, O]|[Mona Lisa]| ++--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------+-----------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_dl| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|176.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel +- LemmatizerModel +- Stemmer +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-20-explain_document_md_de.md b/docs/_posts/ahmedlone127/2023-05-20-explain_document_md_de.md new file mode 100644 index 00000000000000..716a8c7ef93bda --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-20-explain_document_md_de.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for German +author: John Snow Labs +name: explain_document_md +date: 2023-05-20 +tags: [open_source, german, explain_document_md, pipeline, de] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_de_4.4.2_3.0_1684626936563.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_de_4.4.2_3.0_1684626936563.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.explain.document').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.explain.document').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo aus John Snow Labs! '] | ['Hallo aus John Snow Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|470.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-20-explain_document_md_fr.md b/docs/_posts/ahmedlone127/2023-05-20-explain_document_md_fr.md new file mode 100644 index 00000000000000..7242cd87aba84a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-20-explain_document_md_fr.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for French +author: John Snow Labs +name: explain_document_md +date: 2023-05-20 +tags: [open_source, french, explain_document_md, pipeline, fr] +task: Named Entity Recognition +language: fr +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_fr_4.4.2_3.0_1684622823988.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_fr_4.4.2_3.0_1684622823988.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:--------------------------------|:-------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------------------| +| 0 | ['Bonjour de John Snow Labs! '] | ['Bonjour de John Snow Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | ['INTJ', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0783179998397827,.,...]] | ['I-MISC', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['Bonjour', 'John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fr| +|Size:|467.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-20-explain_document_md_it.md b/docs/_posts/ahmedlone127/2023-05-20-explain_document_md_it.md new file mode 100644 index 00000000000000..1024efaa39dc1b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-20-explain_document_md_it.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Italian +author: John Snow Labs +name: explain_document_md +date: 2023-05-20 +tags: [open_source, italian, explain_document_md, pipeline, it] +task: Named Entity Recognition +language: it +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_it_4.4.2_3.0_1684623315612.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_it_4.4.2_3.0_1684623315612.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.explain.document').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.explain.document').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Ciao da John Snow Labs! '] | ['Ciao da John Snow Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.146050006151199,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|it| +|Size:|468.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-20-explain_document_ml_en.md b/docs/_posts/ahmedlone127/2023-05-20-explain_document_ml_en.md new file mode 100644 index 00000000000000..710aa1f053ff23 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-20-explain_document_ml_en.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document ML Pipeline for English +author: John Snow Labs +name: explain_document_ml +date: 2023-05-20 +tags: [open_source, english, explain_document_ml, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_ml is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_ml_en_4.4.2_3.0_1684625649608.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_ml_en_4.4.2_3.0_1684625649608.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_ml', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala + + +val pipeline = new PretrainedPipeline("explain_document_ml", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python + + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.explain').predict(text) +result_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_ml', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_ml", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + + +| | document | sentence | token | spell | lemmas | stems | pos | +|---:|:---------------------------------|:---------------------------------|:-------------------------------------------------|:------------------------------------------------|:------------------------------------------------|:-----------------------------------------------|:---------------------------------------| +| 0 | ['Hello fronm John Snwow Labs!'] | ['Hello fronm John Snwow Labs!'] | ['Hello', 'fronm', 'John', 'Snwow', 'Labs', '!'] | ['Hello', 'front', 'John', 'Snow', 'Labs', '!'] | ['Hello', 'front', 'John', 'Snow', 'Labs', '!'] | ['hello', 'front', 'john', 'snow', 'lab', '!'] | ['UH', 'NN', 'NNP', 'NNP', 'NNP', '.'] || | document | sentence | token | spell | lemmas | stems | pos | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_ml| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|9.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel +- LemmatizerModel +- Stemmer +- PerceptronModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-20-match_datetime_en.md b/docs/_posts/ahmedlone127/2023-05-20-match_datetime_en.md new file mode 100644 index 00000000000000..3fcefb1d7eb349 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-20-match_datetime_en.md @@ -0,0 +1,127 @@ +--- +layout: model +title: Match Datetime in Texts +author: John Snow Labs +name: match_datetime +date: 2023-05-20 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +DateMatcher based on yyyy/MM/dd + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_datetime_en_4.4.2_3.0_1684624920994.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_datetime_en_4.4.2_3.0_1684624920994.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline_local = PretrainedPipeline('match_datetime') + +tres = pipeline_local.fullAnnotate(input_list)[0] +for dte in tres['date']: + sent = tres['sentence'][int(dte.metadata['sentence'])] + print (f'text/chunk {sent.result[dte.begin:dte.end+1]} | mapped_date: {dte.result}') +``` +```scala + +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. +He also visited and the day before, but at that time he was alone. +David again visited today with his colleagues. +He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_datetime", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline_local = PretrainedPipeline('match_datetime') + +tres = pipeline_local.fullAnnotate(input_list)[0] +for dte in tres['date']: + sent = tres['sentence'][int(dte.metadata['sentence'])] + print (f'text/chunk {sent.result[dte.begin:dte.end+1]} | mapped_date: {dte.result}') +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. +He also visited and the day before, but at that time he was alone. +David again visited today with his colleagues. +He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_datetime", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` +
+ +## Results + +```bash +Results + + + +text/chunk yesterday | mapped_date: 2022/01/02 +text/chunk day before | mapped_date: 2022/01/02 +text/chunk today | mapped_date: 2022/01/03 +text/chunk tomorrow | mapped_date: 2022/01/04 + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|match_datetime| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|13.0 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- MultiDateMatcher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-20-match_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-20-match_pattern_en.md new file mode 100644 index 00000000000000..055d160c31f8bf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-20-match_pattern_en.md @@ -0,0 +1,77 @@ +--- +layout: model +title: Match Pattern +author: John Snow Labs +name: match_pattern +date: 2023-05-20 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The match_pattern is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and matches pattrens . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_pattern_en_4.4.2_3.0_1684624999250.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_pattern_en_4.4.2_3.0_1684624999250.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models") + result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models") + result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|match_pattern| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|17.4 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- RegexMatcherModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-20-movies_sentiment_analysis_en.md b/docs/_posts/ahmedlone127/2023-05-20-movies_sentiment_analysis_en.md new file mode 100644 index 00000000000000..95b2ce6a9e50d9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-20-movies_sentiment_analysis_en.md @@ -0,0 +1,79 @@ +--- +layout: model +title: Movies Sentiment Analysis +author: John Snow Labs +name: movies_sentiment_analysis +date: 2023-05-20 +tags: [en, open_source] +task: Sentiment Analysis +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The movies_sentiment_analysis is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and predicts sentiment . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/movies_sentiment_analysis_en_4.4.2_3.0_1684625520341.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/movies_sentiment_analysis_en_4.4.2_3.0_1684625520341.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("movies_sentiment_analysis", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("movies_sentiment_analysis", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|movies_sentiment_analysis| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|207.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- SymmetricDeleteModel +- SentimentDetectorModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-20-recognize_entities_bert_en.md b/docs/_posts/ahmedlone127/2023-05-20-recognize_entities_bert_en.md new file mode 100644 index 00000000000000..926f24a29a77c3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-20-recognize_entities_bert_en.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Recognize Entities DL pipeline for English - BERT +author: John Snow Labs +name: recognize_entities_bert +date: 2023-05-20 +tags: [open_source, english, recognize_entities_bert, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The recognize_entities_bert is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recognize_entities_bert_en_4.4.2_3.0_1684626456905.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recognize_entities_bert_en_4.4.2_3.0_1684626456905.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_bert', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("recognize_entities_bert", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.bert').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_bert', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("recognize_entities_bert", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.bert').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.085488274693489,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-ORG', 'O'] | ['John Snow Labs'] || | document | sentence | token | embeddings | ner | entities | + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recognize_entities_bert| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|424.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-20-recognize_entities_dl_en.md b/docs/_posts/ahmedlone127/2023-05-20-recognize_entities_dl_en.md new file mode 100644 index 00000000000000..fd6967e6b74e1c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-20-recognize_entities_dl_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for English +author: John Snow Labs +name: recognize_entities_dl +date: 2023-05-20 +tags: [open_source, english, recognize_entities_dl, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The recognize_entities_dl is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_en_4.4.2_3.0_1684626299390.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_en_4.4.2_3.0_1684626299390.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:---------------------------------------------------|:------------------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2668800055980682,.,...]] | ['B-ORG', 'I-ORG', 'I-ORG', 'I-ORG', 'I-ORG', 'O'] | ['Hello from John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recognize_entities_dl| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-albert_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-albert_base_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..c67e8675e84d6b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-albert_base_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: ALBERT Base CoNNL-03 NER Pipeline +author: John Snow Labs +name: albert_base_token_classifier_conll03_pipeline +date: 2023-05-21 +tags: [open_source, ner, token_classifier, albert, conll03, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [albert_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/albert_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_token_classifier_conll03_pipeline_en_4.4.2_3.0_1684653753684.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_token_classifier_conll03_pipeline_en_4.4.2_3.0_1684653753684.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|43.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- AlbertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-albert_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-albert_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..d1d0a027101f98 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-albert_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: ALBERT Large CoNNL-03 NER Pipeline +author: John Snow Labs +name: albert_large_token_classifier_conll03_pipeline +date: 2023-05-21 +tags: [open_source, ner, token_classifier, albert, conll03, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [albert_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/albert_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_token_classifier_conll03_pipeline_en_4.4.2_3.0_1684653839636.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_token_classifier_conll03_pipeline_en_4.4.2_3.0_1684653839636.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|64.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- AlbertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-albert_xlarge_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-albert_xlarge_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..1e109782e0404e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-albert_xlarge_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: ALBERT XLarge CoNNL-03 NER Pipeline +author: John Snow Labs +name: albert_xlarge_token_classifier_conll03_pipeline +date: 2023-05-21 +tags: [open_source, ner, token_classifier, albert, conll03, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [albert_xlarge_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/albert_xlarge_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_xlarge_token_classifier_conll03_pipeline_en_4.4.2_3.0_1684653940548.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_xlarge_token_classifier_conll03_pipeline_en_4.4.2_3.0_1684653940548.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_xlarge_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|206.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- AlbertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-analyze_sentimentdl_glove_imdb_en.md b/docs/_posts/ahmedlone127/2023-05-21-analyze_sentimentdl_glove_imdb_en.md new file mode 100644 index 00000000000000..bb3533370321b9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-analyze_sentimentdl_glove_imdb_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Sentiment Analysis pipeline for English (analyze_sentimentdl_glove_imdb) +author: John Snow Labs +name: analyze_sentimentdl_glove_imdb +date: 2023-05-21 +tags: [open_source, english, analyze_sentimentdl_glove_imdb, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The analyze_sentimentdl_glove_imdb is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/analyze_sentimentdl_glove_imdb_en_4.4.2_3.0_1684638979985.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/analyze_sentimentdl_glove_imdb_en_4.4.2_3.0_1684638979985.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("analyze_sentimentdl_glove_imdb", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.sentiment.glove').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("analyze_sentimentdl_glove_imdb", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.sentiment.glove').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | tokens | word_embeddings | sentence_embeddings | sentiment | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-----------------------------|:------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2668800055980682,.,...]] | [[0.0771183446049690,.,...]] | ['neg'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|analyze_sentimentdl_glove_imdb| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|161.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- SentenceEmbeddings +- SentimentDLModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-bert_sequence_classifier_age_news_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-bert_sequence_classifier_age_news_pipeline_en.md new file mode 100644 index 00000000000000..5d271a57a015e8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-bert_sequence_classifier_age_news_pipeline_en.md @@ -0,0 +1,98 @@ +--- +layout: model +title: Pipeline to Classify Texts into 4 News Categories +author: John Snow Labs +name: bert_sequence_classifier_age_news_pipeline +date: 2023-05-21 +tags: [ag_news, news, bert, bert_sequence, classification, en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [bert_sequence_classifier_age_news_en](https://nlp.johnsnowlabs.com/2021/11/07/bert_sequence_classifier_age_news_en.html) which is imported from `HuggingFace`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_age_news_pipeline_en_4.4.2_3.0_1684649894789.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_age_news_pipeline_en_4.4.2_3.0_1684649894789.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +news_pipeline = PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +```scala + + +val news_pipeline = new PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +news_pipeline = PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +```scala +val news_pipeline = new PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +
+ +## Results + +```bash +Results + + + + +['Sci/Tech'] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_age_news_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|42.4 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- BertForSequenceClassification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-bert_sequence_classifier_trec_coarse_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-bert_sequence_classifier_trec_coarse_pipeline_en.md new file mode 100644 index 00000000000000..52cb640bdcccfa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-bert_sequence_classifier_trec_coarse_pipeline_en.md @@ -0,0 +1,100 @@ +--- +layout: model +title: Pipeline to Classify Texts into TREC-6 Classes +author: John Snow Labs +name: bert_sequence_classifier_trec_coarse_pipeline +date: 2023-05-21 +tags: [bert_sequence, trec, coarse, bert, en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [bert_sequence_classifier_trec_coarse_en](https://nlp.johnsnowlabs.com/2021/11/06/bert_sequence_classifier_trec_coarse_en.html). + +The TREC dataset for question classification consists of open-domain, fact-based questions divided into broad semantic categories. You can check the official documentation of the dataset, entities, etc. [here](https://search.r-project.org/CRAN/refmans/textdata/html/dataset_trec.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_trec_coarse_pipeline_en_4.4.2_3.0_1684649817385.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_trec_coarse_pipeline_en_4.4.2_3.0_1684649817385.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +trec_pipeline = PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +```scala + + +val trec_pipeline = new PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +trec_pipeline = PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +```scala +val trec_pipeline = new PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +
+ +## Results + +```bash +Results + + + + +['LOC'] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_trec_coarse_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|406.6 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- BertForSequenceClassification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md b/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md new file mode 100644 index 00000000000000..49ca3e37d26312 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Dutch NER Pipeline +author: John Snow Labs +name: bert_token_classifier_dutch_udlassy_ner_pipeline +date: 2023-05-21 +tags: [open_source, ner, dutch, token_classifier, bert, treatment, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [bert_token_classifier_dutch_udlassy_ner](https://nlp.johnsnowlabs.com/2021/12/08/bert_token_classifier_dutch_udlassy_ner_nl.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_token_classifier_dutch_udlassy_ner_pipeline_nl_4.4.2_3.0_1684651513743.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_token_classifier_dutch_udlassy_ner_pipeline_nl_4.4.2_3.0_1684651513743.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +```scala + +val pipeline = new PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +```scala +val pipeline = new PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +
+ +## Results + +```bash +Results + + + ++---------------+---------+ +|chunk |ner_label| ++---------------+---------+ +|Peter Fergusson|PERSON | +|oktober 2011 |DATE | +|New York |GPE | +|5 jaar |DATE | +|Tesla Motor |ORG | ++---------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_token_classifier_dutch_udlassy_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|408.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_hi_en_ner_pipeline_hi.md b/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_hi_en_ner_pipeline_hi.md new file mode 100644 index 00000000000000..0b61c71f04caf9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_hi_en_ner_pipeline_hi.md @@ -0,0 +1,113 @@ +--- +layout: model +title: NER Pipeline for Hindi+English +author: John Snow Labs +name: bert_token_classifier_hi_en_ner_pipeline +date: 2023-05-21 +tags: [hindi, bert_token, hi, open_source] +task: Named Entity Recognition +language: hi +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on [bert_token_classifier_hi_en_ner](https://nlp.johnsnowlabs.com/2021/12/27/bert_token_classifier_hi_en_ner_hi.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_token_classifier_hi_en_ner_pipeline_hi_4.4.2_3.0_1684650604589.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_token_classifier_hi_en_ner_pipeline_hi_4.4.2_3.0_1684650604589.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +```scala + +val pipeline = new PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +val pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +```scala +val pipeline = new PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +val pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +
+ +## Results + +```bash +Results + + + ++---------------------------+------------+ +|chunk |ner_label | ++---------------------------+------------+ +|रिलायंस इंडस्ट्रीज़ लिमिटेड |ORGANISATION| +|Reliance Industries Limited|ORGANISATION| +|भारतीय |PLACE | +|मुंबई |PLACE | +|महाराष्ट्र |PLACE | +|Maharashtra) |PLACE | +|नवल टाटा |PERSON | +|मुम्बई |PLACE | +|Mumbai |PLACE | +|टाटा समुह |ORGANISATION| +|भारत |PLACE | +|जमशेदजी टाटा |PERSON | ++---------------------------+------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_token_classifier_hi_en_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|hi| +|Size:|665.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- BertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_scandi_ner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_scandi_ner_pipeline_xx.md new file mode 100644 index 00000000000000..e87e9642c6170b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_scandi_ner_pipeline_xx.md @@ -0,0 +1,103 @@ +--- +layout: model +title: NER Pipeline for 6 Scandinavian Languages +author: John Snow Labs +name: bert_token_classifier_scandi_ner_pipeline +date: 2023-05-21 +tags: [danish, norwegian, swedish, icelandic, faroese, bert, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on [bert_token_classifier_scandi_ner](https://nlp.johnsnowlabs.com/2021/12/09/bert_token_classifier_scandi_ner_xx.html) model which is imported from `HuggingFace`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_token_classifier_scandi_ner_pipeline_xx_4.4.2_3.0_1684649668401.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_token_classifier_scandi_ner_pipeline_xx_4.4.2_3.0_1684649668401.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +scandiner_pipeline = PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") +scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +```scala + +val scandiner_pipeline = new PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") + +val scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +scandiner_pipeline = PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") +scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +```scala +val scandiner_pipeline = new PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") + +val scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +
+ +## Results + +```bash +Results + + + ++-------------------+---------+ +|chunk |ner_label| ++-------------------+---------+ +|Hans |PER | +|Statens Universitet|ORG | +|København |LOC | +|københavner |MISC | ++-------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_token_classifier_scandi_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|666.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- BertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-distilbert_base_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-21-distilbert_base_token_classifier_masakhaner_pipeline_xx.md new file mode 100644 index 00000000000000..d0bc40acb6ec8d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-distilbert_base_token_classifier_masakhaner_pipeline_xx.md @@ -0,0 +1,108 @@ +--- +layout: model +title: NER Pipeline for 9 African Languages +author: John Snow Labs +name: distilbert_base_token_classifier_masakhaner_pipeline +date: 2023-05-21 +tags: [hausa, igbo, kinyarwanda, luganda, nigerian, pidgin, swahilu, wolof, yoruba, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilbert_base_token_classifier_masakhaner](https://nlp.johnsnowlabs.com/2022/01/18/distilbert_base_token_classifier_masakhaner_xx.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_token_classifier_masakhaner_pipeline_xx_4.4.2_3.0_1684650178459.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_token_classifier_masakhaner_pipeline_xx_4.4.2_3.0_1684650178459.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +masakhaner_pipeline = PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +```scala + +val masakhaner_pipeline = new PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +masakhaner_pipeline = PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +```scala +val masakhaner_pipeline = new PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +
+ +## Results + +```bash +Results + + + ++-----------------------------+---------+ +|chunk |ner_label| ++-----------------------------+---------+ +|Mohammed Sani Musa |PER | +|Activate Technologies Limited|ORG | +|ọdún-un 2019 |DATE | +|All rogressives Congress |ORG | +|APC |ORG | +|Aṣojú Ìlà-Oòrùn Niger |LOC | +|Premium Times |ORG | ++-----------------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_token_classifier_masakhaner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|505.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- DistilBertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-distilbert_token_classifier_typo_detector_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-distilbert_token_classifier_typo_detector_pipeline_en.md new file mode 100644 index 00000000000000..cfeb1b836428a1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-distilbert_token_classifier_typo_detector_pipeline_en.md @@ -0,0 +1,105 @@ +--- +layout: model +title: Typo Detector Pipeline for English +author: John Snow Labs +name: distilbert_token_classifier_typo_detector_pipeline +date: 2023-05-21 +tags: [ner, bert, bert_for_token, typo, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilbert_token_classifier_typo_detector](https://nlp.johnsnowlabs.com/2022/01/19/distilbert_token_classifier_typo_detector_en.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_en_4.4.2_3.0_1684654635894.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_en_4.4.2_3.0_1684654635894.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +```scala + + +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +```scala +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +
+ +## Results + +```bash +Results + + + + ++----------+---------+ +|chunk |ner_label| ++----------+---------+ +|stgruggled|PO | +|tine |PO | ++----------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_token_classifier_typo_detector_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|244.1 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- DistilBertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-distilbert_token_classifier_typo_detector_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-21-distilbert_token_classifier_typo_detector_pipeline_is.md new file mode 100644 index 00000000000000..f71f8d6fbd6c55 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-distilbert_token_classifier_typo_detector_pipeline_is.md @@ -0,0 +1,102 @@ +--- +layout: model +title: Typo Detector Pipeline for Icelandic +author: John Snow Labs +name: distilbert_token_classifier_typo_detector_pipeline +date: 2023-05-21 +tags: [icelandic, typo, ner, distilbert, is, open_source] +task: Named Entity Recognition +language: is +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilbert_token_classifier_typo_detector_is](https://nlp.johnsnowlabs.com/2022/01/19/distilbert_token_classifier_typo_detector_is.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_is_4.4.2_3.0_1684650029386.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_is_4.4.2_3.0_1684650029386.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +```scala + +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +```scala +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +
+ +## Results + +```bash +Results + + + ++--------+---------+ +|chunk |ner_label| ++--------+---------+ +|miög |PO | +|álykanir|PO | ++--------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_token_classifier_typo_detector_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|is| +|Size:|505.8 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- DistilBertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-distilroberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-distilroberta_base_token_classifier_ontonotes_pipeline_en.md new file mode 100644 index 00000000000000..4213fa76ba1ff2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-distilroberta_base_token_classifier_ontonotes_pipeline_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: DistilRoBERTa Base Ontonotes NER Pipeline +author: John Snow Labs +name: distilroberta_base_token_classifier_ontonotes_pipeline +date: 2023-05-21 +tags: [open_source, ner, token_classifier, distilroberta, ontonotes, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilroberta_base_token_classifier_ontonotes](https://nlp.johnsnowlabs.com/2021/09/26/distilroberta_base_token_classifier_ontonotes_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilroberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.0_1684654068942.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilroberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.0_1684654068942.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala + + +val pipeline = new PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala +val pipeline = new PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | +|November 2020 |DATE | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilroberta_base_token_classifier_ontonotes_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|307.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_da.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_da.md new file mode 100644 index 00000000000000..8955e9f3dade76 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_da.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-21 +tags: [da, open_source] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_da_4.4.2_3.0_1684643501546.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_da_4.4.2_3.0_1684643501546.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "da") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("da.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "da") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("da.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_de.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_de.md new file mode 100644 index 00000000000000..cc54bb0dab8119 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_de.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL pipeline for German - Large +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-21 +tags: [open_source, german, entity_recognizer_lg, pipeline, de] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_de_4.4.2_3.0_1684627484230.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_de_4.4.2_3.0_1684627484230.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo aus John Snow Labs! '] | ['Hallo aus John Snow Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | [[-0.245989993214607,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_es.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_es.md new file mode 100644 index 00000000000000..75475181299702 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_es.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognition Pipeline (Large, Spanish) +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-21 +tags: [es, open_source] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_es_4.4.2_3.0_1684630228186.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_es_4.4.2_3.0_1684630228186.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "es") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("es.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "es") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_fi.md new file mode 100644 index 00000000000000..55ebf123c1df74 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_fi.md @@ -0,0 +1,95 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-21 +tags: [fi, open_source] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fi_4.4.2_3.0_1684645053355.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fi_4.4.2_3.0_1684645053355.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "fi") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("fi.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "fi") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("fi.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_nl.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_nl.md new file mode 100644 index 00000000000000..dff7cd9661e907 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_nl.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-21 +tags: [nl, open_source] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_nl_4.4.2_3.0_1684638276742.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_nl_4.4.2_3.0_1684638276742.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "nl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("nl.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "nl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("nl.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_no.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_no.md new file mode 100644 index 00000000000000..8699ac760ad0f3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_no.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-21 +tags: ["no", open_source] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_no_4.4.2_3.0_1684640295003.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_no_4.4.2_3.0_1684640295003.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "no") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("no.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "no") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("no.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_pl.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_pl.md new file mode 100644 index 00000000000000..b25eb321ead9c0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_pl.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-21 +tags: [pl, open_source] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pl_4.4.2_3.0_1684636325692.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pl_4.4.2_3.0_1684636325692.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("pl.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pl.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_pt.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_pt.md new file mode 100644 index 00000000000000..141a06d8077ec9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_pt.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-21 +tags: [pt, open_source] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pt_4.4.2_3.0_1684634333081.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pt_4.4.2_3.0_1684634333081.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pt") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pt") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_ru.md new file mode 100644 index 00000000000000..fcf058e50dc668 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_ru.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-21 +tags: [ru, open_source] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_ru_4.4.2_3.0_1684632224398.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_ru_4.4.2_3.0_1684632224398.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "ru") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("ru.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "ru") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ru.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_sv.md new file mode 100644 index 00000000000000..7f3490502e1367 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_sv.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-21 +tags: [sv, open_source] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_sv_4.4.2_3.0_1684641919225.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_sv_4.4.2_3.0_1684641919225.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "sv") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("sv.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "sv") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("sv.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_da.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_da.md new file mode 100644 index 00000000000000..65da905933269c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_da.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Danish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-21 +tags: [open_source, danish, entity_recognizer_md, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_da_4.4.2_3.0_1684643723219.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_da_4.4.2_3.0_1684643723219.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|461.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_de.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_de.md new file mode 100644 index 00000000000000..a9a6c2bdd55d90 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_de.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for German - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-21 +tags: [open_source, german, entity_recognizer_md, pipeline, de] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_de_4.4.2_3.0_1684627775106.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_de_4.4.2_3.0_1684627775106.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo aus John Snow Labs! '] | ['Hallo aus John Snow Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|461.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_es.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_es.md new file mode 100644 index 00000000000000..3529cfbc437fe0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_es.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Spanish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-21 +tags: [open_source, spanish, entity_recognizer_md, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_es_4.4.2_3.0_1684630510421.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_es_4.4.2_3.0_1684630510421.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | [[0.5123000144958496,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|461.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_fi.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_fi.md new file mode 100644 index 00000000000000..e67072c399a524 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_fi.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Finnish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-21 +tags: [open_source, finnish, entity_recognizer_md, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fi_4.4.2_3.0_1684645288276.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fi_4.4.2_3.0_1684645288276.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | [[0.1868100017309188,.,...]] | ['O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|462.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_nl.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_nl.md new file mode 100644 index 00000000000000..c8b719fba4fa30 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_nl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Dutch - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-21 +tags: [open_source, dutch, entity_recognizer_md, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_nl_4.4.2_3.0_1684638555435.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_nl_4.4.2_3.0_1684638555435.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|462.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_no.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_no.md new file mode 100644 index 00000000000000..3f575cab2d81aa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_no.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Norwegian (Bokmal) - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-21 +tags: [open_source, norwegian_bokmal, entity_recognizer_md, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_no_4.4.2_3.0_1684640513821.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_no_4.4.2_3.0_1684640513821.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | [[0.1868100017309188,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|462.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_pl.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_pl.md new file mode 100644 index 00000000000000..b86a93ee891b41 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_pl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Polish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-21 +tags: [open_source, polish, entity_recognizer_md, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pl_4.4.2_3.0_1684636611883.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pl_4.4.2_3.0_1684636611883.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|462.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_pt.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_pt.md new file mode 100644 index 00000000000000..e0dca02bbcb78a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_pt.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Portuguese - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-21 +tags: [open_source, portuguese, entity_recognizer_md, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pt_4.4.2_3.0_1684634611526.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pt_4.4.2_3.0_1684634611526.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|462.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_ru.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_ru.md new file mode 100644 index 00000000000000..0c1f23aa64fe70 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_ru.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Russian - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-21 +tags: [open_source, russian, entity_recognizer_md, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_ru_4.4.2_3.0_1684632514655.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_ru_4.4.2_3.0_1684632514655.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-LOC', 'I-LOC', 'I-LOC'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|462.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_sv.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_sv.md new file mode 100644 index 00000000000000..a3be16bd99c74f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_sv.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Swedish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-21 +tags: [open_source, swedish, entity_recognizer_md, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_sv_4.4.2_3.0_1684642147875.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_sv_4.4.2_3.0_1684642147875.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|461.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_da.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_da.md new file mode 100644 index 00000000000000..303ae0989da47d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_da.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Danish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-21 +tags: [open_source, danish, entity_recognizer_sm, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_da_4.4.2_3.0_1684643832055.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_da_4.4.2_3.0_1684643832055.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_es.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_es.md new file mode 100644 index 00000000000000..481dbe7d2460cc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_es.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Spanish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-21 +tags: [open_source, spanish, entity_recognizer_sm, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_es_4.4.2_3.0_1684630683320.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_es_4.4.2_3.0_1684630683320.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:---------------------------------------|:-----------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | [[0.1754499971866607,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'B-MISC'] | ['John Snow', 'Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_fi.md new file mode 100644 index 00000000000000..793d02168e2078 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_fi.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Finnish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-21 +tags: [open_source, finnish, entity_recognizer_sm, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_fi_4.4.2_3.0_1684645397574.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_fi_4.4.2_3.0_1684645397574.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | [[-0.394499987363815,.,...]] | ['O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|166.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_nl.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_nl.md new file mode 100644 index 00000000000000..2b785ae319e8be --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_nl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Dutch - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-21 +tags: [open_source, dutch, entity_recognizer_sm, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_nl_4.4.2_3.0_1684638694186.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_nl_4.4.2_3.0_1684638694186.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | [[0.3653799891471863,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|166.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_no.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_no.md new file mode 100644 index 00000000000000..40887242a00edb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_no.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Norwegian (Bokmal) - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-21 +tags: [open_source, norwegian_bokmal, entity_recognizer_sm, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_no_4.4.2_3.0_1684640623882.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_no_4.4.2_3.0_1684640623882.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | [[-0.394499987363815,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|167.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_pl.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_pl.md new file mode 100644 index 00000000000000..689f09ca4cf5a9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_pl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Polish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-21 +tags: [open_source, polish, entity_recognizer_sm, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pl_4.4.2_3.0_1684636778236.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pl_4.4.2_3.0_1684636778236.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|167.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_pt.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_pt.md new file mode 100644 index 00000000000000..1adc52a341a687 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_pt.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Portuguese - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-21 +tags: [open_source, portuguese, entity_recognizer_sm, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pt_4.4.2_3.0_1684634777199.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pt_4.4.2_3.0_1684634777199.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|167.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_ru.md new file mode 100644 index 00000000000000..2077b8eb5eaf1e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_ru.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Russian - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-21 +tags: [open_source, russian, entity_recognizer_sm, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_ru_4.4.2_3.0_1684632691695.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_ru_4.4.2_3.0_1684632691695.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|167.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_sv.md new file mode 100644 index 00000000000000..26691aca25af85 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_sv.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Swedish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-21 +tags: [open_source, swedish, entity_recognizer_sm, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_sv_4.4.2_3.0_1684642270813.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_sv_4.4.2_3.0_1684642270813.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_da.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_da.md new file mode 100644 index 00000000000000..8084d8e7b4fee4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_da.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Danish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-21 +tags: [open_source, danish, explain_document_lg, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_da_4.4.2_3.0_1684642717450.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_da_4.4.2_3.0_1684642717450.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.025171000510454,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_es.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_es.md new file mode 100644 index 00000000000000..440ff38002a317 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_es.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Spanish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-21 +tags: [open_source, spanish, explain_document_lg, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_es_4.4.2_3.0_1684629207774.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_es_4.4.2_3.0_1684629207774.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['PART', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.016199000179767,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_fi.md new file mode 100644 index 00000000000000..dcf425acc83419 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_fi.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Finnish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-21 +tags: [open_source, finnish, explain_document_lg, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_fi_4.4.2_3.0_1684644276942.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_fi_4.4.2_3.0_1684644276942.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:---------------------------------|:------------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | ['hei', 'John', 'Snow', 'Labs!'] | ['INTJ', 'PROPN', 'PROPN', 'PROPN'] | [[0.0639619976282119,.,...]] | ['O', 'B-PRO', 'I-PRO', 'I-PRO'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_nl.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_nl.md new file mode 100644 index 00000000000000..05b601046d0b94 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_nl.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Dutch (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-21 +tags: [open_source, dutch, explain_document_lg, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_nl_4.4.2_3.0_1684637315077.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_nl_4.4.2_3.0_1684637315077.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:-----------------------------|:------------------------------------------|:-----------------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.245989993214607,.,...]] | ['B-PER', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Hallo', 'John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_no.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_no.md new file mode 100644 index 00000000000000..5a2c307dbda354 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_no.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Norwegian (Bokmal) (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-21 +tags: [open_source, norwegian_bokmal, explain_document_lg, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_no_4.4.2_3.0_1684639461035.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_no_4.4.2_3.0_1684639461035.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:--------------------------------------------|:-----------------------------|:---------------------------------------|:-----------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0639619976282119,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'B-PROD'] | ['John Snow', 'Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_pl.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_pl.md new file mode 100644 index 00000000000000..f0ba1686e91181 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_pl.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Polish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-21 +tags: [open_source, polish, explain_document_lg, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_pl_4.4.2_3.0_1684635370392.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_pl_4.4.2_3.0_1684635370392.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | ['witać', 'z', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4977500140666961,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_pt.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_pt.md new file mode 100644 index 00000000000000..f0a7bd6c6ed595 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_pt.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Portuguese (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-21 +tags: [open_source, portuguese, explain_document_lg, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_pt_4.4.2_3.0_1684633339307.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_pt_4.4.2_3.0_1684633339307.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:---------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4388400018215179,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_ru.md new file mode 100644 index 00000000000000..a72929290d4ee7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_ru.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Russian (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-21 +tags: [open_source, russian, explain_document_lg, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_ru_4.4.2_3.0_1684631229487.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_ru_4.4.2_3.0_1684631229487.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | ['здравствовать', 'из', 'Джон', 'Снежных', 'Лабораторий!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_sv.md new file mode 100644 index 00000000000000..916e0c895d7905 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_sv.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Swedish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-21 +tags: [open_source, swedish, explain_document_lg, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_sv_4.4.2_3.0_1684641138379.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_sv_4.4.2_3.0_1684641138379.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_da.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_da.md new file mode 100644 index 00000000000000..2ea14f5b2331cd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_da.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Danish +author: John Snow Labs +name: explain_document_md +date: 2023-05-21 +tags: [open_source, danish, explain_document_md, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_da_4.4.2_3.0_1684642961909.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_da_4.4.2_3.0_1684642961909.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|463.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_es.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_es.md new file mode 100644 index 00000000000000..2907d7a7c35f51 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_es.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Spanish +author: John Snow Labs +name: explain_document_md +date: 2023-05-21 +tags: [open_source, spanish, explain_document_md, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_es_4.4.2_3.0_1684629524995.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_es_4.4.2_3.0_1684629524995.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['PART', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.5123000144958496,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|472.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_fi.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_fi.md new file mode 100644 index 00000000000000..71b87ab1245b42 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_fi.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Finnish +author: John Snow Labs +name: explain_document_md +date: 2023-05-21 +tags: [open_source, finnish, explain_document_md, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_fi_4.4.2_3.0_1684644512467.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_fi_4.4.2_3.0_1684644512467.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:---------------------------------|:------------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | ['hei', 'John', 'Snow', 'Labs!'] | ['INTJ', 'PROPN', 'PROPN', 'PROPN'] | [[0.1868100017309188,.,...]] | ['O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|467.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_nl.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_nl.md new file mode 100644 index 00000000000000..6b7dfa51463cf6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_nl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Dutch +author: John Snow Labs +name: explain_document_md +date: 2023-05-21 +tags: [open_source, dutch, explain_document_md, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_nl_4.4.2_3.0_1684637608234.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_nl_4.4.2_3.0_1684637608234.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|464.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_no.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_no.md new file mode 100644 index 00000000000000..8558af1efebade --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_no.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Norwegian (Bokmal) +author: John Snow Labs +name: explain_document_md +date: 2023-05-21 +tags: [open_source, norwegian_bokmal, explain_document_md, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_no_4.4.2_3.0_1684639716214.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_no_4.4.2_3.0_1684639716214.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.1868100017309188,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|465.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_pl.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_pl.md new file mode 100644 index 00000000000000..36b991f68edd19 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_pl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Polish +author: John Snow Labs +name: explain_document_md +date: 2023-05-21 +tags: [open_source, polish, explain_document_md, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_pl_4.4.2_3.0_1684635642227.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_pl_4.4.2_3.0_1684635642227.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | ['witać', 'z', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|464.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_pt.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_pt.md new file mode 100644 index 00000000000000..6cf27ba2036565 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_pt.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Portuguese +author: John Snow Labs +name: explain_document_md +date: 2023-05-21 +tags: [open_source, portuguese, explain_document_md, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_pt_4.4.2_3.0_1684633642977.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_pt_4.4.2_3.0_1684633642977.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:---------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|473.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_ru.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_ru.md new file mode 100644 index 00000000000000..c679540f9b0387 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_ru.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Russian +author: John Snow Labs +name: explain_document_md +date: 2023-05-21 +tags: [open_source, russian, explain_document_md, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_ru_4.4.2_3.0_1684631533294.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_ru_4.4.2_3.0_1684631533294.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | ['здравствовать', 'из', 'Джон', 'Снежных', 'Лабораторий!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-LOC', 'I-LOC', 'I-LOC'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|465.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_sv.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_sv.md new file mode 100644 index 00000000000000..0483eb82713997 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_sv.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Swedish +author: John Snow Labs +name: explain_document_md +date: 2023-05-21 +tags: [open_source, swedish, explain_document_md, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_sv_4.4.2_3.0_1684641378893.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_sv_4.4.2_3.0_1684641378893.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|471.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_da.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_da.md new file mode 100644 index 00000000000000..9d60898c5ec6d1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_da.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Danish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-21 +tags: [open_source, danish, explain_document_sm, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_da_4.4.2_3.0_1684643083685.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_da_4.4.2_3.0_1684643083685.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|168.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_es.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_es.md new file mode 100644 index 00000000000000..9fb4075ae18c3c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_es.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Spanish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-21 +tags: [open_source, spanish, explain_document_sm, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_es_4.4.2_3.0_1684629732604.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_es_4.4.2_3.0_1684629732604.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:---------------------------------------|:-----------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['PART', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.1754499971866607,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'B-MISC'] | ['John Snow', 'Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|177.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_fi.md new file mode 100644 index 00000000000000..85d99ba6092594 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_fi.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Finnish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-21 +tags: [open_source, finnish, explain_document_sm, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_fi_4.4.2_3.0_1684644636020.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_fi_4.4.2_3.0_1684644636020.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:---------------------------------|:------------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | ['hei', 'John', 'Snow', 'Labs!'] | ['INTJ', 'PROPN', 'PROPN', 'PROPN'] | [[-0.394499987363815,.,...]] | ['O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|172.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_nl.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_nl.md new file mode 100644 index 00000000000000..1d7adcc0517e6a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_nl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Dutch +author: John Snow Labs +name: explain_document_sm +date: 2023-05-21 +tags: [open_source, dutch, explain_document_sm, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_nl_4.4.2_3.0_1684637799452.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_nl_4.4.2_3.0_1684637799452.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.3653799891471863,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|169.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_no.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_no.md new file mode 100644 index 00000000000000..21fbb60d5981b6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_no.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Norwegian (Bokmal) +author: John Snow Labs +name: explain_document_sm +date: 2023-05-21 +tags: [open_source, norwegian_bokmal, explain_document_sm, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_no_4.4.2_3.0_1684639851807.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_no_4.4.2_3.0_1684639851807.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.394499987363815,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|170.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_pl.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_pl.md new file mode 100644 index 00000000000000..678671d456d788 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_pl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Polish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-21 +tags: [open_source, polish, explain_document_sm, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_pl_4.4.2_3.0_1684635836520.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_pl_4.4.2_3.0_1684635836520.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | ['witać', 'z', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|169.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_pt.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_pt.md new file mode 100644 index 00000000000000..e2cc711812194d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_pt.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Portuguese +author: John Snow Labs +name: explain_document_sm +date: 2023-05-21 +tags: [open_source, portuguese, explain_document_sm, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_pt_4.4.2_3.0_1684633838458.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_pt_4.4.2_3.0_1684633838458.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:---------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|178.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_ru.md new file mode 100644 index 00000000000000..38585aedaa18ff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_ru.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Russian +author: John Snow Labs +name: explain_document_sm +date: 2023-05-21 +tags: [open_source, russian, explain_document_sm, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_ru_4.4.2_3.0_1684631727283.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_ru_4.4.2_3.0_1684631727283.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | ['здравствовать', 'из', 'Джон', 'Снежных', 'Лабораторий!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|170.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_sv.md new file mode 100644 index 00000000000000..ec8aec2971da3f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_sv.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Swedish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-21 +tags: [open_source, swedish, explain_document_sm, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_sv_4.4.2_3.0_1684641506395.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_sv_4.4.2_3.0_1684641506395.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|176.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-longformer_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-longformer_base_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..23ff94d863572b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-longformer_base_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Longformer Base NER Pipeline +author: John Snow Labs +name: longformer_base_token_classifier_conll03_pipeline +date: 2023-05-21 +tags: [ner, longformer, pipeline, conll, token_classification, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [longformer_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/10/09/longformer_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/longformer_base_token_classifier_conll03_pipeline_en_4.4.2_3.0_1684652524851.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/longformer_base_token_classifier_conll03_pipeline_en_4.4.2_3.0_1684652524851.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|longformer_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|516.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- LongformerForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-longformer_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-longformer_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..1172ee55ae06b5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-longformer_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Longformer Large NER Pipeline +author: John Snow Labs +name: longformer_large_token_classifier_conll03_pipeline +date: 2023-05-21 +tags: [open_source, ner, token_classifier, longformer, conll, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [longformer_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/10/09/longformer_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/longformer_large_token_classifier_conll03_pipeline_en_4.4.2_3.0_1684652890632.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/longformer_large_token_classifier_conll03_pipeline_en_4.4.2_3.0_1684652890632.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|longformer_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- LongformerForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-match_chunks_en.md b/docs/_posts/ahmedlone127/2023-05-21-match_chunks_en.md new file mode 100644 index 00000000000000..0e7bb1cabc639e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-match_chunks_en.md @@ -0,0 +1,141 @@ +--- +layout: model +title: Match Chunks in Texts +author: John Snow Labs +name: match_chunks +date: 2023-05-21 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The pipeline uses regex `
?/*+` + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_chunks_en_4.4.2_3.0_1684628637228.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_chunks_en_4.4.2_3.0_1684628637228.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline + +pipeline_local = PretrainedPipeline('match_chunks') + +result = pipeline_local.annotate("David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.") + +result['chunk'] +``` +```scala + +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_chunks", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.match.chunks").predict("""David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline_local = PretrainedPipeline('match_chunks') + +result = pipeline_local.annotate("David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.") + +result['chunk'] +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_chunks", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.match.chunks").predict("""David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.""") +``` +
+ +## Results + +```bash +Results + + + +['the restaurant yesterday', +'family', +'the day', +'that time', +'today', +'the food', +'tomorrow'] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|match_chunks| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|4.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel +- Chunker \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-nerdl_fewnerd_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-nerdl_fewnerd_100d_pipeline_en.md new file mode 100644 index 00000000000000..a400d3f53f1cfb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-nerdl_fewnerd_100d_pipeline_en.md @@ -0,0 +1,109 @@ +--- +layout: model +title: Pretrained Pipeline for Few-NERD-General NER Model +author: John Snow Labs +name: nerdl_fewnerd_100d_pipeline +date: 2023-05-21 +tags: [fewnerd, nerdl, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on Few-NERD model and it detects : + +`PERSON`, `ORGANIZATION`, `LOCATION`, `ART`, `BUILDING`, `PRODUCT`, `EVENT`, `OTHER` + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_100d_pipeline_en_4.4.2_3.0_1684648729860.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_100d_pipeline_en_4.4.2_3.0_1684648729860.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).""") +``` +```scala + +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).")(0) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).""") +``` +```scala +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).")(0) +``` +
+ +## Results + +```bash +Results + + + ++-----------------------+------------+ +|chunk |ner_label | ++-----------------------+------------+ +|Kentucky Fried Chicken |ORGANIZATION| +|Anglo-Egyptian War |EVENT | +|battle of Tell El Kebir|EVENT | +|Egypt Medal |OTHER | +|Order of Medjidie |OTHER | ++-----------------------+------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nerdl_fewnerd_100d_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|167.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-nerdl_fewnerd_subentity_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-nerdl_fewnerd_subentity_100d_pipeline_en.md new file mode 100644 index 00000000000000..6e5bda94df3f1b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-nerdl_fewnerd_subentity_100d_pipeline_en.md @@ -0,0 +1,110 @@ +--- +layout: model +title: Pre-trained Pipeline for Few-NERD NER Model +author: John Snow Labs +name: nerdl_fewnerd_subentity_100d_pipeline +date: 2023-05-21 +tags: [fewnerd, ner, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on Few-NERD/inter public dataset and it extracts 66 entities that are in general scope. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_subentity_100d_pipeline_en_4.4.2_3.0_1684647887382.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_subentity_100d_pipeline_en_4.4.2_3.0_1684647887382.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.""") +``` +```scala + +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.")(0) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.""") +``` +```scala +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.")(0) +``` +
+ +## Results + +```bash +Results + + + ++-----------------------+----------------------------+ +|chunk |ner_label | ++-----------------------+----------------------------+ +|Corazones ('12 Hearts')|art-broadcastprogram | +|Spanish-language |other-language | +|United States |location-GPE | +|Telemundo |organization-media/newspaper| +|Argentine TV |organization-media/newspaper| +|Los Angeles |location-GPE | +|Steven Spielberg |person-director | +|Cloverfield Paradox |art-film | ++-----------------------+----------------------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nerdl_fewnerd_subentity_100d_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|167.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-nerdl_restaurant_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-nerdl_restaurant_100d_pipeline_en.md new file mode 100644 index 00000000000000..b0d7072f07eb69 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-nerdl_restaurant_100d_pipeline_en.md @@ -0,0 +1,119 @@ +--- +layout: model +title: Pipeline to Detect Restaurant-related Terminology +author: John Snow Labs +name: nerdl_restaurant_100d_pipeline +date: 2023-05-21 +tags: [restaurant, ner, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [nerdl_restaurant_100d](https://nlp.johnsnowlabs.com/2021/12/31/nerdl_restaurant_100d_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nerdl_restaurant_100d_pipeline_en_4.4.2_3.0_1684650284287.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nerdl_restaurant_100d_pipeline_en_4.4.2_3.0_1684650284287.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +restaurant_pipeline = PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +```scala + +val restaurant_pipeline = new PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +restaurant_pipeline = PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +```scala +val restaurant_pipeline = new PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +
+ +## Results + +```bash +Results + + + ++---------------------------+---------------+ +|chunk |ner_label | ++---------------------------+---------------+ +|Hong Kong’s |Restaurant_Name| +|favourite |Rating | +|pasta bar |Dish | +|most reasonably |Price | +|lunch |Hours | +|in town! |Location | +|Sha Tin – Pici’s |Restaurant_Name| +|burrata |Dish | +|arugula salad |Dish | +|freshly tossed tuna tartare|Dish | +|reliable |Price | +|handmade pasta |Dish | +|pappardelle |Dish | +|effortless |Amenity | +|Italian |Cuisine | +|tidy one-pot |Amenity | +|espresso |Dish | ++---------------------------+---------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nerdl_restaurant_100d_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_base_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_base_en.md new file mode 100644 index 00000000000000..87e96cf134ef92 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_base_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Base +author: John Snow Labs +name: onto_recognize_entities_bert_base +date: 2023-05-21 +tags: [open_source, english, onto_recognize_entities_bert_base, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_base is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_base_en_4.4.2_3.0_1684645534266.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_base_en_4.4.2_3.0_1684645534266.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_base', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_base", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.base').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_base', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_base", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.base').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.085488274693489,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_base| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|425.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_large_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_large_en.md new file mode 100644 index 00000000000000..b922543e2b7a56 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_large_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Large +author: John Snow Labs +name: onto_recognize_entities_bert_large +date: 2023-05-21 +tags: [open_source, english, onto_recognize_entities_bert_large, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_large is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_large_en_4.4.2_3.0_1684645790943.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_large_en_4.4.2_3.0_1684645790943.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_large', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_large", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.large').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_large', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_large", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.large').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.262016534805297,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_large| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.3 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_medium_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_medium_en.md new file mode 100644 index 00000000000000..3bc0840d862e29 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_medium_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Medium +author: John Snow Labs +name: onto_recognize_entities_bert_medium +date: 2023-05-21 +tags: [open_source, english, onto_recognize_entities_bert_medium, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_medium is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_medium_en_4.4.2_3.0_1684645924742.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_medium_en_4.4.2_3.0_1684645924742.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_medium', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_medium", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.medium').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_medium', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_medium", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.medium').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.0365490540862083,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_medium| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|172.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_mini_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_mini_en.md new file mode 100644 index 00000000000000..14b73191a94566 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_mini_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Mini +author: John Snow Labs +name: onto_recognize_entities_bert_mini +date: 2023-05-21 +tags: [open_source, english, onto_recognize_entities_bert_mini, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_mini is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_mini_en_4.4.2_3.0_1684646004112.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_mini_en_4.4.2_3.0_1684646004112.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_mini', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_mini", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.mini').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_mini', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_mini", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.mini').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.147406503558158,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_mini| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|57.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_small_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_small_en.md new file mode 100644 index 00000000000000..d1cccd62c63532 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_small_en.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Small +author: John Snow Labs +name: onto_recognize_entities_bert_small +date: 2023-05-21 +tags: [open_source, english, onto_recognize_entities_bert_small, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_small is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_small_en_4.4.2_3.0_1684646088785.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_small_en_4.4.2_3.0_1684646088785.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_small', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_small", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.small').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_small', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_small", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.small').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:----------------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.9379079937934875,.,...]] | ['O', 'O', 'B-PERSON', 'I-PERSON', 'I-PERSON', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_small| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|125.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_tiny_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_tiny_en.md new file mode 100644 index 00000000000000..558a056a497f46 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_tiny_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Tiny +author: John Snow Labs +name: onto_recognize_entities_bert_tiny +date: 2023-05-21 +tags: [open_source, english, onto_recognize_entities_bert_tiny, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_tiny is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_tiny_en_4.4.2_3.0_1684646160343.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_tiny_en_4.4.2_3.0_1684646160343.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_tiny', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_tiny", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.tiny').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_tiny', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_tiny", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.tiny').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:----------------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-1.526878952980041,.,...]] | ['O', 'O', 'B-PERSON', 'I-PERSON', 'I-PERSON', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_tiny| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|31.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_base_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_base_en.md new file mode 100644 index 00000000000000..fe1c34d91f31e8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_base_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - ELECTRA Base +author: John Snow Labs +name: onto_recognize_entities_electra_base +date: 2023-05-21 +tags: [open_source, english, onto_recognize_entities_electra_base, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_electra_base is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_base_en_4.4.2_3.0_1684646292310.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_base_en_4.4.2_3.0_1684646292310.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_electra_base', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_base", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.electra.base').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_electra_base', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_base", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.electra.base').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2088415920734405,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_electra_base| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|424.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_large_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_large_en.md new file mode 100644 index 00000000000000..ac1128949d45af --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_large_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - ELECTRA Large +author: John Snow Labs +name: onto_recognize_entities_electra_large +date: 2023-05-21 +tags: [open_source, english, onto_recognize_entities_electra_large, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_electra_large is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_large_en_4.4.2_3.0_1684646637999.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_large_en_4.4.2_3.0_1684646637999.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('onto_recognize_entities_electra_large', lang = 'en') +annotations = pipeline.fullAnnotate("Hello from John Snow Labs!")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_large", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.large').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('onto_recognize_entities_electra_large', lang = 'en') +annotations = pipeline.fullAnnotate("Hello from John Snow Labs!")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_large", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.large').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.264069110155105,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_electra_large| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.3 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_small_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_small_en.md new file mode 100644 index 00000000000000..3dda6bffca3e74 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_small_en.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - ELECTRA Small +author: John Snow Labs +name: onto_recognize_entities_electra_small +date: 2023-05-21 +tags: [open_source, english, onto_recognize_entities_electra_small, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_electra_small is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_small_en_4.4.2_3.0_1684646382329.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_small_en_4.4.2_3.0_1684646382329.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_electra_small', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_small", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.electra.small').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_electra_small', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_small", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.electra.small').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2279076874256134,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_electra_small| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|66.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_lg_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_lg_en.md new file mode 100644 index 00000000000000..ed9a31526cc8be --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_lg_en.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Onto Recognize Entities Lg +author: John Snow Labs +name: onto_recognize_entities_lg +date: 2023-05-21 +tags: [en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entites. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_lg_en_4.4.2_3.0_1684628442699.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_lg_en_4.4.2_3.0_1684628442699.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("onto_recognize_entities_lg", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("en.ner.onto.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("onto_recognize_entities_lg", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.ner.onto.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_sm_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_sm_en.md new file mode 100644 index 00000000000000..973ef3cece1364 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_sm_en.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL pipeline for English - Small +author: John Snow Labs +name: onto_recognize_entities_sm +date: 2023-05-21 +tags: [open_source, english, onto_recognize_entities_sm, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_sm_en_4.4.2_3.0_1684627952482.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_sm_en_4.4.2_3.0_1684627952482.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_sm', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_sm", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.sm').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_sm', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_sm", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.sm').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2668800055980682,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-pos_ud_bokmaal_nb.md b/docs/_posts/ahmedlone127/2023-05-21-pos_ud_bokmaal_nb.md new file mode 100644 index 00000000000000..0d388eff34fad8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-pos_ud_bokmaal_nb.md @@ -0,0 +1,138 @@ +--- +layout: model +title: Part of Speech for Norwegian +author: John Snow Labs +name: pos_ud_bokmaal +date: 2023-05-21 +tags: [pos, norwegian, nb, open_source] +task: Part of Speech Tagging +language: nb +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model annotates the part of speech of tokens in a text. The parts of speech annotated include PRON (pronoun), CCONJ (coordinating conjunction), and 15 others. The part of speech model is useful for extracting the grammatical structure of a piece of text automatically. + +This model was trained using the dataset available at https://universaldependencies.org + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pos_ud_bokmaal_nb_4.4.2_3.0_1684648789557.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pos_ud_bokmaal_nb_4.4.2_3.0_1684648789557.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("pos") + +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos]) +light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text"))) +results = light_pipeline.fullAnnotate("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.") +``` +```scala + +val pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") + .setInputCols(Array("document", "token")) + .setOutputCol("pos") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos)) +val data = Seq("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python + +import nlu + +text = ["""Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene."""] +pos_df = nlu.load('nb.pos.ud_bokmaal').predict(text) +pos_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("pos") + +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos]) +light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text"))) +results = light_pipeline.fullAnnotate("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.") +``` +```scala +val pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") + .setInputCols(Array("document", "token")) + .setOutputCol("pos") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos)) +val data = Seq("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu + +text = ["""Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene."""] +pos_df = nlu.load('nb.pos.ud_bokmaal').predict(text) +pos_df +``` +
+ +## Results + +```bash +Results + + + +[Row(annotatorType='pos', begin=0, end=4, result='DET', metadata={'word': 'Annet'}), +Row(annotatorType='pos', begin=6, end=8, result='SCONJ', metadata={'word': 'enn'}), +Row(annotatorType='pos', begin=10, end=10, result='PART', metadata={'word': 'å'}), +Row(annotatorType='pos', begin=12, end=15, result='AUX', metadata={'word': 'være'}), +Row(annotatorType='pos', begin=17, end=22, result='NOUN', metadata={'word': 'kongen'}), +...] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pos_ud_bokmaal| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nb| +|Size:|17.7 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-recognize_entities_dl_fa.md b/docs/_posts/ahmedlone127/2023-05-21-recognize_entities_dl_fa.md new file mode 100644 index 00000000000000..9e6cd9bc8b4e0a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-recognize_entities_dl_fa.md @@ -0,0 +1,169 @@ +--- +layout: model +title: Explain Document DL Pipeline for Farsi/Persian +author: John Snow Labs +name: recognize_entities_dl +date: 2023-05-21 +tags: [pipeline, ner, fa, open_source] +task: Named Entity Recognition +language: fa +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_dl is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities . It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_fa_4.4.2_3.0_1684647040883.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_fa_4.4.2_3.0_1684647040883.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'fa') + +annotations = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")[0] + +annotations.keys() +``` +```scala + +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "fa") + +val result = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")(0) +``` + +{:.nlu-block} +```python + +import nlu + +text = ["""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند"""] + +result_df = nlu.load('fa.recognize_entities_dl').predict(text) + +result_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'fa') + +annotations = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")[0] + +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "fa") + +val result = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")(0) +``` + +{:.nlu-block} +```python +import nlu + +text = ["""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند"""] + +result_df = nlu.load('fa.recognize_entities_dl').predict(text) + +result_df +``` +
+ +## Results + +```bash +Results + + + +| | document | sentence | token | clean_tokens | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------|:---------------|:---------|:------|:-------------|:------|:---------------------| +| 0 | "به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند | "به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند | " | " | " | PUNCT | " | O | خبرنگار ایرنا | +| 1 | | | به | گزارش | به | ADP | به | O | محمد قمی | +| 2 | | | گزارش | خبرنگار | گزارش | NOUN | گزارش | O | پاکدشت | +| 3 | | | خبرنگار | ایرنا | خبرنگار | NOUN | خبرنگار | B-ORG | علی‌اکبر موسوی خوئینی | +| 4 | | | ایرنا | ، | ایرنا | PROPN | ایرنا | I-ORG | شمس‌الدین وهابی | +| 5 | | | ، | اساس | ؛ | PUNCT | ، | O | تهران | +| 6 | | | بر | تصمیم | بر | ADP | بر | O | | +| 7 | | | اساس | این | اساس | NOUN | اساس | O | | +| 8 | | | تصمیم | مجمع | تصمیم | NOUN | تصمیم | O | | +| 9 | | | این | ، | این | DET | این | O | | +| 10 | | | مجمع | محمد | مجمع | NOUN | مجمع | O | | +| 11 | | | ، | قمی | ؛ | PUNCT | ، | O | | +| 12 | | | محمد | نماینده | محمد | PROPN | محمد | B-PER | | +| 13 | | | قمی | پاکدشت | قمی | PROPN | قمی | I-PER | | +| 14 | | | نماینده | عنوان | نماینده | NOUN | نماینده | O | | +| 15 | | | مردم | رئیس | مردم | NOUN | مردم | O | | +| 16 | | | پاکدشت | علی‌اکبر | پاکدشت | PROPN | پاکدشت | B-LOC | | +| 17 | | | به | موسوی | به | ADP | به | O | | +| 18 | | | عنوان | خوئینی | عنوان | NOUN | عنوان | O | | +| 19 | | | رئیس | شمس‌الدین | رئیس | NOUN | رئیس | O | | +| 20 | | | و | وهابی | او | CCONJ | و | O | | +| 21 | | | علی‌اکبر | نمایندگان | علی‌اکبر | PROPN | علی‌اکبر | B-PER | | +| 22 | | | موسوی | تهران | موسوی | PROPN | موسوی | I-PER | | +| 23 | | | خوئینی | عنوان | خوئینی | PROPN | خوئینی | I-PER | | +| 24 | | | و | نواب | او | CCONJ | و | O | | +| 25 | | | شمس‌الدین | رئیس | شمس‌الدین | PROPN | شمس‌الدین | B-PER | | +| 26 | | | وهابی | انتخاب | وهابی | PROPN | وهابی | I-PER | | +| 27 | | | نمایندگان | | نماینده | NOUN | نمایندگان | O | | +| 28 | | | مردم | | مردم | NOUN | مردم | O | | +| 29 | | | تهران | | تهران | PROPN | تهران | B-LOC | | +| 30 | | | به | | به | ADP | به | O | | +| 31 | | | عنوان | | عنوان | NOUN | عنوان | O | | +| 32 | | | نواب | | نواب | NOUN | نواب | O | | +| 33 | | | رئیس | | رئیس | NOUN | رئیس | O | | +| 34 | | | انتخاب | | انتخاب | NOUN | انتخاب | O | | +| 35 | | | شدند | | کرد#کن | VERB | شدند | O | | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recognize_entities_dl| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fa| +|Size:|1.2 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- StopWordsCleaner +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-roberta_base_token_classifier_ontonotes_pipeline_en.md new file mode 100644 index 00000000000000..0778fc9a699292 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -0,0 +1,104 @@ +--- +layout: model +title: RoBERTa Base Ontonotes NER Pipeline +author: John Snow Labs +name: roberta_base_token_classifier_ontonotes_pipeline +date: 2023-05-21 +tags: [open_source, ner, token_classifier, roberta, ontonotes, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_base_token_classifier_ontonotes](https://nlp.johnsnowlabs.com/2021/09/26/roberta_base_token_classifier_ontonotes_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.0_1684654215838.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.0_1684654215838.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala + +val pipeline = new PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +## Results + +```bash +Results + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | +|November 2020 |DATE | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_base_token_classifier_ontonotes_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|456.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-roberta_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-roberta_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..896a3bee6473e6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-roberta_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: RoBERTa Large CoNLL-03 NER Pipeline +author: John Snow Labs +name: roberta_large_token_classifier_conll03_pipeline +date: 2023-05-21 +tags: [open_source, ner, token_classifier, roberta, conll03, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/roberta_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_large_token_classifier_conll03_pipeline_en_4.4.2_3.0_1684654494236.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_large_token_classifier_conll03_pipeline_en_4.4.2_3.0_1684654494236.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.3 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_bne_capitel_ner_pipeline_es.md b/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_bne_capitel_ner_pipeline_es.md new file mode 100644 index 00000000000000..43d61d82783359 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_bne_capitel_ner_pipeline_es.md @@ -0,0 +1,104 @@ +--- +layout: model +title: Spanish NER Pipeline +author: John Snow Labs +name: roberta_token_classifier_bne_capitel_ner_pipeline +date: 2023-05-21 +tags: [roberta, token_classifier, spanish, ner, es, open_source] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_token_classifier_bne_capitel_ner_es](https://nlp.johnsnowlabs.com/2021/12/07/roberta_token_classifier_bne_capitel_ner_es.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_bne_capitel_ner_pipeline_es_4.4.2_3.0_1684652188124.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_bne_capitel_ner_pipeline_es_4.4.2_3.0_1684652188124.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +```scala + +val pipeline = new PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +
+ +## Results + +```bash +Results + + + ++------------------------+---------+ +|chunk |ner_label| ++------------------------+---------+ +|Antonio |PER | +|fábrica de Mercedes-Benz|ORG | +|Madrid |LOC | ++------------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_token_classifier_bne_capitel_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|459.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_icelandic_ner_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_icelandic_ner_pipeline_is.md new file mode 100644 index 00000000000000..5636ff0f7d2ac6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_icelandic_ner_pipeline_is.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Icelandic NER Pipeline +author: John Snow Labs +name: roberta_token_classifier_icelandic_ner_pipeline +date: 2023-05-21 +tags: [open_source, ner, token_classifier, roberta, icelandic, is] +task: Named Entity Recognition +language: is +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_token_classifier_icelandic_ner](https://nlp.johnsnowlabs.com/2021/12/06/roberta_token_classifier_icelandic_ner_is.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_icelandic_ner_pipeline_is_4.4.2_3.0_1684652336165.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_icelandic_ner_pipeline_is_4.4.2_3.0_1684652336165.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +```scala + +val pipeline = new PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +
+ +## Results + +```bash +Results + + + ++----------------+------------+ +|chunk |ner_label | ++----------------+------------+ +|Peter Fergusson |Person | +|New York |Location | +|október 2011 |Date | +|Tesla Motor |Organization| +|100K $ |Money | ++----------------+------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_token_classifier_icelandic_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|is| +|Size:|457.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_timex_semeval_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_timex_semeval_pipeline_en.md new file mode 100644 index 00000000000000..f49f81cfbb1ea6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_timex_semeval_pipeline_en.md @@ -0,0 +1,110 @@ +--- +layout: model +title: Pipeline to Detect Time-related Terminology +author: John Snow Labs +name: roberta_token_classifier_timex_semeval_pipeline +date: 2023-05-21 +tags: [timex, semeval, ner, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_token_classifier_timex_semeval](https://nlp.johnsnowlabs.com/2021/12/28/roberta_token_classifier_timex_semeval_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_timex_semeval_pipeline_en_4.4.2_3.0_1684650430965.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_timex_semeval_pipeline_en_4.4.2_3.0_1684650430965.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +timex_pipeline = PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +```scala + + +val timex_pipeline = new PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +timex_pipeline = PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +```scala +val timex_pipeline = new PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +
+ +## Results + +```bash +Results + + + + ++-------+-----------------+ +|chunk |ner_label | ++-------+-----------------+ +|22:12C |Period | +|3 |Number | +|days |Calendar-Interval| +|Tuesday|Day-Of-Week | +|to |Between | +|Friday |Day-Of-Week | ++-------+-----------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_token_classifier_timex_semeval_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|439.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-spellcheck_dl_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-spellcheck_dl_pipeline_en.md new file mode 100644 index 00000000000000..50b9658933ce7d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-spellcheck_dl_pipeline_en.md @@ -0,0 +1,112 @@ +--- +layout: model +title: Context Spell Checker Pipeline for English +author: John Snow Labs +name: spellcheck_dl_pipeline +date: 2023-05-21 +tags: [spellcheck, spell, spellcheck_pipeline, spelling_corrector, en, open_source] +task: Spell Check +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained spellchecker pipeline is built on the top of [spellcheck_dl](https://nlp.johnsnowlabs.com/2022/04/02/spellcheck_dl_en_2_4.html) model. This pipeline is for PySpark 2.4.x users with SparkNLP 3.4.2 and above. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/spellcheck_dl_pipeline_en_4.4.2_3.0_1684651384893.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/spellcheck_dl_pipeline_en_4.4.2_3.0_1684651384893.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +text = ["During the summer we have the best ueather.", "I have a black ueather jacket, so nice."] + +pipeline.annotate(text) +``` +```scala + + +val pipeline = new PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +val example = Array("During the summer we have the best ueather.", "I have a black ueather jacket, so nice.") + +pipeline.annotate(example) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +text = ["During the summer we have the best ueather.", "I have a black ueather jacket, so nice."] + +pipeline.annotate(text) +``` +```scala +val pipeline = new PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +val example = Array("During the summer we have the best ueather.", "I have a black ueather jacket, so nice.") + +pipeline.annotate(example) +``` +
+ +## Results + +```bash +Results + + + + +[{'checked': ['During', 'the', 'summer', 'we', 'have', 'the', 'best', 'weather', '.'], + 'document': ['During the summer we have the best ueather.'], + 'token': ['During', 'the', 'summer', 'we', 'have', 'the', 'best', 'ueather', '.']}, + + {'checked': ['I', 'have', 'a', 'black', 'leather', 'jacket', ',', 'so', 'nice', '.'], + 'document': ['I have a black ueather jacket, so nice.'], + 'token': ['I', 'have', 'a', 'black', 'ueather', 'jacket', ',', 'so', 'nice', '.']}] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|spellcheck_dl_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|99.7 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- ContextSpellCheckerModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-text_cleaning_en.md b/docs/_posts/ahmedlone127/2023-05-21-text_cleaning_en.md new file mode 100644 index 00000000000000..2450fe258fcc9a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-text_cleaning_en.md @@ -0,0 +1,80 @@ +--- +layout: model +title: Text Cleaning +author: John Snow Labs +name: text_cleaning +date: 2023-05-21 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The text_cleaning is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and cleans text. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/text_cleaning_en_4.4.2_3.0_1684632793360.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/text_cleaning_en_4.4.2_3.0_1684632793360.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("text_cleaning", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("text_cleaning", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|text_cleaning| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|944.5 KB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- NormalizerModel +- StopWordsCleaner +- LemmatizerModel +- TokenAssembler \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_base_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..66efd285422515 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_base_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: XLM-RoBERTa Base, CoNLL-03 NER Pipeline +author: John Snow Labs +name: xlm_roberta_base_token_classifier_conll03_pipeline +date: 2023-05-21 +tags: [open_source, ner, token_classifier, xlm_roberta, conll03, xlm, base, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/10/03/xlm_roberta_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_conll03_pipeline_en_4.4.2_3.0_1684653563611.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_conll03_pipeline_en_4.4.2_3.0_1684653563611.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|851.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md new file mode 100644 index 00000000000000..61da217d710f2d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: XLM-RoBERTa Base NER Pipeline +author: John Snow Labs +name: xlm_roberta_base_token_classifier_ontonotes_pipeline +date: 2023-05-21 +tags: [open_source, ner, token_classifier, xlm_roberta, ontonotes, xlm, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifier_ontonotes](https://nlp.johnsnowlabs.com/2021/10/03/xlm_roberta_base_token_classifier_ontonotes_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.0_1684653311042.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.0_1684653311042.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | +|November 2020 |DATE | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_base_token_classifier_ontonotes_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|858.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_conll03_pipeline_de.md b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_conll03_pipeline_de.md new file mode 100644 index 00000000000000..bb9753961cb0a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_conll03_pipeline_de.md @@ -0,0 +1,104 @@ +--- +layout: model +title: NER Pipeline for German +author: John Snow Labs +name: xlm_roberta_large_token_classifier_conll03_pipeline +date: 2023-05-21 +tags: [german, roberta, xlm, ner, conll03, de, open_source] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_large_token_classifier_conll03_de](https://nlp.johnsnowlabs.com/2021/12/25/xlm_roberta_large_token_classifier_conll03_de.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_conll03_pipeline_de_4.4.2_3.0_1684651992754.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_conll03_pipeline_de_4.4.2_3.0_1684651992754.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +```scala + +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +
+ +## Results + +```bash +Results + + + ++----------------------+---------+ +|chunk |ner_label| ++----------------------+---------+ +|Ibser |PER | +|ASK Ebreichsdorf |ORG | +|Admira Wacker Mödling |ORG | ++----------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|1.8 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md new file mode 100644 index 00000000000000..549d4b28e9709b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md @@ -0,0 +1,104 @@ +--- +layout: model +title: NER Pipeline for 10 High Resourced Languages +author: John Snow Labs +name: xlm_roberta_large_token_classifier_hrl_pipeline +date: 2023-05-21 +tags: [arabic, german, english, spanish, french, italian, latvian, dutch, portuguese, chinese, xlm, roberta, ner, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_large_token_classifier_hrl](https://nlp.johnsnowlabs.com/2021/12/26/xlm_roberta_large_token_classifier_hrl_xx.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_hrl_pipeline_xx_4.4.2_3.0_1684651206434.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_hrl_pipeline_xx_4.4.2_3.0_1684651206434.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +```scala + +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +
+ +## Results + +```bash +Results + + + ++---------------------------+---------+ +|chunk |ner_label| ++---------------------------+---------+ +|الرياض |LOC | +|فيصل بن بندر بن عبد العزيز |PER | +|الرياض |LOC | ++---------------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_large_token_classifier_hrl_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|1.8 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md new file mode 100644 index 00000000000000..4f77ba5205735f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md @@ -0,0 +1,104 @@ +--- +layout: model +title: NER Pipeline for 10 African Languages +author: John Snow Labs +name: xlm_roberta_large_token_classifier_masakhaner_pipeline +date: 2023-05-21 +tags: [masakhaner, african, xlm_roberta, multilingual, pipeline, amharic, hausa, igbo, kinyarwanda, luganda, swahilu, wolof, yoruba, nigerian, pidgin, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on [xlm_roberta_large_token_classifier_masakhaner](https://nlp.johnsnowlabs.com/2021/12/06/xlm_roberta_large_token_classifier_masakhaner_xx.html) ner model which is imported from `HuggingFace`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_masakhaner_pipeline_xx_4.4.2_3.0_1684649452893.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_masakhaner_pipeline_xx_4.4.2_3.0_1684649452893.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +masakhaner_pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +```scala + +val masakhaner_pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +val masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +masakhaner_pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +```scala +val masakhaner_pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +val masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +
+ +## Results + +```bash +Results + + + ++----------------+---------+ +|chunk |ner_label| ++----------------+---------+ +|አህመድ ቫንዳ |PER | +|ከ3-10-2000 ጀምሮ|DATE | +|በአዲስ አበባ |LOC | ++----------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_large_token_classifier_masakhaner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|1.8 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-xlnet_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-xlnet_base_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..74dfe5133cd1c1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-xlnet_base_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: XLNet Base CoNLL-03 NER Pipeline +author: John Snow Labs +name: xlnet_base_token_classifier_conll03_pipeline +date: 2023-05-21 +tags: [ner, english, xlnet, base, token_classification, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlnet_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/28/xlnet_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlnet_base_token_classifier_conll03_pipeline_en_4.4.2_3.0_1684653077251.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlnet_base_token_classifier_conll03_pipeline_en_4.4.2_3.0_1684653077251.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlnet_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|438.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlnetForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-21-xlnet_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-xlnet_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..8fa9ae510aa17e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-21-xlnet_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,103 @@ +--- +layout: model +title: XLNet Large CoNLL-03 NER Pipeline +author: John Snow Labs +name: xlnet_large_token_classifier_conll03_pipeline +date: 2023-05-21 +tags: [open_source, ner, token_classifier, xlnet, conll03, large, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlnet_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/28/xlnet_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlnet_large_token_classifier_conll03_pipeline_en_4.4.2_3.0_1684653645790.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlnet_large_token_classifier_conll03_pipeline_en_4.4.2_3.0_1684653645790.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlnet_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|19.1 KB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- NormalizerModel \ No newline at end of file From f28ea8eb784d91442c1603d66fcd78a5ba2e6c88 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Tue, 23 May 2023 20:26:18 +0700 Subject: [PATCH 08/55] 2023-05-22-explain_document_md_fr (#13811) * Add model 2023-05-22-explain_document_md_fr * Add model 2023-05-22-dependency_parse_en * Add model 2023-05-22-explain_document_md_it * Add model 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+--- + +## Description + +This pretrained pipeline is built on the top of [albert_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/albert_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_token_classifier_conll03_pipeline_en_4.4.2_3.2_1684764563581.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_token_classifier_conll03_pipeline_en_4.4.2_3.2_1684764563581.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|43.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- AlbertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-albert_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-albert_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..491f9477630b3a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-albert_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: ALBERT Large CoNNL-03 NER Pipeline +author: John Snow Labs +name: albert_large_token_classifier_conll03_pipeline +date: 2023-05-22 +tags: [open_source, ner, token_classifier, albert, conll03, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [albert_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/albert_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_token_classifier_conll03_pipeline_en_4.4.2_3.2_1684764638897.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_token_classifier_conll03_pipeline_en_4.4.2_3.2_1684764638897.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|64.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- AlbertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-albert_xlarge_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-albert_xlarge_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..f941f83ee783e6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-albert_xlarge_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: ALBERT XLarge CoNNL-03 NER Pipeline +author: John Snow Labs +name: albert_xlarge_token_classifier_conll03_pipeline +date: 2023-05-22 +tags: [open_source, ner, token_classifier, albert, conll03, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [albert_xlarge_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/albert_xlarge_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_xlarge_token_classifier_conll03_pipeline_en_4.4.2_3.2_1684764737089.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_xlarge_token_classifier_conll03_pipeline_en_4.4.2_3.2_1684764737089.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_xlarge_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|206.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- AlbertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-analyze_sentiment_en.md b/docs/_posts/ahmedlone127/2023-05-22-analyze_sentiment_en.md new file mode 100644 index 00000000000000..b28842b096cb8f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-analyze_sentiment_en.md @@ -0,0 +1,130 @@ +--- +layout: model +title: Sentiment Analysis pipeline for English +author: John Snow Labs +name: analyze_sentiment +date: 2023-05-22 +tags: [open_source, english, analyze_sentiment, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The analyze_sentiment is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/analyze_sentiment_en_4.4.2_3.2_1684740585084.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/analyze_sentiment_en_4.4.2_3.2_1684740585084.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python + +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en') + +result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") + + +``` +```scala + +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en") + +val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") + +``` + +{:.nlu-block} +```python + +import nlu +text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""] +result_df = nlu.load('en.classify').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en') + +result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en") + +val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") +``` + +{:.nlu-block} +```python +import nlu +text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""] +result_df = nlu.load('en.classify').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | text | sentiment | +|---:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------| +| 0 | Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now! | positive | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|analyze_sentiment| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|5.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel +- ViveknSentimentModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-analyze_sentimentdl_glove_imdb_en.md b/docs/_posts/ahmedlone127/2023-05-22-analyze_sentimentdl_glove_imdb_en.md new file mode 100644 index 00000000000000..12dad070707716 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-analyze_sentimentdl_glove_imdb_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Sentiment Analysis pipeline for English (analyze_sentimentdl_glove_imdb) +author: John Snow Labs +name: analyze_sentimentdl_glove_imdb +date: 2023-05-22 +tags: [open_source, english, analyze_sentimentdl_glove_imdb, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The analyze_sentimentdl_glove_imdb is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/analyze_sentimentdl_glove_imdb_en_4.4.2_3.2_1684750145035.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/analyze_sentimentdl_glove_imdb_en_4.4.2_3.2_1684750145035.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("analyze_sentimentdl_glove_imdb", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.sentiment.glove').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("analyze_sentimentdl_glove_imdb", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.sentiment.glove').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | tokens | word_embeddings | sentence_embeddings | sentiment | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-----------------------------|:------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2668800055980682,.,...]] | [[0.0771183446049690,.,...]] | ['neg'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|analyze_sentimentdl_glove_imdb| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|161.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- SentenceEmbeddings +- SentimentDLModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-bert_sequence_classifier_age_news_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-bert_sequence_classifier_age_news_pipeline_en.md new file mode 100644 index 00000000000000..9a8e3fb42796b3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-bert_sequence_classifier_age_news_pipeline_en.md @@ -0,0 +1,98 @@ +--- +layout: model +title: Pipeline to Classify Texts into 4 News Categories +author: John Snow Labs +name: bert_sequence_classifier_age_news_pipeline +date: 2023-05-22 +tags: [ag_news, news, bert, bert_sequence, classification, en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [bert_sequence_classifier_age_news_en](https://nlp.johnsnowlabs.com/2021/11/07/bert_sequence_classifier_age_news_en.html) which is imported from `HuggingFace`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_age_news_pipeline_en_4.4.2_3.2_1684760588184.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_age_news_pipeline_en_4.4.2_3.2_1684760588184.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +news_pipeline = PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +```scala + + +val news_pipeline = new PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +news_pipeline = PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +```scala +val news_pipeline = new PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +
+ +## Results + +```bash +Results + + + + +['Sci/Tech'] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_age_news_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|42.4 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- BertForSequenceClassification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-bert_sequence_classifier_trec_coarse_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-bert_sequence_classifier_trec_coarse_pipeline_en.md new file mode 100644 index 00000000000000..42d4d0ad25c7db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-bert_sequence_classifier_trec_coarse_pipeline_en.md @@ -0,0 +1,100 @@ +--- +layout: model +title: Pipeline to Classify Texts into TREC-6 Classes +author: John Snow Labs +name: bert_sequence_classifier_trec_coarse_pipeline +date: 2023-05-22 +tags: [bert_sequence, trec, coarse, bert, en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [bert_sequence_classifier_trec_coarse_en](https://nlp.johnsnowlabs.com/2021/11/06/bert_sequence_classifier_trec_coarse_en.html). + +The TREC dataset for question classification consists of open-domain, fact-based questions divided into broad semantic categories. You can check the official documentation of the dataset, entities, etc. [here](https://search.r-project.org/CRAN/refmans/textdata/html/dataset_trec.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_trec_coarse_pipeline_en_4.4.2_3.2_1684760511228.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_trec_coarse_pipeline_en_4.4.2_3.2_1684760511228.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +trec_pipeline = PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +```scala + + +val trec_pipeline = new PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +trec_pipeline = PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +```scala +val trec_pipeline = new PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +
+ +## Results + +```bash +Results + + + + +['LOC'] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_trec_coarse_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|406.6 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- BertForSequenceClassification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md b/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md new file mode 100644 index 00000000000000..e344aa5f0b4f91 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Dutch NER Pipeline +author: John Snow Labs +name: bert_token_classifier_dutch_udlassy_ner_pipeline +date: 2023-05-22 +tags: [open_source, ner, dutch, token_classifier, bert, treatment, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [bert_token_classifier_dutch_udlassy_ner](https://nlp.johnsnowlabs.com/2021/12/08/bert_token_classifier_dutch_udlassy_ner_nl.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_token_classifier_dutch_udlassy_ner_pipeline_nl_4.4.2_3.2_1684762242282.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_token_classifier_dutch_udlassy_ner_pipeline_nl_4.4.2_3.2_1684762242282.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +```scala + +val pipeline = new PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +```scala +val pipeline = new PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +
+ +## Results + +```bash +Results + + + ++---------------+---------+ +|chunk |ner_label| ++---------------+---------+ +|Peter Fergusson|PERSON | +|oktober 2011 |DATE | +|New York |GPE | +|5 jaar |DATE | +|Tesla Motor |ORG | ++---------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_token_classifier_dutch_udlassy_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|408.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_hi_en_ner_pipeline_hi.md b/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_hi_en_ner_pipeline_hi.md new file mode 100644 index 00000000000000..0dbde13ed54557 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_hi_en_ner_pipeline_hi.md @@ -0,0 +1,113 @@ +--- +layout: model +title: NER Pipeline for Hindi+English +author: John Snow Labs +name: bert_token_classifier_hi_en_ner_pipeline +date: 2023-05-22 +tags: [hindi, bert_token, hi, open_source] +task: Named Entity Recognition +language: hi +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on [bert_token_classifier_hi_en_ner](https://nlp.johnsnowlabs.com/2021/12/27/bert_token_classifier_hi_en_ner_hi.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_token_classifier_hi_en_ner_pipeline_hi_4.4.2_3.2_1684761323620.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_token_classifier_hi_en_ner_pipeline_hi_4.4.2_3.2_1684761323620.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +```scala + +val pipeline = new PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +val pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +```scala +val pipeline = new PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +val pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +
+ +## Results + +```bash +Results + + + ++---------------------------+------------+ +|chunk |ner_label | ++---------------------------+------------+ +|रिलायंस इंडस्ट्रीज़ लिमिटेड |ORGANISATION| +|Reliance Industries Limited|ORGANISATION| +|भारतीय |PLACE | +|मुंबई |PLACE | +|महाराष्ट्र |PLACE | +|Maharashtra) |PLACE | +|नवल टाटा |PERSON | +|मुम्बई |PLACE | +|Mumbai |PLACE | +|टाटा समुह |ORGANISATION| +|भारत |PLACE | +|जमशेदजी टाटा |PERSON | ++---------------------------+------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_token_classifier_hi_en_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|hi| +|Size:|665.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- BertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_scandi_ner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_scandi_ner_pipeline_xx.md new file mode 100644 index 00000000000000..96ecea3ee6a270 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_scandi_ner_pipeline_xx.md @@ -0,0 +1,103 @@ +--- +layout: model +title: NER Pipeline for 6 Scandinavian Languages +author: John Snow Labs +name: bert_token_classifier_scandi_ner_pipeline +date: 2023-05-22 +tags: [danish, norwegian, swedish, icelandic, faroese, bert, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on [bert_token_classifier_scandi_ner](https://nlp.johnsnowlabs.com/2021/12/09/bert_token_classifier_scandi_ner_xx.html) model which is imported from `HuggingFace`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_token_classifier_scandi_ner_pipeline_xx_4.4.2_3.2_1684760367866.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_token_classifier_scandi_ner_pipeline_xx_4.4.2_3.2_1684760367866.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +scandiner_pipeline = PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") +scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +```scala + +val scandiner_pipeline = new PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") + +val scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +scandiner_pipeline = PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") +scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +```scala +val scandiner_pipeline = new PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") + +val scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +
+ +## Results + +```bash +Results + + + ++-------------------+---------+ +|chunk |ner_label| ++-------------------+---------+ +|Hans |PER | +|Statens Universitet|ORG | +|København |LOC | +|københavner |MISC | ++-------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_token_classifier_scandi_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|666.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- BertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-check_spelling_en.md b/docs/_posts/ahmedlone127/2023-05-22-check_spelling_en.md new file mode 100644 index 00000000000000..de2fe4ed013f0d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-check_spelling_en.md @@ -0,0 +1,120 @@ +--- +layout: model +title: Spell Checking Pipeline for English +author: John Snow Labs +name: check_spelling +date: 2023-05-22 +tags: [open_source, english, check_spelling, pipeline, en] +task: Spell Check +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The check_spelling is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/check_spelling_en_4.4.2_3.2_1684739703561.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/check_spelling_en_4.4.2_3.2_1684739703561.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('check_spelling', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("check_spelling", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('check_spelling', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("check_spelling", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | checked | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------------------------| +| 0 | ['I liek to live dangertus ! '] | ['I liek to live dangertus !'] | ['I', 'liek', 'to', 'live', 'dangertus', '!'] | ['I', 'like', 'to', 'live', 'dangerous', '!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|check_spelling| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|906.2 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-clean_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-22-clean_pattern_en.md new file mode 100644 index 00000000000000..d1a29a03fad8ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-clean_pattern_en.md @@ -0,0 +1,120 @@ +--- +layout: model +title: Clean patterns pipeline for English +author: John Snow Labs +name: clean_pattern +date: 2023-05-22 +tags: [open_source, english, clean_pattern, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The clean_pattern is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clean_pattern_en_4.4.2_3.2_1684739857350.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clean_pattern_en_4.4.2_3.2_1684739857350.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_pattern', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala + + +val pipeline = new PretrainedPipeline("clean_pattern", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python + + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.pattern').predict(text) +result_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_pattern', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("clean_pattern", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.pattern').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + + +| | document | sentence | token | normal | +|---:|:-----------|:-----------|:----------|:----------| +| 0 | ['Hello'] | ['Hello'] | ['Hello'] | ['Hello'] || | document | sentence | token | normal | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clean_pattern| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|17.2 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NormalizerModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-clean_stop_en.md b/docs/_posts/ahmedlone127/2023-05-22-clean_stop_en.md new file mode 100644 index 00000000000000..d3ce8b84318824 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-clean_stop_en.md @@ -0,0 +1,120 @@ +--- +layout: model +title: Clean documents pipeline for English +author: John Snow Labs +name: clean_stop +date: 2023-05-22 +tags: [open_source, english, clean_stop, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The clean_stop is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clean_stop_en_4.4.2_3.2_1684739913044.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clean_stop_en_4.4.2_3.2_1684739913044.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_stop', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("clean_stop", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.stop').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_stop', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("clean_stop", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.stop').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | cleanTokens | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:---------------------------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | ['Hello', 'John', 'Snow', 'Labs', '!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clean_stop| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|14.2 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- StopWordsCleaner \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-dependency_parse_en.md b/docs/_posts/ahmedlone127/2023-05-22-dependency_parse_en.md new file mode 100644 index 00000000000000..4ccf3bb9676eab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-dependency_parse_en.md @@ -0,0 +1,119 @@ +--- +layout: model +title: Typed Dependency Parsing pipeline for English +author: John Snow Labs +name: dependency_parse +date: 2023-05-22 +tags: [pipeline, dependency_parsing, untyped_dependency_parsing, typed_dependency_parsing, laballed_depdency_parsing, unlaballed_depdency_parsing, en, open_source] +task: Dependency Parser +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Typed Dependency parser, trained on the on the CONLL dataset. + +Dependency parsing is the task of extracting a dependency parse of a sentence that represents its grammatical structure and defines the relationships between “head” words and words, which modify those heads. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dependency_parse_en_4.4.2_3.2_1684738088939.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dependency_parse_en_4.4.2_3.2_1684738088939.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('dependency_parse', lang = 'en') +annotations = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("dependency_parse", lang = "en") +val result = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence")(0) + +``` + +{:.nlu-block} +```python + +nlu.load("dep.typed").predict("Dependencies represents relationships betweens words in a Sentence") + + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('dependency_parse', lang = 'en') +annotations = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("dependency_parse", lang = "en") +val result = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence")(0) +``` + +{:.nlu-block} +```python +nlu.load("dep.typed").predict("Dependencies represents relationships betweens words in a Sentence") +``` +
+ +## Results + +```bash +Results + + ++---------------------------------------------------------------------------------+--------------------------------------------------------+ +|result |result | ++---------------------------------------------------------------------------------+--------------------------------------------------------+ +|[ROOT, Dependencies, represents, words, relationships, Sentence, Sentence, words]|[root, parataxis, nsubj, amod, nsubj, case, nsubj, flat]| ++---------------------------------------------------------------------------------+--------------------------------------------------------+ + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dependency_parse| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|23.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel +- DependencyParserModel +- TypedDependencyParserModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-distilbert_base_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-22-distilbert_base_token_classifier_masakhaner_pipeline_xx.md new file mode 100644 index 00000000000000..b9e9ad4659bce5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-distilbert_base_token_classifier_masakhaner_pipeline_xx.md @@ -0,0 +1,108 @@ +--- +layout: model +title: NER Pipeline for 9 African Languages +author: John Snow Labs +name: distilbert_base_token_classifier_masakhaner_pipeline +date: 2023-05-22 +tags: [hausa, igbo, kinyarwanda, luganda, nigerian, pidgin, swahilu, wolof, yoruba, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilbert_base_token_classifier_masakhaner](https://nlp.johnsnowlabs.com/2022/01/18/distilbert_base_token_classifier_masakhaner_xx.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_token_classifier_masakhaner_pipeline_xx_4.4.2_3.2_1684760887859.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_token_classifier_masakhaner_pipeline_xx_4.4.2_3.2_1684760887859.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +masakhaner_pipeline = PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +```scala + +val masakhaner_pipeline = new PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +masakhaner_pipeline = PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +```scala +val masakhaner_pipeline = new PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +
+ +## Results + +```bash +Results + + + ++-----------------------------+---------+ +|chunk |ner_label| ++-----------------------------+---------+ +|Mohammed Sani Musa |PER | +|Activate Technologies Limited|ORG | +|ọdún-un 2019 |DATE | +|All rogressives Congress |ORG | +|APC |ORG | +|Aṣojú Ìlà-Oòrùn Niger |LOC | +|Premium Times |ORG | ++-----------------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_token_classifier_masakhaner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|505.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- DistilBertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-distilbert_token_classifier_typo_detector_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-distilbert_token_classifier_typo_detector_pipeline_en.md new file mode 100644 index 00000000000000..68fd4a0067cb56 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-distilbert_token_classifier_typo_detector_pipeline_en.md @@ -0,0 +1,105 @@ +--- +layout: model +title: Typo Detector Pipeline for English +author: John Snow Labs +name: distilbert_token_classifier_typo_detector_pipeline +date: 2023-05-22 +tags: [ner, bert, bert_for_token, typo, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilbert_token_classifier_typo_detector](https://nlp.johnsnowlabs.com/2022/01/19/distilbert_token_classifier_typo_detector_en.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_en_4.4.2_3.2_1684765440650.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_en_4.4.2_3.2_1684765440650.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +```scala + + +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +```scala +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +
+ +## Results + +```bash +Results + + + + ++----------+---------+ +|chunk |ner_label| ++----------+---------+ +|stgruggled|PO | +|tine |PO | ++----------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_token_classifier_typo_detector_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|244.1 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- DistilBertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-distilbert_token_classifier_typo_detector_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-22-distilbert_token_classifier_typo_detector_pipeline_is.md new file mode 100644 index 00000000000000..5264facb573761 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-distilbert_token_classifier_typo_detector_pipeline_is.md @@ -0,0 +1,102 @@ +--- +layout: model +title: Typo Detector Pipeline for Icelandic +author: John Snow Labs +name: distilbert_token_classifier_typo_detector_pipeline +date: 2023-05-22 +tags: [icelandic, typo, ner, distilbert, is, open_source] +task: Named Entity Recognition +language: is +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilbert_token_classifier_typo_detector_is](https://nlp.johnsnowlabs.com/2022/01/19/distilbert_token_classifier_typo_detector_is.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_is_4.4.2_3.2_1684760726687.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_is_4.4.2_3.2_1684760726687.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +```scala + +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +```scala +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +
+ +## Results + +```bash +Results + + + ++--------+---------+ +|chunk |ner_label| ++--------+---------+ +|miög |PO | +|álykanir|PO | ++--------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_token_classifier_typo_detector_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|is| +|Size:|505.8 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- DistilBertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-distilroberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-distilroberta_base_token_classifier_ontonotes_pipeline_en.md new file mode 100644 index 00000000000000..27235c5826115d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-distilroberta_base_token_classifier_ontonotes_pipeline_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: DistilRoBERTa Base Ontonotes NER Pipeline +author: John Snow Labs +name: distilroberta_base_token_classifier_ontonotes_pipeline +date: 2023-05-22 +tags: [open_source, ner, token_classifier, distilroberta, ontonotes, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilroberta_base_token_classifier_ontonotes](https://nlp.johnsnowlabs.com/2021/09/26/distilroberta_base_token_classifier_ontonotes_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilroberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.2_1684764854469.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilroberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.2_1684764854469.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala + + +val pipeline = new PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala +val pipeline = new PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | +|November 2020 |DATE | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilroberta_base_token_classifier_ontonotes_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|307.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_da.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_da.md new file mode 100644 index 00000000000000..3db719c608008b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_da.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-22 +tags: [da, open_source] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_da_4.4.2_3.2_1684754325851.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_da_4.4.2_3.2_1684754325851.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "da") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("da.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "da") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("da.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_de.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_de.md new file mode 100644 index 00000000000000..e610fe29fc9466 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_de.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL pipeline for German - Large +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-22 +tags: [open_source, german, entity_recognizer_lg, pipeline, de] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_de_4.4.2_3.2_1684741714892.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_de_4.4.2_3.2_1684741714892.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo aus John Snow Labs! '] | ['Hallo aus John Snow Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | [[-0.245989993214607,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_es.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_es.md new file mode 100644 index 00000000000000..3379529b426ecc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_es.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognition Pipeline (Large, Spanish) +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-22 +tags: [es, open_source] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_es_4.4.2_3.2_1684743745132.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_es_4.4.2_3.2_1684743745132.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "es") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("es.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "es") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_fi.md new file mode 100644 index 00000000000000..19276be9edc20f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_fi.md @@ -0,0 +1,95 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-22 +tags: [fi, open_source] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fi_4.4.2_3.2_1684755830767.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fi_4.4.2_3.2_1684755830767.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "fi") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("fi.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "fi") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("fi.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_fr.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_fr.md new file mode 100644 index 00000000000000..b0724b67df638f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_fr.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL pipeline for French - Large +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-22 +tags: [open_source, french, entity_recognizer_lg, pipeline, fr] +task: Named Entity Recognition +language: fr +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fr_4.4.2_3.2_1684738793356.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fr_4.4.2_3.2_1684738793356.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:--------------------------------|:-------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Bonjour de John Snow Labs! '] | ['Bonjour de John Snow Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | [[-0.010997000150382,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fr| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_it.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_it.md new file mode 100644 index 00000000000000..3ef359aa5617df --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_it.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL pipeline for Italian - Large +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-22 +tags: [open_source, italian, entity_recognizer_lg, pipeline, it] +task: Named Entity Recognition +language: it +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_it_4.4.2_3.2_1684739416407.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_it_4.4.2_3.2_1684739416407.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Ciao da John Snow Labs! '] | ['Ciao da John Snow Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | [[-0.238279998302459,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|it| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_nl.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_nl.md new file mode 100644 index 00000000000000..a833bd49ae918d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_nl.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-22 +tags: [nl, open_source] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_nl_4.4.2_3.2_1684749617370.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_nl_4.4.2_3.2_1684749617370.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "nl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("nl.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "nl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("nl.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_pl.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_pl.md new file mode 100644 index 00000000000000..9b7cfa38872a5e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_pl.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-22 +tags: [pl, open_source] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pl_4.4.2_3.2_1684748190518.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pl_4.4.2_3.2_1684748190518.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("pl.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pl.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_pt.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_pt.md new file mode 100644 index 00000000000000..b62f280417de8a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_pt.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-22 +tags: [pt, open_source] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pt_4.4.2_3.2_1684746737662.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pt_4.4.2_3.2_1684746737662.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pt") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pt") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_ru.md new file mode 100644 index 00000000000000..592f8f3daee790 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_ru.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-22 +tags: [ru, open_source] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_ru_4.4.2_3.2_1684745198275.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_ru_4.4.2_3.2_1684745198275.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "ru") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("ru.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "ru") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ru.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_sv.md new file mode 100644 index 00000000000000..ea42724fa41ac2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_sv.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-22 +tags: [sv, open_source] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_sv_4.4.2_3.2_1684752852321.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_sv_4.4.2_3.2_1684752852321.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "sv") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("sv.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "sv") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("sv.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_da.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_da.md new file mode 100644 index 00000000000000..250bce40234d48 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_da.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Danish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-22 +tags: [open_source, danish, entity_recognizer_md, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_da_4.4.2_3.2_1684754550183.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_da_4.4.2_3.2_1684754550183.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|461.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_de.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_de.md new file mode 100644 index 00000000000000..f65fb8fc1941c5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_de.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for German - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-22 +tags: [open_source, german, entity_recognizer_md, pipeline, de] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_de_4.4.2_3.2_1684741919759.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_de_4.4.2_3.2_1684741919759.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo aus John Snow Labs! '] | ['Hallo aus John Snow Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|461.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_es.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_es.md new file mode 100644 index 00000000000000..481e6272b3ea80 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_es.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Spanish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-22 +tags: [open_source, spanish, entity_recognizer_md, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_es_4.4.2_3.2_1684743970696.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_es_4.4.2_3.2_1684743970696.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | [[0.5123000144958496,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|461.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_fi.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_fi.md new file mode 100644 index 00000000000000..a136eb2862bb2a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_fi.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Finnish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-22 +tags: [open_source, finnish, entity_recognizer_md, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fi_4.4.2_3.2_1684756054603.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fi_4.4.2_3.2_1684756054603.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | [[0.1868100017309188,.,...]] | ['O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|462.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_fr.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_fr.md new file mode 100644 index 00000000000000..f1cc75df9acfff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_fr.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for French - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-22 +tags: [open_source, french, entity_recognizer_md, pipeline, fr] +task: Named Entity Recognition +language: fr +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fr_4.4.2_3.2_1684739005452.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fr_4.4.2_3.2_1684739005452.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:--------------------------------|:-------------------------------|:-------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------------------| +| 0 | ['Bonjour de John Snow Labs! '] | ['Bonjour de John Snow Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | [[0.0783179998397827,.,...]] | ['I-MISC', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['Bonjour', 'John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fr| +|Size:|462.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_it.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_it.md new file mode 100644 index 00000000000000..40bc0b41363862 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_it.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Italian - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-22 +tags: [open_source, italian, entity_recognizer_md, pipeline, it] +task: Named Entity Recognition +language: it +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_it_4.4.2_3.2_1684739634384.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_it_4.4.2_3.2_1684739634384.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Ciao da John Snow Labs! '] | ['Ciao da John Snow Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | [[-0.146050006151199,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|it| +|Size:|462.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_nl.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_nl.md new file mode 100644 index 00000000000000..0f20b201d841a1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_nl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Dutch - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-22 +tags: [open_source, dutch, entity_recognizer_md, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_nl_4.4.2_3.2_1684749837378.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_nl_4.4.2_3.2_1684749837378.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|462.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_no.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_no.md new file mode 100644 index 00000000000000..ce25923c56e400 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_no.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Norwegian (Bokmal) - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-22 +tags: [open_source, norwegian_bokmal, entity_recognizer_md, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_no_4.4.2_3.2_1684751523799.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_no_4.4.2_3.2_1684751523799.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | [[0.1868100017309188,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|462.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_pl.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_pl.md new file mode 100644 index 00000000000000..365ab682a741a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_pl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Polish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-22 +tags: [open_source, polish, entity_recognizer_md, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pl_4.4.2_3.2_1684748402156.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pl_4.4.2_3.2_1684748402156.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|462.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_pt.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_pt.md new file mode 100644 index 00000000000000..33299d41ce3e97 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_pt.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Portuguese - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-22 +tags: [open_source, portuguese, entity_recognizer_md, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pt_4.4.2_3.2_1684746958863.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pt_4.4.2_3.2_1684746958863.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|462.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_ru.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_ru.md new file mode 100644 index 00000000000000..99598085e1433e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_ru.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Russian - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-22 +tags: [open_source, russian, entity_recognizer_md, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_ru_4.4.2_3.2_1684745411097.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_ru_4.4.2_3.2_1684745411097.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-LOC', 'I-LOC', 'I-LOC'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|462.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_sv.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_sv.md new file mode 100644 index 00000000000000..d4099b83261023 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_sv.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Swedish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-22 +tags: [open_source, swedish, entity_recognizer_md, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_sv_4.4.2_3.2_1684753062630.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_sv_4.4.2_3.2_1684753062630.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|461.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_da.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_da.md new file mode 100644 index 00000000000000..01a6ba6a68fa5f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_da.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Danish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-22 +tags: [open_source, danish, entity_recognizer_sm, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_da_4.4.2_3.2_1684754651058.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_da_4.4.2_3.2_1684754651058.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_es.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_es.md new file mode 100644 index 00000000000000..c034fd3a3a45f3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_es.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Spanish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-22 +tags: [open_source, spanish, entity_recognizer_sm, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_es_4.4.2_3.2_1684744068030.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_es_4.4.2_3.2_1684744068030.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:---------------------------------------|:-----------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | [[0.1754499971866607,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'B-MISC'] | ['John Snow', 'Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_fi.md new file mode 100644 index 00000000000000..a31fd716168455 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_fi.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Finnish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-22 +tags: [open_source, finnish, entity_recognizer_sm, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_fi_4.4.2_3.2_1684756154843.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_fi_4.4.2_3.2_1684756154843.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | [[-0.394499987363815,.,...]] | ['O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|166.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_nl.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_nl.md new file mode 100644 index 00000000000000..df6ae842bd61ad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_nl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Dutch - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-22 +tags: [open_source, dutch, entity_recognizer_sm, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_nl_4.4.2_3.2_1684749934987.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_nl_4.4.2_3.2_1684749934987.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | [[0.3653799891471863,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|166.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_no.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_no.md new file mode 100644 index 00000000000000..59ff12c659a006 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_no.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Norwegian (Bokmal) - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-22 +tags: [open_source, norwegian_bokmal, entity_recognizer_sm, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_no_4.4.2_3.2_1684751621553.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_no_4.4.2_3.2_1684751621553.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | [[-0.394499987363815,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|167.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_pl.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_pl.md new file mode 100644 index 00000000000000..9409417b3ed33f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_pl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Polish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-22 +tags: [open_source, polish, entity_recognizer_sm, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pl_4.4.2_3.2_1684748499659.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pl_4.4.2_3.2_1684748499659.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|167.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_pt.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_pt.md new file mode 100644 index 00000000000000..9ea506944c48ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_pt.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Portuguese - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-22 +tags: [open_source, portuguese, entity_recognizer_sm, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pt_4.4.2_3.2_1684747056347.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pt_4.4.2_3.2_1684747056347.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|167.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_ru.md new file mode 100644 index 00000000000000..d791648f8b3f4a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_ru.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Russian - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-22 +tags: [open_source, russian, entity_recognizer_sm, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_ru_4.4.2_3.2_1684745509125.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_ru_4.4.2_3.2_1684745509125.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|167.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_sv.md new file mode 100644 index 00000000000000..be54bafe49fec0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_sv.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Swedish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-22 +tags: [open_source, swedish, entity_recognizer_sm, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_sv_4.4.2_3.2_1684753162570.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_sv_4.4.2_3.2_1684753162570.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_dl_en.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_dl_en.md new file mode 100644 index 00000000000000..22525f8c0909a0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_dl_en.md @@ -0,0 +1,128 @@ +--- +layout: model +title: Explain Document DL Pipeline for English +author: John Snow Labs +name: explain_document_dl +date: 2023-05-22 +tags: [open_source, english, explain_document_dl, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_dl is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_dl_en_4.4.2_3.2_1684740694393.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_dl_en_4.4.2_3.2_1684740694393.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_dl', lang = 'en') +annotations = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_dl", lang = "en") +val result = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = ["The Mona Lisa is an oil painting from the 16th century."] +result_df = nlu.load('en.explain.dl').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_dl', lang = 'en') +annotations = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_dl", lang = "en") +val result = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")(0) +``` + +{:.nlu-block} +```python +import nlu +text = ["The Mona Lisa is an oil painting from the 16th century."] +result_df = nlu.load('en.explain.dl').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + ++--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------+-----------+ +| text| document| sentence| token| checked| lemma| stem| pos| embeddings| ner| entities| ++--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------+-----------+ +|The Mona Lisa is an oil painting from the 16th ...|[The Mona Lisa is an oil painting from the 16th...|[The Mona Lisa is an oil painting from the 16th...|[The, Mona, Lisa, is, an, oil, painting, from, ...|[The, Mona, Lisa, is, an, oil, painting, from, ...|[The, Mona, Lisa, be, an, oil, painting, from, ...|[the, mona, lisa, i, an, oil, paint, from, the,...|[DT, NNP, NNP, VBZ, DT, NN, NN, IN, DT, JJ, NN, .]|[[-0.038194, -0.24487, 0.72812, -0.39961, 0.083...|[O, B-PER, I-PER, O, O, O, O, O, O, O, O, O]|[Mona Lisa]| ++--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------+-----------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_dl| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|176.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel +- LemmatizerModel +- Stemmer +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_da.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_da.md new file mode 100644 index 00000000000000..b763647ec89c5f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_da.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Danish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-22 +tags: [open_source, danish, explain_document_lg, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_da_4.4.2_3.2_1684753576517.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_da_4.4.2_3.2_1684753576517.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.025171000510454,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_es.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_es.md new file mode 100644 index 00000000000000..76a63466d126db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_es.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Spanish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-22 +tags: [open_source, spanish, explain_document_lg, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_es_4.4.2_3.2_1684742967821.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_es_4.4.2_3.2_1684742967821.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['PART', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.016199000179767,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_fi.md new file mode 100644 index 00000000000000..84e43520c89a9e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_fi.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Finnish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-22 +tags: [open_source, finnish, explain_document_lg, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_fi_4.4.2_3.2_1684755070483.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_fi_4.4.2_3.2_1684755070483.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:---------------------------------|:------------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | ['hei', 'John', 'Snow', 'Labs!'] | ['INTJ', 'PROPN', 'PROPN', 'PROPN'] | [[0.0639619976282119,.,...]] | ['O', 'B-PRO', 'I-PRO', 'I-PRO'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_nl.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_nl.md new file mode 100644 index 00000000000000..0b99ce03ad00a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_nl.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Dutch (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-22 +tags: [open_source, dutch, explain_document_lg, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_nl_4.4.2_3.2_1684748901083.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_nl_4.4.2_3.2_1684748901083.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:-----------------------------|:------------------------------------------|:-----------------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.245989993214607,.,...]] | ['B-PER', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Hallo', 'John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_no.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_no.md new file mode 100644 index 00000000000000..58e772adc29637 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_no.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Norwegian (Bokmal) (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-22 +tags: [open_source, norwegian_bokmal, explain_document_lg, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_no_4.4.2_3.2_1684750556546.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_no_4.4.2_3.2_1684750556546.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:--------------------------------------------|:-----------------------------|:---------------------------------------|:-----------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0639619976282119,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'B-PROD'] | ['John Snow', 'Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_pl.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_pl.md new file mode 100644 index 00000000000000..50da010661309a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_pl.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Polish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-22 +tags: [open_source, polish, explain_document_lg, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_pl_4.4.2_3.2_1684747465006.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_pl_4.4.2_3.2_1684747465006.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | ['witać', 'z', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4977500140666961,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_pt.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_pt.md new file mode 100644 index 00000000000000..1520ef306ca72d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_pt.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Portuguese (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-22 +tags: [open_source, portuguese, explain_document_lg, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_pt_4.4.2_3.2_1684745973159.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_pt_4.4.2_3.2_1684745973159.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:---------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4388400018215179,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_ru.md new file mode 100644 index 00000000000000..b323facb0ee86b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_ru.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Russian (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-22 +tags: [open_source, russian, explain_document_lg, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_ru_4.4.2_3.2_1684744476695.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_ru_4.4.2_3.2_1684744476695.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | ['здравствовать', 'из', 'Джон', 'Снежных', 'Лабораторий!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_sv.md new file mode 100644 index 00000000000000..0d05cd01531234 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_sv.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Swedish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-22 +tags: [open_source, swedish, explain_document_lg, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_sv_4.4.2_3.2_1684752095991.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_sv_4.4.2_3.2_1684752095991.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_da.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_da.md new file mode 100644 index 00000000000000..d0511eec69f4f3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_da.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Danish +author: John Snow Labs +name: explain_document_md +date: 2023-05-22 +tags: [open_source, danish, explain_document_md, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_da_4.4.2_3.2_1684753810963.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_da_4.4.2_3.2_1684753810963.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|463.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_de.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_de.md new file mode 100644 index 00000000000000..7d8c9d76430d9d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_de.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for German +author: John Snow Labs +name: explain_document_md +date: 2023-05-22 +tags: [open_source, german, explain_document_md, pipeline, de] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_de_4.4.2_3.2_1684741307799.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_de_4.4.2_3.2_1684741307799.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.explain.document').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.explain.document').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo aus John Snow Labs! '] | ['Hallo aus John Snow Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|470.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_es.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_es.md new file mode 100644 index 00000000000000..dd22a740c2dade --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_es.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Spanish +author: John Snow Labs +name: explain_document_md +date: 2023-05-22 +tags: [open_source, spanish, explain_document_md, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_es_4.4.2_3.2_1684743215360.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_es_4.4.2_3.2_1684743215360.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['PART', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.5123000144958496,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|472.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_fi.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_fi.md new file mode 100644 index 00000000000000..90c527826353da --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_fi.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Finnish +author: John Snow Labs +name: explain_document_md +date: 2023-05-22 +tags: [open_source, finnish, explain_document_md, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_fi_4.4.2_3.2_1684755309033.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_fi_4.4.2_3.2_1684755309033.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:---------------------------------|:------------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | ['hei', 'John', 'Snow', 'Labs!'] | ['INTJ', 'PROPN', 'PROPN', 'PROPN'] | [[0.1868100017309188,.,...]] | ['O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|467.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_fr.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_fr.md new file mode 100644 index 00000000000000..914f74e1b22ebc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_fr.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for French +author: John Snow Labs +name: explain_document_md +date: 2023-05-22 +tags: [open_source, french, explain_document_md, pipeline, fr] +task: Named Entity Recognition +language: fr +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_fr_4.4.2_3.2_1684737940167.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_fr_4.4.2_3.2_1684737940167.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:--------------------------------|:-------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------------------| +| 0 | ['Bonjour de John Snow Labs! '] | ['Bonjour de John Snow Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | ['INTJ', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0783179998397827,.,...]] | ['I-MISC', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['Bonjour', 'John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fr| +|Size:|467.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_it.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_it.md new file mode 100644 index 00000000000000..f39a873efab07d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_it.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Italian +author: John Snow Labs +name: explain_document_md +date: 2023-05-22 +tags: [open_source, italian, explain_document_md, pipeline, it] +task: Named Entity Recognition +language: it +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_it_4.4.2_3.2_1684738395048.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_it_4.4.2_3.2_1684738395048.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.explain.document').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.explain.document').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Ciao da John Snow Labs! '] | ['Ciao da John Snow Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.146050006151199,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|it| +|Size:|468.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_nl.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_nl.md new file mode 100644 index 00000000000000..3db7dac3e6b3a2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_nl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Dutch +author: John Snow Labs +name: explain_document_md +date: 2023-05-22 +tags: [open_source, dutch, explain_document_md, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_nl_4.4.2_3.2_1684749121949.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_nl_4.4.2_3.2_1684749121949.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|464.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_no.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_no.md new file mode 100644 index 00000000000000..5d10909e8fc22a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_no.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Norwegian (Bokmal) +author: John Snow Labs +name: explain_document_md +date: 2023-05-22 +tags: [open_source, norwegian_bokmal, explain_document_md, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_no_4.4.2_3.2_1684750792985.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_no_4.4.2_3.2_1684750792985.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.1868100017309188,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|465.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_pl.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_pl.md new file mode 100644 index 00000000000000..0cd34d95fd228d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_pl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Polish +author: John Snow Labs +name: explain_document_md +date: 2023-05-22 +tags: [open_source, polish, explain_document_md, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_pl_4.4.2_3.2_1684747685683.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_pl_4.4.2_3.2_1684747685683.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | ['witać', 'z', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|464.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_pt.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_pt.md new file mode 100644 index 00000000000000..f2f09673c76407 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_pt.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Portuguese +author: John Snow Labs +name: explain_document_md +date: 2023-05-22 +tags: [open_source, portuguese, explain_document_md, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_pt_4.4.2_3.2_1684746207929.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_pt_4.4.2_3.2_1684746207929.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:---------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|473.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_ru.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_ru.md new file mode 100644 index 00000000000000..cbc9c302074826 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_ru.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Russian +author: John Snow Labs +name: explain_document_md +date: 2023-05-22 +tags: [open_source, russian, explain_document_md, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_ru_4.4.2_3.2_1684744687037.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_ru_4.4.2_3.2_1684744687037.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | ['здравствовать', 'из', 'Джон', 'Снежных', 'Лабораторий!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-LOC', 'I-LOC', 'I-LOC'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|465.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_sv.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_sv.md new file mode 100644 index 00000000000000..994633443fdff2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_sv.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Swedish +author: John Snow Labs +name: explain_document_md +date: 2023-05-22 +tags: [open_source, swedish, explain_document_md, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_sv_4.4.2_3.2_1684752335611.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_sv_4.4.2_3.2_1684752335611.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|471.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_ml_en.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_ml_en.md new file mode 100644 index 00000000000000..c172b6ae4150d9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_ml_en.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document ML Pipeline for English +author: John Snow Labs +name: explain_document_ml +date: 2023-05-22 +tags: [open_source, english, explain_document_ml, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_ml is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_ml_en_4.4.2_3.2_1684740490743.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_ml_en_4.4.2_3.2_1684740490743.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_ml', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala + + +val pipeline = new PretrainedPipeline("explain_document_ml", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python + + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.explain').predict(text) +result_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_ml', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_ml", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + + +| | document | sentence | token | spell | lemmas | stems | pos | +|---:|:---------------------------------|:---------------------------------|:-------------------------------------------------|:------------------------------------------------|:------------------------------------------------|:-----------------------------------------------|:---------------------------------------| +| 0 | ['Hello fronm John Snwow Labs!'] | ['Hello fronm John Snwow Labs!'] | ['Hello', 'fronm', 'John', 'Snwow', 'Labs', '!'] | ['Hello', 'front', 'John', 'Snow', 'Labs', '!'] | ['Hello', 'front', 'John', 'Snow', 'Labs', '!'] | ['hello', 'front', 'john', 'snow', 'lab', '!'] | ['UH', 'NN', 'NNP', 'NNP', 'NNP', '.'] || | document | sentence | token | spell | lemmas | stems | pos | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_ml| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|9.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel +- LemmatizerModel +- Stemmer +- PerceptronModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_da.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_da.md new file mode 100644 index 00000000000000..099cacbd81241c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_da.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Danish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-22 +tags: [open_source, danish, explain_document_sm, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_da_4.4.2_3.2_1684753918091.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_da_4.4.2_3.2_1684753918091.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|168.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_es.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_es.md new file mode 100644 index 00000000000000..bd110694edd184 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_es.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Spanish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-22 +tags: [open_source, spanish, explain_document_sm, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_es_4.4.2_3.2_1684743338997.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_es_4.4.2_3.2_1684743338997.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:---------------------------------------|:-----------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['PART', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.1754499971866607,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'B-MISC'] | ['John Snow', 'Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|177.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_fi.md new file mode 100644 index 00000000000000..8d69d6805b998c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_fi.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Finnish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-22 +tags: [open_source, finnish, explain_document_sm, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_fi_4.4.2_3.2_1684755428685.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_fi_4.4.2_3.2_1684755428685.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:---------------------------------|:------------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | ['hei', 'John', 'Snow', 'Labs!'] | ['INTJ', 'PROPN', 'PROPN', 'PROPN'] | [[-0.394499987363815,.,...]] | ['O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|172.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_nl.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_nl.md new file mode 100644 index 00000000000000..ce83b5f962eaf7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_nl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Dutch +author: John Snow Labs +name: explain_document_sm +date: 2023-05-22 +tags: [open_source, dutch, explain_document_sm, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_nl_4.4.2_3.2_1684749230843.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_nl_4.4.2_3.2_1684749230843.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.3653799891471863,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|169.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_no.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_no.md new file mode 100644 index 00000000000000..9a66e31df60fac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_no.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Norwegian (Bokmal) +author: John Snow Labs +name: explain_document_sm +date: 2023-05-22 +tags: [open_source, norwegian_bokmal, explain_document_sm, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_no_4.4.2_3.2_1684750903407.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_no_4.4.2_3.2_1684750903407.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.394499987363815,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|170.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_pl.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_pl.md new file mode 100644 index 00000000000000..a1ee93b4e960a8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_pl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Polish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-22 +tags: [open_source, polish, explain_document_sm, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_pl_4.4.2_3.2_1684747793377.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_pl_4.4.2_3.2_1684747793377.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | ['witać', 'z', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|169.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_pt.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_pt.md new file mode 100644 index 00000000000000..7d025252667c3e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_pt.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Portuguese +author: John Snow Labs +name: explain_document_sm +date: 2023-05-22 +tags: [open_source, portuguese, explain_document_sm, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_pt_4.4.2_3.2_1684746332576.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_pt_4.4.2_3.2_1684746332576.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:---------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|178.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_ru.md new file mode 100644 index 00000000000000..a81133879157b3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_ru.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Russian +author: John Snow Labs +name: explain_document_sm +date: 2023-05-22 +tags: [open_source, russian, explain_document_sm, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_ru_4.4.2_3.2_1684744799879.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_ru_4.4.2_3.2_1684744799879.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | ['здравствовать', 'из', 'Джон', 'Снежных', 'Лабораторий!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|170.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_sv.md new file mode 100644 index 00000000000000..f401f38e1a3a09 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_sv.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Swedish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-22 +tags: [open_source, swedish, explain_document_sm, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_sv_4.4.2_3.2_1684752455058.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_sv_4.4.2_3.2_1684752455058.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|176.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-longformer_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-longformer_base_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..3878cb8b993593 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-longformer_base_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Longformer Base NER Pipeline +author: John Snow Labs +name: longformer_base_token_classifier_conll03_pipeline +date: 2023-05-22 +tags: [ner, longformer, pipeline, conll, token_classification, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [longformer_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/10/09/longformer_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/longformer_base_token_classifier_conll03_pipeline_en_4.4.2_3.2_1684763298404.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/longformer_base_token_classifier_conll03_pipeline_en_4.4.2_3.2_1684763298404.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|longformer_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|516.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- LongformerForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-longformer_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-longformer_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..d22ba6ca506a8a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-longformer_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Longformer Large NER Pipeline +author: John Snow Labs +name: longformer_large_token_classifier_conll03_pipeline +date: 2023-05-22 +tags: [open_source, ner, token_classifier, longformer, conll, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [longformer_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/10/09/longformer_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/longformer_large_token_classifier_conll03_pipeline_en_4.4.2_3.2_1684763718160.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/longformer_large_token_classifier_conll03_pipeline_en_4.4.2_3.2_1684763718160.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|longformer_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- LongformerForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-match_chunks_en.md b/docs/_posts/ahmedlone127/2023-05-22-match_chunks_en.md new file mode 100644 index 00000000000000..cfb0fd4d59d7de --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-match_chunks_en.md @@ -0,0 +1,141 @@ +--- +layout: model +title: Match Chunks in Texts +author: John Snow Labs +name: match_chunks +date: 2023-05-22 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The pipeline uses regex `
?/*+` + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_chunks_en_4.4.2_3.2_1684742560301.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_chunks_en_4.4.2_3.2_1684742560301.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline + +pipeline_local = PretrainedPipeline('match_chunks') + +result = pipeline_local.annotate("David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.") + +result['chunk'] +``` +```scala + +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_chunks", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.match.chunks").predict("""David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline_local = PretrainedPipeline('match_chunks') + +result = pipeline_local.annotate("David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.") + +result['chunk'] +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_chunks", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.match.chunks").predict("""David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.""") +``` +
+ +## Results + +```bash +Results + + + +['the restaurant yesterday', +'family', +'the day', +'that time', +'today', +'the food', +'tomorrow'] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|match_chunks| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|4.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel +- Chunker \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-match_datetime_en.md b/docs/_posts/ahmedlone127/2023-05-22-match_datetime_en.md new file mode 100644 index 00000000000000..57932b42257f82 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-match_datetime_en.md @@ -0,0 +1,127 @@ +--- +layout: model +title: Match Datetime in Texts +author: John Snow Labs +name: match_datetime +date: 2023-05-22 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +DateMatcher based on yyyy/MM/dd + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_datetime_en_4.4.2_3.2_1684739748828.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_datetime_en_4.4.2_3.2_1684739748828.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline_local = PretrainedPipeline('match_datetime') + +tres = pipeline_local.fullAnnotate(input_list)[0] +for dte in tres['date']: + sent = tres['sentence'][int(dte.metadata['sentence'])] + print (f'text/chunk {sent.result[dte.begin:dte.end+1]} | mapped_date: {dte.result}') +``` +```scala + +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. +He also visited and the day before, but at that time he was alone. +David again visited today with his colleagues. +He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_datetime", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline_local = PretrainedPipeline('match_datetime') + +tres = pipeline_local.fullAnnotate(input_list)[0] +for dte in tres['date']: + sent = tres['sentence'][int(dte.metadata['sentence'])] + print (f'text/chunk {sent.result[dte.begin:dte.end+1]} | mapped_date: {dte.result}') +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. +He also visited and the day before, but at that time he was alone. +David again visited today with his colleagues. +He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_datetime", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` +
+ +## Results + +```bash +Results + + + +text/chunk yesterday | mapped_date: 2022/01/02 +text/chunk day before | mapped_date: 2022/01/02 +text/chunk today | mapped_date: 2022/01/03 +text/chunk tomorrow | mapped_date: 2022/01/04 + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|match_datetime| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|12.9 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- MultiDateMatcher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-match_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-22-match_pattern_en.md new file mode 100644 index 00000000000000..4c55bc1f6ce271 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-match_pattern_en.md @@ -0,0 +1,77 @@ +--- +layout: model +title: Match Pattern +author: John Snow Labs +name: match_pattern +date: 2023-05-22 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The match_pattern is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and matches pattrens . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_pattern_en_4.4.2_3.2_1684739796201.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_pattern_en_4.4.2_3.2_1684739796201.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models") + result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models") + result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|match_pattern| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|17.4 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- RegexMatcherModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-movies_sentiment_analysis_en.md b/docs/_posts/ahmedlone127/2023-05-22-movies_sentiment_analysis_en.md new file mode 100644 index 00000000000000..cc0ac4fc6f624c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-movies_sentiment_analysis_en.md @@ -0,0 +1,79 @@ +--- +layout: model +title: Movies Sentiment Analysis +author: John Snow Labs +name: movies_sentiment_analysis +date: 2023-05-22 +tags: [en, open_source] +task: Sentiment Analysis +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The movies_sentiment_analysis is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and predicts sentiment . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/movies_sentiment_analysis_en_4.4.2_3.2_1684740411708.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/movies_sentiment_analysis_en_4.4.2_3.2_1684740411708.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("movies_sentiment_analysis", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("movies_sentiment_analysis", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|movies_sentiment_analysis| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|207.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- SymmetricDeleteModel +- SentimentDetectorModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-nerdl_fewnerd_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-nerdl_fewnerd_100d_pipeline_en.md new file mode 100644 index 00000000000000..9ca480fccdef9d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-nerdl_fewnerd_100d_pipeline_en.md @@ -0,0 +1,109 @@ +--- +layout: model +title: Pretrained Pipeline for Few-NERD-General NER Model +author: John Snow Labs +name: nerdl_fewnerd_100d_pipeline +date: 2023-05-22 +tags: [fewnerd, nerdl, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on Few-NERD model and it detects : + +`PERSON`, `ORGANIZATION`, `LOCATION`, `ART`, `BUILDING`, `PRODUCT`, `EVENT`, `OTHER` + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_100d_pipeline_en_4.4.2_3.2_1684759421066.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_100d_pipeline_en_4.4.2_3.2_1684759421066.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).""") +``` +```scala + +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).")(0) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).""") +``` +```scala +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).")(0) +``` +
+ +## Results + +```bash +Results + + + ++-----------------------+------------+ +|chunk |ner_label | ++-----------------------+------------+ +|Kentucky Fried Chicken |ORGANIZATION| +|Anglo-Egyptian War |EVENT | +|battle of Tell El Kebir|EVENT | +|Egypt Medal |OTHER | +|Order of Medjidie |OTHER | ++-----------------------+------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nerdl_fewnerd_100d_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|167.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-nerdl_fewnerd_subentity_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-nerdl_fewnerd_subentity_100d_pipeline_en.md new file mode 100644 index 00000000000000..6337f0136a984d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-nerdl_fewnerd_subentity_100d_pipeline_en.md @@ -0,0 +1,110 @@ +--- +layout: model +title: Pre-trained Pipeline for Few-NERD NER Model +author: John Snow Labs +name: nerdl_fewnerd_subentity_100d_pipeline +date: 2023-05-22 +tags: [fewnerd, ner, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on Few-NERD/inter public dataset and it extracts 66 entities that are in general scope. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_subentity_100d_pipeline_en_4.4.2_3.2_1684758585667.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_subentity_100d_pipeline_en_4.4.2_3.2_1684758585667.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.""") +``` +```scala + +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.")(0) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.""") +``` +```scala +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.")(0) +``` +
+ +## Results + +```bash +Results + + + ++-----------------------+----------------------------+ +|chunk |ner_label | ++-----------------------+----------------------------+ +|Corazones ('12 Hearts')|art-broadcastprogram | +|Spanish-language |other-language | +|United States |location-GPE | +|Telemundo |organization-media/newspaper| +|Argentine TV |organization-media/newspaper| +|Los Angeles |location-GPE | +|Steven Spielberg |person-director | +|Cloverfield Paradox |art-film | ++-----------------------+----------------------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nerdl_fewnerd_subentity_100d_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|167.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-nerdl_restaurant_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-nerdl_restaurant_100d_pipeline_en.md new file mode 100644 index 00000000000000..15f6f43e5b8103 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-nerdl_restaurant_100d_pipeline_en.md @@ -0,0 +1,119 @@ +--- +layout: model +title: Pipeline to Detect Restaurant-related Terminology +author: John Snow Labs +name: nerdl_restaurant_100d_pipeline +date: 2023-05-22 +tags: [restaurant, ner, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [nerdl_restaurant_100d](https://nlp.johnsnowlabs.com/2021/12/31/nerdl_restaurant_100d_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nerdl_restaurant_100d_pipeline_en_4.4.2_3.2_1684760993869.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nerdl_restaurant_100d_pipeline_en_4.4.2_3.2_1684760993869.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +restaurant_pipeline = PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +```scala + +val restaurant_pipeline = new PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +restaurant_pipeline = PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +```scala +val restaurant_pipeline = new PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +
+ +## Results + +```bash +Results + + + ++---------------------------+---------------+ +|chunk |ner_label | ++---------------------------+---------------+ +|Hong Kong’s |Restaurant_Name| +|favourite |Rating | +|pasta bar |Dish | +|most reasonably |Price | +|lunch |Hours | +|in town! |Location | +|Sha Tin – Pici’s |Restaurant_Name| +|burrata |Dish | +|arugula salad |Dish | +|freshly tossed tuna tartare|Dish | +|reliable |Price | +|handmade pasta |Dish | +|pappardelle |Dish | +|effortless |Amenity | +|Italian |Cuisine | +|tidy one-pot |Amenity | +|espresso |Dish | ++---------------------------+---------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nerdl_restaurant_100d_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_base_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_base_en.md new file mode 100644 index 00000000000000..d63528bb521f24 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_base_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Base +author: John Snow Labs +name: onto_recognize_entities_bert_base +date: 2023-05-22 +tags: [open_source, english, onto_recognize_entities_bert_base, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_base is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_base_en_4.4.2_3.2_1684756282409.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_base_en_4.4.2_3.2_1684756282409.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_base', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_base", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.base').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_base', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_base", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.base').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.085488274693489,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_base| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|424.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_large_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_large_en.md new file mode 100644 index 00000000000000..f362f3d323a62b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_large_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Large +author: John Snow Labs +name: onto_recognize_entities_bert_large +date: 2023-05-22 +tags: [open_source, english, onto_recognize_entities_bert_large, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_large is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_large_en_4.4.2_3.2_1684756537388.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_large_en_4.4.2_3.2_1684756537388.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_large', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_large", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.large').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_large', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_large", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.large').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.262016534805297,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_large| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.3 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_medium_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_medium_en.md new file mode 100644 index 00000000000000..519d1c16b5c60a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_medium_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Medium +author: John Snow Labs +name: onto_recognize_entities_bert_medium +date: 2023-05-22 +tags: [open_source, english, onto_recognize_entities_bert_medium, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_medium is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_medium_en_4.4.2_3.2_1684756661648.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_medium_en_4.4.2_3.2_1684756661648.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_medium', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_medium", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.medium').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_medium', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_medium", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.medium').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.0365490540862083,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_medium| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|172.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_mini_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_mini_en.md new file mode 100644 index 00000000000000..9bb36244046f97 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_mini_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Mini +author: John Snow Labs +name: onto_recognize_entities_bert_mini +date: 2023-05-22 +tags: [open_source, english, onto_recognize_entities_bert_mini, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_mini is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_mini_en_4.4.2_3.2_1684756732617.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_mini_en_4.4.2_3.2_1684756732617.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_mini', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_mini", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.mini').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_mini', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_mini", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.mini').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.147406503558158,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_mini| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|57.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_small_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_small_en.md new file mode 100644 index 00000000000000..07de2dd5f53614 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_small_en.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Small +author: John Snow Labs +name: onto_recognize_entities_bert_small +date: 2023-05-22 +tags: [open_source, english, onto_recognize_entities_bert_small, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_small is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_small_en_4.4.2_3.2_1684756809457.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_small_en_4.4.2_3.2_1684756809457.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_small', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_small", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.small').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_small', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_small", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.small').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:----------------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.9379079937934875,.,...]] | ['O', 'O', 'B-PERSON', 'I-PERSON', 'I-PERSON', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_small| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|125.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_tiny_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_tiny_en.md new file mode 100644 index 00000000000000..1a722c34fa70bd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_tiny_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Tiny +author: John Snow Labs +name: onto_recognize_entities_bert_tiny +date: 2023-05-22 +tags: [open_source, english, onto_recognize_entities_bert_tiny, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_tiny is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_tiny_en_4.4.2_3.2_1684756877770.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_tiny_en_4.4.2_3.2_1684756877770.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_tiny', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_tiny", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.tiny').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_tiny', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_tiny", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.tiny').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:----------------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-1.526878952980041,.,...]] | ['O', 'O', 'B-PERSON', 'I-PERSON', 'I-PERSON', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_tiny| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|31.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_base_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_base_en.md new file mode 100644 index 00000000000000..905ff995657472 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_base_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - ELECTRA Base +author: John Snow Labs +name: onto_recognize_entities_electra_base +date: 2023-05-22 +tags: [open_source, english, onto_recognize_entities_electra_base, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_electra_base is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_base_en_4.4.2_3.2_1684757009817.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_base_en_4.4.2_3.2_1684757009817.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_electra_base', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_base", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.electra.base').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_electra_base', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_base", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.electra.base').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2088415920734405,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_electra_base| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|424.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_large_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_large_en.md new file mode 100644 index 00000000000000..1e68b4e623705d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_large_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - ELECTRA Large +author: John Snow Labs +name: onto_recognize_entities_electra_large +date: 2023-05-22 +tags: [open_source, english, onto_recognize_entities_electra_large, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_electra_large is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_large_en_4.4.2_3.2_1684757364140.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_large_en_4.4.2_3.2_1684757364140.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('onto_recognize_entities_electra_large', lang = 'en') +annotations = pipeline.fullAnnotate("Hello from John Snow Labs!")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_large", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.large').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('onto_recognize_entities_electra_large', lang = 'en') +annotations = pipeline.fullAnnotate("Hello from John Snow Labs!")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_large", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.large').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.264069110155105,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_electra_large| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.3 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_small_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_small_en.md new file mode 100644 index 00000000000000..1177daf831daf2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_small_en.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - ELECTRA Small +author: John Snow Labs +name: onto_recognize_entities_electra_small +date: 2023-05-22 +tags: [open_source, english, onto_recognize_entities_electra_small, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_electra_small is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_small_en_4.4.2_3.2_1684757098462.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_small_en_4.4.2_3.2_1684757098462.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_electra_small', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_small", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.electra.small').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_electra_small', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_small", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.electra.small').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2279076874256134,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_electra_small| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|66.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_lg_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_lg_en.md new file mode 100644 index 00000000000000..a03017071dfa89 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_lg_en.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Onto Recognize Entities Lg +author: John Snow Labs +name: onto_recognize_entities_lg +date: 2023-05-22 +tags: [en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entites. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_lg_en_4.4.2_3.2_1684742400552.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_lg_en_4.4.2_3.2_1684742400552.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("onto_recognize_entities_lg", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("en.ner.onto.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("onto_recognize_entities_lg", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.ner.onto.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_sm_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_sm_en.md new file mode 100644 index 00000000000000..9895cd6398d85c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_sm_en.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL pipeline for English - Small +author: John Snow Labs +name: onto_recognize_entities_sm +date: 2023-05-22 +tags: [open_source, english, onto_recognize_entities_sm, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_sm_en_4.4.2_3.2_1684742020499.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_sm_en_4.4.2_3.2_1684742020499.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_sm', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_sm", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.sm').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_sm', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_sm", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.sm').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2668800055980682,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-pos_ud_bokmaal_nb.md b/docs/_posts/ahmedlone127/2023-05-22-pos_ud_bokmaal_nb.md new file mode 100644 index 00000000000000..e5c889d05ef3a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-pos_ud_bokmaal_nb.md @@ -0,0 +1,138 @@ +--- +layout: model +title: Part of Speech for Norwegian +author: John Snow Labs +name: pos_ud_bokmaal +date: 2023-05-22 +tags: [pos, norwegian, nb, open_source] +task: Part of Speech Tagging +language: nb +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model annotates the part of speech of tokens in a text. The parts of speech annotated include PRON (pronoun), CCONJ (coordinating conjunction), and 15 others. The part of speech model is useful for extracting the grammatical structure of a piece of text automatically. + +This model was trained using the dataset available at https://universaldependencies.org + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pos_ud_bokmaal_nb_4.4.2_3.2_1684759476489.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pos_ud_bokmaal_nb_4.4.2_3.2_1684759476489.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("pos") + +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos]) +light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text"))) +results = light_pipeline.fullAnnotate("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.") +``` +```scala + +val pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") + .setInputCols(Array("document", "token")) + .setOutputCol("pos") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos)) +val data = Seq("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python + +import nlu + +text = ["""Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene."""] +pos_df = nlu.load('nb.pos.ud_bokmaal').predict(text) +pos_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("pos") + +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos]) +light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text"))) +results = light_pipeline.fullAnnotate("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.") +``` +```scala +val pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") + .setInputCols(Array("document", "token")) + .setOutputCol("pos") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos)) +val data = Seq("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu + +text = ["""Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene."""] +pos_df = nlu.load('nb.pos.ud_bokmaal').predict(text) +pos_df +``` +
+ +## Results + +```bash +Results + + + +[Row(annotatorType='pos', begin=0, end=4, result='DET', metadata={'word': 'Annet'}), +Row(annotatorType='pos', begin=6, end=8, result='SCONJ', metadata={'word': 'enn'}), +Row(annotatorType='pos', begin=10, end=10, result='PART', metadata={'word': 'å'}), +Row(annotatorType='pos', begin=12, end=15, result='AUX', metadata={'word': 'være'}), +Row(annotatorType='pos', begin=17, end=22, result='NOUN', metadata={'word': 'kongen'}), +...] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pos_ud_bokmaal| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nb| +|Size:|17.7 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_bert_en.md b/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_bert_en.md new file mode 100644 index 00000000000000..ac7290c2c2f439 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_bert_en.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Recognize Entities DL pipeline for English - BERT +author: John Snow Labs +name: recognize_entities_bert +date: 2023-05-22 +tags: [open_source, english, recognize_entities_bert, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The recognize_entities_bert is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recognize_entities_bert_en_4.4.2_3.2_1684740971785.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recognize_entities_bert_en_4.4.2_3.2_1684740971785.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_bert', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("recognize_entities_bert", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.bert').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_bert', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("recognize_entities_bert", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.bert').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.085488274693489,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-ORG', 'O'] | ['John Snow Labs'] || | document | sentence | token | embeddings | ner | entities | + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recognize_entities_bert| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|424.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_dl_en.md b/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_dl_en.md new file mode 100644 index 00000000000000..ee25fc3f75a502 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_dl_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for English +author: John Snow Labs +name: recognize_entities_dl +date: 2023-05-22 +tags: [open_source, english, recognize_entities_dl, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The recognize_entities_dl is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_en_4.4.2_3.2_1684740843629.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_en_4.4.2_3.2_1684740843629.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:---------------------------------------------------|:------------------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2668800055980682,.,...]] | ['B-ORG', 'I-ORG', 'I-ORG', 'I-ORG', 'I-ORG', 'O'] | ['Hello from John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recognize_entities_dl| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_dl_fa.md b/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_dl_fa.md new file mode 100644 index 00000000000000..722e3d58623246 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_dl_fa.md @@ -0,0 +1,169 @@ +--- +layout: model +title: Explain Document DL Pipeline for Farsi/Persian +author: John Snow Labs +name: recognize_entities_dl +date: 2023-05-22 +tags: [pipeline, ner, fa, open_source] +task: Named Entity Recognition +language: fa +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_dl is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities . It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_fa_4.4.2_3.2_1684757760941.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_fa_4.4.2_3.2_1684757760941.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'fa') + +annotations = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")[0] + +annotations.keys() +``` +```scala + +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "fa") + +val result = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")(0) +``` + +{:.nlu-block} +```python + +import nlu + +text = ["""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند"""] + +result_df = nlu.load('fa.recognize_entities_dl').predict(text) + +result_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'fa') + +annotations = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")[0] + +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "fa") + +val result = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")(0) +``` + +{:.nlu-block} +```python +import nlu + +text = ["""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند"""] + +result_df = nlu.load('fa.recognize_entities_dl').predict(text) + +result_df +``` +
+ +## Results + +```bash +Results + + + +| | document | sentence | token | clean_tokens | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------|:---------------|:---------|:------|:-------------|:------|:---------------------| +| 0 | "به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند | "به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند | " | " | " | PUNCT | " | O | خبرنگار ایرنا | +| 1 | | | به | گزارش | به | ADP | به | O | محمد قمی | +| 2 | | | گزارش | خبرنگار | گزارش | NOUN | گزارش | O | پاکدشت | +| 3 | | | خبرنگار | ایرنا | خبرنگار | NOUN | خبرنگار | B-ORG | علی‌اکبر موسوی خوئینی | +| 4 | | | ایرنا | ، | ایرنا | PROPN | ایرنا | I-ORG | شمس‌الدین وهابی | +| 5 | | | ، | اساس | ؛ | PUNCT | ، | O | تهران | +| 6 | | | بر | تصمیم | بر | ADP | بر | O | | +| 7 | | | اساس | این | اساس | NOUN | اساس | O | | +| 8 | | | تصمیم | مجمع | تصمیم | NOUN | تصمیم | O | | +| 9 | | | این | ، | این | DET | این | O | | +| 10 | | | مجمع | محمد | مجمع | NOUN | مجمع | O | | +| 11 | | | ، | قمی | ؛ | PUNCT | ، | O | | +| 12 | | | محمد | نماینده | محمد | PROPN | محمد | B-PER | | +| 13 | | | قمی | پاکدشت | قمی | PROPN | قمی | I-PER | | +| 14 | | | نماینده | عنوان | نماینده | NOUN | نماینده | O | | +| 15 | | | مردم | رئیس | مردم | NOUN | مردم | O | | +| 16 | | | پاکدشت | علی‌اکبر | پاکدشت | PROPN | پاکدشت | B-LOC | | +| 17 | | | به | موسوی | به | ADP | به | O | | +| 18 | | | عنوان | خوئینی | عنوان | NOUN | عنوان | O | | +| 19 | | | رئیس | شمس‌الدین | رئیس | NOUN | رئیس | O | | +| 20 | | | و | وهابی | او | CCONJ | و | O | | +| 21 | | | علی‌اکبر | نمایندگان | علی‌اکبر | PROPN | علی‌اکبر | B-PER | | +| 22 | | | موسوی | تهران | موسوی | PROPN | موسوی | I-PER | | +| 23 | | | خوئینی | عنوان | خوئینی | PROPN | خوئینی | I-PER | | +| 24 | | | و | نواب | او | CCONJ | و | O | | +| 25 | | | شمس‌الدین | رئیس | شمس‌الدین | PROPN | شمس‌الدین | B-PER | | +| 26 | | | وهابی | انتخاب | وهابی | PROPN | وهابی | I-PER | | +| 27 | | | نمایندگان | | نماینده | NOUN | نمایندگان | O | | +| 28 | | | مردم | | مردم | NOUN | مردم | O | | +| 29 | | | تهران | | تهران | PROPN | تهران | B-LOC | | +| 30 | | | به | | به | ADP | به | O | | +| 31 | | | عنوان | | عنوان | NOUN | عنوان | O | | +| 32 | | | نواب | | نواب | NOUN | نواب | O | | +| 33 | | | رئیس | | رئیس | NOUN | رئیس | O | | +| 34 | | | انتخاب | | انتخاب | NOUN | انتخاب | O | | +| 35 | | | شدند | | کرد#کن | VERB | شدند | O | | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recognize_entities_dl| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fa| +|Size:|1.2 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- StopWordsCleaner +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-roberta_base_token_classifier_ontonotes_pipeline_en.md new file mode 100644 index 00000000000000..0ec192b82de752 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -0,0 +1,104 @@ +--- +layout: model +title: RoBERTa Base Ontonotes NER Pipeline +author: John Snow Labs +name: roberta_base_token_classifier_ontonotes_pipeline +date: 2023-05-22 +tags: [open_source, ner, token_classifier, roberta, ontonotes, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_base_token_classifier_ontonotes](https://nlp.johnsnowlabs.com/2021/09/26/roberta_base_token_classifier_ontonotes_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.2_1684765002678.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.2_1684765002678.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala + +val pipeline = new PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +## Results + +```bash +Results + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | +|November 2020 |DATE | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_base_token_classifier_ontonotes_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|456.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-roberta_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-roberta_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..a7d710875988c0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-roberta_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: RoBERTa Large CoNLL-03 NER Pipeline +author: John Snow Labs +name: roberta_large_token_classifier_conll03_pipeline +date: 2023-05-22 +tags: [open_source, ner, token_classifier, roberta, conll03, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/roberta_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_large_token_classifier_conll03_pipeline_en_4.4.2_3.2_1684765300363.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_large_token_classifier_conll03_pipeline_en_4.4.2_3.2_1684765300363.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.3 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_bne_capitel_ner_pipeline_es.md b/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_bne_capitel_ner_pipeline_es.md new file mode 100644 index 00000000000000..ff1f64782cf1ee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_bne_capitel_ner_pipeline_es.md @@ -0,0 +1,104 @@ +--- +layout: model +title: Spanish NER Pipeline +author: John Snow Labs +name: roberta_token_classifier_bne_capitel_ner_pipeline +date: 2023-05-22 +tags: [roberta, token_classifier, spanish, ner, es, open_source] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_token_classifier_bne_capitel_ner_es](https://nlp.johnsnowlabs.com/2021/12/07/roberta_token_classifier_bne_capitel_ner_es.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_bne_capitel_ner_pipeline_es_4.4.2_3.2_1684762930548.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_bne_capitel_ner_pipeline_es_4.4.2_3.2_1684762930548.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +```scala + +val pipeline = new PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +
+ +## Results + +```bash +Results + + + ++------------------------+---------+ +|chunk |ner_label| ++------------------------+---------+ +|Antonio |PER | +|fábrica de Mercedes-Benz|ORG | +|Madrid |LOC | ++------------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_token_classifier_bne_capitel_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|459.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_icelandic_ner_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_icelandic_ner_pipeline_is.md new file mode 100644 index 00000000000000..61688280e98e16 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_icelandic_ner_pipeline_is.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Icelandic NER Pipeline +author: John Snow Labs +name: roberta_token_classifier_icelandic_ner_pipeline +date: 2023-05-22 +tags: [open_source, ner, token_classifier, roberta, icelandic, is] +task: Named Entity Recognition +language: is +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_token_classifier_icelandic_ner](https://nlp.johnsnowlabs.com/2021/12/06/roberta_token_classifier_icelandic_ner_is.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_icelandic_ner_pipeline_is_4.4.2_3.2_1684763086202.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_icelandic_ner_pipeline_is_4.4.2_3.2_1684763086202.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +```scala + +val pipeline = new PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +
+ +## Results + +```bash +Results + + + ++----------------+------------+ +|chunk |ner_label | ++----------------+------------+ +|Peter Fergusson |Person | +|New York |Location | +|október 2011 |Date | +|Tesla Motor |Organization| +|100K $ |Money | ++----------------+------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_token_classifier_icelandic_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|is| +|Size:|457.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_timex_semeval_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_timex_semeval_pipeline_en.md new file mode 100644 index 00000000000000..93eb4f1b9a89a0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_timex_semeval_pipeline_en.md @@ -0,0 +1,110 @@ +--- +layout: model +title: Pipeline to Detect Time-related Terminology +author: John Snow Labs +name: roberta_token_classifier_timex_semeval_pipeline +date: 2023-05-22 +tags: [timex, semeval, ner, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_token_classifier_timex_semeval](https://nlp.johnsnowlabs.com/2021/12/28/roberta_token_classifier_timex_semeval_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_timex_semeval_pipeline_en_4.4.2_3.2_1684761138759.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_timex_semeval_pipeline_en_4.4.2_3.2_1684761138759.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +timex_pipeline = PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +```scala + + +val timex_pipeline = new PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +timex_pipeline = PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +```scala +val timex_pipeline = new PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +
+ +## Results + +```bash +Results + + + + ++-------+-----------------+ +|chunk |ner_label | ++-------+-----------------+ +|22:12C |Period | +|3 |Number | +|days |Calendar-Interval| +|Tuesday|Day-Of-Week | +|to |Between | +|Friday |Day-Of-Week | ++-------+-----------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_token_classifier_timex_semeval_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|439.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-spellcheck_dl_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-spellcheck_dl_pipeline_en.md new file mode 100644 index 00000000000000..02d60cf8b49fa2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-spellcheck_dl_pipeline_en.md @@ -0,0 +1,112 @@ +--- +layout: model +title: Context Spell Checker Pipeline for English +author: John Snow Labs +name: spellcheck_dl_pipeline +date: 2023-05-22 +tags: [spellcheck, spell, spellcheck_pipeline, spelling_corrector, en, open_source] +task: Spell Check +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained spellchecker pipeline is built on the top of [spellcheck_dl](https://nlp.johnsnowlabs.com/2022/04/02/spellcheck_dl_en_2_4.html) model. This pipeline is for PySpark 2.4.x users with SparkNLP 3.4.2 and above. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/spellcheck_dl_pipeline_en_4.4.2_3.2_1684762113786.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/spellcheck_dl_pipeline_en_4.4.2_3.2_1684762113786.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +text = ["During the summer we have the best ueather.", "I have a black ueather jacket, so nice."] + +pipeline.annotate(text) +``` +```scala + + +val pipeline = new PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +val example = Array("During the summer we have the best ueather.", "I have a black ueather jacket, so nice.") + +pipeline.annotate(example) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +text = ["During the summer we have the best ueather.", "I have a black ueather jacket, so nice."] + +pipeline.annotate(text) +``` +```scala +val pipeline = new PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +val example = Array("During the summer we have the best ueather.", "I have a black ueather jacket, so nice.") + +pipeline.annotate(example) +``` +
+ +## Results + +```bash +Results + + + + +[{'checked': ['During', 'the', 'summer', 'we', 'have', 'the', 'best', 'weather', '.'], + 'document': ['During the summer we have the best ueather.'], + 'token': ['During', 'the', 'summer', 'we', 'have', 'the', 'best', 'ueather', '.']}, + + {'checked': ['I', 'have', 'a', 'black', 'leather', 'jacket', ',', 'so', 'nice', '.'], + 'document': ['I have a black ueather jacket, so nice.'], + 'token': ['I', 'have', 'a', 'black', 'ueather', 'jacket', ',', 'so', 'nice', '.']}] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|spellcheck_dl_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|99.7 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- ContextSpellCheckerModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-text_cleaning_en.md b/docs/_posts/ahmedlone127/2023-05-22-text_cleaning_en.md new file mode 100644 index 00000000000000..bd21c3c74648b7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-text_cleaning_en.md @@ -0,0 +1,80 @@ +--- +layout: model +title: Text Cleaning +author: John Snow Labs +name: text_cleaning +date: 2023-05-22 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The text_cleaning is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and cleans text. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/text_cleaning_en_4.4.2_3.2_1684745567650.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/text_cleaning_en_4.4.2_3.2_1684745567650.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("text_cleaning", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("text_cleaning", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|text_cleaning| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|944.5 KB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- NormalizerModel +- StopWordsCleaner +- LemmatizerModel +- TokenAssembler \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_base_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..b01501066476f8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_base_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: XLM-RoBERTa Base, CoNLL-03 NER Pipeline +author: John Snow Labs +name: xlm_roberta_base_token_classifier_conll03_pipeline +date: 2023-05-22 +tags: [open_source, ner, token_classifier, xlm_roberta, conll03, xlm, base, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/10/03/xlm_roberta_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_conll03_pipeline_en_4.4.2_3.2_1684764388839.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_conll03_pipeline_en_4.4.2_3.2_1684764388839.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|851.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md new file mode 100644 index 00000000000000..9b0a4e00a3cbec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: XLM-RoBERTa Base NER Pipeline +author: John Snow Labs +name: xlm_roberta_base_token_classifier_ontonotes_pipeline +date: 2023-05-22 +tags: [open_source, ner, token_classifier, xlm_roberta, ontonotes, xlm, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifier_ontonotes](https://nlp.johnsnowlabs.com/2021/10/03/xlm_roberta_base_token_classifier_ontonotes_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.2_1684764132113.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.2_1684764132113.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | +|November 2020 |DATE | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_base_token_classifier_ontonotes_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|858.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_conll03_pipeline_de.md b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_conll03_pipeline_de.md new file mode 100644 index 00000000000000..a5cde6f39d05a4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_conll03_pipeline_de.md @@ -0,0 +1,104 @@ +--- +layout: model +title: NER Pipeline for German +author: John Snow Labs +name: xlm_roberta_large_token_classifier_conll03_pipeline +date: 2023-05-22 +tags: [german, roberta, xlm, ner, conll03, de, open_source] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_large_token_classifier_conll03_de](https://nlp.johnsnowlabs.com/2021/12/25/xlm_roberta_large_token_classifier_conll03_de.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_conll03_pipeline_de_4.4.2_3.2_1684762736094.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_conll03_pipeline_de_4.4.2_3.2_1684762736094.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +```scala + +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +
+ +## Results + +```bash +Results + + + ++----------------------+---------+ +|chunk |ner_label| ++----------------------+---------+ +|Ibser |PER | +|ASK Ebreichsdorf |ORG | +|Admira Wacker Mödling |ORG | ++----------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|1.8 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md new file mode 100644 index 00000000000000..b50cc0a119e8ce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md @@ -0,0 +1,104 @@ +--- +layout: model +title: NER Pipeline for 10 High Resourced Languages +author: John Snow Labs +name: xlm_roberta_large_token_classifier_hrl_pipeline +date: 2023-05-22 +tags: [arabic, german, english, spanish, french, italian, latvian, dutch, portuguese, chinese, xlm, roberta, ner, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_large_token_classifier_hrl](https://nlp.johnsnowlabs.com/2021/12/26/xlm_roberta_large_token_classifier_hrl_xx.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_hrl_pipeline_xx_4.4.2_3.2_1684761928074.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_hrl_pipeline_xx_4.4.2_3.2_1684761928074.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +```scala + +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +
+ +## Results + +```bash +Results + + + ++---------------------------+---------+ +|chunk |ner_label| ++---------------------------+---------+ +|الرياض |LOC | +|فيصل بن بندر بن عبد العزيز |PER | +|الرياض |LOC | ++---------------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_large_token_classifier_hrl_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|1.8 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md new file mode 100644 index 00000000000000..33594be3b84966 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md @@ -0,0 +1,104 @@ +--- +layout: model +title: NER Pipeline for 10 African Languages +author: John Snow Labs +name: xlm_roberta_large_token_classifier_masakhaner_pipeline +date: 2023-05-22 +tags: [masakhaner, african, xlm_roberta, multilingual, pipeline, amharic, hausa, igbo, kinyarwanda, luganda, swahilu, wolof, yoruba, nigerian, pidgin, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on [xlm_roberta_large_token_classifier_masakhaner](https://nlp.johnsnowlabs.com/2021/12/06/xlm_roberta_large_token_classifier_masakhaner_xx.html) ner model which is imported from `HuggingFace`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_masakhaner_pipeline_xx_4.4.2_3.2_1684760146360.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_masakhaner_pipeline_xx_4.4.2_3.2_1684760146360.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +masakhaner_pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +```scala + +val masakhaner_pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +val masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +masakhaner_pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +```scala +val masakhaner_pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +val masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +
+ +## Results + +```bash +Results + + + ++----------------+---------+ +|chunk |ner_label| ++----------------+---------+ +|አህመድ ቫንዳ |PER | +|ከ3-10-2000 ጀምሮ|DATE | +|በአዲስ አበባ |LOC | ++----------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_large_token_classifier_masakhaner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|1.8 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-xlnet_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-xlnet_base_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..e11013676e7752 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-xlnet_base_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: XLNet Base CoNLL-03 NER Pipeline +author: John Snow Labs +name: xlnet_base_token_classifier_conll03_pipeline +date: 2023-05-22 +tags: [ner, english, xlnet, base, token_classification, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlnet_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/28/xlnet_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlnet_base_token_classifier_conll03_pipeline_en_4.4.2_3.2_1684763897898.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlnet_base_token_classifier_conll03_pipeline_en_4.4.2_3.2_1684763897898.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlnet_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|438.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlnetForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-22-xlnet_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-xlnet_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..58dc4fccd0d7f8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-22-xlnet_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,103 @@ +--- +layout: model +title: XLNet Large CoNLL-03 NER Pipeline +author: John Snow Labs +name: xlnet_large_token_classifier_conll03_pipeline +date: 2023-05-22 +tags: [open_source, ner, token_classifier, xlnet, conll03, large, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlnet_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/28/xlnet_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlnet_large_token_classifier_conll03_pipeline_en_4.4.2_3.2_1684764466783.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlnet_large_token_classifier_conll03_pipeline_en_4.4.2_3.2_1684764466783.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlnet_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|19.1 KB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- NormalizerModel \ No newline at end of file From 40498817afe264c7aa1d00b1ec4d0eb77daf49f1 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Thu, 25 May 2023 18:15:57 +0700 Subject: [PATCH 09/55] 2023-05-24-explain_document_md_fr (#13821) * Add model 2023-05-24-explain_document_md_fr * Add model 2023-05-24-dependency_parse_en * Add model 2023-05-24-explain_document_md_it * Add model 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"Python-Scala-Java" +--- + +## Description + +The analyze_sentiment is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/analyze_sentiment_en_4.4.2_3.4_1684942601855.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/analyze_sentiment_en_4.4.2_3.4_1684942601855.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python + +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en') + +result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") + + +``` +```scala + +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en") + +val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") + +``` + +{:.nlu-block} +```python + +import nlu +text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""] +result_df = nlu.load('en.classify').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en') + +result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en") + +val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") +``` + +{:.nlu-block} +```python +import nlu +text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""] +result_df = nlu.load('en.classify').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | text | sentiment | +|---:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------| +| 0 | Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now! | positive | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|analyze_sentiment| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|5.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel +- ViveknSentimentModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-analyze_sentimentdl_glove_imdb_en.md b/docs/_posts/ahmedlone127/2023-05-24-analyze_sentimentdl_glove_imdb_en.md new file mode 100644 index 00000000000000..b9f91f44783fce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-analyze_sentimentdl_glove_imdb_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Sentiment Analysis pipeline for English (analyze_sentimentdl_glove_imdb) +author: John Snow Labs +name: analyze_sentimentdl_glove_imdb +date: 2023-05-24 +tags: [open_source, english, analyze_sentimentdl_glove_imdb, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The analyze_sentimentdl_glove_imdb is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/analyze_sentimentdl_glove_imdb_en_4.4.2_3.4_1684952698619.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/analyze_sentimentdl_glove_imdb_en_4.4.2_3.4_1684952698619.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("analyze_sentimentdl_glove_imdb", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.sentiment.glove').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("analyze_sentimentdl_glove_imdb", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.sentiment.glove').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | tokens | word_embeddings | sentence_embeddings | sentiment | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-----------------------------|:------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2668800055980682,.,...]] | [[0.0771183446049690,.,...]] | ['neg'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|analyze_sentimentdl_glove_imdb| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|161.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- SentenceEmbeddings +- SentimentDLModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-check_spelling_en.md b/docs/_posts/ahmedlone127/2023-05-24-check_spelling_en.md new file mode 100644 index 00000000000000..0470181a65b65c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-check_spelling_en.md @@ -0,0 +1,120 @@ +--- +layout: model +title: Spell Checking Pipeline for English +author: John Snow Labs +name: check_spelling +date: 2023-05-24 +tags: [open_source, english, check_spelling, pipeline, en] +task: Spell Check +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The check_spelling is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/check_spelling_en_4.4.2_3.4_1684941780505.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/check_spelling_en_4.4.2_3.4_1684941780505.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('check_spelling', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("check_spelling", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('check_spelling', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("check_spelling", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | checked | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------------------------| +| 0 | ['I liek to live dangertus ! '] | ['I liek to live dangertus !'] | ['I', 'liek', 'to', 'live', 'dangertus', '!'] | ['I', 'like', 'to', 'live', 'dangerous', '!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|check_spelling| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|906.2 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-clean_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-24-clean_pattern_en.md new file mode 100644 index 00000000000000..b13244944aa964 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-clean_pattern_en.md @@ -0,0 +1,120 @@ +--- +layout: model +title: Clean patterns pipeline for English +author: John Snow Labs +name: clean_pattern +date: 2023-05-24 +tags: [open_source, english, clean_pattern, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The clean_pattern is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clean_pattern_en_4.4.2_3.4_1684941915345.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clean_pattern_en_4.4.2_3.4_1684941915345.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_pattern', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala + + +val pipeline = new PretrainedPipeline("clean_pattern", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python + + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.pattern').predict(text) +result_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_pattern', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("clean_pattern", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.pattern').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + + +| | document | sentence | token | normal | +|---:|:-----------|:-----------|:----------|:----------| +| 0 | ['Hello'] | ['Hello'] | ['Hello'] | ['Hello'] || | document | sentence | token | normal | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clean_pattern| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|17.2 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NormalizerModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-clean_stop_en.md b/docs/_posts/ahmedlone127/2023-05-24-clean_stop_en.md new file mode 100644 index 00000000000000..6068e038ad1ee6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-clean_stop_en.md @@ -0,0 +1,120 @@ +--- +layout: model +title: Clean documents pipeline for English +author: John Snow Labs +name: clean_stop +date: 2023-05-24 +tags: [open_source, english, clean_stop, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The clean_stop is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clean_stop_en_4.4.2_3.4_1684941961003.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clean_stop_en_4.4.2_3.4_1684941961003.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_stop', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("clean_stop", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.stop').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_stop', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("clean_stop", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.stop').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | cleanTokens | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:---------------------------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | ['Hello', 'John', 'Snow', 'Labs', '!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clean_stop| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|14.2 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- StopWordsCleaner \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-dependency_parse_en.md b/docs/_posts/ahmedlone127/2023-05-24-dependency_parse_en.md new file mode 100644 index 00000000000000..698243dd02a138 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-dependency_parse_en.md @@ -0,0 +1,119 @@ +--- +layout: model +title: Typed Dependency Parsing pipeline for English +author: John Snow Labs +name: dependency_parse +date: 2023-05-24 +tags: [pipeline, dependency_parsing, untyped_dependency_parsing, typed_dependency_parsing, laballed_depdency_parsing, unlaballed_depdency_parsing, en, open_source] +task: Dependency Parser +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Typed Dependency parser, trained on the on the CONLL dataset. + +Dependency parsing is the task of extracting a dependency parse of a sentence that represents its grammatical structure and defines the relationships between “head” words and words, which modify those heads. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dependency_parse_en_4.4.2_3.4_1684940085159.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dependency_parse_en_4.4.2_3.4_1684940085159.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('dependency_parse', lang = 'en') +annotations = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("dependency_parse", lang = "en") +val result = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence")(0) + +``` + +{:.nlu-block} +```python + +nlu.load("dep.typed").predict("Dependencies represents relationships betweens words in a Sentence") + + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('dependency_parse', lang = 'en') +annotations = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("dependency_parse", lang = "en") +val result = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence")(0) +``` + +{:.nlu-block} +```python +nlu.load("dep.typed").predict("Dependencies represents relationships betweens words in a Sentence") +``` +
+ +## Results + +```bash +Results + + ++---------------------------------------------------------------------------------+--------------------------------------------------------+ +|result |result | ++---------------------------------------------------------------------------------+--------------------------------------------------------+ +|[ROOT, Dependencies, represents, words, relationships, Sentence, Sentence, words]|[root, parataxis, nsubj, amod, nsubj, case, nsubj, flat]| ++---------------------------------------------------------------------------------+--------------------------------------------------------+ + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dependency_parse| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|23.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel +- DependencyParserModel +- TypedDependencyParserModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_de.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_de.md new file mode 100644 index 00000000000000..dd930bea9c5bf6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_de.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL pipeline for German - Large +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-24 +tags: [open_source, german, entity_recognizer_lg, pipeline, de] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_de_4.4.2_3.4_1684943746142.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_de_4.4.2_3.4_1684943746142.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo aus John Snow Labs! '] | ['Hallo aus John Snow Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | [[-0.245989993214607,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_es.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_es.md new file mode 100644 index 00000000000000..51c2508bf37b4e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_es.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognition Pipeline (Large, Spanish) +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-24 +tags: [es, open_source] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_es_4.4.2_3.4_1684945914760.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_es_4.4.2_3.4_1684945914760.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "es") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("es.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "es") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_fr.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_fr.md new file mode 100644 index 00000000000000..18c3447a08a0bc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_fr.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL pipeline for French - Large +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-24 +tags: [open_source, french, entity_recognizer_lg, pipeline, fr] +task: Named Entity Recognition +language: fr +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fr_4.4.2_3.4_1684940831641.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fr_4.4.2_3.4_1684940831641.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:--------------------------------|:-------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Bonjour de John Snow Labs! '] | ['Bonjour de John Snow Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | [[-0.010997000150382,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fr| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_it.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_it.md new file mode 100644 index 00000000000000..60a4947371eb1d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_it.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL pipeline for Italian - Large +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-24 +tags: [open_source, italian, entity_recognizer_lg, pipeline, it] +task: Named Entity Recognition +language: it +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_it_4.4.2_3.4_1684941483648.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_it_4.4.2_3.4_1684941483648.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Ciao da John Snow Labs! '] | ['Ciao da John Snow Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | [[-0.238279998302459,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|it| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_nl.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_nl.md new file mode 100644 index 00000000000000..2c911b74026429 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_nl.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-24 +tags: [nl, open_source] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_nl_4.4.2_3.4_1684952173643.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_nl_4.4.2_3.4_1684952173643.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "nl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("nl.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "nl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("nl.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_no.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_no.md new file mode 100644 index 00000000000000..4cefe051b56add --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_no.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-24 +tags: ["no", open_source] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_no_4.4.2_3.4_1684953911347.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_no_4.4.2_3.4_1684953911347.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "no") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("no.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "no") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("no.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_pl.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_pl.md new file mode 100644 index 00000000000000..2955701471bfe1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_pl.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-24 +tags: [pl, open_source] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pl_4.4.2_3.4_1684950613328.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pl_4.4.2_3.4_1684950613328.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("pl.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pl.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_pt.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_pt.md new file mode 100644 index 00000000000000..4b1cde8456ac3c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_pt.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-24 +tags: [pt, open_source] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pt_4.4.2_3.4_1684949065163.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pt_4.4.2_3.4_1684949065163.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pt") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pt") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_ru.md new file mode 100644 index 00000000000000..49dfad88bac238 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_ru.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-24 +tags: [ru, open_source] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_ru_4.4.2_3.4_1684947444959.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_ru_4.4.2_3.4_1684947444959.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "ru") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("ru.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "ru") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ru.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_sv.md new file mode 100644 index 00000000000000..2d7712cc130eec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_sv.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-24 +tags: [sv, open_source] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_sv_4.4.2_3.4_1684955596215.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_sv_4.4.2_3.4_1684955596215.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "sv") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("sv.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "sv") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("sv.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_de.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_de.md new file mode 100644 index 00000000000000..2598aa24330417 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_de.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for German - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-24 +tags: [open_source, german, entity_recognizer_md, pipeline, de] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_de_4.4.2_3.4_1684943971785.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_de_4.4.2_3.4_1684943971785.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo aus John Snow Labs! '] | ['Hallo aus John Snow Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|461.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_es.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_es.md new file mode 100644 index 00000000000000..16fc2ffd0cc20b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_es.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Spanish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-24 +tags: [open_source, spanish, entity_recognizer_md, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_es_4.4.2_3.4_1684946121502.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_es_4.4.2_3.4_1684946121502.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | [[0.5123000144958496,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|461.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_fr.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_fr.md new file mode 100644 index 00000000000000..72dac1583e9b59 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_fr.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for French - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-24 +tags: [open_source, french, entity_recognizer_md, pipeline, fr] +task: Named Entity Recognition +language: fr +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fr_4.4.2_3.4_1684941045540.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fr_4.4.2_3.4_1684941045540.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:--------------------------------|:-------------------------------|:-------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------------------| +| 0 | ['Bonjour de John Snow Labs! '] | ['Bonjour de John Snow Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | [[0.0783179998397827,.,...]] | ['I-MISC', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['Bonjour', 'John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fr| +|Size:|462.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_it.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_it.md new file mode 100644 index 00000000000000..8e77c3508b4c21 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_it.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Italian - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-24 +tags: [open_source, italian, entity_recognizer_md, pipeline, it] +task: Named Entity Recognition +language: it +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_it_4.4.2_3.4_1684941708703.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_it_4.4.2_3.4_1684941708703.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Ciao da John Snow Labs! '] | ['Ciao da John Snow Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | [[-0.146050006151199,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|it| +|Size:|462.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_nl.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_nl.md new file mode 100644 index 00000000000000..18ddd100595790 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_nl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Dutch - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-24 +tags: [open_source, dutch, entity_recognizer_md, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_nl_4.4.2_3.4_1684952390695.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_nl_4.4.2_3.4_1684952390695.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|462.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_no.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_no.md new file mode 100644 index 00000000000000..5e43ac4f67f878 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_no.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Norwegian (Bokmal) - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-24 +tags: [open_source, norwegian_bokmal, entity_recognizer_md, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_no_4.4.2_3.4_1684954132143.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_no_4.4.2_3.4_1684954132143.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | [[0.1868100017309188,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|462.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_pl.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_pl.md new file mode 100644 index 00000000000000..6126a136cac579 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_pl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Polish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-24 +tags: [open_source, polish, entity_recognizer_md, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pl_4.4.2_3.4_1684950848216.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pl_4.4.2_3.4_1684950848216.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|462.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_pt.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_pt.md new file mode 100644 index 00000000000000..56d0da59e48fe5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_pt.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Portuguese - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-24 +tags: [open_source, portuguese, entity_recognizer_md, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pt_4.4.2_3.4_1684949287268.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pt_4.4.2_3.4_1684949287268.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|462.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_ru.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_ru.md new file mode 100644 index 00000000000000..daa6bd25d17025 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_ru.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Russian - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-24 +tags: [open_source, russian, entity_recognizer_md, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_ru_4.4.2_3.4_1684947665804.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_ru_4.4.2_3.4_1684947665804.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-LOC', 'I-LOC', 'I-LOC'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|462.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_sv.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_sv.md new file mode 100644 index 00000000000000..e2153d5b120cfc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_sv.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Swedish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-24 +tags: [open_source, swedish, entity_recognizer_md, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_sv_4.4.2_3.4_1684955821354.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_sv_4.4.2_3.4_1684955821354.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|461.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_es.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_es.md new file mode 100644 index 00000000000000..53cfb3b430866a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_es.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Spanish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-24 +tags: [open_source, spanish, entity_recognizer_sm, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_es_4.4.2_3.4_1684946222304.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_es_4.4.2_3.4_1684946222304.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:---------------------------------------|:-----------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | [[0.1754499971866607,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'B-MISC'] | ['John Snow', 'Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_nl.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_nl.md new file mode 100644 index 00000000000000..6575de09f1f697 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_nl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Dutch - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-24 +tags: [open_source, dutch, entity_recognizer_sm, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_nl_4.4.2_3.4_1684952488440.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_nl_4.4.2_3.4_1684952488440.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | [[0.3653799891471863,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|166.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_no.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_no.md new file mode 100644 index 00000000000000..37a8591fe2ef78 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_no.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Norwegian (Bokmal) - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-24 +tags: [open_source, norwegian_bokmal, entity_recognizer_sm, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_no_4.4.2_3.4_1684954232934.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_no_4.4.2_3.4_1684954232934.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | [[-0.394499987363815,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|167.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_pl.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_pl.md new file mode 100644 index 00000000000000..9ab4379c655969 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_pl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Polish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-24 +tags: [open_source, polish, entity_recognizer_sm, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pl_4.4.2_3.4_1684950948020.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pl_4.4.2_3.4_1684950948020.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|167.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_pt.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_pt.md new file mode 100644 index 00000000000000..11474b38b3477a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_pt.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Portuguese - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-24 +tags: [open_source, portuguese, entity_recognizer_sm, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pt_4.4.2_3.4_1684949390472.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pt_4.4.2_3.4_1684949390472.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|167.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_ru.md new file mode 100644 index 00000000000000..6ae2893777183f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_ru.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Russian - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-24 +tags: [open_source, russian, entity_recognizer_sm, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_ru_4.4.2_3.4_1684947763425.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_ru_4.4.2_3.4_1684947763425.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|167.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_sv.md new file mode 100644 index 00000000000000..f7642a41d11171 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_sv.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Swedish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-24 +tags: [open_source, swedish, entity_recognizer_sm, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_sv_4.4.2_3.4_1684955921393.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_sv_4.4.2_3.4_1684955921393.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_dl_en.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_dl_en.md new file mode 100644 index 00000000000000..ce04a272f78d0b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_dl_en.md @@ -0,0 +1,128 @@ +--- +layout: model +title: Explain Document DL Pipeline for English +author: John Snow Labs +name: explain_document_dl +date: 2023-05-24 +tags: [open_source, english, explain_document_dl, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_dl is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_dl_en_4.4.2_3.4_1684942709629.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_dl_en_4.4.2_3.4_1684942709629.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_dl', lang = 'en') +annotations = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_dl", lang = "en") +val result = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = ["The Mona Lisa is an oil painting from the 16th century."] +result_df = nlu.load('en.explain.dl').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_dl', lang = 'en') +annotations = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_dl", lang = "en") +val result = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")(0) +``` + +{:.nlu-block} +```python +import nlu +text = ["The Mona Lisa is an oil painting from the 16th century."] +result_df = nlu.load('en.explain.dl').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + ++--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------+-----------+ +| text| document| sentence| token| checked| lemma| stem| pos| embeddings| ner| entities| ++--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------+-----------+ +|The Mona Lisa is an oil painting from the 16th ...|[The Mona Lisa is an oil painting from the 16th...|[The Mona Lisa is an oil painting from the 16th...|[The, Mona, Lisa, is, an, oil, painting, from, ...|[The, Mona, Lisa, is, an, oil, painting, from, ...|[The, Mona, Lisa, be, an, oil, painting, from, ...|[the, mona, lisa, i, an, oil, paint, from, the,...|[DT, NNP, NNP, VBZ, DT, NN, NN, IN, DT, JJ, NN, .]|[[-0.038194, -0.24487, 0.72812, -0.39961, 0.083...|[O, B-PER, I-PER, O, O, O, O, O, O, O, O, O]|[Mona Lisa]| ++--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------+-----------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_dl| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|176.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel +- LemmatizerModel +- Stemmer +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_es.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_es.md new file mode 100644 index 00000000000000..81358c6d035804 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_es.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Spanish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-24 +tags: [open_source, spanish, explain_document_lg, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_es_4.4.2_3.4_1684945123820.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_es_4.4.2_3.4_1684945123820.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['PART', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.016199000179767,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_nl.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_nl.md new file mode 100644 index 00000000000000..d110e2ec77bd8d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_nl.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Dutch (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-24 +tags: [open_source, dutch, explain_document_lg, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_nl_4.4.2_3.4_1684951389869.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_nl_4.4.2_3.4_1684951389869.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:-----------------------------|:------------------------------------------|:-----------------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.245989993214607,.,...]] | ['B-PER', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Hallo', 'John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_no.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_no.md new file mode 100644 index 00000000000000..9702d232220434 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_no.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Norwegian (Bokmal) (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-24 +tags: [open_source, norwegian_bokmal, explain_document_lg, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_no_4.4.2_3.4_1684953143709.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_no_4.4.2_3.4_1684953143709.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:--------------------------------------------|:-----------------------------|:---------------------------------------|:-----------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0639619976282119,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'B-PROD'] | ['John Snow', 'Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_pl.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_pl.md new file mode 100644 index 00000000000000..405508f1aa9aff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_pl.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Polish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-24 +tags: [open_source, polish, explain_document_lg, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_pl_4.4.2_3.4_1684949836920.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_pl_4.4.2_3.4_1684949836920.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | ['witać', 'z', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4977500140666961,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_pt.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_pt.md new file mode 100644 index 00000000000000..24b2225c9d0b01 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_pt.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Portuguese (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-24 +tags: [open_source, portuguese, explain_document_lg, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_pt_4.4.2_3.4_1684948271616.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_pt_4.4.2_3.4_1684948271616.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:---------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4388400018215179,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_ru.md new file mode 100644 index 00000000000000..9fdae922e46e8a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_ru.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Russian (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-24 +tags: [open_source, russian, explain_document_lg, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_ru_4.4.2_3.4_1684946671420.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_ru_4.4.2_3.4_1684946671420.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | ['здравствовать', 'из', 'Джон', 'Снежных', 'Лабораторий!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_sv.md new file mode 100644 index 00000000000000..132f8cdbb495a2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_sv.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Swedish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-24 +tags: [open_source, swedish, explain_document_lg, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_sv_4.4.2_3.4_1684954783812.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_sv_4.4.2_3.4_1684954783812.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_de.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_de.md new file mode 100644 index 00000000000000..3d4f4dc3ef291d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_de.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for German +author: John Snow Labs +name: explain_document_md +date: 2023-05-24 +tags: [open_source, german, explain_document_md, pipeline, de] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_de_4.4.2_3.4_1684943309681.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_de_4.4.2_3.4_1684943309681.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.explain.document').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.explain.document').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo aus John Snow Labs! '] | ['Hallo aus John Snow Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|470.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_es.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_es.md new file mode 100644 index 00000000000000..0ce6e3cfe83274 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_es.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Spanish +author: John Snow Labs +name: explain_document_md +date: 2023-05-24 +tags: [open_source, spanish, explain_document_md, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_es_4.4.2_3.4_1684945354554.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_es_4.4.2_3.4_1684945354554.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['PART', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.5123000144958496,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|472.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_fr.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_fr.md new file mode 100644 index 00000000000000..a43059872d0343 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_fr.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for French +author: John Snow Labs +name: explain_document_md +date: 2023-05-24 +tags: [open_source, french, explain_document_md, pipeline, fr] +task: Named Entity Recognition +language: fr +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_fr_4.4.2_3.4_1684939814735.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_fr_4.4.2_3.4_1684939814735.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:--------------------------------|:-------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------------------| +| 0 | ['Bonjour de John Snow Labs! '] | ['Bonjour de John Snow Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | ['INTJ', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0783179998397827,.,...]] | ['I-MISC', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['Bonjour', 'John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fr| +|Size:|467.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_it.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_it.md new file mode 100644 index 00000000000000..19fbe7fcc45acb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_it.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Italian +author: John Snow Labs +name: explain_document_md +date: 2023-05-24 +tags: [open_source, italian, explain_document_md, pipeline, it] +task: Named Entity Recognition +language: it +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_it_4.4.2_3.4_1684940391635.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_it_4.4.2_3.4_1684940391635.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.explain.document').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.explain.document').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Ciao da John Snow Labs! '] | ['Ciao da John Snow Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.146050006151199,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|it| +|Size:|468.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_nl.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_nl.md new file mode 100644 index 00000000000000..8788c085221711 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_nl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Dutch +author: John Snow Labs +name: explain_document_md +date: 2023-05-24 +tags: [open_source, dutch, explain_document_md, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_nl_4.4.2_3.4_1684951619874.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_nl_4.4.2_3.4_1684951619874.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|464.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_no.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_no.md new file mode 100644 index 00000000000000..d0fc0624d92ee7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_no.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Norwegian (Bokmal) +author: John Snow Labs +name: explain_document_md +date: 2023-05-24 +tags: [open_source, norwegian_bokmal, explain_document_md, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_no_4.4.2_3.4_1684953369855.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_no_4.4.2_3.4_1684953369855.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.1868100017309188,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|465.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_pl.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_pl.md new file mode 100644 index 00000000000000..b9b53745d2be90 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_pl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Polish +author: John Snow Labs +name: explain_document_md +date: 2023-05-24 +tags: [open_source, polish, explain_document_md, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_pl_4.4.2_3.4_1684950075052.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_pl_4.4.2_3.4_1684950075052.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | ['witać', 'z', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|464.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_pt.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_pt.md new file mode 100644 index 00000000000000..b82c26f2445b85 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_pt.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Portuguese +author: John Snow Labs +name: explain_document_md +date: 2023-05-24 +tags: [open_source, portuguese, explain_document_md, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_pt_4.4.2_3.4_1684948506078.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_pt_4.4.2_3.4_1684948506078.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:---------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|473.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_ru.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_ru.md new file mode 100644 index 00000000000000..761bb44e1825bd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_ru.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Russian +author: John Snow Labs +name: explain_document_md +date: 2023-05-24 +tags: [open_source, russian, explain_document_md, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_ru_4.4.2_3.4_1684946894518.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_ru_4.4.2_3.4_1684946894518.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | ['здравствовать', 'из', 'Джон', 'Снежных', 'Лабораторий!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-LOC', 'I-LOC', 'I-LOC'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|465.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_sv.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_sv.md new file mode 100644 index 00000000000000..395727eadff20a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_sv.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Swedish +author: John Snow Labs +name: explain_document_md +date: 2023-05-24 +tags: [open_source, swedish, explain_document_md, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_sv_4.4.2_3.4_1684955031982.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_sv_4.4.2_3.4_1684955031982.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|471.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_ml_en.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_ml_en.md new file mode 100644 index 00000000000000..c8c470caefcf24 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_ml_en.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document ML Pipeline for English +author: John Snow Labs +name: explain_document_ml +date: 2023-05-24 +tags: [open_source, english, explain_document_ml, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_ml is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_ml_en_4.4.2_3.4_1684942508061.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_ml_en_4.4.2_3.4_1684942508061.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_ml', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala + + +val pipeline = new PretrainedPipeline("explain_document_ml", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python + + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.explain').predict(text) +result_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_ml', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_ml", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + + +| | document | sentence | token | spell | lemmas | stems | pos | +|---:|:---------------------------------|:---------------------------------|:-------------------------------------------------|:------------------------------------------------|:------------------------------------------------|:-----------------------------------------------|:---------------------------------------| +| 0 | ['Hello fronm John Snwow Labs!'] | ['Hello fronm John Snwow Labs!'] | ['Hello', 'fronm', 'John', 'Snwow', 'Labs', '!'] | ['Hello', 'front', 'John', 'Snow', 'Labs', '!'] | ['Hello', 'front', 'John', 'Snow', 'Labs', '!'] | ['hello', 'front', 'john', 'snow', 'lab', '!'] | ['UH', 'NN', 'NNP', 'NNP', 'NNP', '.'] || | document | sentence | token | spell | lemmas | stems | pos | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_ml| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|9.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel +- LemmatizerModel +- Stemmer +- PerceptronModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_es.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_es.md new file mode 100644 index 00000000000000..edcce5d9a5a792 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_es.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Spanish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-24 +tags: [open_source, spanish, explain_document_sm, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_es_4.4.2_3.4_1684945478693.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_es_4.4.2_3.4_1684945478693.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:---------------------------------------|:-----------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['PART', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.1754499971866607,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'B-MISC'] | ['John Snow', 'Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|177.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_nl.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_nl.md new file mode 100644 index 00000000000000..384c5bf945844a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_nl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Dutch +author: John Snow Labs +name: explain_document_sm +date: 2023-05-24 +tags: [open_source, dutch, explain_document_sm, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_nl_4.4.2_3.4_1684951728575.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_nl_4.4.2_3.4_1684951728575.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.3653799891471863,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|169.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_no.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_no.md new file mode 100644 index 00000000000000..52a43cf866c269 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_no.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Norwegian (Bokmal) +author: John Snow Labs +name: explain_document_sm +date: 2023-05-24 +tags: [open_source, norwegian_bokmal, explain_document_sm, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_no_4.4.2_3.4_1684953479768.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_no_4.4.2_3.4_1684953479768.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.394499987363815,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|170.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_pl.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_pl.md new file mode 100644 index 00000000000000..6d2918d63f41ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_pl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Polish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-24 +tags: [open_source, polish, explain_document_sm, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_pl_4.4.2_3.4_1684950181773.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_pl_4.4.2_3.4_1684950181773.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | ['witać', 'z', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|169.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_pt.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_pt.md new file mode 100644 index 00000000000000..0e0b40663ca15f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_pt.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Portuguese +author: John Snow Labs +name: explain_document_sm +date: 2023-05-24 +tags: [open_source, portuguese, explain_document_sm, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_pt_4.4.2_3.4_1684948627317.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_pt_4.4.2_3.4_1684948627317.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:---------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|178.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_ru.md new file mode 100644 index 00000000000000..782cc68a2a9e61 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_ru.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Russian +author: John Snow Labs +name: explain_document_sm +date: 2023-05-24 +tags: [open_source, russian, explain_document_sm, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_ru_4.4.2_3.4_1684947008574.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_ru_4.4.2_3.4_1684947008574.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | ['здравствовать', 'из', 'Джон', 'Снежных', 'Лабораторий!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|170.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_sv.md new file mode 100644 index 00000000000000..eb558461247667 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_sv.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Swedish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-24 +tags: [open_source, swedish, explain_document_sm, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_sv_4.4.2_3.4_1684955152911.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_sv_4.4.2_3.4_1684955152911.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|176.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-match_chunks_en.md b/docs/_posts/ahmedlone127/2023-05-24-match_chunks_en.md new file mode 100644 index 00000000000000..bc4e550333f299 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-match_chunks_en.md @@ -0,0 +1,141 @@ +--- +layout: model +title: Match Chunks in Texts +author: John Snow Labs +name: match_chunks +date: 2023-05-24 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The pipeline uses regex `
?/*+` + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_chunks_en_4.4.2_3.4_1684944669443.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_chunks_en_4.4.2_3.4_1684944669443.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline + +pipeline_local = PretrainedPipeline('match_chunks') + +result = pipeline_local.annotate("David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.") + +result['chunk'] +``` +```scala + +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_chunks", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.match.chunks").predict("""David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline_local = PretrainedPipeline('match_chunks') + +result = pipeline_local.annotate("David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.") + +result['chunk'] +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_chunks", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.match.chunks").predict("""David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.""") +``` +
+ +## Results + +```bash +Results + + + +['the restaurant yesterday', +'family', +'the day', +'that time', +'today', +'the food', +'tomorrow'] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|match_chunks| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|4.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel +- Chunker \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-match_datetime_en.md b/docs/_posts/ahmedlone127/2023-05-24-match_datetime_en.md new file mode 100644 index 00000000000000..14a0f3958959fb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-match_datetime_en.md @@ -0,0 +1,127 @@ +--- +layout: model +title: Match Datetime in Texts +author: John Snow Labs +name: match_datetime +date: 2023-05-24 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +DateMatcher based on yyyy/MM/dd + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_datetime_en_4.4.2_3.4_1684941825146.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_datetime_en_4.4.2_3.4_1684941825146.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline_local = PretrainedPipeline('match_datetime') + +tres = pipeline_local.fullAnnotate(input_list)[0] +for dte in tres['date']: + sent = tres['sentence'][int(dte.metadata['sentence'])] + print (f'text/chunk {sent.result[dte.begin:dte.end+1]} | mapped_date: {dte.result}') +``` +```scala + +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. +He also visited and the day before, but at that time he was alone. +David again visited today with his colleagues. +He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_datetime", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline_local = PretrainedPipeline('match_datetime') + +tres = pipeline_local.fullAnnotate(input_list)[0] +for dte in tres['date']: + sent = tres['sentence'][int(dte.metadata['sentence'])] + print (f'text/chunk {sent.result[dte.begin:dte.end+1]} | mapped_date: {dte.result}') +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. +He also visited and the day before, but at that time he was alone. +David again visited today with his colleagues. +He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_datetime", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` +
+ +## Results + +```bash +Results + + + +text/chunk yesterday | mapped_date: 2022/01/02 +text/chunk day before | mapped_date: 2022/01/02 +text/chunk today | mapped_date: 2022/01/03 +text/chunk tomorrow | mapped_date: 2022/01/04 + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|match_datetime| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|12.9 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- MultiDateMatcher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-match_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-24-match_pattern_en.md new file mode 100644 index 00000000000000..f6664a3ddd1cb7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-match_pattern_en.md @@ -0,0 +1,77 @@ +--- +layout: model +title: Match Pattern +author: John Snow Labs +name: match_pattern +date: 2023-05-24 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The match_pattern is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and matches pattrens . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_pattern_en_4.4.2_3.4_1684941870922.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_pattern_en_4.4.2_3.4_1684941870922.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models") + result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models") + result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|match_pattern| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|17.4 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- RegexMatcherModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-movies_sentiment_analysis_en.md b/docs/_posts/ahmedlone127/2023-05-24-movies_sentiment_analysis_en.md new file mode 100644 index 00000000000000..ffc88a8a21c0af --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-movies_sentiment_analysis_en.md @@ -0,0 +1,79 @@ +--- +layout: model +title: Movies Sentiment Analysis +author: John Snow Labs +name: movies_sentiment_analysis +date: 2023-05-24 +tags: [en, open_source] +task: Sentiment Analysis +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The movies_sentiment_analysis is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and predicts sentiment . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/movies_sentiment_analysis_en_4.4.2_3.4_1684942432293.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/movies_sentiment_analysis_en_4.4.2_3.4_1684942432293.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("movies_sentiment_analysis", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("movies_sentiment_analysis", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|movies_sentiment_analysis| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|207.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- SymmetricDeleteModel +- SentimentDetectorModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-onto_recognize_entities_lg_en.md b/docs/_posts/ahmedlone127/2023-05-24-onto_recognize_entities_lg_en.md new file mode 100644 index 00000000000000..c53073ddb80d2f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-onto_recognize_entities_lg_en.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Onto Recognize Entities Lg +author: John Snow Labs +name: onto_recognize_entities_lg +date: 2023-05-24 +tags: [en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entites. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_lg_en_4.4.2_3.4_1684944508258.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_lg_en_4.4.2_3.4_1684944508258.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("onto_recognize_entities_lg", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("en.ner.onto.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("onto_recognize_entities_lg", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.ner.onto.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-onto_recognize_entities_sm_en.md b/docs/_posts/ahmedlone127/2023-05-24-onto_recognize_entities_sm_en.md new file mode 100644 index 00000000000000..61c9e4f510886b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-onto_recognize_entities_sm_en.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL pipeline for English - Small +author: John Snow Labs +name: onto_recognize_entities_sm +date: 2023-05-24 +tags: [open_source, english, onto_recognize_entities_sm, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_sm_en_4.4.2_3.4_1684944069140.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_sm_en_4.4.2_3.4_1684944069140.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_sm', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_sm", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.sm').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_sm', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_sm", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.sm').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2668800055980682,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-recognize_entities_bert_en.md b/docs/_posts/ahmedlone127/2023-05-24-recognize_entities_bert_en.md new file mode 100644 index 00000000000000..b57803f6d35a4e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-recognize_entities_bert_en.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Recognize Entities DL pipeline for English - BERT +author: John Snow Labs +name: recognize_entities_bert +date: 2023-05-24 +tags: [open_source, english, recognize_entities_bert, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The recognize_entities_bert is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recognize_entities_bert_en_4.4.2_3.4_1684942985233.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recognize_entities_bert_en_4.4.2_3.4_1684942985233.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_bert', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("recognize_entities_bert", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.bert').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_bert', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("recognize_entities_bert", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.bert').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.085488274693489,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-ORG', 'O'] | ['John Snow Labs'] || | document | sentence | token | embeddings | ner | entities | + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recognize_entities_bert| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|424.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-recognize_entities_dl_en.md b/docs/_posts/ahmedlone127/2023-05-24-recognize_entities_dl_en.md new file mode 100644 index 00000000000000..aad937ec1cdd60 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-recognize_entities_dl_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for English +author: John Snow Labs +name: recognize_entities_dl +date: 2023-05-24 +tags: [open_source, english, recognize_entities_dl, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The recognize_entities_dl is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_en_4.4.2_3.4_1684942858167.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_en_4.4.2_3.4_1684942858167.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:---------------------------------------------------|:------------------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2668800055980682,.,...]] | ['B-ORG', 'I-ORG', 'I-ORG', 'I-ORG', 'I-ORG', 'O'] | ['Hello from John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recognize_entities_dl| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-24-text_cleaning_en.md b/docs/_posts/ahmedlone127/2023-05-24-text_cleaning_en.md new file mode 100644 index 00000000000000..df3850fb08eb8e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-24-text_cleaning_en.md @@ -0,0 +1,80 @@ +--- +layout: model +title: Text Cleaning +author: John Snow Labs +name: text_cleaning +date: 2023-05-24 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The text_cleaning is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and cleans text. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/text_cleaning_en_4.4.2_3.4_1684947820896.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/text_cleaning_en_4.4.2_3.4_1684947820896.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("text_cleaning", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("text_cleaning", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|text_cleaning| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|944.5 KB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- NormalizerModel +- StopWordsCleaner +- LemmatizerModel +- TokenAssembler \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-albert_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-albert_base_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..e2e1cc820d93d9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-albert_base_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: ALBERT Base CoNNL-03 NER Pipeline +author: John Snow Labs +name: albert_base_token_classifier_conll03_pipeline +date: 2023-05-25 +tags: [open_source, ner, token_classifier, albert, conll03, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [albert_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/albert_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685011000388.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685011000388.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|43.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- AlbertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-albert_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-albert_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..8e78af9a31809a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-albert_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: ALBERT Large CoNNL-03 NER Pipeline +author: John Snow Labs +name: albert_large_token_classifier_conll03_pipeline +date: 2023-05-25 +tags: [open_source, ner, token_classifier, albert, conll03, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [albert_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/albert_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685011078237.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685011078237.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|64.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- AlbertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-albert_xlarge_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-albert_xlarge_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..4ae7fcc69bac01 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-albert_xlarge_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: ALBERT XLarge CoNNL-03 NER Pipeline +author: John Snow Labs +name: albert_xlarge_token_classifier_conll03_pipeline +date: 2023-05-25 +tags: [open_source, ner, token_classifier, albert, conll03, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [albert_xlarge_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/albert_xlarge_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_xlarge_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685011576149.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_xlarge_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685011576149.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_xlarge_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|206.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- AlbertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-bert_sequence_classifier_age_news_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-bert_sequence_classifier_age_news_pipeline_en.md new file mode 100644 index 00000000000000..e58bd487c8d55e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-bert_sequence_classifier_age_news_pipeline_en.md @@ -0,0 +1,98 @@ +--- +layout: model +title: Pipeline to Classify Texts into 4 News Categories +author: John Snow Labs +name: bert_sequence_classifier_age_news_pipeline +date: 2023-05-25 +tags: [ag_news, news, bert, bert_sequence, classification, en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [bert_sequence_classifier_age_news_en](https://nlp.johnsnowlabs.com/2021/11/07/bert_sequence_classifier_age_news_en.html) which is imported from `HuggingFace`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_age_news_pipeline_en_4.4.2_3.4_1685006986387.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_age_news_pipeline_en_4.4.2_3.4_1685006986387.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +news_pipeline = PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +```scala + + +val news_pipeline = new PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +news_pipeline = PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +```scala +val news_pipeline = new PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +
+ +## Results + +```bash +Results + + + + +['Sci/Tech'] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_age_news_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|42.4 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- BertForSequenceClassification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-bert_sequence_classifier_trec_coarse_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-bert_sequence_classifier_trec_coarse_pipeline_en.md new file mode 100644 index 00000000000000..a60580df832493 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-bert_sequence_classifier_trec_coarse_pipeline_en.md @@ -0,0 +1,100 @@ +--- +layout: model +title: Pipeline to Classify Texts into TREC-6 Classes +author: John Snow Labs +name: bert_sequence_classifier_trec_coarse_pipeline +date: 2023-05-25 +tags: [bert_sequence, trec, coarse, bert, en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [bert_sequence_classifier_trec_coarse_en](https://nlp.johnsnowlabs.com/2021/11/06/bert_sequence_classifier_trec_coarse_en.html). + +The TREC dataset for question classification consists of open-domain, fact-based questions divided into broad semantic categories. You can check the official documentation of the dataset, entities, etc. [here](https://search.r-project.org/CRAN/refmans/textdata/html/dataset_trec.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_trec_coarse_pipeline_en_4.4.2_3.4_1685006908563.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_trec_coarse_pipeline_en_4.4.2_3.4_1685006908563.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +trec_pipeline = PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +```scala + + +val trec_pipeline = new PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +trec_pipeline = PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +```scala +val trec_pipeline = new PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +
+ +## Results + +```bash +Results + + + + +['LOC'] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_trec_coarse_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|406.6 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- BertForSequenceClassification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md b/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md new file mode 100644 index 00000000000000..334a754c8aadbe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Dutch NER Pipeline +author: John Snow Labs +name: bert_token_classifier_dutch_udlassy_ner_pipeline +date: 2023-05-25 +tags: [open_source, ner, dutch, token_classifier, bert, treatment, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [bert_token_classifier_dutch_udlassy_ner](https://nlp.johnsnowlabs.com/2021/12/08/bert_token_classifier_dutch_udlassy_ner_nl.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_token_classifier_dutch_udlassy_ner_pipeline_nl_4.4.2_3.4_1685008643426.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_token_classifier_dutch_udlassy_ner_pipeline_nl_4.4.2_3.4_1685008643426.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +```scala + +val pipeline = new PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +```scala +val pipeline = new PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +
+ +## Results + +```bash +Results + + + ++---------------+---------+ +|chunk |ner_label| ++---------------+---------+ +|Peter Fergusson|PERSON | +|oktober 2011 |DATE | +|New York |GPE | +|5 jaar |DATE | +|Tesla Motor |ORG | ++---------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_token_classifier_dutch_udlassy_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|408.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_hi_en_ner_pipeline_hi.md b/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_hi_en_ner_pipeline_hi.md new file mode 100644 index 00000000000000..cec6fc28e13e46 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_hi_en_ner_pipeline_hi.md @@ -0,0 +1,113 @@ +--- +layout: model +title: NER Pipeline for Hindi+English +author: John Snow Labs +name: bert_token_classifier_hi_en_ner_pipeline +date: 2023-05-25 +tags: [hindi, bert_token, hi, open_source] +task: Named Entity Recognition +language: hi +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on [bert_token_classifier_hi_en_ner](https://nlp.johnsnowlabs.com/2021/12/27/bert_token_classifier_hi_en_ner_hi.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_token_classifier_hi_en_ner_pipeline_hi_4.4.2_3.4_1685007718456.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_token_classifier_hi_en_ner_pipeline_hi_4.4.2_3.4_1685007718456.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +```scala + +val pipeline = new PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +val pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +```scala +val pipeline = new PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +val pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +
+ +## Results + +```bash +Results + + + ++---------------------------+------------+ +|chunk |ner_label | ++---------------------------+------------+ +|रिलायंस इंडस्ट्रीज़ लिमिटेड |ORGANISATION| +|Reliance Industries Limited|ORGANISATION| +|भारतीय |PLACE | +|मुंबई |PLACE | +|महाराष्ट्र |PLACE | +|Maharashtra) |PLACE | +|नवल टाटा |PERSON | +|मुम्बई |PLACE | +|Mumbai |PLACE | +|टाटा समुह |ORGANISATION| +|भारत |PLACE | +|जमशेदजी टाटा |PERSON | ++---------------------------+------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_token_classifier_hi_en_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|hi| +|Size:|665.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- BertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_scandi_ner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_scandi_ner_pipeline_xx.md new file mode 100644 index 00000000000000..1d374a3449d63e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_scandi_ner_pipeline_xx.md @@ -0,0 +1,103 @@ +--- +layout: model +title: NER Pipeline for 6 Scandinavian Languages +author: John Snow Labs +name: bert_token_classifier_scandi_ner_pipeline +date: 2023-05-25 +tags: [danish, norwegian, swedish, icelandic, faroese, bert, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on [bert_token_classifier_scandi_ner](https://nlp.johnsnowlabs.com/2021/12/09/bert_token_classifier_scandi_ner_xx.html) model which is imported from `HuggingFace`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_token_classifier_scandi_ner_pipeline_xx_4.4.2_3.4_1685006761481.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_token_classifier_scandi_ner_pipeline_xx_4.4.2_3.4_1685006761481.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +scandiner_pipeline = PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") +scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +```scala + +val scandiner_pipeline = new PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") + +val scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +scandiner_pipeline = PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") +scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +```scala +val scandiner_pipeline = new PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") + +val scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +
+ +## Results + +```bash +Results + + + ++-------------------+---------+ +|chunk |ner_label| ++-------------------+---------+ +|Hans |PER | +|Statens Universitet|ORG | +|København |LOC | +|københavner |MISC | ++-------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_token_classifier_scandi_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|666.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- BertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-distilbert_base_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-25-distilbert_base_token_classifier_masakhaner_pipeline_xx.md new file mode 100644 index 00000000000000..51bb9ddb9ce828 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-distilbert_base_token_classifier_masakhaner_pipeline_xx.md @@ -0,0 +1,108 @@ +--- +layout: model +title: NER Pipeline for 9 African Languages +author: John Snow Labs +name: distilbert_base_token_classifier_masakhaner_pipeline +date: 2023-05-25 +tags: [hausa, igbo, kinyarwanda, luganda, nigerian, pidgin, swahilu, wolof, yoruba, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilbert_base_token_classifier_masakhaner](https://nlp.johnsnowlabs.com/2022/01/18/distilbert_base_token_classifier_masakhaner_xx.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_token_classifier_masakhaner_pipeline_xx_4.4.2_3.4_1685007288058.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_token_classifier_masakhaner_pipeline_xx_4.4.2_3.4_1685007288058.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +masakhaner_pipeline = PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +```scala + +val masakhaner_pipeline = new PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +masakhaner_pipeline = PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +```scala +val masakhaner_pipeline = new PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +
+ +## Results + +```bash +Results + + + ++-----------------------------+---------+ +|chunk |ner_label| ++-----------------------------+---------+ +|Mohammed Sani Musa |PER | +|Activate Technologies Limited|ORG | +|ọdún-un 2019 |DATE | +|All rogressives Congress |ORG | +|APC |ORG | +|Aṣojú Ìlà-Oòrùn Niger |LOC | +|Premium Times |ORG | ++-----------------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_token_classifier_masakhaner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|505.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- DistilBertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-distilbert_token_classifier_typo_detector_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-distilbert_token_classifier_typo_detector_pipeline_en.md new file mode 100644 index 00000000000000..16dcc217ef93ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-distilbert_token_classifier_typo_detector_pipeline_en.md @@ -0,0 +1,105 @@ +--- +layout: model +title: Typo Detector Pipeline for English +author: John Snow Labs +name: distilbert_token_classifier_typo_detector_pipeline +date: 2023-05-25 +tags: [ner, bert, bert_for_token, typo, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilbert_token_classifier_typo_detector](https://nlp.johnsnowlabs.com/2022/01/19/distilbert_token_classifier_typo_detector_en.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_en_4.4.2_3.4_1685012275410.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_en_4.4.2_3.4_1685012275410.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +```scala + + +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +```scala +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +
+ +## Results + +```bash +Results + + + + ++----------+---------+ +|chunk |ner_label| ++----------+---------+ +|stgruggled|PO | +|tine |PO | ++----------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_token_classifier_typo_detector_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|244.1 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- DistilBertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-distilbert_token_classifier_typo_detector_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-25-distilbert_token_classifier_typo_detector_pipeline_is.md new file mode 100644 index 00000000000000..02feb817995b9d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-distilbert_token_classifier_typo_detector_pipeline_is.md @@ -0,0 +1,102 @@ +--- +layout: model +title: Typo Detector Pipeline for Icelandic +author: John Snow Labs +name: distilbert_token_classifier_typo_detector_pipeline +date: 2023-05-25 +tags: [icelandic, typo, ner, distilbert, is, open_source] +task: Named Entity Recognition +language: is +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilbert_token_classifier_typo_detector_is](https://nlp.johnsnowlabs.com/2022/01/19/distilbert_token_classifier_typo_detector_is.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_is_4.4.2_3.4_1685007129872.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_is_4.4.2_3.4_1685007129872.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +```scala + +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +```scala +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +
+ +## Results + +```bash +Results + + + ++--------+---------+ +|chunk |ner_label| ++--------+---------+ +|miög |PO | +|álykanir|PO | ++--------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_token_classifier_typo_detector_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|is| +|Size:|505.8 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- DistilBertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-distilroberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-distilroberta_base_token_classifier_ontonotes_pipeline_en.md new file mode 100644 index 00000000000000..7d8c43b01b758d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-distilroberta_base_token_classifier_ontonotes_pipeline_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: DistilRoBERTa Base Ontonotes NER Pipeline +author: John Snow Labs +name: distilroberta_base_token_classifier_ontonotes_pipeline +date: 2023-05-25 +tags: [open_source, ner, token_classifier, distilroberta, ontonotes, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilroberta_base_token_classifier_ontonotes](https://nlp.johnsnowlabs.com/2021/09/26/distilroberta_base_token_classifier_ontonotes_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilroberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.4_1685011696461.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilroberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.4_1685011696461.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala + + +val pipeline = new PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala +val pipeline = new PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | +|November 2020 |DATE | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilroberta_base_token_classifier_ontonotes_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|307.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_da.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_da.md new file mode 100644 index 00000000000000..e6ef48c03cb90f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_da.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-25 +tags: [da, open_source] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_da_4.4.2_3.4_1685000597398.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_da_4.4.2_3.4_1685000597398.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "da") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("da.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "da") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("da.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fi.md new file mode 100644 index 00000000000000..ed5f7f9a393147 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fi.md @@ -0,0 +1,95 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-25 +tags: [fi, open_source] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fi_4.4.2_3.4_1685002127724.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fi_4.4.2_3.4_1685002127724.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "fi") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("fi.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "fi") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("fi.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_da.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_da.md new file mode 100644 index 00000000000000..f83e7485baf99d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_da.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Danish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-25 +tags: [open_source, danish, entity_recognizer_md, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_da_4.4.2_3.4_1685000811210.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_da_4.4.2_3.4_1685000811210.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|461.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fi.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fi.md new file mode 100644 index 00000000000000..6b8bd69f15a865 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fi.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Finnish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-25 +tags: [open_source, finnish, entity_recognizer_md, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fi_4.4.2_3.4_1685002357393.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fi_4.4.2_3.4_1685002357393.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | [[0.1868100017309188,.,...]] | ['O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|462.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_da.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_da.md new file mode 100644 index 00000000000000..253d488b819fa2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_da.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Danish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-25 +tags: [open_source, danish, entity_recognizer_sm, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_da_4.4.2_3.4_1685000915897.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_da_4.4.2_3.4_1685000915897.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_fi.md new file mode 100644 index 00000000000000..1965380568e49c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_fi.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Finnish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-25 +tags: [open_source, finnish, entity_recognizer_sm, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_fi_4.4.2_3.4_1685002462888.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_fi_4.4.2_3.4_1685002462888.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | [[-0.394499987363815,.,...]] | ['O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|166.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_da.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_da.md new file mode 100644 index 00000000000000..288382a9d9610d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_da.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Danish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-25 +tags: [open_source, danish, explain_document_lg, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_da_4.4.2_3.4_1684999826923.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_da_4.4.2_3.4_1684999826923.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.025171000510454,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_fi.md new file mode 100644 index 00000000000000..680c5e0c3550cb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_fi.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Finnish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-25 +tags: [open_source, finnish, explain_document_lg, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_fi_4.4.2_3.4_1685001344456.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_fi_4.4.2_3.4_1685001344456.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:---------------------------------|:------------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | ['hei', 'John', 'Snow', 'Labs!'] | ['INTJ', 'PROPN', 'PROPN', 'PROPN'] | [[0.0639619976282119,.,...]] | ['O', 'B-PRO', 'I-PRO', 'I-PRO'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_da.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_da.md new file mode 100644 index 00000000000000..ca902b34fb791e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_da.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Danish +author: John Snow Labs +name: explain_document_md +date: 2023-05-25 +tags: [open_source, danish, explain_document_md, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_da_4.4.2_3.4_1685000055604.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_da_4.4.2_3.4_1685000055604.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|463.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fi.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fi.md new file mode 100644 index 00000000000000..c6f312c9fc6ece --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fi.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Finnish +author: John Snow Labs +name: explain_document_md +date: 2023-05-25 +tags: [open_source, finnish, explain_document_md, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_fi_4.4.2_3.4_1685001588322.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_fi_4.4.2_3.4_1685001588322.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:---------------------------------|:------------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | ['hei', 'John', 'Snow', 'Labs!'] | ['INTJ', 'PROPN', 'PROPN', 'PROPN'] | [[0.1868100017309188,.,...]] | ['O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|467.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_da.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_da.md new file mode 100644 index 00000000000000..aa006a5390110b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_da.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Danish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-25 +tags: [open_source, danish, explain_document_sm, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_da_4.4.2_3.4_1685000168570.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_da_4.4.2_3.4_1685000168570.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|168.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_fi.md new file mode 100644 index 00000000000000..2c72443ea6ba1c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_fi.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Finnish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-25 +tags: [open_source, finnish, explain_document_sm, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_fi_4.4.2_3.4_1685001706482.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_fi_4.4.2_3.4_1685001706482.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:---------------------------------|:------------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | ['hei', 'John', 'Snow', 'Labs!'] | ['INTJ', 'PROPN', 'PROPN', 'PROPN'] | [[-0.394499987363815,.,...]] | ['O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|172.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-longformer_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-longformer_base_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..5aa26c0b319b5a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-longformer_base_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Longformer Base NER Pipeline +author: John Snow Labs +name: longformer_base_token_classifier_conll03_pipeline +date: 2023-05-25 +tags: [ner, longformer, pipeline, conll, token_classification, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [longformer_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/10/09/longformer_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/longformer_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685009700125.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/longformer_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685009700125.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|longformer_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|516.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- LongformerForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-longformer_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-longformer_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..648159c17e83ac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-longformer_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Longformer Large NER Pipeline +author: John Snow Labs +name: longformer_large_token_classifier_conll03_pipeline +date: 2023-05-25 +tags: [open_source, ner, token_classifier, longformer, conll, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [longformer_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/10/09/longformer_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/longformer_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685010120411.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/longformer_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685010120411.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|longformer_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- LongformerForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-nerdl_fewnerd_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-nerdl_fewnerd_100d_pipeline_en.md new file mode 100644 index 00000000000000..576e051d5bc96f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-nerdl_fewnerd_100d_pipeline_en.md @@ -0,0 +1,109 @@ +--- +layout: model +title: Pretrained Pipeline for Few-NERD-General NER Model +author: John Snow Labs +name: nerdl_fewnerd_100d_pipeline +date: 2023-05-25 +tags: [fewnerd, nerdl, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on Few-NERD model and it detects : + +`PERSON`, `ORGANIZATION`, `LOCATION`, `ART`, `BUILDING`, `PRODUCT`, `EVENT`, `OTHER` + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_100d_pipeline_en_4.4.2_3.4_1685005798637.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_100d_pipeline_en_4.4.2_3.4_1685005798637.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).""") +``` +```scala + +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).")(0) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).""") +``` +```scala +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).")(0) +``` +
+ +## Results + +```bash +Results + + + ++-----------------------+------------+ +|chunk |ner_label | ++-----------------------+------------+ +|Kentucky Fried Chicken |ORGANIZATION| +|Anglo-Egyptian War |EVENT | +|battle of Tell El Kebir|EVENT | +|Egypt Medal |OTHER | +|Order of Medjidie |OTHER | ++-----------------------+------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nerdl_fewnerd_100d_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|167.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-nerdl_fewnerd_subentity_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-nerdl_fewnerd_subentity_100d_pipeline_en.md new file mode 100644 index 00000000000000..df56e314588370 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-nerdl_fewnerd_subentity_100d_pipeline_en.md @@ -0,0 +1,110 @@ +--- +layout: model +title: Pre-trained Pipeline for Few-NERD NER Model +author: John Snow Labs +name: nerdl_fewnerd_subentity_100d_pipeline +date: 2023-05-25 +tags: [fewnerd, ner, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on Few-NERD/inter public dataset and it extracts 66 entities that are in general scope. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_subentity_100d_pipeline_en_4.4.2_3.4_1685004953893.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_subentity_100d_pipeline_en_4.4.2_3.4_1685004953893.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.""") +``` +```scala + +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.")(0) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.""") +``` +```scala +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.")(0) +``` +
+ +## Results + +```bash +Results + + + ++-----------------------+----------------------------+ +|chunk |ner_label | ++-----------------------+----------------------------+ +|Corazones ('12 Hearts')|art-broadcastprogram | +|Spanish-language |other-language | +|United States |location-GPE | +|Telemundo |organization-media/newspaper| +|Argentine TV |organization-media/newspaper| +|Los Angeles |location-GPE | +|Steven Spielberg |person-director | +|Cloverfield Paradox |art-film | ++-----------------------+----------------------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nerdl_fewnerd_subentity_100d_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|167.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-nerdl_restaurant_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-nerdl_restaurant_100d_pipeline_en.md new file mode 100644 index 00000000000000..35b24d5bdc7092 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-nerdl_restaurant_100d_pipeline_en.md @@ -0,0 +1,119 @@ +--- +layout: model +title: Pipeline to Detect Restaurant-related Terminology +author: John Snow Labs +name: nerdl_restaurant_100d_pipeline +date: 2023-05-25 +tags: [restaurant, ner, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [nerdl_restaurant_100d](https://nlp.johnsnowlabs.com/2021/12/31/nerdl_restaurant_100d_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nerdl_restaurant_100d_pipeline_en_4.4.2_3.4_1685007392423.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nerdl_restaurant_100d_pipeline_en_4.4.2_3.4_1685007392423.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +restaurant_pipeline = PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +```scala + +val restaurant_pipeline = new PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +restaurant_pipeline = PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +```scala +val restaurant_pipeline = new PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +
+ +## Results + +```bash +Results + + + ++---------------------------+---------------+ +|chunk |ner_label | ++---------------------------+---------------+ +|Hong Kong’s |Restaurant_Name| +|favourite |Rating | +|pasta bar |Dish | +|most reasonably |Price | +|lunch |Hours | +|in town! |Location | +|Sha Tin – Pici’s |Restaurant_Name| +|burrata |Dish | +|arugula salad |Dish | +|freshly tossed tuna tartare|Dish | +|reliable |Price | +|handmade pasta |Dish | +|pappardelle |Dish | +|effortless |Amenity | +|Italian |Cuisine | +|tidy one-pot |Amenity | +|espresso |Dish | ++---------------------------+---------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nerdl_restaurant_100d_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_base_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_base_en.md new file mode 100644 index 00000000000000..2b784c34e2b082 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_base_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Base +author: John Snow Labs +name: onto_recognize_entities_bert_base +date: 2023-05-25 +tags: [open_source, english, onto_recognize_entities_bert_base, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_base is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_base_en_4.4.2_3.4_1685002595738.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_base_en_4.4.2_3.4_1685002595738.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_base', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_base", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.base').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_base', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_base", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.base').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.085488274693489,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_base| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|424.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_large_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_large_en.md new file mode 100644 index 00000000000000..e57085f3457201 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_large_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Large +author: John Snow Labs +name: onto_recognize_entities_bert_large +date: 2023-05-25 +tags: [open_source, english, onto_recognize_entities_bert_large, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_large is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_large_en_4.4.2_3.4_1685002876888.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_large_en_4.4.2_3.4_1685002876888.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_large', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_large", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.large').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_large', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_large", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.large').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.262016534805297,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_large| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.3 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_medium_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_medium_en.md new file mode 100644 index 00000000000000..0111a1a5b603c0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_medium_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Medium +author: John Snow Labs +name: onto_recognize_entities_bert_medium +date: 2023-05-25 +tags: [open_source, english, onto_recognize_entities_bert_medium, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_medium is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_medium_en_4.4.2_3.4_1685003004663.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_medium_en_4.4.2_3.4_1685003004663.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_medium', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_medium", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.medium').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_medium', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_medium", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.medium').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.0365490540862083,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_medium| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|172.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_mini_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_mini_en.md new file mode 100644 index 00000000000000..3b0399d4c97731 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_mini_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Mini +author: John Snow Labs +name: onto_recognize_entities_bert_mini +date: 2023-05-25 +tags: [open_source, english, onto_recognize_entities_bert_mini, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_mini is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_mini_en_4.4.2_3.4_1685003078331.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_mini_en_4.4.2_3.4_1685003078331.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_mini', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_mini", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.mini').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_mini', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_mini", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.mini').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.147406503558158,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_mini| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|57.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_small_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_small_en.md new file mode 100644 index 00000000000000..8159fb8f3542e0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_small_en.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Small +author: John Snow Labs +name: onto_recognize_entities_bert_small +date: 2023-05-25 +tags: [open_source, english, onto_recognize_entities_bert_small, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_small is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_small_en_4.4.2_3.4_1685003156550.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_small_en_4.4.2_3.4_1685003156550.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_small', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_small", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.small').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_small', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_small", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.small').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:----------------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.9379079937934875,.,...]] | ['O', 'O', 'B-PERSON', 'I-PERSON', 'I-PERSON', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_small| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|125.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_tiny_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_tiny_en.md new file mode 100644 index 00000000000000..3422a929ec3015 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_tiny_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Tiny +author: John Snow Labs +name: onto_recognize_entities_bert_tiny +date: 2023-05-25 +tags: [open_source, english, onto_recognize_entities_bert_tiny, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_tiny is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_tiny_en_4.4.2_3.4_1685003227086.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_tiny_en_4.4.2_3.4_1685003227086.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_tiny', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_tiny", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.tiny').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_tiny', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_tiny", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.tiny').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:----------------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-1.526878952980041,.,...]] | ['O', 'O', 'B-PERSON', 'I-PERSON', 'I-PERSON', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_tiny| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|31.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_base_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_base_en.md new file mode 100644 index 00000000000000..675da0be7072ec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_base_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - ELECTRA Base +author: John Snow Labs +name: onto_recognize_entities_electra_base +date: 2023-05-25 +tags: [open_source, english, onto_recognize_entities_electra_base, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_electra_base is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_base_en_4.4.2_3.4_1685003364230.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_base_en_4.4.2_3.4_1685003364230.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_electra_base', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_base", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.electra.base').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_electra_base', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_base", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.electra.base').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2088415920734405,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_electra_base| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|424.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_large_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_large_en.md new file mode 100644 index 00000000000000..b9322a0f4b7d99 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_large_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - ELECTRA Large +author: John Snow Labs +name: onto_recognize_entities_electra_large +date: 2023-05-25 +tags: [open_source, english, onto_recognize_entities_electra_large, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_electra_large is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_large_en_4.4.2_3.4_1685003723900.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_large_en_4.4.2_3.4_1685003723900.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('onto_recognize_entities_electra_large', lang = 'en') +annotations = pipeline.fullAnnotate("Hello from John Snow Labs!")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_large", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.large').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('onto_recognize_entities_electra_large', lang = 'en') +annotations = pipeline.fullAnnotate("Hello from John Snow Labs!")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_large", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.large').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.264069110155105,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_electra_large| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.3 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_small_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_small_en.md new file mode 100644 index 00000000000000..05fab26fa6a07f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_small_en.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - ELECTRA Small +author: John Snow Labs +name: onto_recognize_entities_electra_small +date: 2023-05-25 +tags: [open_source, english, onto_recognize_entities_electra_small, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_electra_small is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_small_en_4.4.2_3.4_1685003455062.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_small_en_4.4.2_3.4_1685003455062.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_electra_small', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_small", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.electra.small').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_electra_small', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_small", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.electra.small').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2279076874256134,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_electra_small| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|66.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-pos_ud_bokmaal_nb.md b/docs/_posts/ahmedlone127/2023-05-25-pos_ud_bokmaal_nb.md new file mode 100644 index 00000000000000..29450208088378 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-pos_ud_bokmaal_nb.md @@ -0,0 +1,138 @@ +--- +layout: model +title: Part of Speech for Norwegian +author: John Snow Labs +name: pos_ud_bokmaal +date: 2023-05-25 +tags: [pos, norwegian, nb, open_source] +task: Part of Speech Tagging +language: nb +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model annotates the part of speech of tokens in a text. The parts of speech annotated include PRON (pronoun), CCONJ (coordinating conjunction), and 15 others. The part of speech model is useful for extracting the grammatical structure of a piece of text automatically. + +This model was trained using the dataset available at https://universaldependencies.org + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pos_ud_bokmaal_nb_4.4.2_3.4_1685005857238.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pos_ud_bokmaal_nb_4.4.2_3.4_1685005857238.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("pos") + +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos]) +light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text"))) +results = light_pipeline.fullAnnotate("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.") +``` +```scala + +val pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") + .setInputCols(Array("document", "token")) + .setOutputCol("pos") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos)) +val data = Seq("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python + +import nlu + +text = ["""Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene."""] +pos_df = nlu.load('nb.pos.ud_bokmaal').predict(text) +pos_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("pos") + +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos]) +light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text"))) +results = light_pipeline.fullAnnotate("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.") +``` +```scala +val pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") + .setInputCols(Array("document", "token")) + .setOutputCol("pos") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos)) +val data = Seq("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu + +text = ["""Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene."""] +pos_df = nlu.load('nb.pos.ud_bokmaal').predict(text) +pos_df +``` +
+ +## Results + +```bash +Results + + + +[Row(annotatorType='pos', begin=0, end=4, result='DET', metadata={'word': 'Annet'}), +Row(annotatorType='pos', begin=6, end=8, result='SCONJ', metadata={'word': 'enn'}), +Row(annotatorType='pos', begin=10, end=10, result='PART', metadata={'word': 'å'}), +Row(annotatorType='pos', begin=12, end=15, result='AUX', metadata={'word': 'være'}), +Row(annotatorType='pos', begin=17, end=22, result='NOUN', metadata={'word': 'kongen'}), +...] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pos_ud_bokmaal| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nb| +|Size:|17.7 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_dl_fa.md b/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_dl_fa.md new file mode 100644 index 00000000000000..67c967a63ba643 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_dl_fa.md @@ -0,0 +1,169 @@ +--- +layout: model +title: Explain Document DL Pipeline for Farsi/Persian +author: John Snow Labs +name: recognize_entities_dl +date: 2023-05-25 +tags: [pipeline, ner, fa, open_source] +task: Named Entity Recognition +language: fa +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_dl is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities . It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_fa_4.4.2_3.4_1685004123691.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_fa_4.4.2_3.4_1685004123691.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'fa') + +annotations = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")[0] + +annotations.keys() +``` +```scala + +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "fa") + +val result = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")(0) +``` + +{:.nlu-block} +```python + +import nlu + +text = ["""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند"""] + +result_df = nlu.load('fa.recognize_entities_dl').predict(text) + +result_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'fa') + +annotations = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")[0] + +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "fa") + +val result = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")(0) +``` + +{:.nlu-block} +```python +import nlu + +text = ["""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند"""] + +result_df = nlu.load('fa.recognize_entities_dl').predict(text) + +result_df +``` +
+ +## Results + +```bash +Results + + + +| | document | sentence | token | clean_tokens | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------|:---------------|:---------|:------|:-------------|:------|:---------------------| +| 0 | "به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند | "به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند | " | " | " | PUNCT | " | O | خبرنگار ایرنا | +| 1 | | | به | گزارش | به | ADP | به | O | محمد قمی | +| 2 | | | گزارش | خبرنگار | گزارش | NOUN | گزارش | O | پاکدشت | +| 3 | | | خبرنگار | ایرنا | خبرنگار | NOUN | خبرنگار | B-ORG | علی‌اکبر موسوی خوئینی | +| 4 | | | ایرنا | ، | ایرنا | PROPN | ایرنا | I-ORG | شمس‌الدین وهابی | +| 5 | | | ، | اساس | ؛ | PUNCT | ، | O | تهران | +| 6 | | | بر | تصمیم | بر | ADP | بر | O | | +| 7 | | | اساس | این | اساس | NOUN | اساس | O | | +| 8 | | | تصمیم | مجمع | تصمیم | NOUN | تصمیم | O | | +| 9 | | | این | ، | این | DET | این | O | | +| 10 | | | مجمع | محمد | مجمع | NOUN | مجمع | O | | +| 11 | | | ، | قمی | ؛ | PUNCT | ، | O | | +| 12 | | | محمد | نماینده | محمد | PROPN | محمد | B-PER | | +| 13 | | | قمی | پاکدشت | قمی | PROPN | قمی | I-PER | | +| 14 | | | نماینده | عنوان | نماینده | NOUN | نماینده | O | | +| 15 | | | مردم | رئیس | مردم | NOUN | مردم | O | | +| 16 | | | پاکدشت | علی‌اکبر | پاکدشت | PROPN | پاکدشت | B-LOC | | +| 17 | | | به | موسوی | به | ADP | به | O | | +| 18 | | | عنوان | خوئینی | عنوان | NOUN | عنوان | O | | +| 19 | | | رئیس | شمس‌الدین | رئیس | NOUN | رئیس | O | | +| 20 | | | و | وهابی | او | CCONJ | و | O | | +| 21 | | | علی‌اکبر | نمایندگان | علی‌اکبر | PROPN | علی‌اکبر | B-PER | | +| 22 | | | موسوی | تهران | موسوی | PROPN | موسوی | I-PER | | +| 23 | | | خوئینی | عنوان | خوئینی | PROPN | خوئینی | I-PER | | +| 24 | | | و | نواب | او | CCONJ | و | O | | +| 25 | | | شمس‌الدین | رئیس | شمس‌الدین | PROPN | شمس‌الدین | B-PER | | +| 26 | | | وهابی | انتخاب | وهابی | PROPN | وهابی | I-PER | | +| 27 | | | نمایندگان | | نماینده | NOUN | نمایندگان | O | | +| 28 | | | مردم | | مردم | NOUN | مردم | O | | +| 29 | | | تهران | | تهران | PROPN | تهران | B-LOC | | +| 30 | | | به | | به | ADP | به | O | | +| 31 | | | عنوان | | عنوان | NOUN | عنوان | O | | +| 32 | | | نواب | | نواب | NOUN | نواب | O | | +| 33 | | | رئیس | | رئیس | NOUN | رئیس | O | | +| 34 | | | انتخاب | | انتخاب | NOUN | انتخاب | O | | +| 35 | | | شدند | | کرد#کن | VERB | شدند | O | | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recognize_entities_dl| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fa| +|Size:|1.2 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- StopWordsCleaner +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-roberta_base_token_classifier_ontonotes_pipeline_en.md new file mode 100644 index 00000000000000..4d4c5e36796b92 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -0,0 +1,104 @@ +--- +layout: model +title: RoBERTa Base Ontonotes NER Pipeline +author: John Snow Labs +name: roberta_base_token_classifier_ontonotes_pipeline +date: 2023-05-25 +tags: [open_source, ner, token_classifier, roberta, ontonotes, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_base_token_classifier_ontonotes](https://nlp.johnsnowlabs.com/2021/09/26/roberta_base_token_classifier_ontonotes_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.4_1685011848544.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.4_1685011848544.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala + +val pipeline = new PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +## Results + +```bash +Results + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | +|November 2020 |DATE | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_base_token_classifier_ontonotes_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|456.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-roberta_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-roberta_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..35380043fb7944 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-roberta_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: RoBERTa Large CoNLL-03 NER Pipeline +author: John Snow Labs +name: roberta_large_token_classifier_conll03_pipeline +date: 2023-05-25 +tags: [open_source, ner, token_classifier, roberta, conll03, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/roberta_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685012136542.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685012136542.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.3 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_bne_capitel_ner_pipeline_es.md b/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_bne_capitel_ner_pipeline_es.md new file mode 100644 index 00000000000000..26d479aef03036 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_bne_capitel_ner_pipeline_es.md @@ -0,0 +1,104 @@ +--- +layout: model +title: Spanish NER Pipeline +author: John Snow Labs +name: roberta_token_classifier_bne_capitel_ner_pipeline +date: 2023-05-25 +tags: [roberta, token_classifier, spanish, ner, es, open_source] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_token_classifier_bne_capitel_ner_es](https://nlp.johnsnowlabs.com/2021/12/07/roberta_token_classifier_bne_capitel_ner_es.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_bne_capitel_ner_pipeline_es_4.4.2_3.4_1685009328345.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_bne_capitel_ner_pipeline_es_4.4.2_3.4_1685009328345.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +```scala + +val pipeline = new PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +
+ +## Results + +```bash +Results + + + ++------------------------+---------+ +|chunk |ner_label| ++------------------------+---------+ +|Antonio |PER | +|fábrica de Mercedes-Benz|ORG | +|Madrid |LOC | ++------------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_token_classifier_bne_capitel_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|459.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_icelandic_ner_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_icelandic_ner_pipeline_is.md new file mode 100644 index 00000000000000..fa8f705e0b014e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_icelandic_ner_pipeline_is.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Icelandic NER Pipeline +author: John Snow Labs +name: roberta_token_classifier_icelandic_ner_pipeline +date: 2023-05-25 +tags: [open_source, ner, token_classifier, roberta, icelandic, is] +task: Named Entity Recognition +language: is +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_token_classifier_icelandic_ner](https://nlp.johnsnowlabs.com/2021/12/06/roberta_token_classifier_icelandic_ner_is.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_icelandic_ner_pipeline_is_4.4.2_3.4_1685009487617.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_icelandic_ner_pipeline_is_4.4.2_3.4_1685009487617.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +```scala + +val pipeline = new PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +
+ +## Results + +```bash +Results + + + ++----------------+------------+ +|chunk |ner_label | ++----------------+------------+ +|Peter Fergusson |Person | +|New York |Location | +|október 2011 |Date | +|Tesla Motor |Organization| +|100K $ |Money | ++----------------+------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_token_classifier_icelandic_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|is| +|Size:|457.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_timex_semeval_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_timex_semeval_pipeline_en.md new file mode 100644 index 00000000000000..16f045eb439192 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_timex_semeval_pipeline_en.md @@ -0,0 +1,110 @@ +--- +layout: model +title: Pipeline to Detect Time-related Terminology +author: John Snow Labs +name: roberta_token_classifier_timex_semeval_pipeline +date: 2023-05-25 +tags: [timex, semeval, ner, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_token_classifier_timex_semeval](https://nlp.johnsnowlabs.com/2021/12/28/roberta_token_classifier_timex_semeval_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_timex_semeval_pipeline_en_4.4.2_3.4_1685007537027.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_timex_semeval_pipeline_en_4.4.2_3.4_1685007537027.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +timex_pipeline = PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +```scala + + +val timex_pipeline = new PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +timex_pipeline = PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +```scala +val timex_pipeline = new PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +
+ +## Results + +```bash +Results + + + + ++-------+-----------------+ +|chunk |ner_label | ++-------+-----------------+ +|22:12C |Period | +|3 |Number | +|days |Calendar-Interval| +|Tuesday|Day-Of-Week | +|to |Between | +|Friday |Day-Of-Week | ++-------+-----------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_token_classifier_timex_semeval_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|439.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-spellcheck_dl_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-spellcheck_dl_pipeline_en.md new file mode 100644 index 00000000000000..16069df0f43f0c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-spellcheck_dl_pipeline_en.md @@ -0,0 +1,112 @@ +--- +layout: model +title: Context Spell Checker Pipeline for English +author: John Snow Labs +name: spellcheck_dl_pipeline +date: 2023-05-25 +tags: [spellcheck, spell, spellcheck_pipeline, spelling_corrector, en, open_source] +task: Spell Check +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained spellchecker pipeline is built on the top of [spellcheck_dl](https://nlp.johnsnowlabs.com/2022/04/02/spellcheck_dl_en_2_4.html) model. This pipeline is for PySpark 2.4.x users with SparkNLP 3.4.2 and above. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/spellcheck_dl_pipeline_en_4.4.2_3.4_1685008513632.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/spellcheck_dl_pipeline_en_4.4.2_3.4_1685008513632.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +text = ["During the summer we have the best ueather.", "I have a black ueather jacket, so nice."] + +pipeline.annotate(text) +``` +```scala + + +val pipeline = new PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +val example = Array("During the summer we have the best ueather.", "I have a black ueather jacket, so nice.") + +pipeline.annotate(example) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +text = ["During the summer we have the best ueather.", "I have a black ueather jacket, so nice."] + +pipeline.annotate(text) +``` +```scala +val pipeline = new PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +val example = Array("During the summer we have the best ueather.", "I have a black ueather jacket, so nice.") + +pipeline.annotate(example) +``` +
+ +## Results + +```bash +Results + + + + +[{'checked': ['During', 'the', 'summer', 'we', 'have', 'the', 'best', 'weather', '.'], + 'document': ['During the summer we have the best ueather.'], + 'token': ['During', 'the', 'summer', 'we', 'have', 'the', 'best', 'ueather', '.']}, + + {'checked': ['I', 'have', 'a', 'black', 'leather', 'jacket', ',', 'so', 'nice', '.'], + 'document': ['I have a black ueather jacket, so nice.'], + 'token': ['I', 'have', 'a', 'black', 'ueather', 'jacket', ',', 'so', 'nice', '.']}] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|spellcheck_dl_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|99.7 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- ContextSpellCheckerModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_base_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..9de2e5ceda88cc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_base_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: XLM-RoBERTa Base, CoNLL-03 NER Pipeline +author: John Snow Labs +name: xlm_roberta_base_token_classifier_conll03_pipeline +date: 2023-05-25 +tags: [open_source, ner, token_classifier, xlm_roberta, conll03, xlm, base, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/10/03/xlm_roberta_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685010816849.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685010816849.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|851.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md new file mode 100644 index 00000000000000..3b4f28b8401058 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: XLM-RoBERTa Base NER Pipeline +author: John Snow Labs +name: xlm_roberta_base_token_classifier_ontonotes_pipeline +date: 2023-05-25 +tags: [open_source, ner, token_classifier, xlm_roberta, ontonotes, xlm, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifier_ontonotes](https://nlp.johnsnowlabs.com/2021/10/03/xlm_roberta_base_token_classifier_ontonotes_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.4_1685010558936.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.4_1685010558936.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | +|November 2020 |DATE | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_base_token_classifier_ontonotes_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|858.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_conll03_pipeline_de.md b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_conll03_pipeline_de.md new file mode 100644 index 00000000000000..a66592362aefc1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_conll03_pipeline_de.md @@ -0,0 +1,104 @@ +--- +layout: model +title: NER Pipeline for German +author: John Snow Labs +name: xlm_roberta_large_token_classifier_conll03_pipeline +date: 2023-05-25 +tags: [german, roberta, xlm, ner, conll03, de, open_source] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_large_token_classifier_conll03_de](https://nlp.johnsnowlabs.com/2021/12/25/xlm_roberta_large_token_classifier_conll03_de.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_conll03_pipeline_de_4.4.2_3.4_1685009132607.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_conll03_pipeline_de_4.4.2_3.4_1685009132607.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +```scala + +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +
+ +## Results + +```bash +Results + + + ++----------------------+---------+ +|chunk |ner_label| ++----------------------+---------+ +|Ibser |PER | +|ASK Ebreichsdorf |ORG | +|Admira Wacker Mödling |ORG | ++----------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|1.8 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md new file mode 100644 index 00000000000000..0644860ec4af4f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md @@ -0,0 +1,104 @@ +--- +layout: model +title: NER Pipeline for 10 High Resourced Languages +author: John Snow Labs +name: xlm_roberta_large_token_classifier_hrl_pipeline +date: 2023-05-25 +tags: [arabic, german, english, spanish, french, italian, latvian, dutch, portuguese, chinese, xlm, roberta, ner, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_large_token_classifier_hrl](https://nlp.johnsnowlabs.com/2021/12/26/xlm_roberta_large_token_classifier_hrl_xx.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_hrl_pipeline_xx_4.4.2_3.4_1685008327318.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_hrl_pipeline_xx_4.4.2_3.4_1685008327318.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +```scala + +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +
+ +## Results + +```bash +Results + + + ++---------------------------+---------+ +|chunk |ner_label| ++---------------------------+---------+ +|الرياض |LOC | +|فيصل بن بندر بن عبد العزيز |PER | +|الرياض |LOC | ++---------------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_large_token_classifier_hrl_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|1.8 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md new file mode 100644 index 00000000000000..ef6310376136df --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md @@ -0,0 +1,104 @@ +--- +layout: model +title: NER Pipeline for 10 African Languages +author: John Snow Labs +name: xlm_roberta_large_token_classifier_masakhaner_pipeline +date: 2023-05-25 +tags: [masakhaner, african, xlm_roberta, multilingual, pipeline, amharic, hausa, igbo, kinyarwanda, luganda, swahilu, wolof, yoruba, nigerian, pidgin, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on [xlm_roberta_large_token_classifier_masakhaner](https://nlp.johnsnowlabs.com/2021/12/06/xlm_roberta_large_token_classifier_masakhaner_xx.html) ner model which is imported from `HuggingFace`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_masakhaner_pipeline_xx_4.4.2_3.4_1685006535738.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_masakhaner_pipeline_xx_4.4.2_3.4_1685006535738.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +masakhaner_pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +```scala + +val masakhaner_pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +val masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +masakhaner_pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +```scala +val masakhaner_pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +val masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +
+ +## Results + +```bash +Results + + + ++----------------+---------+ +|chunk |ner_label| ++----------------+---------+ +|አህመድ ቫንዳ |PER | +|ከ3-10-2000 ጀምሮ|DATE | +|በአዲስ አበባ |LOC | ++----------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_large_token_classifier_masakhaner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|1.8 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-xlnet_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-xlnet_base_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..ab857ce93611fa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-xlnet_base_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: XLNet Base CoNLL-03 NER Pipeline +author: John Snow Labs +name: xlnet_base_token_classifier_conll03_pipeline +date: 2023-05-25 +tags: [ner, english, xlnet, base, token_classification, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlnet_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/28/xlnet_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlnet_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685010312802.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlnet_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685010312802.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlnet_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|438.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlnetForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-xlnet_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-xlnet_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..a7692a3b4a989f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-xlnet_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,103 @@ +--- +layout: model +title: XLNet Large CoNLL-03 NER Pipeline +author: John Snow Labs +name: xlnet_large_token_classifier_conll03_pipeline +date: 2023-05-25 +tags: [open_source, ner, token_classifier, xlnet, conll03, large, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlnet_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/28/xlnet_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlnet_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685010899121.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlnet_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685010899121.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlnet_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|19.0 KB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- NormalizerModel \ No newline at end of file From e4e465e87c640924838e10a24acf4f360228b323 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Fri, 26 May 2023 00:46:46 +0700 Subject: [PATCH 10/55] Add model 2023-05-25-explain_document_md_fr (#13827) Co-authored-by: ahmedlone127 --- .../2023-05-25-explain_document_md_fr.md | 123 ++++++++++++++++++ 1 file changed, 123 insertions(+) create mode 100644 docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fr.md diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fr.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fr.md new file mode 100644 index 00000000000000..4e3bd64d36f4b1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fr.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for French +author: John Snow Labs +name: explain_document_md +date: 2023-05-25 +tags: [open_source, french, explain_document_md, pipeline, fr] +task: Named Entity Recognition +language: fr +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_fr_4.4.2_3.4_1685035526859.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_fr_4.4.2_3.4_1685035526859.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:--------------------------------|:-------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------------------| +| 0 | ['Bonjour de John Snow Labs! '] | ['Bonjour de John Snow Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | ['INTJ', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0783179998397827,.,...]] | ['I-MISC', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['Bonjour', 'John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fr| +|Size:|467.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file From e8e01a5e5e84ea69072ce14cca4fc007aa6fc17e Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Fri, 26 May 2023 13:10:34 +0700 Subject: [PATCH 11/55] 2023-05-25-dependency_parse_en (#13828) * Add model 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2023-05-26-xlnet_large_token_classifier_conll03_pipeline_en * Add model 2023-05-26-albert_base_token_classifier_conll03_pipeline_en * Add model 2023-05-26-albert_large_token_classifier_conll03_pipeline_en * Add model 2023-05-26-albert_xlarge_token_classifier_conll03_pipeline_en * Add model 2023-05-26-distilroberta_base_token_classifier_ontonotes_pipeline_en * Add model 2023-05-26-roberta_base_token_classifier_ontonotes_pipeline_en * Add model 2023-05-26-roberta_large_token_classifier_conll03_pipeline_en * Add model 2023-05-26-distilbert_token_classifier_typo_detector_pipeline_en --------- Co-authored-by: ahmedlone127 --- .../2023-05-25-analyze_sentiment_en.md | 130 ++++++++++++++ ...05-25-analyze_sentimentdl_glove_imdb_en.md | 122 +++++++++++++ .../2023-05-25-check_spelling_en.md | 120 +++++++++++++ .../2023-05-25-clean_pattern_en.md | 120 +++++++++++++ .../ahmedlone127/2023-05-25-clean_stop_en.md | 120 +++++++++++++ .../2023-05-25-dependency_parse_en.md | 119 ++++++++++++ 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a/docs/_posts/ahmedlone127/2023-05-25-analyze_sentiment_en.md b/docs/_posts/ahmedlone127/2023-05-25-analyze_sentiment_en.md new file mode 100644 index 00000000000000..8bdc4ac6e9d31a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-analyze_sentiment_en.md @@ -0,0 +1,130 @@ +--- +layout: model +title: Sentiment Analysis pipeline for English +author: John Snow Labs +name: analyze_sentiment +date: 2023-05-25 +tags: [open_source, english, analyze_sentiment, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The analyze_sentiment is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/analyze_sentiment_en_4.4.2_3.4_1685040876208.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/analyze_sentiment_en_4.4.2_3.4_1685040876208.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python + +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en') + +result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") + + +``` +```scala + +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en") + +val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") + +``` + +{:.nlu-block} +```python + +import nlu +text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""] +result_df = nlu.load('en.classify').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en') + +result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en") + +val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") +``` + +{:.nlu-block} +```python +import nlu +text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""] +result_df = nlu.load('en.classify').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | text | sentiment | +|---:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------| +| 0 | Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now! | positive | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|analyze_sentiment| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|5.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel +- ViveknSentimentModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-analyze_sentimentdl_glove_imdb_en.md b/docs/_posts/ahmedlone127/2023-05-25-analyze_sentimentdl_glove_imdb_en.md new file mode 100644 index 00000000000000..2706b7b2ff19b3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-analyze_sentimentdl_glove_imdb_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Sentiment Analysis pipeline for English (analyze_sentimentdl_glove_imdb) +author: John Snow Labs +name: analyze_sentimentdl_glove_imdb +date: 2023-05-25 +tags: [open_source, english, analyze_sentimentdl_glove_imdb, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The analyze_sentimentdl_glove_imdb is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/analyze_sentimentdl_glove_imdb_en_4.4.2_3.4_1685051273135.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/analyze_sentimentdl_glove_imdb_en_4.4.2_3.4_1685051273135.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("analyze_sentimentdl_glove_imdb", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.sentiment.glove').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("analyze_sentimentdl_glove_imdb", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.sentiment.glove').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | tokens | word_embeddings | sentence_embeddings | sentiment | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-----------------------------|:------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2668800055980682,.,...]] | [[0.0771183446049690,.,...]] | ['neg'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|analyze_sentimentdl_glove_imdb| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|161.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- SentenceEmbeddings +- SentimentDLModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-check_spelling_en.md b/docs/_posts/ahmedlone127/2023-05-25-check_spelling_en.md new file mode 100644 index 00000000000000..3569d4a7d52c3a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-check_spelling_en.md @@ -0,0 +1,120 @@ +--- +layout: model +title: Spell Checking Pipeline for English +author: John Snow Labs +name: check_spelling +date: 2023-05-25 +tags: [open_source, english, check_spelling, pipeline, en] +task: Spell Check +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The check_spelling is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/check_spelling_en_4.4.2_3.4_1685039983204.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/check_spelling_en_4.4.2_3.4_1685039983204.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('check_spelling', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("check_spelling", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('check_spelling', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("check_spelling", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | checked | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------------------------| +| 0 | ['I liek to live dangertus ! '] | ['I liek to live dangertus !'] | ['I', 'liek', 'to', 'live', 'dangertus', '!'] | ['I', 'like', 'to', 'live', 'dangerous', '!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|check_spelling| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|906.2 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-clean_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-25-clean_pattern_en.md new file mode 100644 index 00000000000000..f8f32369a4eda7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-clean_pattern_en.md @@ -0,0 +1,120 @@ +--- +layout: model +title: Clean patterns pipeline for English +author: John Snow Labs +name: clean_pattern +date: 2023-05-25 +tags: [open_source, english, clean_pattern, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The clean_pattern is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clean_pattern_en_4.4.2_3.4_1685040134078.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clean_pattern_en_4.4.2_3.4_1685040134078.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_pattern', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala + + +val pipeline = new PretrainedPipeline("clean_pattern", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python + + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.pattern').predict(text) +result_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_pattern', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("clean_pattern", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.pattern').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + + +| | document | sentence | token | normal | +|---:|:-----------|:-----------|:----------|:----------| +| 0 | ['Hello'] | ['Hello'] | ['Hello'] | ['Hello'] || | document | sentence | token | normal | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clean_pattern| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|17.2 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NormalizerModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-clean_stop_en.md b/docs/_posts/ahmedlone127/2023-05-25-clean_stop_en.md new file mode 100644 index 00000000000000..0f2a945ce528cf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-clean_stop_en.md @@ -0,0 +1,120 @@ +--- +layout: model +title: Clean documents pipeline for English +author: John Snow Labs +name: clean_stop +date: 2023-05-25 +tags: [open_source, english, clean_stop, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The clean_stop is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clean_stop_en_4.4.2_3.4_1685040188479.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clean_stop_en_4.4.2_3.4_1685040188479.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_stop', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("clean_stop", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.stop').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_stop', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("clean_stop", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.stop').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | cleanTokens | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:---------------------------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | ['Hello', 'John', 'Snow', 'Labs', '!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clean_stop| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|14.1 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- StopWordsCleaner \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-dependency_parse_en.md b/docs/_posts/ahmedlone127/2023-05-25-dependency_parse_en.md new file mode 100644 index 00000000000000..9f45e70779dbe1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-dependency_parse_en.md @@ -0,0 +1,119 @@ +--- +layout: model +title: Typed Dependency Parsing pipeline for English +author: John Snow Labs +name: dependency_parse +date: 2023-05-25 +tags: [pipeline, dependency_parsing, untyped_dependency_parsing, typed_dependency_parsing, laballed_depdency_parsing, unlaballed_depdency_parsing, en, open_source] +task: Dependency Parser +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Typed Dependency parser, trained on the on the CONLL dataset. + +Dependency parsing is the task of extracting a dependency parse of a sentence that represents its grammatical structure and defines the relationships between “head” words and words, which modify those heads. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dependency_parse_en_4.4.2_3.4_1685038231137.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dependency_parse_en_4.4.2_3.4_1685038231137.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('dependency_parse', lang = 'en') +annotations = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("dependency_parse", lang = "en") +val result = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence")(0) + +``` + +{:.nlu-block} +```python + +nlu.load("dep.typed").predict("Dependencies represents relationships betweens words in a Sentence") + + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('dependency_parse', lang = 'en') +annotations = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("dependency_parse", lang = "en") +val result = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence")(0) +``` + +{:.nlu-block} +```python +nlu.load("dep.typed").predict("Dependencies represents relationships betweens words in a Sentence") +``` +
+ +## Results + +```bash +Results + + ++---------------------------------------------------------------------------------+--------------------------------------------------------+ +|result |result | ++---------------------------------------------------------------------------------+--------------------------------------------------------+ +|[ROOT, Dependencies, represents, words, relationships, Sentence, Sentence, words]|[root, parataxis, nsubj, amod, nsubj, case, nsubj, flat]| ++---------------------------------------------------------------------------------+--------------------------------------------------------+ + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dependency_parse| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|23.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel +- DependencyParserModel +- TypedDependencyParserModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_da.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_da.md index e6ef48c03cb90f..a914f9245e6972 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_da.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_da.md @@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_da_4.4.2_3.4_1685000597398.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_da_4.4.2_3.4_1685000597398.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_da_4.4.2_3.4_1685055716132.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_da_4.4.2_3.4_1685055716132.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_de.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_de.md new file mode 100644 index 00000000000000..ac74fffbb10209 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_de.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL pipeline for German - Large +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-25 +tags: [open_source, german, entity_recognizer_lg, pipeline, de] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_de_4.4.2_3.4_1685042092802.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_de_4.4.2_3.4_1685042092802.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo aus John Snow Labs! '] | ['Hallo aus John Snow Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | [[-0.245989993214607,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_es.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_es.md new file mode 100644 index 00000000000000..dcaf88d51af2a1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_es.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognition Pipeline (Large, Spanish) +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-25 +tags: [es, open_source] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_es_4.4.2_3.4_1685044314081.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_es_4.4.2_3.4_1685044314081.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "es") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("es.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "es") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fi.md index ed5f7f9a393147..013f16d4dbdb1c 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fi.md @@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fi_4.4.2_3.4_1685002127724.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fi_4.4.2_3.4_1685002127724.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fi_4.4.2_3.4_1685057277652.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fi_4.4.2_3.4_1685057277652.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fr.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fr.md new file mode 100644 index 00000000000000..9c4e73c2af609a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fr.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL pipeline for French - Large +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-25 +tags: [open_source, french, entity_recognizer_lg, pipeline, fr] +task: Named Entity Recognition +language: fr +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fr_4.4.2_3.4_1685039016234.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fr_4.4.2_3.4_1685039016234.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:--------------------------------|:-------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Bonjour de John Snow Labs! '] | ['Bonjour de John Snow Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | [[-0.010997000150382,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fr| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_it.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_it.md new file mode 100644 index 00000000000000..8c194b43e3c24a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_it.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL pipeline for Italian - Large +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-25 +tags: [open_source, italian, entity_recognizer_lg, pipeline, it] +task: Named Entity Recognition +language: it +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_it_4.4.2_3.4_1685039686865.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_it_4.4.2_3.4_1685039686865.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Ciao da John Snow Labs! '] | ['Ciao da John Snow Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | [[-0.238279998302459,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|it| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_nl.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_nl.md new file mode 100644 index 00000000000000..fd4314e0006bfc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_nl.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-25 +tags: [nl, open_source] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_nl_4.4.2_3.4_1685050724969.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_nl_4.4.2_3.4_1685050724969.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "nl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("nl.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "nl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("nl.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_no.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_no.md new file mode 100644 index 00000000000000..35fa6de66de6f0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_no.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-25 +tags: ["no", open_source] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_no_4.4.2_3.4_1685052497867.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_no_4.4.2_3.4_1685052497867.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "no") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("no.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "no") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("no.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pl.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pl.md new file mode 100644 index 00000000000000..44db088ba2847b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pl.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-25 +tags: [pl, open_source] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pl_4.4.2_3.4_1685049158345.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pl_4.4.2_3.4_1685049158345.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("pl.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pl.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pt.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pt.md new file mode 100644 index 00000000000000..82b823e2afca0a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pt.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-25 +tags: [pt, open_source] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pt_4.4.2_3.4_1685047557531.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pt_4.4.2_3.4_1685047557531.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pt") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pt") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_ru.md new file mode 100644 index 00000000000000..a4c97678e65201 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_ru.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-25 +tags: [ru, open_source] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_ru_4.4.2_3.4_1685045880932.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_ru_4.4.2_3.4_1685045880932.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "ru") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("ru.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "ru") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ru.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_sv.md new file mode 100644 index 00000000000000..d7bb9221fbf93c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_sv.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-25 +tags: [sv, open_source] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_sv_4.4.2_3.4_1685054155985.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_sv_4.4.2_3.4_1685054155985.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "sv") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("sv.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "sv") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("sv.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_da.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_da.md index f83e7485baf99d..8aaa0181ed988d 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_da.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_da.md @@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_da_4.4.2_3.4_1685000811210.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_da_4.4.2_3.4_1685000811210.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_da_4.4.2_3.4_1685055942476.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_da_4.4.2_3.4_1685055942476.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_de.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_de.md new file mode 100644 index 00000000000000..ad7bc5c4ca7a30 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_de.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for German - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-25 +tags: [open_source, german, entity_recognizer_md, pipeline, de] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_de_4.4.2_3.4_1685042320125.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_de_4.4.2_3.4_1685042320125.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo aus John Snow Labs! '] | ['Hallo aus John Snow Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|461.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_es.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_es.md new file mode 100644 index 00000000000000..83f9541829c9aa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_es.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Spanish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-25 +tags: [open_source, spanish, entity_recognizer_md, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_es_4.4.2_3.4_1685044529658.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_es_4.4.2_3.4_1685044529658.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | [[0.5123000144958496,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|461.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fi.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fi.md index 6b8bd69f15a865..fe05574fe2541a 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fi.md @@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fi_4.4.2_3.4_1685002357393.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fi_4.4.2_3.4_1685002357393.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fi_4.4.2_3.4_1685057503410.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fi_4.4.2_3.4_1685057503410.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fr.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fr.md new file mode 100644 index 00000000000000..3b4d8d57efb687 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fr.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for French - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-25 +tags: [open_source, french, entity_recognizer_md, pipeline, fr] +task: Named Entity Recognition +language: fr +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fr_4.4.2_3.4_1685039236291.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fr_4.4.2_3.4_1685039236291.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:--------------------------------|:-------------------------------|:-------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------------------| +| 0 | ['Bonjour de John Snow Labs! '] | ['Bonjour de John Snow Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | [[0.0783179998397827,.,...]] | ['I-MISC', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['Bonjour', 'John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fr| +|Size:|462.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_it.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_it.md new file mode 100644 index 00000000000000..6faebb940e6857 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_it.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Italian - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-25 +tags: [open_source, italian, entity_recognizer_md, pipeline, it] +task: Named Entity Recognition +language: it +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_it_4.4.2_3.4_1685039912274.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_it_4.4.2_3.4_1685039912274.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Ciao da John Snow Labs! '] | ['Ciao da John Snow Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | [[-0.146050006151199,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|it| +|Size:|462.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_nl.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_nl.md new file mode 100644 index 00000000000000..ead207b4c33eb7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_nl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Dutch - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-25 +tags: [open_source, dutch, entity_recognizer_md, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_nl_4.4.2_3.4_1685050943374.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_nl_4.4.2_3.4_1685050943374.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|462.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_no.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_no.md new file mode 100644 index 00000000000000..fd95d668ad2f48 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_no.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Norwegian (Bokmal) - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-25 +tags: [open_source, norwegian_bokmal, entity_recognizer_md, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_no_4.4.2_3.4_1685052723511.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_no_4.4.2_3.4_1685052723511.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | [[0.1868100017309188,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|462.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_pl.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_pl.md new file mode 100644 index 00000000000000..0daaff9cd34996 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_pl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Polish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-25 +tags: [open_source, polish, entity_recognizer_md, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pl_4.4.2_3.4_1685049374697.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pl_4.4.2_3.4_1685049374697.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|462.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_pt.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_pt.md new file mode 100644 index 00000000000000..697bdc63f341cb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_pt.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Portuguese - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-25 +tags: [open_source, portuguese, entity_recognizer_md, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pt_4.4.2_3.4_1685047791424.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pt_4.4.2_3.4_1685047791424.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|462.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_ru.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_ru.md new file mode 100644 index 00000000000000..c05bbe85ae6a23 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_ru.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Russian - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-25 +tags: [open_source, russian, entity_recognizer_md, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_ru_4.4.2_3.4_1685046110885.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_ru_4.4.2_3.4_1685046110885.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-LOC', 'I-LOC', 'I-LOC'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|462.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_sv.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_sv.md new file mode 100644 index 00000000000000..0d31ac7e45bec8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_sv.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Swedish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-25 +tags: [open_source, swedish, entity_recognizer_md, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_sv_4.4.2_3.4_1685054380902.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_sv_4.4.2_3.4_1685054380902.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|461.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_da.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_da.md index 253d488b819fa2..b51c4758a0cf36 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_da.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_da.md @@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_da_4.4.2_3.4_1685000915897.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_da_4.4.2_3.4_1685000915897.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_da_4.4.2_3.4_1685056048244.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_da_4.4.2_3.4_1685056048244.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_es.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_es.md new file mode 100644 index 00000000000000..d6b573223d9fb9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_es.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Spanish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-25 +tags: [open_source, spanish, entity_recognizer_sm, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_es_4.4.2_3.4_1685044640150.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_es_4.4.2_3.4_1685044640150.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:---------------------------------------|:-----------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | [[0.1754499971866607,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'B-MISC'] | ['John Snow', 'Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_fi.md index 1965380568e49c..b3a87b93f6edbd 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_fi.md @@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_fi_4.4.2_3.4_1685002462888.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_fi_4.4.2_3.4_1685002462888.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_fi_4.4.2_3.4_1685057608991.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_fi_4.4.2_3.4_1685057608991.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_nl.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_nl.md new file mode 100644 index 00000000000000..838aeccec01e45 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_nl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Dutch - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-25 +tags: [open_source, dutch, entity_recognizer_sm, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_nl_4.4.2_3.4_1685051046186.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_nl_4.4.2_3.4_1685051046186.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | [[0.3653799891471863,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|166.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_no.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_no.md new file mode 100644 index 00000000000000..123e862837174b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_no.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Norwegian (Bokmal) - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-25 +tags: [open_source, norwegian_bokmal, entity_recognizer_sm, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_no_4.4.2_3.4_1685052827860.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_no_4.4.2_3.4_1685052827860.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | [[-0.394499987363815,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|167.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_pl.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_pl.md new file mode 100644 index 00000000000000..0f83d5a4d2903d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_pl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Polish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-25 +tags: [open_source, polish, entity_recognizer_sm, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pl_4.4.2_3.4_1685049478793.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pl_4.4.2_3.4_1685049478793.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|167.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_pt.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_pt.md new file mode 100644 index 00000000000000..f9c6d47bbf9089 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_pt.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Portuguese - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-25 +tags: [open_source, portuguese, entity_recognizer_sm, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pt_4.4.2_3.4_1685047899396.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pt_4.4.2_3.4_1685047899396.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|167.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_ru.md new file mode 100644 index 00000000000000..0ed56858781ab1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_ru.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Russian - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-25 +tags: [open_source, russian, entity_recognizer_sm, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_ru_4.4.2_3.4_1685046218620.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_ru_4.4.2_3.4_1685046218620.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|167.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_sv.md new file mode 100644 index 00000000000000..5d8cd94201d0f6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_sv.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Swedish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-25 +tags: [open_source, swedish, entity_recognizer_sm, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_sv_4.4.2_3.4_1685054487398.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_sv_4.4.2_3.4_1685054487398.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_dl_en.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_dl_en.md new file mode 100644 index 00000000000000..48ac092cf76157 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_dl_en.md @@ -0,0 +1,128 @@ +--- +layout: model +title: Explain Document DL Pipeline for English +author: John Snow Labs +name: explain_document_dl +date: 2023-05-25 +tags: [open_source, english, explain_document_dl, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_dl is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_dl_en_4.4.2_3.4_1685040992507.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_dl_en_4.4.2_3.4_1685040992507.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_dl', lang = 'en') +annotations = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_dl", lang = "en") +val result = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = ["The Mona Lisa is an oil painting from the 16th century."] +result_df = nlu.load('en.explain.dl').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_dl', lang = 'en') +annotations = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_dl", lang = "en") +val result = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")(0) +``` + +{:.nlu-block} +```python +import nlu +text = ["The Mona Lisa is an oil painting from the 16th century."] +result_df = nlu.load('en.explain.dl').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + ++--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------+-----------+ +| text| document| sentence| token| checked| lemma| stem| pos| embeddings| ner| entities| ++--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------+-----------+ +|The Mona Lisa is an oil painting from the 16th ...|[The Mona Lisa is an oil painting from the 16th...|[The Mona Lisa is an oil painting from the 16th...|[The, Mona, Lisa, is, an, oil, painting, from, ...|[The, Mona, Lisa, is, an, oil, painting, from, ...|[The, Mona, Lisa, be, an, oil, painting, from, ...|[the, mona, lisa, i, an, oil, paint, from, the,...|[DT, NNP, NNP, VBZ, DT, NN, NN, IN, DT, JJ, NN, .]|[[-0.038194, -0.24487, 0.72812, -0.39961, 0.083...|[O, B-PER, I-PER, O, O, O, O, O, O, O, O, O]|[Mona Lisa]| ++--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------+-----------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_dl| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|176.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel +- LemmatizerModel +- Stemmer +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_da.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_da.md index 288382a9d9610d..0ccf156f06db78 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_da.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_da.md @@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_da_4.4.2_3.4_1684999826923.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_da_4.4.2_3.4_1684999826923.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_da_4.4.2_3.4_1685054923023.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_da_4.4.2_3.4_1685054923023.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_es.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_es.md new file mode 100644 index 00000000000000..60436844a502a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_es.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Spanish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-25 +tags: [open_source, spanish, explain_document_lg, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_es_4.4.2_3.4_1685043480517.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_es_4.4.2_3.4_1685043480517.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['PART', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.016199000179767,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_fi.md index 680c5e0c3550cb..159abee951a035 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_fi.md @@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_fi_4.4.2_3.4_1685001344456.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_fi_4.4.2_3.4_1685001344456.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_fi_4.4.2_3.4_1685056502478.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_fi_4.4.2_3.4_1685056502478.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_nl.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_nl.md new file mode 100644 index 00000000000000..c1ae88f159d9af --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_nl.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Dutch (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-25 +tags: [open_source, dutch, explain_document_lg, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_nl_4.4.2_3.4_1685049928456.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_nl_4.4.2_3.4_1685049928456.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:-----------------------------|:------------------------------------------|:-----------------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.245989993214607,.,...]] | ['B-PER', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Hallo', 'John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_no.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_no.md new file mode 100644 index 00000000000000..43f95fdfc89ad0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_no.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Norwegian (Bokmal) (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-25 +tags: [open_source, norwegian_bokmal, explain_document_lg, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_no_4.4.2_3.4_1685051715152.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_no_4.4.2_3.4_1685051715152.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:--------------------------------------------|:-----------------------------|:---------------------------------------|:-----------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0639619976282119,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'B-PROD'] | ['John Snow', 'Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_pl.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_pl.md new file mode 100644 index 00000000000000..b7a4d9d8b2f91e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_pl.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Polish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-25 +tags: [open_source, polish, explain_document_lg, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_pl_4.4.2_3.4_1685048347765.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_pl_4.4.2_3.4_1685048347765.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | ['witać', 'z', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4977500140666961,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_pt.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_pt.md new file mode 100644 index 00000000000000..1a8369c1d8560c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_pt.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Portuguese (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-25 +tags: [open_source, portuguese, explain_document_lg, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_pt_4.4.2_3.4_1685046739453.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_pt_4.4.2_3.4_1685046739453.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:---------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4388400018215179,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_ru.md new file mode 100644 index 00000000000000..4aef430d1f1da0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_ru.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Russian (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-25 +tags: [open_source, russian, explain_document_lg, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_ru_4.4.2_3.4_1685045082115.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_ru_4.4.2_3.4_1685045082115.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | ['здравствовать', 'из', 'Джон', 'Снежных', 'Лабораторий!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_sv.md new file mode 100644 index 00000000000000..61113b16764d0a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_sv.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Swedish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-25 +tags: [open_source, swedish, explain_document_lg, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_sv_4.4.2_3.4_1685053346707.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_sv_4.4.2_3.4_1685053346707.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_da.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_da.md index ca902b34fb791e..fbfeb598e42d42 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_da.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_da.md @@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_da_4.4.2_3.4_1685000055604.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_da_4.4.2_3.4_1685000055604.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_da_4.4.2_3.4_1685055174051.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_da_4.4.2_3.4_1685055174051.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_de.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_de.md new file mode 100644 index 00000000000000..61a671d663cc95 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_de.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for German +author: John Snow Labs +name: explain_document_md +date: 2023-05-25 +tags: [open_source, german, explain_document_md, pipeline, de] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_de_4.4.2_3.4_1685041632110.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_de_4.4.2_3.4_1685041632110.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.explain.document').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.explain.document').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo aus John Snow Labs! '] | ['Hallo aus John Snow Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|470.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_es.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_es.md new file mode 100644 index 00000000000000..750f307ec55d0e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_es.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Spanish +author: John Snow Labs +name: explain_document_md +date: 2023-05-25 +tags: [open_source, spanish, explain_document_md, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_es_4.4.2_3.4_1685043736525.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_es_4.4.2_3.4_1685043736525.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['PART', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.5123000144958496,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|472.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fi.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fi.md index c6f312c9fc6ece..a5a183487a9fd5 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fi.md @@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_fi_4.4.2_3.4_1685001588322.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_fi_4.4.2_3.4_1685001588322.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_fi_4.4.2_3.4_1685056729372.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_fi_4.4.2_3.4_1685056729372.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_it.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_it.md new file mode 100644 index 00000000000000..2964ed3333b5c5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_it.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Italian +author: John Snow Labs +name: explain_document_md +date: 2023-05-25 +tags: [open_source, italian, explain_document_md, pipeline, it] +task: Named Entity Recognition +language: it +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_it_4.4.2_3.4_1685038565599.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_it_4.4.2_3.4_1685038565599.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.explain.document').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.explain.document').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Ciao da John Snow Labs! '] | ['Ciao da John Snow Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.146050006151199,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|it| +|Size:|468.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_nl.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_nl.md new file mode 100644 index 00000000000000..45db3304164c2d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_nl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Dutch +author: John Snow Labs +name: explain_document_md +date: 2023-05-25 +tags: [open_source, dutch, explain_document_md, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_nl_4.4.2_3.4_1685050177103.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_nl_4.4.2_3.4_1685050177103.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|464.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_no.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_no.md new file mode 100644 index 00000000000000..ed848865a3f17e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_no.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Norwegian (Bokmal) +author: John Snow Labs +name: explain_document_md +date: 2023-05-25 +tags: [open_source, norwegian_bokmal, explain_document_md, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_no_4.4.2_3.4_1685051937316.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_no_4.4.2_3.4_1685051937316.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.1868100017309188,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|465.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_pl.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_pl.md new file mode 100644 index 00000000000000..fde99cd1e8891f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_pl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Polish +author: John Snow Labs +name: explain_document_md +date: 2023-05-25 +tags: [open_source, polish, explain_document_md, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_pl_4.4.2_3.4_1685048596474.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_pl_4.4.2_3.4_1685048596474.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | ['witać', 'z', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|464.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_pt.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_pt.md new file mode 100644 index 00000000000000..6ff7d29502f92f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_pt.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Portuguese +author: John Snow Labs +name: explain_document_md +date: 2023-05-25 +tags: [open_source, portuguese, explain_document_md, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_pt_4.4.2_3.4_1685046975096.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_pt_4.4.2_3.4_1685046975096.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:---------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|473.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_ru.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_ru.md new file mode 100644 index 00000000000000..10741f307e1adf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_ru.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Russian +author: John Snow Labs +name: explain_document_md +date: 2023-05-25 +tags: [open_source, russian, explain_document_md, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_ru_4.4.2_3.4_1685045315176.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_ru_4.4.2_3.4_1685045315176.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | ['здравствовать', 'из', 'Джон', 'Снежных', 'Лабораторий!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-LOC', 'I-LOC', 'I-LOC'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|465.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_sv.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_sv.md new file mode 100644 index 00000000000000..bd082756480915 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_sv.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Swedish +author: John Snow Labs +name: explain_document_md +date: 2023-05-25 +tags: [open_source, swedish, explain_document_md, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_sv_4.4.2_3.4_1685053591124.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_sv_4.4.2_3.4_1685053591124.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|471.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_ml_en.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_ml_en.md new file mode 100644 index 00000000000000..ab3e62efb1a1d2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_ml_en.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document ML Pipeline for English +author: John Snow Labs +name: explain_document_ml +date: 2023-05-25 +tags: [open_source, english, explain_document_ml, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_ml is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_ml_en_4.4.2_3.4_1685040778279.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_ml_en_4.4.2_3.4_1685040778279.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_ml', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala + + +val pipeline = new PretrainedPipeline("explain_document_ml", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python + + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.explain').predict(text) +result_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_ml', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_ml", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + + +| | document | sentence | token | spell | lemmas | stems | pos | +|---:|:---------------------------------|:---------------------------------|:-------------------------------------------------|:------------------------------------------------|:------------------------------------------------|:-----------------------------------------------|:---------------------------------------| +| 0 | ['Hello fronm John Snwow Labs!'] | ['Hello fronm John Snwow Labs!'] | ['Hello', 'fronm', 'John', 'Snwow', 'Labs', '!'] | ['Hello', 'front', 'John', 'Snow', 'Labs', '!'] | ['Hello', 'front', 'John', 'Snow', 'Labs', '!'] | ['hello', 'front', 'john', 'snow', 'lab', '!'] | ['UH', 'NN', 'NNP', 'NNP', 'NNP', '.'] || | document | sentence | token | spell | lemmas | stems | pos | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_ml| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|9.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel +- LemmatizerModel +- Stemmer +- PerceptronModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_da.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_da.md index aa006a5390110b..3bdacc90f82667 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_da.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_da.md @@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_da_4.4.2_3.4_1685000168570.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_da_4.4.2_3.4_1685000168570.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_da_4.4.2_3.4_1685055287722.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_da_4.4.2_3.4_1685055287722.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_es.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_es.md new file mode 100644 index 00000000000000..38a2e76ff4e209 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_es.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Spanish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-25 +tags: [open_source, spanish, explain_document_sm, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_es_4.4.2_3.4_1685043872827.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_es_4.4.2_3.4_1685043872827.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:---------------------------------------|:-----------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['PART', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.1754499971866607,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'B-MISC'] | ['John Snow', 'Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|177.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_fi.md index 2c72443ea6ba1c..be1359fc083280 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_fi.md @@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_fi_4.4.2_3.4_1685001706482.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_fi_4.4.2_3.4_1685001706482.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_fi_4.4.2_3.4_1685056849986.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_fi_4.4.2_3.4_1685056849986.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_nl.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_nl.md new file mode 100644 index 00000000000000..1efa36b19fec31 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_nl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Dutch +author: John Snow Labs +name: explain_document_sm +date: 2023-05-25 +tags: [open_source, dutch, explain_document_sm, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_nl_4.4.2_3.4_1685050292506.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_nl_4.4.2_3.4_1685050292506.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.3653799891471863,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|169.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_no.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_no.md new file mode 100644 index 00000000000000..1e76fef347c49a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_no.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Norwegian (Bokmal) +author: John Snow Labs +name: explain_document_sm +date: 2023-05-25 +tags: [open_source, norwegian_bokmal, explain_document_sm, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_no_4.4.2_3.4_1685052052087.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_no_4.4.2_3.4_1685052052087.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.394499987363815,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|170.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_pl.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_pl.md new file mode 100644 index 00000000000000..1aafb36a4573b7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_pl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Polish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-25 +tags: [open_source, polish, explain_document_sm, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_pl_4.4.2_3.4_1685048715273.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_pl_4.4.2_3.4_1685048715273.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | ['witać', 'z', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|169.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_pt.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_pt.md new file mode 100644 index 00000000000000..941b5d493829bc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_pt.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Portuguese +author: John Snow Labs +name: explain_document_sm +date: 2023-05-25 +tags: [open_source, portuguese, explain_document_sm, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_pt_4.4.2_3.4_1685047107909.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_pt_4.4.2_3.4_1685047107909.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:---------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|178.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_ru.md new file mode 100644 index 00000000000000..73af8d334fe53d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_ru.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Russian +author: John Snow Labs +name: explain_document_sm +date: 2023-05-25 +tags: [open_source, russian, explain_document_sm, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_ru_4.4.2_3.4_1685045434988.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_ru_4.4.2_3.4_1685045434988.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | ['здравствовать', 'из', 'Джон', 'Снежных', 'Лабораторий!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|170.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_sv.md new file mode 100644 index 00000000000000..014650d0410a7b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_sv.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Swedish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-25 +tags: [open_source, swedish, explain_document_sm, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_sv_4.4.2_3.4_1685053721244.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_sv_4.4.2_3.4_1685053721244.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|176.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-match_chunks_en.md b/docs/_posts/ahmedlone127/2023-05-25-match_chunks_en.md new file mode 100644 index 00000000000000..903012f4a225c2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-match_chunks_en.md @@ -0,0 +1,141 @@ +--- +layout: model +title: Match Chunks in Texts +author: John Snow Labs +name: match_chunks +date: 2023-05-25 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The pipeline uses regex `
?/*+` + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_chunks_en_4.4.2_3.4_1685043019049.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_chunks_en_4.4.2_3.4_1685043019049.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline + +pipeline_local = PretrainedPipeline('match_chunks') + +result = pipeline_local.annotate("David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.") + +result['chunk'] +``` +```scala + +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_chunks", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.match.chunks").predict("""David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline_local = PretrainedPipeline('match_chunks') + +result = pipeline_local.annotate("David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.") + +result['chunk'] +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_chunks", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.match.chunks").predict("""David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.""") +``` +
+ +## Results + +```bash +Results + + + +['the restaurant yesterday', +'family', +'the day', +'that time', +'today', +'the food', +'tomorrow'] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|match_chunks| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|4.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel +- Chunker \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-match_datetime_en.md b/docs/_posts/ahmedlone127/2023-05-25-match_datetime_en.md new file mode 100644 index 00000000000000..e5f01baffac33f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-match_datetime_en.md @@ -0,0 +1,127 @@ +--- +layout: model +title: Match Datetime in Texts +author: John Snow Labs +name: match_datetime +date: 2023-05-25 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +DateMatcher based on yyyy/MM/dd + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_datetime_en_4.4.2_3.4_1685040031472.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_datetime_en_4.4.2_3.4_1685040031472.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline_local = PretrainedPipeline('match_datetime') + +tres = pipeline_local.fullAnnotate(input_list)[0] +for dte in tres['date']: + sent = tres['sentence'][int(dte.metadata['sentence'])] + print (f'text/chunk {sent.result[dte.begin:dte.end+1]} | mapped_date: {dte.result}') +``` +```scala + +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. +He also visited and the day before, but at that time he was alone. +David again visited today with his colleagues. +He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_datetime", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline_local = PretrainedPipeline('match_datetime') + +tres = pipeline_local.fullAnnotate(input_list)[0] +for dte in tres['date']: + sent = tres['sentence'][int(dte.metadata['sentence'])] + print (f'text/chunk {sent.result[dte.begin:dte.end+1]} | mapped_date: {dte.result}') +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. +He also visited and the day before, but at that time he was alone. +David again visited today with his colleagues. +He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_datetime", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` +
+ +## Results + +```bash +Results + + + +text/chunk yesterday | mapped_date: 2022/01/02 +text/chunk day before | mapped_date: 2022/01/02 +text/chunk today | mapped_date: 2022/01/03 +text/chunk tomorrow | mapped_date: 2022/01/04 + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|match_datetime| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|12.9 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- MultiDateMatcher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-match_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-25-match_pattern_en.md new file mode 100644 index 00000000000000..67b720cb60cbc5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-match_pattern_en.md @@ -0,0 +1,77 @@ +--- +layout: model +title: Match Pattern +author: John Snow Labs +name: match_pattern +date: 2023-05-25 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The match_pattern is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and matches pattrens . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_pattern_en_4.4.2_3.4_1685040085566.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_pattern_en_4.4.2_3.4_1685040085566.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models") + result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models") + result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|match_pattern| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|17.4 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- RegexMatcherModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-movies_sentiment_analysis_en.md b/docs/_posts/ahmedlone127/2023-05-25-movies_sentiment_analysis_en.md new file mode 100644 index 00000000000000..1aa1e43fe345b0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-movies_sentiment_analysis_en.md @@ -0,0 +1,79 @@ +--- +layout: model +title: Movies Sentiment Analysis +author: John Snow Labs +name: movies_sentiment_analysis +date: 2023-05-25 +tags: [en, open_source] +task: Sentiment Analysis +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The movies_sentiment_analysis is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and predicts sentiment . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/movies_sentiment_analysis_en_4.4.2_3.4_1685040688121.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/movies_sentiment_analysis_en_4.4.2_3.4_1685040688121.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("movies_sentiment_analysis", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("movies_sentiment_analysis", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|movies_sentiment_analysis| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|207.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- SymmetricDeleteModel +- SentimentDetectorModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_base_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_base_en.md index 2b784c34e2b082..4991fe05619320 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_base_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_base_en.md @@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_base_en_4.4.2_3.4_1685002595738.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_base_en_4.4.2_3.4_1685002595738.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_base_en_4.4.2_3.4_1685057740362.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_base_en_4.4.2_3.4_1685057740362.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_large_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_large_en.md index e57085f3457201..e34af1639fe2b7 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_large_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_large_en.md @@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_large_en_4.4.2_3.4_1685002876888.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_large_en_4.4.2_3.4_1685002876888.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_large_en_4.4.2_3.4_1685058011750.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_large_en_4.4.2_3.4_1685058011750.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_medium_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_medium_en.md index 0111a1a5b603c0..5e5b33a34999f0 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_medium_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_medium_en.md @@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_medium_en_4.4.2_3.4_1685003004663.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_medium_en_4.4.2_3.4_1685003004663.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_medium_en_4.4.2_3.4_1685058141036.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_medium_en_4.4.2_3.4_1685058141036.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_mini_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_mini_en.md index 3b0399d4c97731..18514bae287899 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_mini_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_mini_en.md @@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_mini_en_4.4.2_3.4_1685003078331.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_mini_en_4.4.2_3.4_1685003078331.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_mini_en_4.4.2_3.4_1685058217920.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_mini_en_4.4.2_3.4_1685058217920.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_small_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_small_en.md index 8159fb8f3542e0..b6278456400e9e 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_small_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_small_en.md @@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_small_en_4.4.2_3.4_1685003156550.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_small_en_4.4.2_3.4_1685003156550.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_small_en_4.4.2_3.4_1685058297770.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_small_en_4.4.2_3.4_1685058297770.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_tiny_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_tiny_en.md index 3422a929ec3015..7efda03ad07cda 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_tiny_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_tiny_en.md @@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_tiny_en_4.4.2_3.4_1685003227086.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_tiny_en_4.4.2_3.4_1685003227086.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_tiny_en_4.4.2_3.4_1685058370008.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_tiny_en_4.4.2_3.4_1685058370008.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_base_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_base_en.md index 675da0be7072ec..b85ab4b633b442 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_base_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_base_en.md @@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_base_en_4.4.2_3.4_1685003364230.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_base_en_4.4.2_3.4_1685003364230.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_base_en_4.4.2_3.4_1685058505292.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_base_en_4.4.2_3.4_1685058505292.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_large_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_large_en.md index b9322a0f4b7d99..91b5c23ebbd365 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_large_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_large_en.md @@ -29,8 +29,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_large_en_4.4.2_3.4_1685003723900.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_large_en_4.4.2_3.4_1685003723900.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_large_en_4.4.2_3.4_1685058869048.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_large_en_4.4.2_3.4_1685058869048.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_small_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_small_en.md index 05fab26fa6a07f..8ccf11eed91884 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_small_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_small_en.md @@ -28,8 +28,8 @@ It performs most of the common text processing tasks on your dataframe {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_small_en_4.4.2_3.4_1685003455062.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_small_en_4.4.2_3.4_1685003455062.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_small_en_4.4.2_3.4_1685058599061.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_small_en_4.4.2_3.4_1685058599061.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_lg_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_lg_en.md new file mode 100644 index 00000000000000..0425702cf6b0d8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_lg_en.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Onto Recognize Entities Lg +author: John Snow Labs +name: onto_recognize_entities_lg +date: 2023-05-25 +tags: [en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entites. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_lg_en_4.4.2_3.4_1685042868130.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_lg_en_4.4.2_3.4_1685042868130.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("onto_recognize_entities_lg", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("en.ner.onto.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("onto_recognize_entities_lg", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.ner.onto.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_sm_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_sm_en.md new file mode 100644 index 00000000000000..91b9700a46de0f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_sm_en.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL pipeline for English - Small +author: John Snow Labs +name: onto_recognize_entities_sm +date: 2023-05-25 +tags: [open_source, english, onto_recognize_entities_sm, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_sm_en_4.4.2_3.4_1685042425870.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_sm_en_4.4.2_3.4_1685042425870.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_sm', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_sm", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.sm').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_sm', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_sm", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.sm').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2668800055980682,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_bert_en.md b/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_bert_en.md new file mode 100644 index 00000000000000..404c38a15a40b7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_bert_en.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Recognize Entities DL pipeline for English - BERT +author: John Snow Labs +name: recognize_entities_bert +date: 2023-05-25 +tags: [open_source, english, recognize_entities_bert, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The recognize_entities_bert is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recognize_entities_bert_en_4.4.2_3.4_1685041284903.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recognize_entities_bert_en_4.4.2_3.4_1685041284903.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_bert', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("recognize_entities_bert", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.bert').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_bert', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("recognize_entities_bert", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.bert').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.085488274693489,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-ORG', 'O'] | ['John Snow Labs'] || | document | sentence | token | embeddings | ner | entities | + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recognize_entities_bert| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|424.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_dl_en.md b/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_dl_en.md new file mode 100644 index 00000000000000..0c3dc5fcf95ca4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_dl_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for English +author: John Snow Labs +name: recognize_entities_dl +date: 2023-05-25 +tags: [open_source, english, recognize_entities_dl, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The recognize_entities_dl is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_en_4.4.2_3.4_1685041151703.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_en_4.4.2_3.4_1685041151703.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:---------------------------------------------------|:------------------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2668800055980682,.,...]] | ['B-ORG', 'I-ORG', 'I-ORG', 'I-ORG', 'I-ORG', 'O'] | ['Hello from John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recognize_entities_dl| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-25-text_cleaning_en.md b/docs/_posts/ahmedlone127/2023-05-25-text_cleaning_en.md new file mode 100644 index 00000000000000..52c450ba6f87c4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-25-text_cleaning_en.md @@ -0,0 +1,80 @@ +--- +layout: model +title: Text Cleaning +author: John Snow Labs +name: text_cleaning +date: 2023-05-25 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The text_cleaning is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and cleans text. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/text_cleaning_en_4.4.2_3.4_1685046282536.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/text_cleaning_en_4.4.2_3.4_1685046282536.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("text_cleaning", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("text_cleaning", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|text_cleaning| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|944.5 KB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- NormalizerModel +- StopWordsCleaner +- LemmatizerModel +- TokenAssembler \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-albert_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-albert_base_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..78c671f91f89da --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-albert_base_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: ALBERT Base CoNNL-03 NER Pipeline +author: John Snow Labs +name: albert_base_token_classifier_conll03_pipeline +date: 2023-05-26 +tags: [open_source, ner, token_classifier, albert, conll03, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [albert_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/albert_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066409500.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066409500.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|43.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- AlbertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-albert_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-albert_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..4f4443a9414ff3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-albert_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: ALBERT Large CoNNL-03 NER Pipeline +author: John Snow Labs +name: albert_large_token_classifier_conll03_pipeline +date: 2023-05-26 +tags: [open_source, ner, token_classifier, albert, conll03, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [albert_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/albert_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066487956.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066487956.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|64.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- AlbertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-albert_xlarge_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-albert_xlarge_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..1cfd1f3b1eea22 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-albert_xlarge_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: ALBERT XLarge CoNNL-03 NER Pipeline +author: John Snow Labs +name: albert_xlarge_token_classifier_conll03_pipeline +date: 2023-05-26 +tags: [open_source, ner, token_classifier, albert, conll03, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [albert_xlarge_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/albert_xlarge_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_xlarge_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066592482.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_xlarge_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066592482.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_xlarge_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|206.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- AlbertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_age_news_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_age_news_pipeline_en.md new file mode 100644 index 00000000000000..7f4939e8c4dcf9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_age_news_pipeline_en.md @@ -0,0 +1,98 @@ +--- +layout: model +title: Pipeline to Classify Texts into 4 News Categories +author: John Snow Labs +name: bert_sequence_classifier_age_news_pipeline +date: 2023-05-26 +tags: [ag_news, news, bert, bert_sequence, classification, en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [bert_sequence_classifier_age_news_en](https://nlp.johnsnowlabs.com/2021/11/07/bert_sequence_classifier_age_news_en.html) which is imported from `HuggingFace`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_age_news_pipeline_en_4.4.2_3.4_1685062332212.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_age_news_pipeline_en_4.4.2_3.4_1685062332212.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +news_pipeline = PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +```scala + + +val news_pipeline = new PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +news_pipeline = PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +```scala +val news_pipeline = new PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +
+ +## Results + +```bash +Results + + + + +['Sci/Tech'] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_age_news_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|42.4 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- BertForSequenceClassification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_trec_coarse_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_trec_coarse_pipeline_en.md new file mode 100644 index 00000000000000..3990534aee8c8b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_trec_coarse_pipeline_en.md @@ -0,0 +1,100 @@ +--- +layout: model +title: Pipeline to Classify Texts into TREC-6 Classes +author: John Snow Labs +name: bert_sequence_classifier_trec_coarse_pipeline +date: 2023-05-26 +tags: [bert_sequence, trec, coarse, bert, en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [bert_sequence_classifier_trec_coarse_en](https://nlp.johnsnowlabs.com/2021/11/06/bert_sequence_classifier_trec_coarse_en.html). + +The TREC dataset for question classification consists of open-domain, fact-based questions divided into broad semantic categories. You can check the official documentation of the dataset, entities, etc. [here](https://search.r-project.org/CRAN/refmans/textdata/html/dataset_trec.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_trec_coarse_pipeline_en_4.4.2_3.4_1685062251815.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_trec_coarse_pipeline_en_4.4.2_3.4_1685062251815.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +trec_pipeline = PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +```scala + + +val trec_pipeline = new PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +trec_pipeline = PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +```scala +val trec_pipeline = new PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +
+ +## Results + +```bash +Results + + + + +['LOC'] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_trec_coarse_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|406.6 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- BertForSequenceClassification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md b/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md new file mode 100644 index 00000000000000..76147872e1abee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Dutch NER Pipeline +author: John Snow Labs +name: bert_token_classifier_dutch_udlassy_ner_pipeline +date: 2023-05-26 +tags: [open_source, ner, dutch, token_classifier, bert, treatment, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [bert_token_classifier_dutch_udlassy_ner](https://nlp.johnsnowlabs.com/2021/12/08/bert_token_classifier_dutch_udlassy_ner_nl.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_token_classifier_dutch_udlassy_ner_pipeline_nl_4.4.2_3.4_1685064033791.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_token_classifier_dutch_udlassy_ner_pipeline_nl_4.4.2_3.4_1685064033791.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +```scala + +val pipeline = new PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +```scala +val pipeline = new PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +
+ +## Results + +```bash +Results + + + ++---------------+---------+ +|chunk |ner_label| ++---------------+---------+ +|Peter Fergusson|PERSON | +|oktober 2011 |DATE | +|New York |GPE | +|5 jaar |DATE | +|Tesla Motor |ORG | ++---------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_token_classifier_dutch_udlassy_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|408.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_hi_en_ner_pipeline_hi.md b/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_hi_en_ner_pipeline_hi.md new file mode 100644 index 00000000000000..bc5223db30914c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_hi_en_ner_pipeline_hi.md @@ -0,0 +1,113 @@ +--- +layout: model +title: NER Pipeline for Hindi+English +author: John Snow Labs +name: bert_token_classifier_hi_en_ner_pipeline +date: 2023-05-26 +tags: [hindi, bert_token, hi, open_source] +task: Named Entity Recognition +language: hi +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on [bert_token_classifier_hi_en_ner](https://nlp.johnsnowlabs.com/2021/12/27/bert_token_classifier_hi_en_ner_hi.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_token_classifier_hi_en_ner_pipeline_hi_4.4.2_3.4_1685063082391.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_token_classifier_hi_en_ner_pipeline_hi_4.4.2_3.4_1685063082391.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +```scala + +val pipeline = new PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +val pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +```scala +val pipeline = new PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +val pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +
+ +## Results + +```bash +Results + + + ++---------------------------+------------+ +|chunk |ner_label | ++---------------------------+------------+ +|रिलायंस इंडस्ट्रीज़ लिमिटेड |ORGANISATION| +|Reliance Industries Limited|ORGANISATION| +|भारतीय |PLACE | +|मुंबई |PLACE | +|महाराष्ट्र |PLACE | +|Maharashtra) |PLACE | +|नवल टाटा |PERSON | +|मुम्बई |PLACE | +|Mumbai |PLACE | +|टाटा समुह |ORGANISATION| +|भारत |PLACE | +|जमशेदजी टाटा |PERSON | ++---------------------------+------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_token_classifier_hi_en_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|hi| +|Size:|665.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- BertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_scandi_ner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_scandi_ner_pipeline_xx.md new file mode 100644 index 00000000000000..e2f6ac52e6214b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_scandi_ner_pipeline_xx.md @@ -0,0 +1,103 @@ +--- +layout: model +title: NER Pipeline for 6 Scandinavian Languages +author: John Snow Labs +name: bert_token_classifier_scandi_ner_pipeline +date: 2023-05-26 +tags: [danish, norwegian, swedish, icelandic, faroese, bert, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on [bert_token_classifier_scandi_ner](https://nlp.johnsnowlabs.com/2021/12/09/bert_token_classifier_scandi_ner_xx.html) model which is imported from `HuggingFace`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_token_classifier_scandi_ner_pipeline_xx_4.4.2_3.4_1685062102199.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_token_classifier_scandi_ner_pipeline_xx_4.4.2_3.4_1685062102199.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +scandiner_pipeline = PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") +scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +```scala + +val scandiner_pipeline = new PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") + +val scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +scandiner_pipeline = PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") +scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +```scala +val scandiner_pipeline = new PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") + +val scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +
+ +## Results + +```bash +Results + + + ++-------------------+---------+ +|chunk |ner_label| ++-------------------+---------+ +|Hans |PER | +|Statens Universitet|ORG | +|København |LOC | +|københavner |MISC | ++-------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_token_classifier_scandi_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|666.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- BertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-distilbert_base_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-26-distilbert_base_token_classifier_masakhaner_pipeline_xx.md new file mode 100644 index 00000000000000..9fb584b272e041 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-distilbert_base_token_classifier_masakhaner_pipeline_xx.md @@ -0,0 +1,108 @@ +--- +layout: model +title: NER Pipeline for 9 African Languages +author: John Snow Labs +name: distilbert_base_token_classifier_masakhaner_pipeline +date: 2023-05-26 +tags: [hausa, igbo, kinyarwanda, luganda, nigerian, pidgin, swahilu, wolof, yoruba, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilbert_base_token_classifier_masakhaner](https://nlp.johnsnowlabs.com/2022/01/18/distilbert_base_token_classifier_masakhaner_xx.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_token_classifier_masakhaner_pipeline_xx_4.4.2_3.4_1685062637711.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_token_classifier_masakhaner_pipeline_xx_4.4.2_3.4_1685062637711.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +masakhaner_pipeline = PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +```scala + +val masakhaner_pipeline = new PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +masakhaner_pipeline = PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +```scala +val masakhaner_pipeline = new PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +
+ +## Results + +```bash +Results + + + ++-----------------------------+---------+ +|chunk |ner_label| ++-----------------------------+---------+ +|Mohammed Sani Musa |PER | +|Activate Technologies Limited|ORG | +|ọdún-un 2019 |DATE | +|All rogressives Congress |ORG | +|APC |ORG | +|Aṣojú Ìlà-Oòrùn Niger |LOC | +|Premium Times |ORG | ++-----------------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_token_classifier_masakhaner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|505.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- DistilBertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_en.md new file mode 100644 index 00000000000000..2892cf85cbea2a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_en.md @@ -0,0 +1,105 @@ +--- +layout: model +title: Typo Detector Pipeline for English +author: John Snow Labs +name: distilbert_token_classifier_typo_detector_pipeline +date: 2023-05-26 +tags: [ner, bert, bert_for_token, typo, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilbert_token_classifier_typo_detector](https://nlp.johnsnowlabs.com/2022/01/19/distilbert_token_classifier_typo_detector_en.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_en_4.4.2_3.4_1685067324460.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_en_4.4.2_3.4_1685067324460.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +```scala + + +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +```scala +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +
+ +## Results + +```bash +Results + + + + ++----------+---------+ +|chunk |ner_label| ++----------+---------+ +|stgruggled|PO | +|tine |PO | ++----------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_token_classifier_typo_detector_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|244.1 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- DistilBertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_is.md new file mode 100644 index 00000000000000..050826db384303 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_is.md @@ -0,0 +1,102 @@ +--- +layout: model +title: Typo Detector Pipeline for Icelandic +author: John Snow Labs +name: distilbert_token_classifier_typo_detector_pipeline +date: 2023-05-26 +tags: [icelandic, typo, ner, distilbert, is, open_source] +task: Named Entity Recognition +language: is +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilbert_token_classifier_typo_detector_is](https://nlp.johnsnowlabs.com/2022/01/19/distilbert_token_classifier_typo_detector_is.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_is_4.4.2_3.4_1685062477110.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_is_4.4.2_3.4_1685062477110.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +```scala + +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +```scala +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +
+ +## Results + +```bash +Results + + + ++--------+---------+ +|chunk |ner_label| ++--------+---------+ +|miög |PO | +|álykanir|PO | ++--------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_token_classifier_typo_detector_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|is| +|Size:|505.8 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- DistilBertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-distilroberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-distilroberta_base_token_classifier_ontonotes_pipeline_en.md new file mode 100644 index 00000000000000..61a39c2b402af3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-distilroberta_base_token_classifier_ontonotes_pipeline_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: DistilRoBERTa Base Ontonotes NER Pipeline +author: John Snow Labs +name: distilroberta_base_token_classifier_ontonotes_pipeline +date: 2023-05-26 +tags: [open_source, ner, token_classifier, distilroberta, ontonotes, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilroberta_base_token_classifier_ontonotes](https://nlp.johnsnowlabs.com/2021/09/26/distilroberta_base_token_classifier_ontonotes_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilroberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.4_1685066716004.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilroberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.4_1685066716004.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala + + +val pipeline = new PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala +val pipeline = new PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | +|November 2020 |DATE | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilroberta_base_token_classifier_ontonotes_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|307.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-longformer_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-longformer_base_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..8d3a12f5bdae5a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-longformer_base_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Longformer Base NER Pipeline +author: John Snow Labs +name: longformer_base_token_classifier_conll03_pipeline +date: 2023-05-26 +tags: [ner, longformer, pipeline, conll, token_classification, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [longformer_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/10/09/longformer_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/longformer_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685065123047.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/longformer_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685065123047.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|longformer_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|516.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- LongformerForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-longformer_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-longformer_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..bb275a83e66ad3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-longformer_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Longformer Large NER Pipeline +author: John Snow Labs +name: longformer_large_token_classifier_conll03_pipeline +date: 2023-05-26 +tags: [open_source, ner, token_classifier, longformer, conll, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [longformer_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/10/09/longformer_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/longformer_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685065544572.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/longformer_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685065544572.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|longformer_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- LongformerForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_100d_pipeline_en.md new file mode 100644 index 00000000000000..27eca880193ab7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_100d_pipeline_en.md @@ -0,0 +1,109 @@ +--- +layout: model +title: Pretrained Pipeline for Few-NERD-General NER Model +author: John Snow Labs +name: nerdl_fewnerd_100d_pipeline +date: 2023-05-26 +tags: [fewnerd, nerdl, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on Few-NERD model and it detects : + +`PERSON`, `ORGANIZATION`, `LOCATION`, `ART`, `BUILDING`, `PRODUCT`, `EVENT`, `OTHER` + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_100d_pipeline_en_4.4.2_3.4_1685061095892.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_100d_pipeline_en_4.4.2_3.4_1685061095892.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).""") +``` +```scala + +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).")(0) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).""") +``` +```scala +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).")(0) +``` +
+ +## Results + +```bash +Results + + + ++-----------------------+------------+ +|chunk |ner_label | ++-----------------------+------------+ +|Kentucky Fried Chicken |ORGANIZATION| +|Anglo-Egyptian War |EVENT | +|battle of Tell El Kebir|EVENT | +|Egypt Medal |OTHER | +|Order of Medjidie |OTHER | ++-----------------------+------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nerdl_fewnerd_100d_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|167.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_subentity_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_subentity_100d_pipeline_en.md new file mode 100644 index 00000000000000..5275f8a0281ee1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_subentity_100d_pipeline_en.md @@ -0,0 +1,110 @@ +--- +layout: model +title: Pre-trained Pipeline for Few-NERD NER Model +author: John Snow Labs +name: nerdl_fewnerd_subentity_100d_pipeline +date: 2023-05-26 +tags: [fewnerd, ner, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on Few-NERD/inter public dataset and it extracts 66 entities that are in general scope. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_subentity_100d_pipeline_en_4.4.2_3.4_1685060170575.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_subentity_100d_pipeline_en_4.4.2_3.4_1685060170575.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.""") +``` +```scala + +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.")(0) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.""") +``` +```scala +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.")(0) +``` +
+ +## Results + +```bash +Results + + + ++-----------------------+----------------------------+ +|chunk |ner_label | ++-----------------------+----------------------------+ +|Corazones ('12 Hearts')|art-broadcastprogram | +|Spanish-language |other-language | +|United States |location-GPE | +|Telemundo |organization-media/newspaper| +|Argentine TV |organization-media/newspaper| +|Los Angeles |location-GPE | +|Steven Spielberg |person-director | +|Cloverfield Paradox |art-film | ++-----------------------+----------------------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nerdl_fewnerd_subentity_100d_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|167.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-nerdl_restaurant_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-nerdl_restaurant_100d_pipeline_en.md new file mode 100644 index 00000000000000..1cd9bd857b7dcb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-nerdl_restaurant_100d_pipeline_en.md @@ -0,0 +1,119 @@ +--- +layout: model +title: Pipeline to Detect Restaurant-related Terminology +author: John Snow Labs +name: nerdl_restaurant_100d_pipeline +date: 2023-05-26 +tags: [restaurant, ner, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [nerdl_restaurant_100d](https://nlp.johnsnowlabs.com/2021/12/31/nerdl_restaurant_100d_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nerdl_restaurant_100d_pipeline_en_4.4.2_3.4_1685062743991.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nerdl_restaurant_100d_pipeline_en_4.4.2_3.4_1685062743991.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +restaurant_pipeline = PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +```scala + +val restaurant_pipeline = new PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +restaurant_pipeline = PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +```scala +val restaurant_pipeline = new PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +
+ +## Results + +```bash +Results + + + ++---------------------------+---------------+ +|chunk |ner_label | ++---------------------------+---------------+ +|Hong Kong’s |Restaurant_Name| +|favourite |Rating | +|pasta bar |Dish | +|most reasonably |Price | +|lunch |Hours | +|in town! |Location | +|Sha Tin – Pici’s |Restaurant_Name| +|burrata |Dish | +|arugula salad |Dish | +|freshly tossed tuna tartare|Dish | +|reliable |Price | +|handmade pasta |Dish | +|pappardelle |Dish | +|effortless |Amenity | +|Italian |Cuisine | +|tidy one-pot |Amenity | +|espresso |Dish | ++---------------------------+---------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nerdl_restaurant_100d_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-pos_ud_bokmaal_nb.md b/docs/_posts/ahmedlone127/2023-05-26-pos_ud_bokmaal_nb.md new file mode 100644 index 00000000000000..5fef7a9e1b2912 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-pos_ud_bokmaal_nb.md @@ -0,0 +1,138 @@ +--- +layout: model +title: Part of Speech for Norwegian +author: John Snow Labs +name: pos_ud_bokmaal +date: 2023-05-26 +tags: [pos, norwegian, nb, open_source] +task: Part of Speech Tagging +language: nb +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model annotates the part of speech of tokens in a text. The parts of speech annotated include PRON (pronoun), CCONJ (coordinating conjunction), and 15 others. The part of speech model is useful for extracting the grammatical structure of a piece of text automatically. + +This model was trained using the dataset available at https://universaldependencies.org + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pos_ud_bokmaal_nb_4.4.2_3.4_1685061155019.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pos_ud_bokmaal_nb_4.4.2_3.4_1685061155019.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("pos") + +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos]) +light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text"))) +results = light_pipeline.fullAnnotate("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.") +``` +```scala + +val pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") + .setInputCols(Array("document", "token")) + .setOutputCol("pos") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos)) +val data = Seq("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python + +import nlu + +text = ["""Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene."""] +pos_df = nlu.load('nb.pos.ud_bokmaal').predict(text) +pos_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("pos") + +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos]) +light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text"))) +results = light_pipeline.fullAnnotate("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.") +``` +```scala +val pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") + .setInputCols(Array("document", "token")) + .setOutputCol("pos") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos)) +val data = Seq("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu + +text = ["""Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene."""] +pos_df = nlu.load('nb.pos.ud_bokmaal').predict(text) +pos_df +``` +
+ +## Results + +```bash +Results + + + +[Row(annotatorType='pos', begin=0, end=4, result='DET', metadata={'word': 'Annet'}), +Row(annotatorType='pos', begin=6, end=8, result='SCONJ', metadata={'word': 'enn'}), +Row(annotatorType='pos', begin=10, end=10, result='PART', metadata={'word': 'å'}), +Row(annotatorType='pos', begin=12, end=15, result='AUX', metadata={'word': 'være'}), +Row(annotatorType='pos', begin=17, end=22, result='NOUN', metadata={'word': 'kongen'}), +...] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pos_ud_bokmaal| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nb| +|Size:|17.7 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-recognize_entities_dl_fa.md b/docs/_posts/ahmedlone127/2023-05-26-recognize_entities_dl_fa.md new file mode 100644 index 00000000000000..f3050a29f18a85 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-recognize_entities_dl_fa.md @@ -0,0 +1,169 @@ +--- +layout: model +title: Explain Document DL Pipeline for Farsi/Persian +author: John Snow Labs +name: recognize_entities_dl +date: 2023-05-26 +tags: [pipeline, ner, fa, open_source] +task: Named Entity Recognition +language: fa +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_dl is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities . It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_fa_4.4.2_3.4_1685059282514.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_fa_4.4.2_3.4_1685059282514.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'fa') + +annotations = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")[0] + +annotations.keys() +``` +```scala + +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "fa") + +val result = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")(0) +``` + +{:.nlu-block} +```python + +import nlu + +text = ["""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند"""] + +result_df = nlu.load('fa.recognize_entities_dl').predict(text) + +result_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'fa') + +annotations = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")[0] + +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "fa") + +val result = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")(0) +``` + +{:.nlu-block} +```python +import nlu + +text = ["""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند"""] + +result_df = nlu.load('fa.recognize_entities_dl').predict(text) + +result_df +``` +
+ +## Results + +```bash +Results + + + +| | document | sentence | token | clean_tokens | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------|:---------------|:---------|:------|:-------------|:------|:---------------------| +| 0 | "به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند | "به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند | " | " | " | PUNCT | " | O | خبرنگار ایرنا | +| 1 | | | به | گزارش | به | ADP | به | O | محمد قمی | +| 2 | | | گزارش | خبرنگار | گزارش | NOUN | گزارش | O | پاکدشت | +| 3 | | | خبرنگار | ایرنا | خبرنگار | NOUN | خبرنگار | B-ORG | علی‌اکبر موسوی خوئینی | +| 4 | | | ایرنا | ، | ایرنا | PROPN | ایرنا | I-ORG | شمس‌الدین وهابی | +| 5 | | | ، | اساس | ؛ | PUNCT | ، | O | تهران | +| 6 | | | بر | تصمیم | بر | ADP | بر | O | | +| 7 | | | اساس | این | اساس | NOUN | اساس | O | | +| 8 | | | تصمیم | مجمع | تصمیم | NOUN | تصمیم | O | | +| 9 | | | این | ، | این | DET | این | O | | +| 10 | | | مجمع | محمد | مجمع | NOUN | مجمع | O | | +| 11 | | | ، | قمی | ؛ | PUNCT | ، | O | | +| 12 | | | محمد | نماینده | محمد | PROPN | محمد | B-PER | | +| 13 | | | قمی | پاکدشت | قمی | PROPN | قمی | I-PER | | +| 14 | | | نماینده | عنوان | نماینده | NOUN | نماینده | O | | +| 15 | | | مردم | رئیس | مردم | NOUN | مردم | O | | +| 16 | | | پاکدشت | علی‌اکبر | پاکدشت | PROPN | پاکدشت | B-LOC | | +| 17 | | | به | موسوی | به | ADP | به | O | | +| 18 | | | عنوان | خوئینی | عنوان | NOUN | عنوان | O | | +| 19 | | | رئیس | شمس‌الدین | رئیس | NOUN | رئیس | O | | +| 20 | | | و | وهابی | او | CCONJ | و | O | | +| 21 | | | علی‌اکبر | نمایندگان | علی‌اکبر | PROPN | علی‌اکبر | B-PER | | +| 22 | | | موسوی | تهران | موسوی | PROPN | موسوی | I-PER | | +| 23 | | | خوئینی | عنوان | خوئینی | PROPN | خوئینی | I-PER | | +| 24 | | | و | نواب | او | CCONJ | و | O | | +| 25 | | | شمس‌الدین | رئیس | شمس‌الدین | PROPN | شمس‌الدین | B-PER | | +| 26 | | | وهابی | انتخاب | وهابی | PROPN | وهابی | I-PER | | +| 27 | | | نمایندگان | | نماینده | NOUN | نمایندگان | O | | +| 28 | | | مردم | | مردم | NOUN | مردم | O | | +| 29 | | | تهران | | تهران | PROPN | تهران | B-LOC | | +| 30 | | | به | | به | ADP | به | O | | +| 31 | | | عنوان | | عنوان | NOUN | عنوان | O | | +| 32 | | | نواب | | نواب | NOUN | نواب | O | | +| 33 | | | رئیس | | رئیس | NOUN | رئیس | O | | +| 34 | | | انتخاب | | انتخاب | NOUN | انتخاب | O | | +| 35 | | | شدند | | کرد#کن | VERB | شدند | O | | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recognize_entities_dl| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fa| +|Size:|1.2 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- StopWordsCleaner +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-roberta_base_token_classifier_ontonotes_pipeline_en.md new file mode 100644 index 00000000000000..52cb3c799c48dc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -0,0 +1,104 @@ +--- +layout: model +title: RoBERTa Base Ontonotes NER Pipeline +author: John Snow Labs +name: roberta_base_token_classifier_ontonotes_pipeline +date: 2023-05-26 +tags: [open_source, ner, token_classifier, roberta, ontonotes, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_base_token_classifier_ontonotes](https://nlp.johnsnowlabs.com/2021/09/26/roberta_base_token_classifier_ontonotes_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.4_1685066871009.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.4_1685066871009.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala + +val pipeline = new PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +## Results + +```bash +Results + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | +|November 2020 |DATE | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_base_token_classifier_ontonotes_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|456.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-roberta_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-roberta_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..617edc160336e2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-roberta_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: RoBERTa Large CoNLL-03 NER Pipeline +author: John Snow Labs +name: roberta_large_token_classifier_conll03_pipeline +date: 2023-05-26 +tags: [open_source, ner, token_classifier, roberta, conll03, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/roberta_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685067174083.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685067174083.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.3 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_bne_capitel_ner_pipeline_es.md b/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_bne_capitel_ner_pipeline_es.md new file mode 100644 index 00000000000000..8ac615bb296f92 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_bne_capitel_ner_pipeline_es.md @@ -0,0 +1,104 @@ +--- +layout: model +title: Spanish NER Pipeline +author: John Snow Labs +name: roberta_token_classifier_bne_capitel_ner_pipeline +date: 2023-05-26 +tags: [roberta, token_classifier, spanish, ner, es, open_source] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_token_classifier_bne_capitel_ner_es](https://nlp.johnsnowlabs.com/2021/12/07/roberta_token_classifier_bne_capitel_ner_es.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_bne_capitel_ner_pipeline_es_4.4.2_3.4_1685064748634.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_bne_capitel_ner_pipeline_es_4.4.2_3.4_1685064748634.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +```scala + +val pipeline = new PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +
+ +## Results + +```bash +Results + + + ++------------------------+---------+ +|chunk |ner_label| ++------------------------+---------+ +|Antonio |PER | +|fábrica de Mercedes-Benz|ORG | +|Madrid |LOC | ++------------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_token_classifier_bne_capitel_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|459.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_icelandic_ner_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_icelandic_ner_pipeline_is.md new file mode 100644 index 00000000000000..b128a453a67fd2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_icelandic_ner_pipeline_is.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Icelandic NER Pipeline +author: John Snow Labs +name: roberta_token_classifier_icelandic_ner_pipeline +date: 2023-05-26 +tags: [open_source, ner, token_classifier, roberta, icelandic, is] +task: Named Entity Recognition +language: is +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_token_classifier_icelandic_ner](https://nlp.johnsnowlabs.com/2021/12/06/roberta_token_classifier_icelandic_ner_is.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_icelandic_ner_pipeline_is_4.4.2_3.4_1685064909820.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_icelandic_ner_pipeline_is_4.4.2_3.4_1685064909820.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +```scala + +val pipeline = new PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +
+ +## Results + +```bash +Results + + + ++----------------+------------+ +|chunk |ner_label | ++----------------+------------+ +|Peter Fergusson |Person | +|New York |Location | +|október 2011 |Date | +|Tesla Motor |Organization| +|100K $ |Money | ++----------------+------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_token_classifier_icelandic_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|is| +|Size:|457.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_timex_semeval_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_timex_semeval_pipeline_en.md new file mode 100644 index 00000000000000..973b35a190fd97 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_timex_semeval_pipeline_en.md @@ -0,0 +1,110 @@ +--- +layout: model +title: Pipeline to Detect Time-related Terminology +author: John Snow Labs +name: roberta_token_classifier_timex_semeval_pipeline +date: 2023-05-26 +tags: [timex, semeval, ner, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_token_classifier_timex_semeval](https://nlp.johnsnowlabs.com/2021/12/28/roberta_token_classifier_timex_semeval_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_timex_semeval_pipeline_en_4.4.2_3.4_1685062894274.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_timex_semeval_pipeline_en_4.4.2_3.4_1685062894274.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +timex_pipeline = PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +```scala + + +val timex_pipeline = new PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +timex_pipeline = PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +```scala +val timex_pipeline = new PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +
+ +## Results + +```bash +Results + + + + ++-------+-----------------+ +|chunk |ner_label | ++-------+-----------------+ +|22:12C |Period | +|3 |Number | +|days |Calendar-Interval| +|Tuesday|Day-Of-Week | +|to |Between | +|Friday |Day-Of-Week | ++-------+-----------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_token_classifier_timex_semeval_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|439.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-spellcheck_dl_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-spellcheck_dl_pipeline_en.md new file mode 100644 index 00000000000000..fa564958af1b5c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-spellcheck_dl_pipeline_en.md @@ -0,0 +1,112 @@ +--- +layout: model +title: Context Spell Checker Pipeline for English +author: John Snow Labs +name: spellcheck_dl_pipeline +date: 2023-05-26 +tags: [spellcheck, spell, spellcheck_pipeline, spelling_corrector, en, open_source] +task: Spell Check +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained spellchecker pipeline is built on the top of [spellcheck_dl](https://nlp.johnsnowlabs.com/2022/04/02/spellcheck_dl_en_2_4.html) model. This pipeline is for PySpark 2.4.x users with SparkNLP 3.4.2 and above. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/spellcheck_dl_pipeline_en_4.4.2_3.4_1685063898608.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/spellcheck_dl_pipeline_en_4.4.2_3.4_1685063898608.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +text = ["During the summer we have the best ueather.", "I have a black ueather jacket, so nice."] + +pipeline.annotate(text) +``` +```scala + + +val pipeline = new PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +val example = Array("During the summer we have the best ueather.", "I have a black ueather jacket, so nice.") + +pipeline.annotate(example) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +text = ["During the summer we have the best ueather.", "I have a black ueather jacket, so nice."] + +pipeline.annotate(text) +``` +```scala +val pipeline = new PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +val example = Array("During the summer we have the best ueather.", "I have a black ueather jacket, so nice.") + +pipeline.annotate(example) +``` +
+ +## Results + +```bash +Results + + + + +[{'checked': ['During', 'the', 'summer', 'we', 'have', 'the', 'best', 'weather', '.'], + 'document': ['During the summer we have the best ueather.'], + 'token': ['During', 'the', 'summer', 'we', 'have', 'the', 'best', 'ueather', '.']}, + + {'checked': ['I', 'have', 'a', 'black', 'leather', 'jacket', ',', 'so', 'nice', '.'], + 'document': ['I have a black ueather jacket, so nice.'], + 'token': ['I', 'have', 'a', 'black', 'ueather', 'jacket', ',', 'so', 'nice', '.']}] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|spellcheck_dl_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|99.7 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- ContextSpellCheckerModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..83c7e0e7dc12cd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: XLM-RoBERTa Base, CoNLL-03 NER Pipeline +author: John Snow Labs +name: xlm_roberta_base_token_classifier_conll03_pipeline +date: 2023-05-26 +tags: [open_source, ner, token_classifier, xlm_roberta, conll03, xlm, base, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/10/03/xlm_roberta_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066235304.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066235304.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|851.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md new file mode 100644 index 00000000000000..bfc143f8889183 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: XLM-RoBERTa Base NER Pipeline +author: John Snow Labs +name: xlm_roberta_base_token_classifier_ontonotes_pipeline +date: 2023-05-26 +tags: [open_source, ner, token_classifier, xlm_roberta, ontonotes, xlm, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifier_ontonotes](https://nlp.johnsnowlabs.com/2021/10/03/xlm_roberta_base_token_classifier_ontonotes_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.4_1685065973037.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.4_1685065973037.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | +|November 2020 |DATE | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_base_token_classifier_ontonotes_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|858.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_conll03_pipeline_de.md b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_conll03_pipeline_de.md new file mode 100644 index 00000000000000..d7b6d8ec978ee5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_conll03_pipeline_de.md @@ -0,0 +1,104 @@ +--- +layout: model +title: NER Pipeline for German +author: John Snow Labs +name: xlm_roberta_large_token_classifier_conll03_pipeline +date: 2023-05-26 +tags: [german, roberta, xlm, ner, conll03, de, open_source] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_large_token_classifier_conll03_de](https://nlp.johnsnowlabs.com/2021/12/25/xlm_roberta_large_token_classifier_conll03_de.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_conll03_pipeline_de_4.4.2_3.4_1685064550521.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_conll03_pipeline_de_4.4.2_3.4_1685064550521.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +```scala + +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +
+ +## Results + +```bash +Results + + + ++----------------------+---------+ +|chunk |ner_label| ++----------------------+---------+ +|Ibser |PER | +|ASK Ebreichsdorf |ORG | +|Admira Wacker Mödling |ORG | ++----------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|1.8 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md new file mode 100644 index 00000000000000..32a4f08022b69b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md @@ -0,0 +1,104 @@ +--- +layout: model +title: NER Pipeline for 10 High Resourced Languages +author: John Snow Labs +name: xlm_roberta_large_token_classifier_hrl_pipeline +date: 2023-05-26 +tags: [arabic, german, english, spanish, french, italian, latvian, dutch, portuguese, chinese, xlm, roberta, ner, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_large_token_classifier_hrl](https://nlp.johnsnowlabs.com/2021/12/26/xlm_roberta_large_token_classifier_hrl_xx.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_hrl_pipeline_xx_4.4.2_3.4_1685063715742.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_hrl_pipeline_xx_4.4.2_3.4_1685063715742.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +```scala + +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +
+ +## Results + +```bash +Results + + + ++---------------------------+---------+ +|chunk |ner_label| ++---------------------------+---------+ +|الرياض |LOC | +|فيصل بن بندر بن عبد العزيز |PER | +|الرياض |LOC | ++---------------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_large_token_classifier_hrl_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|1.8 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md new file mode 100644 index 00000000000000..effb202a3b61c5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md @@ -0,0 +1,104 @@ +--- +layout: model +title: NER Pipeline for 10 African Languages +author: John Snow Labs +name: xlm_roberta_large_token_classifier_masakhaner_pipeline +date: 2023-05-26 +tags: [masakhaner, african, xlm_roberta, multilingual, pipeline, amharic, hausa, igbo, kinyarwanda, luganda, swahilu, wolof, yoruba, nigerian, pidgin, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on [xlm_roberta_large_token_classifier_masakhaner](https://nlp.johnsnowlabs.com/2021/12/06/xlm_roberta_large_token_classifier_masakhaner_xx.html) ner model which is imported from `HuggingFace`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_masakhaner_pipeline_xx_4.4.2_3.4_1685061875856.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_masakhaner_pipeline_xx_4.4.2_3.4_1685061875856.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +masakhaner_pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +```scala + +val masakhaner_pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +val masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +masakhaner_pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +```scala +val masakhaner_pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +val masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +
+ +## Results + +```bash +Results + + + ++----------------+---------+ +|chunk |ner_label| ++----------------+---------+ +|አህመድ ቫንዳ |PER | +|ከ3-10-2000 ጀምሮ|DATE | +|በአዲስ አበባ |LOC | ++----------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_large_token_classifier_masakhaner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|1.8 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlnet_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-xlnet_base_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..aa40cf3ff9bbcc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-xlnet_base_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: XLNet Base CoNLL-03 NER Pipeline +author: John Snow Labs +name: xlnet_base_token_classifier_conll03_pipeline +date: 2023-05-26 +tags: [ner, english, xlnet, base, token_classification, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlnet_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/28/xlnet_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlnet_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685065733321.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlnet_base_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685065733321.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlnet_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|438.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlnetForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlnet_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-xlnet_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..ed3e5055d40756 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-26-xlnet_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,103 @@ +--- +layout: model +title: XLNet Large CoNLL-03 NER Pipeline +author: John Snow Labs +name: xlnet_large_token_classifier_conll03_pipeline +date: 2023-05-26 +tags: [open_source, ner, token_classifier, xlnet, conll03, large, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlnet_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/28/xlnet_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlnet_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066312653.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlnet_large_token_classifier_conll03_pipeline_en_4.4.2_3.4_1685066312653.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlnet_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|19.0 KB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- NormalizerModel \ No newline at end of file From 2fd64c3746779fa73529c406e5d6827125e79e89 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Fri, 26 May 2023 13:50:51 +0700 Subject: [PATCH 12/55] 2023-05-25-distilcamembert_french_legal_fr (#13826) * Add model 2023-05-25-distilcamembert_french_legal_fr * Update 2023-05-25-distilcamembert_french_legal_fr.md * Update 2023-05-25-distilcamembert_french_legal_fr.md * Add model 2023-05-25-camembert_french_legal_fr * Update 2023-05-25-camembert_french_legal_fr.md * Update 2023-05-25-camembert_french_legal_fr.md * Update 2023-05-25-distilcamembert_french_legal_fr.md --------- Co-authored-by: Mary-Sci Co-authored-by: Merve Ertas Uslu <67653613+Mary-Sci@users.noreply.github.com> --- .../2023-05-25-camembert_french_legal_fr.md | 96 +++++++++++++++++++ ...3-05-25-distilcamembert_french_legal_fr.md | 96 +++++++++++++++++++ 2 files changed, 192 insertions(+) create mode 100644 docs/_posts/Mary-Sci/2023-05-25-camembert_french_legal_fr.md create mode 100644 docs/_posts/Mary-Sci/2023-05-25-distilcamembert_french_legal_fr.md diff --git a/docs/_posts/Mary-Sci/2023-05-25-camembert_french_legal_fr.md b/docs/_posts/Mary-Sci/2023-05-25-camembert_french_legal_fr.md new file mode 100644 index 00000000000000..6fa3398e68ced2 --- /dev/null +++ b/docs/_posts/Mary-Sci/2023-05-25-camembert_french_legal_fr.md @@ -0,0 +1,96 @@ +--- +layout: model +title: French Legal CamemBert Embeddings Model +author: John Snow Labs +name: camembert_french_legal +date: 2023-05-25 +tags: [open_source, camembert_embeddings, camembertformaskedlm, fr, tensorflow] +task: Embeddings +language: fr +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: CamemBertEmbeddings +article_header: +type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `legal-camembert` is a French model originally trained by `maastrichtlawtech`. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_french_legal_fr_4.4.2_3.0_1685035847575.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_french_legal_fr_4.4.2_3.0_1685035847575.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +documentAssembler = DocumentAssembler() \ + .setInputCols(["text"]) \ + .setOutputCols("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_french_legal","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_french_legal","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_french_legal| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|415.8 MB| +|Case sensitive:|true| + +## References + +https://huggingface.co/maastrichtlawtech/legal-camembert diff --git a/docs/_posts/Mary-Sci/2023-05-25-distilcamembert_french_legal_fr.md b/docs/_posts/Mary-Sci/2023-05-25-distilcamembert_french_legal_fr.md new file mode 100644 index 00000000000000..3a38a1eaf09ab1 --- /dev/null +++ b/docs/_posts/Mary-Sci/2023-05-25-distilcamembert_french_legal_fr.md @@ -0,0 +1,96 @@ +--- +layout: model +title: French Legal CamemBert Embeddings Model +author: John Snow Labs +name: distilcamembert_french_legal +date: 2023-05-25 +tags: [open_source, camembert_embeddings, camembertformaskedlm, fr, tensorflow] +task: Embeddings +language: fr +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: CamemBertEmbeddings +article_header: +type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `legal-distilcamembert` is a French model originally trained by `maastrichtlawtech`. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilcamembert_french_legal_fr_4.4.2_3.0_1685031800112.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilcamembert_french_legal_fr_4.4.2_3.0_1685031800112.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +documentAssembler = DocumentAssembler() \ + .setInputCols(["text"]) \ + .setOutputCols("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("distilcamembert_french_legal","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("distilcamembert_french_legal","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilcamembert_french_legal| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|256.1 MB| +|Case sensitive:|true| + +## References + +https://huggingface.co/maastrichtlawtech/legal-distilcamembert From 795ebf81e93c25b9945877d22369bb12c09a11c7 Mon Sep 17 00:00:00 2001 From: Merve Ertas Uslu <67653613+Mary-Sci@users.noreply.github.com> Date: Fri, 26 May 2023 13:31:09 +0200 Subject: [PATCH 13/55] Update title for 2023-05-25-distilcamembert_french_legal_fr.md (#13831) --- .../Mary-Sci/2023-05-25-distilcamembert_french_legal_fr.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/_posts/Mary-Sci/2023-05-25-distilcamembert_french_legal_fr.md b/docs/_posts/Mary-Sci/2023-05-25-distilcamembert_french_legal_fr.md index 3a38a1eaf09ab1..02fd758632ac5f 100644 --- a/docs/_posts/Mary-Sci/2023-05-25-distilcamembert_french_legal_fr.md +++ b/docs/_posts/Mary-Sci/2023-05-25-distilcamembert_french_legal_fr.md @@ -1,6 +1,6 @@ --- layout: model -title: French Legal CamemBert Embeddings Model +title: French Legal DistilCamemBert Embeddings Model author: John Snow Labs name: distilcamembert_french_legal date: 2023-05-25 From c04ca51550e438b585e71cb6a92b828f52c88584 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Sun, 28 May 2023 02:53:17 +0700 Subject: [PATCH 14/55] 2023-05-27-explain_document_md_fr (#13836) * Add model 2023-05-27-explain_document_md_fr * Add model 2023-05-27-dependency_parse_en * Add model 2023-05-27-explain_document_md_it * Add model 2023-05-27-entity_recognizer_lg_fr * Add model 2023-05-27-entity_recognizer_md_fr * Add model 2023-05-27-entity_recognizer_lg_it * Add model 2023-05-27-entity_recognizer_md_it * Add model 2023-05-27-check_spelling_en * Add model 2023-05-27-match_datetime_en * Add model 2023-05-27-match_pattern_en * Add model 2023-05-27-clean_pattern_en * Add model 2023-05-27-clean_stop_en * Add model 2023-05-27-movies_sentiment_analysis_en * Add model 2023-05-27-explain_document_ml_en * Add model 2023-05-27-analyze_sentiment_en * Add model 2023-05-27-explain_document_dl_en * Add model 2023-05-27-recognize_entities_dl_en * Add model 2023-05-27-recognize_entities_bert_en * Add model 2023-05-27-explain_document_md_de * Add model 2023-05-27-entity_recognizer_lg_de * Add model 2023-05-27-entity_recognizer_md_de * Add model 2023-05-27-onto_recognize_entities_sm_en * Add model 2023-05-27-onto_recognize_entities_lg_en * Add model 2023-05-27-match_chunks_en * Add model 2023-05-27-explain_document_lg_es * Add model 2023-05-27-explain_document_md_es * Add model 2023-05-27-explain_document_sm_es * Add model 2023-05-27-entity_recognizer_lg_es * Add model 2023-05-27-entity_recognizer_md_es * Add model 2023-05-27-entity_recognizer_sm_es * Add model 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+--- + +## Description + +This pretrained pipeline is built on the top of [albert_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/albert_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_token_classifier_conll03_pipeline_en_4.4.2_3.2_1685210192233.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_token_classifier_conll03_pipeline_en_4.4.2_3.2_1685210192233.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|43.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- AlbertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-albert_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-albert_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..308cd3d0f45017 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-albert_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: ALBERT Large CoNNL-03 NER Pipeline +author: John Snow Labs +name: albert_large_token_classifier_conll03_pipeline +date: 2023-05-27 +tags: [open_source, ner, token_classifier, albert, conll03, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [albert_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/albert_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_token_classifier_conll03_pipeline_en_4.4.2_3.2_1685210265440.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_token_classifier_conll03_pipeline_en_4.4.2_3.2_1685210265440.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|64.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- AlbertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-albert_xlarge_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-albert_xlarge_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..9704b0293fda63 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-albert_xlarge_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: ALBERT XLarge CoNNL-03 NER Pipeline +author: John Snow Labs +name: albert_xlarge_token_classifier_conll03_pipeline +date: 2023-05-27 +tags: [open_source, ner, token_classifier, albert, conll03, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [albert_xlarge_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/albert_xlarge_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_xlarge_token_classifier_conll03_pipeline_en_4.4.2_3.2_1685210363132.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_xlarge_token_classifier_conll03_pipeline_en_4.4.2_3.2_1685210363132.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_xlarge_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|206.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- AlbertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-analyze_sentiment_en.md b/docs/_posts/ahmedlone127/2023-05-27-analyze_sentiment_en.md new file mode 100644 index 00000000000000..15ae7376dcae25 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-analyze_sentiment_en.md @@ -0,0 +1,130 @@ +--- +layout: model +title: Sentiment Analysis pipeline for English +author: John Snow Labs +name: analyze_sentiment +date: 2023-05-27 +tags: [open_source, english, analyze_sentiment, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The analyze_sentiment is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/analyze_sentiment_en_4.4.2_3.2_1685186425752.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/analyze_sentiment_en_4.4.2_3.2_1685186425752.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python + +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en') + +result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") + + +``` +```scala + +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en") + +val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") + +``` + +{:.nlu-block} +```python + +import nlu +text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""] +result_df = nlu.load('en.classify').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en') + +result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline + +val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en") + +val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") +``` + +{:.nlu-block} +```python +import nlu +text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""] +result_df = nlu.load('en.classify').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | text | sentiment | +|---:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------| +| 0 | Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now! | positive | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|analyze_sentiment| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|5.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel +- ViveknSentimentModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-analyze_sentimentdl_glove_imdb_en.md b/docs/_posts/ahmedlone127/2023-05-27-analyze_sentimentdl_glove_imdb_en.md new file mode 100644 index 00000000000000..13ee9647ab1e2b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-analyze_sentimentdl_glove_imdb_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Sentiment Analysis pipeline for English (analyze_sentimentdl_glove_imdb) +author: John Snow Labs +name: analyze_sentimentdl_glove_imdb +date: 2023-05-27 +tags: [open_source, english, analyze_sentimentdl_glove_imdb, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The analyze_sentimentdl_glove_imdb is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/analyze_sentimentdl_glove_imdb_en_4.4.2_3.2_1685196050596.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/analyze_sentimentdl_glove_imdb_en_4.4.2_3.2_1685196050596.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("analyze_sentimentdl_glove_imdb", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.sentiment.glove').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("analyze_sentimentdl_glove_imdb", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.sentiment.glove').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | tokens | word_embeddings | sentence_embeddings | sentiment | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-----------------------------|:------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2668800055980682,.,...]] | [[0.0771183446049690,.,...]] | ['neg'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|analyze_sentimentdl_glove_imdb| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|161.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- SentenceEmbeddings +- SentimentDLModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-bert_sequence_classifier_age_news_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-bert_sequence_classifier_age_news_pipeline_en.md new file mode 100644 index 00000000000000..27a4701e743654 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-bert_sequence_classifier_age_news_pipeline_en.md @@ -0,0 +1,98 @@ +--- +layout: model +title: Pipeline to Classify Texts into 4 News Categories +author: John Snow Labs +name: bert_sequence_classifier_age_news_pipeline +date: 2023-05-27 +tags: [ag_news, news, bert, bert_sequence, classification, en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [bert_sequence_classifier_age_news_en](https://nlp.johnsnowlabs.com/2021/11/07/bert_sequence_classifier_age_news_en.html) which is imported from `HuggingFace`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_age_news_pipeline_en_4.4.2_3.2_1685206343556.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_age_news_pipeline_en_4.4.2_3.2_1685206343556.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +news_pipeline = PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +```scala + + +val news_pipeline = new PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +news_pipeline = PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +```scala +val news_pipeline = new PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") + +news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") +``` +
+ +## Results + +```bash +Results + + + + +['Sci/Tech'] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_age_news_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|42.4 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- BertForSequenceClassification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-bert_sequence_classifier_trec_coarse_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-bert_sequence_classifier_trec_coarse_pipeline_en.md new file mode 100644 index 00000000000000..58700391844037 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-bert_sequence_classifier_trec_coarse_pipeline_en.md @@ -0,0 +1,100 @@ +--- +layout: model +title: Pipeline to Classify Texts into TREC-6 Classes +author: John Snow Labs +name: bert_sequence_classifier_trec_coarse_pipeline +date: 2023-05-27 +tags: [bert_sequence, trec, coarse, bert, en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [bert_sequence_classifier_trec_coarse_en](https://nlp.johnsnowlabs.com/2021/11/06/bert_sequence_classifier_trec_coarse_en.html). + +The TREC dataset for question classification consists of open-domain, fact-based questions divided into broad semantic categories. You can check the official documentation of the dataset, entities, etc. [here](https://search.r-project.org/CRAN/refmans/textdata/html/dataset_trec.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_trec_coarse_pipeline_en_4.4.2_3.2_1685206267809.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_trec_coarse_pipeline_en_4.4.2_3.2_1685206267809.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +trec_pipeline = PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +```scala + + +val trec_pipeline = new PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +trec_pipeline = PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +```scala +val trec_pipeline = new PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") + +trec_pipeline.annotate("Germany is the largest country in Europe economically.") +``` +
+ +## Results + +```bash +Results + + + + +['LOC'] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_trec_coarse_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|406.6 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- BertForSequenceClassification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md b/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md new file mode 100644 index 00000000000000..8a0322af18feb8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Dutch NER Pipeline +author: John Snow Labs +name: bert_token_classifier_dutch_udlassy_ner_pipeline +date: 2023-05-27 +tags: [open_source, ner, dutch, token_classifier, bert, treatment, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [bert_token_classifier_dutch_udlassy_ner](https://nlp.johnsnowlabs.com/2021/12/08/bert_token_classifier_dutch_udlassy_ner_nl.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_token_classifier_dutch_udlassy_ner_pipeline_nl_4.4.2_3.2_1685207938259.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_token_classifier_dutch_udlassy_ner_pipeline_nl_4.4.2_3.2_1685207938259.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +```scala + +val pipeline = new PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +```scala +val pipeline = new PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") + +pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") +``` +
+ +## Results + +```bash +Results + + + ++---------------+---------+ +|chunk |ner_label| ++---------------+---------+ +|Peter Fergusson|PERSON | +|oktober 2011 |DATE | +|New York |GPE | +|5 jaar |DATE | +|Tesla Motor |ORG | ++---------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_token_classifier_dutch_udlassy_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|408.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_hi_en_ner_pipeline_hi.md b/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_hi_en_ner_pipeline_hi.md new file mode 100644 index 00000000000000..96a1e85db5fcfb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_hi_en_ner_pipeline_hi.md @@ -0,0 +1,113 @@ +--- +layout: model +title: NER Pipeline for Hindi+English +author: John Snow Labs +name: bert_token_classifier_hi_en_ner_pipeline +date: 2023-05-27 +tags: [hindi, bert_token, hi, open_source] +task: Named Entity Recognition +language: hi +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on [bert_token_classifier_hi_en_ner](https://nlp.johnsnowlabs.com/2021/12/27/bert_token_classifier_hi_en_ner_hi.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_token_classifier_hi_en_ner_pipeline_hi_4.4.2_3.2_1685207050403.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_token_classifier_hi_en_ner_pipeline_hi_4.4.2_3.2_1685207050403.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +```scala + +val pipeline = new PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +val pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +```scala +val pipeline = new PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") + +val pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") +``` +
+ +## Results + +```bash +Results + + + ++---------------------------+------------+ +|chunk |ner_label | ++---------------------------+------------+ +|रिलायंस इंडस्ट्रीज़ लिमिटेड |ORGANISATION| +|Reliance Industries Limited|ORGANISATION| +|भारतीय |PLACE | +|मुंबई |PLACE | +|महाराष्ट्र |PLACE | +|Maharashtra) |PLACE | +|नवल टाटा |PERSON | +|मुम्बई |PLACE | +|Mumbai |PLACE | +|टाटा समुह |ORGANISATION| +|भारत |PLACE | +|जमशेदजी टाटा |PERSON | ++---------------------------+------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_token_classifier_hi_en_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|hi| +|Size:|665.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- BertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_scandi_ner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_scandi_ner_pipeline_xx.md new file mode 100644 index 00000000000000..29eb984bfbc288 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_scandi_ner_pipeline_xx.md @@ -0,0 +1,103 @@ +--- +layout: model +title: NER Pipeline for 6 Scandinavian Languages +author: John Snow Labs +name: bert_token_classifier_scandi_ner_pipeline +date: 2023-05-27 +tags: [danish, norwegian, swedish, icelandic, faroese, bert, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on [bert_token_classifier_scandi_ner](https://nlp.johnsnowlabs.com/2021/12/09/bert_token_classifier_scandi_ner_xx.html) model which is imported from `HuggingFace`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_token_classifier_scandi_ner_pipeline_xx_4.4.2_3.2_1685206123763.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_token_classifier_scandi_ner_pipeline_xx_4.4.2_3.2_1685206123763.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +scandiner_pipeline = PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") +scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +```scala + +val scandiner_pipeline = new PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") + +val scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +scandiner_pipeline = PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") +scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +```scala +val scandiner_pipeline = new PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") + +val scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") +``` +
+ +## Results + +```bash +Results + + + ++-------------------+---------+ +|chunk |ner_label| ++-------------------+---------+ +|Hans |PER | +|Statens Universitet|ORG | +|København |LOC | +|københavner |MISC | ++-------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_token_classifier_scandi_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|666.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- BertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-check_spelling_en.md b/docs/_posts/ahmedlone127/2023-05-27-check_spelling_en.md new file mode 100644 index 00000000000000..ae1cba52c96369 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-check_spelling_en.md @@ -0,0 +1,120 @@ +--- +layout: model +title: Spell Checking Pipeline for English +author: John Snow Labs +name: check_spelling +date: 2023-05-27 +tags: [open_source, english, check_spelling, pipeline, en] +task: Spell Check +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The check_spelling is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/check_spelling_en_4.4.2_3.2_1685185605184.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/check_spelling_en_4.4.2_3.2_1685185605184.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('check_spelling', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("check_spelling", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('check_spelling', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("check_spelling", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | checked | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------------------------| +| 0 | ['I liek to live dangertus ! '] | ['I liek to live dangertus !'] | ['I', 'liek', 'to', 'live', 'dangertus', '!'] | ['I', 'like', 'to', 'live', 'dangerous', '!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|check_spelling| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|906.2 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-clean_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-27-clean_pattern_en.md new file mode 100644 index 00000000000000..c25863d88ce587 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-clean_pattern_en.md @@ -0,0 +1,120 @@ +--- +layout: model +title: Clean patterns pipeline for English +author: John Snow Labs +name: clean_pattern +date: 2023-05-27 +tags: [open_source, english, clean_pattern, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The clean_pattern is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clean_pattern_en_4.4.2_3.2_1685185742070.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clean_pattern_en_4.4.2_3.2_1685185742070.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_pattern', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala + + +val pipeline = new PretrainedPipeline("clean_pattern", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python + + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.pattern').predict(text) +result_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_pattern', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("clean_pattern", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.pattern').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + + +| | document | sentence | token | normal | +|---:|:-----------|:-----------|:----------|:----------| +| 0 | ['Hello'] | ['Hello'] | ['Hello'] | ['Hello'] || | document | sentence | token | normal | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clean_pattern| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|17.2 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NormalizerModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-clean_stop_en.md b/docs/_posts/ahmedlone127/2023-05-27-clean_stop_en.md new file mode 100644 index 00000000000000..f394d0121d9c0c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-clean_stop_en.md @@ -0,0 +1,120 @@ +--- +layout: model +title: Clean documents pipeline for English +author: John Snow Labs +name: clean_stop +date: 2023-05-27 +tags: [open_source, english, clean_stop, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The clean_stop is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clean_stop_en_4.4.2_3.2_1685185789360.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clean_stop_en_4.4.2_3.2_1685185789360.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_stop', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("clean_stop", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.stop').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('clean_stop', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("clean_stop", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.clean.stop').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | cleanTokens | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:---------------------------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | ['Hello', 'John', 'Snow', 'Labs', '!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clean_stop| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|14.1 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- StopWordsCleaner \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-dependency_parse_en.md b/docs/_posts/ahmedlone127/2023-05-27-dependency_parse_en.md new file mode 100644 index 00000000000000..e7c498cdbd3d15 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-dependency_parse_en.md @@ -0,0 +1,119 @@ +--- +layout: model +title: Typed Dependency Parsing pipeline for English +author: John Snow Labs +name: dependency_parse +date: 2023-05-27 +tags: [pipeline, dependency_parsing, untyped_dependency_parsing, typed_dependency_parsing, laballed_depdency_parsing, unlaballed_depdency_parsing, en, open_source] +task: Dependency Parser +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Typed Dependency parser, trained on the on the CONLL dataset. + +Dependency parsing is the task of extracting a dependency parse of a sentence that represents its grammatical structure and defines the relationships between “head” words and words, which modify those heads. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dependency_parse_en_4.4.2_3.2_1685184038289.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dependency_parse_en_4.4.2_3.2_1685184038289.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('dependency_parse', lang = 'en') +annotations = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("dependency_parse", lang = "en") +val result = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence")(0) + +``` + +{:.nlu-block} +```python + +nlu.load("dep.typed").predict("Dependencies represents relationships betweens words in a Sentence") + + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('dependency_parse', lang = 'en') +annotations = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("dependency_parse", lang = "en") +val result = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence")(0) +``` + +{:.nlu-block} +```python +nlu.load("dep.typed").predict("Dependencies represents relationships betweens words in a Sentence") +``` +
+ +## Results + +```bash +Results + + ++---------------------------------------------------------------------------------+--------------------------------------------------------+ +|result |result | ++---------------------------------------------------------------------------------+--------------------------------------------------------+ +|[ROOT, Dependencies, represents, words, relationships, Sentence, Sentence, words]|[root, parataxis, nsubj, amod, nsubj, case, nsubj, flat]| ++---------------------------------------------------------------------------------+--------------------------------------------------------+ + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dependency_parse| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|23.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel +- DependencyParserModel +- TypedDependencyParserModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-distilbert_base_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-27-distilbert_base_token_classifier_masakhaner_pipeline_xx.md new file mode 100644 index 00000000000000..1ae8f9c38f5ea2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-distilbert_base_token_classifier_masakhaner_pipeline_xx.md @@ -0,0 +1,108 @@ +--- +layout: model +title: NER Pipeline for 9 African Languages +author: John Snow Labs +name: distilbert_base_token_classifier_masakhaner_pipeline +date: 2023-05-27 +tags: [hausa, igbo, kinyarwanda, luganda, nigerian, pidgin, swahilu, wolof, yoruba, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilbert_base_token_classifier_masakhaner](https://nlp.johnsnowlabs.com/2022/01/18/distilbert_base_token_classifier_masakhaner_xx.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_token_classifier_masakhaner_pipeline_xx_4.4.2_3.2_1685206635131.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_token_classifier_masakhaner_pipeline_xx_4.4.2_3.2_1685206635131.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +masakhaner_pipeline = PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +```scala + +val masakhaner_pipeline = new PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +masakhaner_pipeline = PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +```scala +val masakhaner_pipeline = new PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") +``` +
+ +## Results + +```bash +Results + + + ++-----------------------------+---------+ +|chunk |ner_label| ++-----------------------------+---------+ +|Mohammed Sani Musa |PER | +|Activate Technologies Limited|ORG | +|ọdún-un 2019 |DATE | +|All rogressives Congress |ORG | +|APC |ORG | +|Aṣojú Ìlà-Oòrùn Niger |LOC | +|Premium Times |ORG | ++-----------------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_token_classifier_masakhaner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|505.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- DistilBertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-distilbert_token_classifier_typo_detector_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-distilbert_token_classifier_typo_detector_pipeline_en.md new file mode 100644 index 00000000000000..43ac09fbb7b9ac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-distilbert_token_classifier_typo_detector_pipeline_en.md @@ -0,0 +1,105 @@ +--- +layout: model +title: Typo Detector Pipeline for English +author: John Snow Labs +name: distilbert_token_classifier_typo_detector_pipeline +date: 2023-05-27 +tags: [ner, bert, bert_for_token, typo, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilbert_token_classifier_typo_detector](https://nlp.johnsnowlabs.com/2022/01/19/distilbert_token_classifier_typo_detector_en.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_en_4.4.2_3.2_1685211046130.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_en_4.4.2_3.2_1685211046130.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +```scala + + +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +```scala +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") + +typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") +``` +
+ +## Results + +```bash +Results + + + + ++----------+---------+ +|chunk |ner_label| ++----------+---------+ +|stgruggled|PO | +|tine |PO | ++----------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_token_classifier_typo_detector_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|244.1 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- DistilBertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-distilbert_token_classifier_typo_detector_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-27-distilbert_token_classifier_typo_detector_pipeline_is.md new file mode 100644 index 00000000000000..6569533a9d3a6c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-distilbert_token_classifier_typo_detector_pipeline_is.md @@ -0,0 +1,102 @@ +--- +layout: model +title: Typo Detector Pipeline for Icelandic +author: John Snow Labs +name: distilbert_token_classifier_typo_detector_pipeline +date: 2023-05-27 +tags: [icelandic, typo, ner, distilbert, is, open_source] +task: Named Entity Recognition +language: is +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilbert_token_classifier_typo_detector_is](https://nlp.johnsnowlabs.com/2022/01/19/distilbert_token_classifier_typo_detector_is.html). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_is_4.4.2_3.2_1685206481363.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_token_classifier_typo_detector_pipeline_is_4.4.2_3.2_1685206481363.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +```scala + +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +```scala +val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") + +typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") +``` +
+ +## Results + +```bash +Results + + + ++--------+---------+ +|chunk |ner_label| ++--------+---------+ +|miög |PO | +|álykanir|PO | ++--------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_token_classifier_typo_detector_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|is| +|Size:|505.8 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- DistilBertForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-distilroberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-distilroberta_base_token_classifier_ontonotes_pipeline_en.md new file mode 100644 index 00000000000000..ccb91b9f89fa58 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-distilroberta_base_token_classifier_ontonotes_pipeline_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: DistilRoBERTa Base Ontonotes NER Pipeline +author: John Snow Labs +name: distilroberta_base_token_classifier_ontonotes_pipeline +date: 2023-05-27 +tags: [open_source, ner, token_classifier, distilroberta, ontonotes, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [distilroberta_base_token_classifier_ontonotes](https://nlp.johnsnowlabs.com/2021/09/26/distilroberta_base_token_classifier_ontonotes_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilroberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.2_1685210478107.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilroberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.2_1685210478107.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala + + +val pipeline = new PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala +val pipeline = new PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | +|November 2020 |DATE | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilroberta_base_token_classifier_ontonotes_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|307.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_da.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_da.md new file mode 100644 index 00000000000000..956306f8d60845 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_da.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-27 +tags: [da, open_source] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_da_4.4.2_3.2_1685200185021.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_da_4.4.2_3.2_1685200185021.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "da") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("da.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "da") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("da.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_de.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_de.md new file mode 100644 index 00000000000000..57ae48b9514af0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_de.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL pipeline for German - Large +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-27 +tags: [open_source, german, entity_recognizer_lg, pipeline, de] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_de_4.4.2_3.2_1685187532191.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_de_4.4.2_3.2_1685187532191.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo aus John Snow Labs! '] | ['Hallo aus John Snow Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | [[-0.245989993214607,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_es.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_es.md new file mode 100644 index 00000000000000..3601a98f89ce0e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_es.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognition Pipeline (Large, Spanish) +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-27 +tags: [es, open_source] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_es_4.4.2_3.2_1685189569463.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_es_4.4.2_3.2_1685189569463.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "es") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("es.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "es") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_fi.md new file mode 100644 index 00000000000000..74e7136b73807f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_fi.md @@ -0,0 +1,95 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-27 +tags: [fi, open_source] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fi_4.4.2_3.2_1685201658836.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fi_4.4.2_3.2_1685201658836.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "fi") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("fi.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "fi") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("fi.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_fr.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_fr.md new file mode 100644 index 00000000000000..9151aaed57cb90 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_fr.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL pipeline for French - Large +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-27 +tags: [open_source, french, entity_recognizer_lg, pipeline, fr] +task: Named Entity Recognition +language: fr +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fr_4.4.2_3.2_1685184723192.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_fr_4.4.2_3.2_1685184723192.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:--------------------------------|:-------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Bonjour de John Snow Labs! '] | ['Bonjour de John Snow Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | [[-0.010997000150382,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fr| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_it.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_it.md new file mode 100644 index 00000000000000..0548107eb0e3e5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_it.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL pipeline for Italian - Large +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-27 +tags: [open_source, italian, entity_recognizer_lg, pipeline, it] +task: Named Entity Recognition +language: it +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_it_4.4.2_3.2_1685185326631.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_it_4.4.2_3.2_1685185326631.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Ciao da John Snow Labs! '] | ['Ciao da John Snow Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | [[-0.238279998302459,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|it| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_nl.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_nl.md new file mode 100644 index 00000000000000..621011ca394ae5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_nl.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-27 +tags: [nl, open_source] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_nl_4.4.2_3.2_1685195531873.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_nl_4.4.2_3.2_1685195531873.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "nl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("nl.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "nl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("nl.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_no.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_no.md new file mode 100644 index 00000000000000..d40cd9f578794e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_no.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-27 +tags: ["no", open_source] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_no_4.4.2_3.2_1685197211034.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_no_4.4.2_3.2_1685197211034.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "no") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("no.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "no") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("no.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_pl.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_pl.md new file mode 100644 index 00000000000000..f0043de69ccb63 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_pl.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-27 +tags: [pl, open_source] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pl_4.4.2_3.2_1685194035213.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pl_4.4.2_3.2_1685194035213.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("pl.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pl") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pl.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_pt.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_pt.md new file mode 100644 index 00000000000000..9a2d737cb0e557 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_pt.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-27 +tags: [pt, open_source] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pt_4.4.2_3.2_1685192565628.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_pt_4.4.2_3.2_1685192565628.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pt") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "pt") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_ru.md new file mode 100644 index 00000000000000..7822530b0cbb2f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_ru.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-27 +tags: [ru, open_source] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_ru_4.4.2_3.2_1685191038635.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_ru_4.4.2_3.2_1685191038635.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "ru") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("ru.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "ru") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ru.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_sv.md new file mode 100644 index 00000000000000..c2f3168eccac11 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_sv.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Entity Recognizer LG +author: John Snow Labs +name: entity_recognizer_lg +date: 2023-05-27 +tags: [sv, open_source] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_sv_4.4.2_3.2_1685198741244.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_lg_sv_4.4.2_3.2_1685198741244.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "sv") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("sv.ner.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("entity_recognizer_lg", "sv") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("sv.ner.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_da.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_da.md new file mode 100644 index 00000000000000..28dffc056a2ee6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_da.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Danish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-27 +tags: [open_source, danish, entity_recognizer_md, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_da_4.4.2_3.2_1685200396175.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_da_4.4.2_3.2_1685200396175.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|461.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_de.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_de.md new file mode 100644 index 00000000000000..8e0f9e44a29a12 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_de.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for German - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-27 +tags: [open_source, german, entity_recognizer_md, pipeline, de] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_de_4.4.2_3.2_1685187740103.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_de_4.4.2_3.2_1685187740103.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.ner.recognizer').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo aus John Snow Labs! '] | ['Hallo aus John Snow Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|461.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_es.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_es.md new file mode 100644 index 00000000000000..bd33f278f598ac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_es.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Spanish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-27 +tags: [open_source, spanish, entity_recognizer_md, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_es_4.4.2_3.2_1685189790233.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_es_4.4.2_3.2_1685189790233.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | [[0.5123000144958496,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|461.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_fi.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_fi.md new file mode 100644 index 00000000000000..3f58127152c001 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_fi.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Finnish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-27 +tags: [open_source, finnish, entity_recognizer_md, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fi_4.4.2_3.2_1685201878265.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fi_4.4.2_3.2_1685201878265.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | [[0.1868100017309188,.,...]] | ['O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|462.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_fr.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_fr.md new file mode 100644 index 00000000000000..d1690b75b4d2da --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_fr.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for French - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-27 +tags: [open_source, french, entity_recognizer_md, pipeline, fr] +task: Named Entity Recognition +language: fr +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fr_4.4.2_3.2_1685184927773.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_fr_4.4.2_3.2_1685184927773.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:--------------------------------|:-------------------------------|:-------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------------------| +| 0 | ['Bonjour de John Snow Labs! '] | ['Bonjour de John Snow Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | [[0.0783179998397827,.,...]] | ['I-MISC', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['Bonjour', 'John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fr| +|Size:|462.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_it.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_it.md new file mode 100644 index 00000000000000..7d65ceb7b19599 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_it.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Italian - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-27 +tags: [open_source, italian, entity_recognizer_md, pipeline, it] +task: Named Entity Recognition +language: it +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_it_4.4.2_3.2_1685185537977.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_it_4.4.2_3.2_1685185537977.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Ciao da John Snow Labs! '] | ['Ciao da John Snow Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | [[-0.146050006151199,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|it| +|Size:|462.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_nl.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_nl.md new file mode 100644 index 00000000000000..ac56a48517233f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_nl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Dutch - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-27 +tags: [open_source, dutch, entity_recognizer_md, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_nl_4.4.2_3.2_1685195743604.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_nl_4.4.2_3.2_1685195743604.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|462.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_no.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_no.md new file mode 100644 index 00000000000000..ff202edb8a36ad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_no.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Norwegian (Bokmal) - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-27 +tags: [open_source, norwegian_bokmal, entity_recognizer_md, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_no_4.4.2_3.2_1685197418614.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_no_4.4.2_3.2_1685197418614.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | [[0.1868100017309188,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|462.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_pl.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_pl.md new file mode 100644 index 00000000000000..d17f30f01f606d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_pl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Polish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-27 +tags: [open_source, polish, entity_recognizer_md, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pl_4.4.2_3.2_1685194242626.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pl_4.4.2_3.2_1685194242626.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|462.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_pt.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_pt.md new file mode 100644 index 00000000000000..d2d11fbe4fbbcf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_pt.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Portuguese - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-27 +tags: [open_source, portuguese, entity_recognizer_md, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pt_4.4.2_3.2_1685192770168.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_pt_4.4.2_3.2_1685192770168.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|462.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_ru.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_ru.md new file mode 100644 index 00000000000000..d3430b136bf947 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_ru.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Russian - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-27 +tags: [open_source, russian, entity_recognizer_md, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_ru_4.4.2_3.2_1685191242357.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_ru_4.4.2_3.2_1685191242357.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-LOC', 'I-LOC', 'I-LOC'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|462.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_sv.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_sv.md new file mode 100644 index 00000000000000..9d062a431575dd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_sv.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Swedish - Medium +author: John Snow Labs +name: entity_recognizer_md +date: 2023-05-27 +tags: [open_source, swedish, entity_recognizer_md, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_sv_4.4.2_3.2_1685198939555.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_md_sv_4.4.2_3.2_1685198939555.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|461.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_da.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_da.md new file mode 100644 index 00000000000000..139ac16d41ff8a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_da.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Danish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-27 +tags: [open_source, danish, entity_recognizer_sm, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_da_4.4.2_3.2_1685200493982.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_da_4.4.2_3.2_1685200493982.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_es.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_es.md new file mode 100644 index 00000000000000..93b1542ce8ee38 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_es.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Spanish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-27 +tags: [open_source, spanish, entity_recognizer_sm, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_es_4.4.2_3.2_1685189886905.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_es_4.4.2_3.2_1685189886905.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:---------------------------------------|:-----------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | [[0.1754499971866607,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'B-MISC'] | ['John Snow', 'Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_fi.md new file mode 100644 index 00000000000000..9c21b117ae969f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_fi.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Finnish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-27 +tags: [open_source, finnish, entity_recognizer_sm, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_fi_4.4.2_3.2_1685201974154.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_fi_4.4.2_3.2_1685201974154.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | [[-0.394499987363815,.,...]] | ['O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|166.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_nl.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_nl.md new file mode 100644 index 00000000000000..aa1c1cde301bf9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_nl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Dutch - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-27 +tags: [open_source, dutch, entity_recognizer_sm, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_nl_4.4.2_3.2_1685195847681.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_nl_4.4.2_3.2_1685195847681.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | [[0.3653799891471863,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|166.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_no.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_no.md new file mode 100644 index 00000000000000..51924c114e80a3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_no.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Norwegian (Bokmal) - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-27 +tags: [open_source, norwegian_bokmal, entity_recognizer_sm, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_no_4.4.2_3.2_1685197515755.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_no_4.4.2_3.2_1685197515755.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | [[-0.394499987363815,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|167.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_pl.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_pl.md new file mode 100644 index 00000000000000..097e40c8af4d75 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_pl.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Polish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-27 +tags: [open_source, polish, entity_recognizer_sm, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pl_4.4.2_3.2_1685194343314.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pl_4.4.2_3.2_1685194343314.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|167.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_pt.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_pt.md new file mode 100644 index 00000000000000..bcb3709145f500 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_pt.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Portuguese - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-27 +tags: [open_source, portuguese, entity_recognizer_sm, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pt_4.4.2_3.2_1685192866707.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_pt_4.4.2_3.2_1685192866707.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|167.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_ru.md new file mode 100644 index 00000000000000..287938422ba350 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_ru.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Russian - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-27 +tags: [open_source, russian, entity_recognizer_sm, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_ru_4.4.2_3.2_1685191339166.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_ru_4.4.2_3.2_1685191339166.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|167.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_sv.md new file mode 100644 index 00000000000000..2541ef1cbada3b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_sv.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for Swedish - Small +author: John Snow Labs +name: entity_recognizer_sm +date: 2023-05-27 +tags: [open_source, swedish, entity_recognizer_sm, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_sv_4.4.2_3.2_1685199035633.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/entity_recognizer_sm_sv_4.4.2_3.2_1685199035633.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|entity_recognizer_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_dl_en.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_dl_en.md new file mode 100644 index 00000000000000..84215d71bd76ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_dl_en.md @@ -0,0 +1,128 @@ +--- +layout: model +title: Explain Document DL Pipeline for English +author: John Snow Labs +name: explain_document_dl +date: 2023-05-27 +tags: [open_source, english, explain_document_dl, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_dl is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_dl_en_4.4.2_3.2_1685186531034.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_dl_en_4.4.2_3.2_1685186531034.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_dl', lang = 'en') +annotations = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_dl", lang = "en") +val result = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = ["The Mona Lisa is an oil painting from the 16th century."] +result_df = nlu.load('en.explain.dl').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_dl', lang = 'en') +annotations = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_dl", lang = "en") +val result = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")(0) +``` + +{:.nlu-block} +```python +import nlu +text = ["The Mona Lisa is an oil painting from the 16th century."] +result_df = nlu.load('en.explain.dl').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + ++--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------+-----------+ +| text| document| sentence| token| checked| lemma| stem| pos| embeddings| ner| entities| ++--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------+-----------+ +|The Mona Lisa is an oil painting from the 16th ...|[The Mona Lisa is an oil painting from the 16th...|[The Mona Lisa is an oil painting from the 16th...|[The, Mona, Lisa, is, an, oil, painting, from, ...|[The, Mona, Lisa, is, an, oil, painting, from, ...|[The, Mona, Lisa, be, an, oil, painting, from, ...|[the, mona, lisa, i, an, oil, paint, from, the,...|[DT, NNP, NNP, VBZ, DT, NN, NN, IN, DT, JJ, NN, .]|[[-0.038194, -0.24487, 0.72812, -0.39961, 0.083...|[O, B-PER, I-PER, O, O, O, O, O, O, O, O, O]|[Mona Lisa]| ++--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------+-----------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_dl| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|176.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel +- LemmatizerModel +- Stemmer +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_da.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_da.md new file mode 100644 index 00000000000000..17117261cdc03a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_da.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Danish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-27 +tags: [open_source, danish, explain_document_lg, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_da_4.4.2_3.2_1685199444127.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_da_4.4.2_3.2_1685199444127.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.025171000510454,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_es.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_es.md new file mode 100644 index 00000000000000..d5356eb6525bed --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_es.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Spanish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-27 +tags: [open_source, spanish, explain_document_lg, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_es_4.4.2_3.2_1685188788527.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_es_4.4.2_3.2_1685188788527.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['PART', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.016199000179767,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_fi.md new file mode 100644 index 00000000000000..36c29d4e22735f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_fi.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Finnish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-27 +tags: [open_source, finnish, explain_document_lg, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_fi_4.4.2_3.2_1685200919173.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_fi_4.4.2_3.2_1685200919173.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:---------------------------------|:------------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | ['hei', 'John', 'Snow', 'Labs!'] | ['INTJ', 'PROPN', 'PROPN', 'PROPN'] | [[0.0639619976282119,.,...]] | ['O', 'B-PRO', 'I-PRO', 'I-PRO'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_nl.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_nl.md new file mode 100644 index 00000000000000..0ec98bf9c38094 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_nl.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Dutch (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-27 +tags: [open_source, dutch, explain_document_lg, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_nl_4.4.2_3.2_1685194759905.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_nl_4.4.2_3.2_1685194759905.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:-----------------------------|:------------------------------------------|:-----------------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.245989993214607,.,...]] | ['B-PER', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Hallo', 'John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_no.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_no.md new file mode 100644 index 00000000000000..e1d8b9650bb7c2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_no.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Norwegian (Bokmal) (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-27 +tags: [open_source, norwegian_bokmal, explain_document_lg, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_no_4.4.2_3.2_1685196471764.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_no_4.4.2_3.2_1685196471764.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:--------------------------------------------|:-----------------------------|:---------------------------------------|:-----------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0639619976282119,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'B-PROD'] | ['John Snow', 'Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_pl.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_pl.md new file mode 100644 index 00000000000000..a03b1e4df2170e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_pl.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Polish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-27 +tags: [open_source, polish, explain_document_lg, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_pl_4.4.2_3.2_1685193283934.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_pl_4.4.2_3.2_1685193283934.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | ['witać', 'z', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4977500140666961,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_pt.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_pt.md new file mode 100644 index 00000000000000..e20dd659bc91ec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_pt.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Portuguese (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-27 +tags: [open_source, portuguese, explain_document_lg, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps + and recognizes entities . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_pt_4.4.2_3.2_1685191816544.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_pt_4.4.2_3.2_1685191816544.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:---------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4388400018215179,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_ru.md new file mode 100644 index 00000000000000..af9a8fcbf5b825 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_ru.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Russian (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-27 +tags: [open_source, russian, explain_document_lg, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_ru_4.4.2_3.2_1685190296688.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_ru_4.4.2_3.2_1685190296688.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | ['здравствовать', 'из', 'Джон', 'Снежных', 'Лабораторий!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_sv.md new file mode 100644 index 00000000000000..deb88f991be635 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_sv.md @@ -0,0 +1,124 @@ +--- +layout: model +title: Explain Document pipeline for Swedish (explain_document_lg) +author: John Snow Labs +name: explain_document_lg +date: 2023-05-27 +tags: [open_source, swedish, explain_document_lg, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_lg_sv_4.4.2_3.2_1685197984968.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_lg_sv_4.4.2_3.2_1685197984968.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.lg').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_lg', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_lg", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.lg').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_da.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_da.md new file mode 100644 index 00000000000000..ad7874fdb22dd4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_da.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Danish +author: John Snow Labs +name: explain_document_md +date: 2023-05-27 +tags: [open_source, danish, explain_document_md, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_da_4.4.2_3.2_1685199663898.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_da_4.4.2_3.2_1685199663898.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|463.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_de.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_de.md new file mode 100644 index 00000000000000..1c44491294c471 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_de.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for German +author: John Snow Labs +name: explain_document_md +date: 2023-05-27 +tags: [open_source, german, explain_document_md, pipeline, de] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_de_4.4.2_3.2_1685187123932.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_de_4.4.2_3.2_1685187123932.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.explain.document').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'de') +annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "de") +val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo aus John Snow Labs! ""] +result_df = nlu.load('de.explain.document').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo aus John Snow Labs! '] | ['Hallo aus John Snow Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | ['Hallo', 'aus', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|470.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_es.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_es.md new file mode 100644 index 00000000000000..7a9a3bf643f297 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_es.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Spanish +author: John Snow Labs +name: explain_document_md +date: 2023-05-27 +tags: [open_source, spanish, explain_document_md, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_es_4.4.2_3.2_1685189038982.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_es_4.4.2_3.2_1685189038982.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['PART', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.5123000144958496,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|472.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_fi.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_fi.md new file mode 100644 index 00000000000000..653cb4d41f77a0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_fi.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Finnish +author: John Snow Labs +name: explain_document_md +date: 2023-05-27 +tags: [open_source, finnish, explain_document_md, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_fi_4.4.2_3.2_1685201138478.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_fi_4.4.2_3.2_1685201138478.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:---------------------------------|:------------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | ['hei', 'John', 'Snow', 'Labs!'] | ['INTJ', 'PROPN', 'PROPN', 'PROPN'] | [[0.1868100017309188,.,...]] | ['O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|467.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_fr.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_fr.md new file mode 100644 index 00000000000000..9548b616c40105 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_fr.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for French +author: John Snow Labs +name: explain_document_md +date: 2023-05-27 +tags: [open_source, french, explain_document_md, pipeline, fr] +task: Named Entity Recognition +language: fr +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_fr_4.4.2_3.2_1685183935667.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_fr_4.4.2_3.2_1685183935667.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'fr') +annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "fr") +val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Bonjour de John Snow Labs! ""] +result_df = nlu.load('fr.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:--------------------------------|:-------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------------------| +| 0 | ['Bonjour de John Snow Labs! '] | ['Bonjour de John Snow Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | ['Bonjour', 'de', 'John', 'Snow', 'Labs!'] | ['INTJ', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0783179998397827,.,...]] | ['I-MISC', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['Bonjour', 'John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fr| +|Size:|467.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_it.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_it.md new file mode 100644 index 00000000000000..7427d6380ffcf0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_it.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Italian +author: John Snow Labs +name: explain_document_md +date: 2023-05-27 +tags: [open_source, italian, explain_document_md, pipeline, it] +task: Named Entity Recognition +language: it +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_it_4.4.2_3.2_1685184327091.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_it_4.4.2_3.2_1685184327091.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.explain.document').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'it') +annotations = pipeline.fullAnnotate(""Ciao da John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "it") +val result = pipeline.fullAnnotate("Ciao da John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Ciao da John Snow Labs! ""] +result_df = nlu.load('it.explain.document').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Ciao da John Snow Labs! '] | ['Ciao da John Snow Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | ['Ciao', 'da', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.146050006151199,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|it| +|Size:|468.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_nl.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_nl.md new file mode 100644 index 00000000000000..32938224b0e131 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_nl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Dutch +author: John Snow Labs +name: explain_document_md +date: 2023-05-27 +tags: [open_source, dutch, explain_document_md, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_nl_4.4.2_3.2_1685194989889.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_nl_4.4.2_3.2_1685194989889.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.5910000205039978,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|464.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_no.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_no.md new file mode 100644 index 00000000000000..b76bcabec18089 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_no.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Norwegian (Bokmal) +author: John Snow Labs +name: explain_document_md +date: 2023-05-27 +tags: [open_source, norwegian_bokmal, explain_document_md, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_no_4.4.2_3.2_1685196700058.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_no_4.4.2_3.2_1685196700058.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.1868100017309188,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|465.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_pl.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_pl.md new file mode 100644 index 00000000000000..bd4b08e1b7ad7b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_pl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Polish +author: John Snow Labs +name: explain_document_md +date: 2023-05-27 +tags: [open_source, polish, explain_document_md, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_pl_4.4.2_3.2_1685193513011.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_pl_4.4.2_3.2_1685193513011.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | ['witać', 'z', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|464.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_pt.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_pt.md new file mode 100644 index 00000000000000..6d16340dbe8eb7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_pt.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Portuguese +author: John Snow Labs +name: explain_document_md +date: 2023-05-27 +tags: [open_source, portuguese, explain_document_md, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_pt_4.4.2_3.2_1685192040133.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_pt_4.4.2_3.2_1685192040133.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:---------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|473.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_ru.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_ru.md new file mode 100644 index 00000000000000..1282459dd46571 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_ru.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Russian +author: John Snow Labs +name: explain_document_md +date: 2023-05-27 +tags: [open_source, russian, explain_document_md, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_ru_4.4.2_3.2_1685190521794.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_ru_4.4.2_3.2_1685190521794.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | ['здравствовать', 'из', 'Джон', 'Снежных', 'Лабораторий!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-LOC', 'I-LOC', 'I-LOC'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|465.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_sv.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_sv.md new file mode 100644 index 00000000000000..6609ffedcfe7d7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_sv.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Swedish +author: John Snow Labs +name: explain_document_md +date: 2023-05-27 +tags: [open_source, swedish, explain_document_md, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_md is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_md_sv_4.4.2_3.2_1685198221561.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_md_sv_4.4.2_3.2_1685198221561.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.md').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_md', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_md", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain.md').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.4006600081920624,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_md| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|471.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_ml_en.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_ml_en.md new file mode 100644 index 00000000000000..1f90a751788864 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_ml_en.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document ML Pipeline for English +author: John Snow Labs +name: explain_document_ml +date: 2023-05-27 +tags: [open_source, english, explain_document_ml, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_ml is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_ml_en_4.4.2_3.2_1685186336939.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_ml_en_4.4.2_3.2_1685186336939.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_ml', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala + + +val pipeline = new PretrainedPipeline("explain_document_ml", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python + + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.explain').predict(text) +result_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('explain_document_ml', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_ml", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + + +| | document | sentence | token | spell | lemmas | stems | pos | +|---:|:---------------------------------|:---------------------------------|:-------------------------------------------------|:------------------------------------------------|:------------------------------------------------|:-----------------------------------------------|:---------------------------------------| +| 0 | ['Hello fronm John Snwow Labs!'] | ['Hello fronm John Snwow Labs!'] | ['Hello', 'fronm', 'John', 'Snwow', 'Labs', '!'] | ['Hello', 'front', 'John', 'Snow', 'Labs', '!'] | ['Hello', 'front', 'John', 'Snow', 'Labs', '!'] | ['hello', 'front', 'john', 'snow', 'lab', '!'] | ['UH', 'NN', 'NNP', 'NNP', 'NNP', '.'] || | document | sentence | token | spell | lemmas | stems | pos | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_ml| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|9.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- NorvigSweetingModel +- LemmatizerModel +- Stemmer +- PerceptronModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_da.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_da.md new file mode 100644 index 00000000000000..6a2168b7edb2fe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_da.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Danish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-27 +tags: [open_source, danish, explain_document_sm, pipeline, da] +task: Named Entity Recognition +language: da +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_da_4.4.2_3.2_1685199771386.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_da_4.4.2_3.2_1685199771386.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'da') +annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "da") +val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej fra John Snow Labs! ""] +result_df = nlu.load('da.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej fra John Snow Labs! '] | ['Hej fra John Snow Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['Hej', 'fra', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|168.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_es.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_es.md new file mode 100644 index 00000000000000..88f723a3345c1f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_es.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Spanish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-27 +tags: [open_source, spanish, explain_document_sm, pipeline, es] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_es_4.4.2_3.2_1685189161597.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_es_4.4.2_3.2_1685189161597.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'es') +annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "es") +val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hola de John Snow Labs! ""] +result_df = nlu.load('es.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:---------------------------------------|:-----------------------| +| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | ['PART', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.1754499971866607,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'B-MISC'] | ['John Snow', 'Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|177.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_fi.md new file mode 100644 index 00000000000000..ba3d50e3fec81d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_fi.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Finnish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-27 +tags: [open_source, finnish, explain_document_sm, pipeline, fi] +task: Named Entity Recognition +language: fi +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_fi_4.4.2_3.2_1685201249805.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_fi_4.4.2_3.2_1685201249805.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'fi') +annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "fi") +val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei John Snow Labs! ""] +result_df = nlu.load('fi.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------|:------------------------|:---------------------------------|:---------------------------------|:------------------------------------|:-----------------------------|:---------------------------------|:--------------------| +| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | ['hei', 'John', 'Snow', 'Labs!'] | ['INTJ', 'PROPN', 'PROPN', 'PROPN'] | [[-0.394499987363815,.,...]] | ['O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|172.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_nl.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_nl.md new file mode 100644 index 00000000000000..1d54d321c856db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_nl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Dutch +author: John Snow Labs +name: explain_document_sm +date: 2023-05-27 +tags: [open_source, dutch, explain_document_sm, pipeline, nl] +task: Named Entity Recognition +language: nl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_nl_4.4.2_3.2_1685195096823.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_nl_4.4.2_3.2_1685195096823.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'nl') +annotations = pipeline.fullAnnotate(""Hallo van John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "nl") +val result = pipeline.fullAnnotate("Hallo van John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hallo van John Snow Labs! ""] +result_df = nlu.load('nl.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-------------------------------|:------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hallo van John Snow Labs! '] | ['Hallo van John Snow Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['Hallo', 'van', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.3653799891471863,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|169.8 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_no.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_no.md new file mode 100644 index 00000000000000..6055beeb1282d2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_no.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Norwegian (Bokmal) +author: John Snow Labs +name: explain_document_sm +date: 2023-05-27 +tags: [open_source, norwegian_bokmal, explain_document_sm, pipeline, "no"] +task: Named Entity Recognition +language: "no" +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_no_4.4.2_3.2_1685196805913.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_no_4.4.2_3.2_1685196805913.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'no') +annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "no") +val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hei fra John Snow Labs! ""] +result_df = nlu.load('no.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hei fra John Snow Labs! '] | ['Hei fra John Snow Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['Hei', 'fra', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[-0.394499987363815,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|170.2 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_pl.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_pl.md new file mode 100644 index 00000000000000..486f94c3c6d8db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_pl.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Polish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-27 +tags: [open_source, polish, explain_document_sm, pipeline, pl] +task: Named Entity Recognition +language: pl +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_pl_4.4.2_3.2_1685193623828.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_pl_4.4.2_3.2_1685193623828.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pl') +annotations = pipeline.fullAnnotate(""Witaj z John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pl") +val result = pipeline.fullAnnotate("Witaj z John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Witaj z John Snow Labs! ""] +result_df = nlu.load('pl.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:-----------------------------|:----------------------------|:----------------------------------------|:----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Witaj z John Snow Labs! '] | ['Witaj z John Snow Labs!'] | ['Witaj', 'z', 'John', 'Snow', 'Labs!'] | ['witać', 'z', 'John', 'Snow', 'Labs!'] | ['VERB', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|169.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_pt.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_pt.md new file mode 100644 index 00000000000000..555ba1926ca9d2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_pt.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Portuguese +author: John Snow Labs +name: explain_document_sm +date: 2023-05-27 +tags: [open_source, portuguese, explain_document_sm, pipeline, pt] +task: Named Entity Recognition +language: pt +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_pt_4.4.2_3.2_1685192164562.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_pt_4.4.2_3.2_1685192164562.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'pt') +annotations = pipeline.fullAnnotate(""Olá de John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "pt") +val result = pipeline.fullAnnotate("Olá de John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Olá de John Snow Labs! ""] +result_df = nlu.load('pt.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:----------------------------|:---------------------------|:---------------------------------------|:---------------------------------------|:--------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Olá de John Snow Labs! '] | ['Olá de John Snow Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['Olá', 'de', 'John', 'Snow', 'Labs!'] | ['PROPN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|pt| +|Size:|178.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_ru.md new file mode 100644 index 00000000000000..687caeeaf5254e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_ru.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Russian +author: John Snow Labs +name: explain_document_sm +date: 2023-05-27 +tags: [open_source, russian, explain_document_sm, pipeline, ru] +task: Named Entity Recognition +language: ru +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_ru_4.4.2_3.2_1685190630368.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_ru_4.4.2_3.2_1685190630368.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'ru') +annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "ru") +val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Здравствуйте из Джона Снежных Лабораторий! ""] +result_df = nlu.load('ru.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:-------------------------------| +| 0 | ['Здравствуйте из Джона Снежных Лабораторий! '] | ['Здравствуйте из Джона Снежных Лабораторий!'] | ['Здравствуйте', 'из', 'Джона', 'Снежных', 'Лабораторий!'] | ['здравствовать', 'из', 'Джон', 'Снежных', 'Лабораторий!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0, 0.0, 0.0, 0.0,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['Джона Снежных Лабораторий!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|ru| +|Size:|170.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_sv.md new file mode 100644 index 00000000000000..a4f21b19d3ecc0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_sv.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Explain Document Pipeline for Swedish +author: John Snow Labs +name: explain_document_sm +date: 2023-05-27 +tags: [open_source, swedish, explain_document_sm, pipeline, sv] +task: Named Entity Recognition +language: sv +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/explain_document_sm_sv_4.4.2_3.2_1685198339255.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_sv_4.4.2_3.2_1685198339255.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('explain_document_sm', lang = 'sv') +annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("explain_document_sm", lang = "sv") +val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hej från John Snow Labs! ""] +result_df = nlu.load('sv.explain').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------| +| 0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|explain_document_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|176.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-longformer_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-longformer_base_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..de9bbef58143e4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-longformer_base_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Longformer Base NER Pipeline +author: John Snow Labs +name: longformer_base_token_classifier_conll03_pipeline +date: 2023-05-27 +tags: [ner, longformer, pipeline, conll, token_classification, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [longformer_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/10/09/longformer_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/longformer_base_token_classifier_conll03_pipeline_en_4.4.2_3.2_1685208974361.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/longformer_base_token_classifier_conll03_pipeline_en_4.4.2_3.2_1685208974361.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|longformer_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|516.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- LongformerForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-longformer_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-longformer_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..15162e39f24930 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-longformer_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Longformer Large NER Pipeline +author: John Snow Labs +name: longformer_large_token_classifier_conll03_pipeline +date: 2023-05-27 +tags: [open_source, ner, token_classifier, longformer, conll, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [longformer_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/10/09/longformer_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/longformer_large_token_classifier_conll03_pipeline_en_4.4.2_3.2_1685209371545.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/longformer_large_token_classifier_conll03_pipeline_en_4.4.2_3.2_1685209371545.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I am working at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|longformer_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- LongformerForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-match_chunks_en.md b/docs/_posts/ahmedlone127/2023-05-27-match_chunks_en.md new file mode 100644 index 00000000000000..9c488db1353a41 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-match_chunks_en.md @@ -0,0 +1,141 @@ +--- +layout: model +title: Match Chunks in Texts +author: John Snow Labs +name: match_chunks +date: 2023-05-27 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The pipeline uses regex `
?/*+` + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_chunks_en_4.4.2_3.2_1685188385436.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_chunks_en_4.4.2_3.2_1685188385436.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline + +pipeline_local = PretrainedPipeline('match_chunks') + +result = pipeline_local.annotate("David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.") + +result['chunk'] +``` +```scala + +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_chunks", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.match.chunks").predict("""David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.""") +``` + +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline_local = PretrainedPipeline('match_chunks') + +result = pipeline_local.annotate("David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.") + +result['chunk'] +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_chunks", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.match.chunks").predict("""David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.""") +``` +
+ +## Results + +```bash +Results + + + +['the restaurant yesterday', +'family', +'the day', +'that time', +'today', +'the food', +'tomorrow'] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|match_chunks| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|4.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel +- Chunker \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-match_datetime_en.md b/docs/_posts/ahmedlone127/2023-05-27-match_datetime_en.md new file mode 100644 index 00000000000000..1cc522ddabbc6e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-match_datetime_en.md @@ -0,0 +1,127 @@ +--- +layout: model +title: Match Datetime in Texts +author: John Snow Labs +name: match_datetime +date: 2023-05-27 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +DateMatcher based on yyyy/MM/dd + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_datetime_en_4.4.2_3.2_1685185649983.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_datetime_en_4.4.2_3.2_1685185649983.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline_local = PretrainedPipeline('match_datetime') + +tres = pipeline_local.fullAnnotate(input_list)[0] +for dte in tres['date']: + sent = tres['sentence'][int(dte.metadata['sentence'])] + print (f'text/chunk {sent.result[dte.begin:dte.end+1]} | mapped_date: {dte.result}') +``` +```scala + +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. +He also visited and the day before, but at that time he was alone. +David again visited today with his colleagues. +He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_datetime", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline_local = PretrainedPipeline('match_datetime') + +tres = pipeline_local.fullAnnotate(input_list)[0] +for dte in tres['date']: + sent = tres['sentence'][int(dte.metadata['sentence'])] + print (f'text/chunk {sent.result[dte.begin:dte.end+1]} | mapped_date: {dte.result}') +``` +```scala +import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP + +SparkNLP.version() + +val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. +He also visited and the day before, but at that time he was alone. +David again visited today with his colleagues. +He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") + +val pipeline = PretrainedPipeline("match_datetime", lang="en") + +val annotation = pipeline.transform(testData) + +annotation.show() +``` +
+ +## Results + +```bash +Results + + + +text/chunk yesterday | mapped_date: 2022/01/02 +text/chunk day before | mapped_date: 2022/01/02 +text/chunk today | mapped_date: 2022/01/03 +text/chunk tomorrow | mapped_date: 2022/01/04 + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|match_datetime| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|12.9 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- MultiDateMatcher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-match_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-27-match_pattern_en.md new file mode 100644 index 00000000000000..c2af72d8b6e50e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-match_pattern_en.md @@ -0,0 +1,77 @@ +--- +layout: model +title: Match Pattern +author: John Snow Labs +name: match_pattern +date: 2023-05-27 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The match_pattern is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and matches pattrens . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/match_pattern_en_4.4.2_3.2_1685185696920.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_pattern_en_4.4.2_3.2_1685185696920.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models") + result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models") + result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|match_pattern| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|17.4 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- RegexMatcherModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-movies_sentiment_analysis_en.md b/docs/_posts/ahmedlone127/2023-05-27-movies_sentiment_analysis_en.md new file mode 100644 index 00000000000000..cfb98943b61a57 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-movies_sentiment_analysis_en.md @@ -0,0 +1,79 @@ +--- +layout: model +title: Movies Sentiment Analysis +author: John Snow Labs +name: movies_sentiment_analysis +date: 2023-05-27 +tags: [en, open_source] +task: Sentiment Analysis +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The movies_sentiment_analysis is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and predicts sentiment . + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/movies_sentiment_analysis_en_4.4.2_3.2_1685186260546.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/movies_sentiment_analysis_en_4.4.2_3.2_1685186260546.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("movies_sentiment_analysis", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("movies_sentiment_analysis", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|movies_sentiment_analysis| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|207.1 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- SymmetricDeleteModel +- SentimentDetectorModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-nerdl_fewnerd_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-nerdl_fewnerd_100d_pipeline_en.md new file mode 100644 index 00000000000000..56e37a064f9508 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-nerdl_fewnerd_100d_pipeline_en.md @@ -0,0 +1,109 @@ +--- +layout: model +title: Pretrained Pipeline for Few-NERD-General NER Model +author: John Snow Labs +name: nerdl_fewnerd_100d_pipeline +date: 2023-05-27 +tags: [fewnerd, nerdl, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on Few-NERD model and it detects : + +`PERSON`, `ORGANIZATION`, `LOCATION`, `ART`, `BUILDING`, `PRODUCT`, `EVENT`, `OTHER` + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_100d_pipeline_en_4.4.2_3.2_1685205191285.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_100d_pipeline_en_4.4.2_3.2_1685205191285.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).""") +``` +```scala + +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).")(0) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).""") +``` +```scala +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).")(0) +``` +
+ +## Results + +```bash +Results + + + ++-----------------------+------------+ +|chunk |ner_label | ++-----------------------+------------+ +|Kentucky Fried Chicken |ORGANIZATION| +|Anglo-Egyptian War |EVENT | +|battle of Tell El Kebir|EVENT | +|Egypt Medal |OTHER | +|Order of Medjidie |OTHER | ++-----------------------+------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nerdl_fewnerd_100d_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|167.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-nerdl_fewnerd_subentity_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-nerdl_fewnerd_subentity_100d_pipeline_en.md new file mode 100644 index 00000000000000..71dbb3a9287c50 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-nerdl_fewnerd_subentity_100d_pipeline_en.md @@ -0,0 +1,110 @@ +--- +layout: model +title: Pre-trained Pipeline for Few-NERD NER Model +author: John Snow Labs +name: nerdl_fewnerd_subentity_100d_pipeline +date: 2023-05-27 +tags: [fewnerd, ner, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on Few-NERD/inter public dataset and it extracts 66 entities that are in general scope. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_subentity_100d_pipeline_en_4.4.2_3.2_1685204361691.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nerdl_fewnerd_subentity_100d_pipeline_en_4.4.2_3.2_1685204361691.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.""") +``` +```scala + +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.")(0) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +fewnerd_pipeline.annotate("""12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.""") +``` +```scala +val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") + +val result = pipeline.fullAnnotate("12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.")(0) +``` +
+ +## Results + +```bash +Results + + + ++-----------------------+----------------------------+ +|chunk |ner_label | ++-----------------------+----------------------------+ +|Corazones ('12 Hearts')|art-broadcastprogram | +|Spanish-language |other-language | +|United States |location-GPE | +|Telemundo |organization-media/newspaper| +|Argentine TV |organization-media/newspaper| +|Los Angeles |location-GPE | +|Steven Spielberg |person-director | +|Cloverfield Paradox |art-film | ++-----------------------+----------------------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nerdl_fewnerd_subentity_100d_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|167.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-nerdl_restaurant_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-nerdl_restaurant_100d_pipeline_en.md new file mode 100644 index 00000000000000..5c3d1ce0babc1b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-nerdl_restaurant_100d_pipeline_en.md @@ -0,0 +1,119 @@ +--- +layout: model +title: Pipeline to Detect Restaurant-related Terminology +author: John Snow Labs +name: nerdl_restaurant_100d_pipeline +date: 2023-05-27 +tags: [restaurant, ner, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [nerdl_restaurant_100d](https://nlp.johnsnowlabs.com/2021/12/31/nerdl_restaurant_100d_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nerdl_restaurant_100d_pipeline_en_4.4.2_3.2_1685206736394.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nerdl_restaurant_100d_pipeline_en_4.4.2_3.2_1685206736394.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +restaurant_pipeline = PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +```scala + +val restaurant_pipeline = new PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +restaurant_pipeline = PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +```scala +val restaurant_pipeline = new PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") + +restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") +``` +
+ +## Results + +```bash +Results + + + ++---------------------------+---------------+ +|chunk |ner_label | ++---------------------------+---------------+ +|Hong Kong’s |Restaurant_Name| +|favourite |Rating | +|pasta bar |Dish | +|most reasonably |Price | +|lunch |Hours | +|in town! |Location | +|Sha Tin – Pici’s |Restaurant_Name| +|burrata |Dish | +|arugula salad |Dish | +|freshly tossed tuna tartare|Dish | +|reliable |Price | +|handmade pasta |Dish | +|pappardelle |Dish | +|effortless |Amenity | +|Italian |Cuisine | +|tidy one-pot |Amenity | +|espresso |Dish | ++---------------------------+---------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nerdl_restaurant_100d_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_base_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_base_en.md new file mode 100644 index 00000000000000..832d938cfe2787 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_base_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Base +author: John Snow Labs +name: onto_recognize_entities_bert_base +date: 2023-05-27 +tags: [open_source, english, onto_recognize_entities_bert_base, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_base is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_base_en_4.4.2_3.2_1685202102790.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_base_en_4.4.2_3.2_1685202102790.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_base', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_base", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.base').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_base', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_base", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.base').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.085488274693489,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_base| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|424.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_large_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_large_en.md new file mode 100644 index 00000000000000..b8df4408ff18b1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_large_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Large +author: John Snow Labs +name: onto_recognize_entities_bert_large +date: 2023-05-27 +tags: [open_source, english, onto_recognize_entities_bert_large, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_large is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_large_en_4.4.2_3.2_1685202359280.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_large_en_4.4.2_3.2_1685202359280.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_large', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_large", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.large').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_large', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_large", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.large').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.262016534805297,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_large| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.3 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_medium_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_medium_en.md new file mode 100644 index 00000000000000..e5666e98d41e01 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_medium_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Medium +author: John Snow Labs +name: onto_recognize_entities_bert_medium +date: 2023-05-27 +tags: [open_source, english, onto_recognize_entities_bert_medium, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_medium is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_medium_en_4.4.2_3.2_1685202490608.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_medium_en_4.4.2_3.2_1685202490608.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_medium', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_medium", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.medium').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_medium', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_medium", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.medium').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.0365490540862083,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_medium| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|172.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_mini_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_mini_en.md new file mode 100644 index 00000000000000..276f8362594d22 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_mini_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Mini +author: John Snow Labs +name: onto_recognize_entities_bert_mini +date: 2023-05-27 +tags: [open_source, english, onto_recognize_entities_bert_mini, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_mini is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_mini_en_4.4.2_3.2_1685202560469.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_mini_en_4.4.2_3.2_1685202560469.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_mini', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_mini", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.mini').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_mini', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_mini", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.mini').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.147406503558158,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_mini| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|57.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_small_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_small_en.md new file mode 100644 index 00000000000000..582c6fa2b9a704 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_small_en.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Small +author: John Snow Labs +name: onto_recognize_entities_bert_small +date: 2023-05-27 +tags: [open_source, english, onto_recognize_entities_bert_small, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_small is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_small_en_4.4.2_3.2_1685202632553.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_small_en_4.4.2_3.2_1685202632553.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_small', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_small", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.small').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_small', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_small", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.small').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:----------------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.9379079937934875,.,...]] | ['O', 'O', 'B-PERSON', 'I-PERSON', 'I-PERSON', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_small| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|125.0 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_tiny_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_tiny_en.md new file mode 100644 index 00000000000000..c2fc9436970439 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_tiny_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - BERT Tiny +author: John Snow Labs +name: onto_recognize_entities_bert_tiny +date: 2023-05-27 +tags: [open_source, english, onto_recognize_entities_bert_tiny, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_bert_tiny is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_tiny_en_4.4.2_3.2_1685202696604.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_bert_tiny_en_4.4.2_3.2_1685202696604.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_tiny', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_tiny", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.tiny').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_bert_tiny', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_tiny", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.bert.tiny').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:----------------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-1.526878952980041,.,...]] | ['O', 'O', 'B-PERSON', 'I-PERSON', 'I-PERSON', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_bert_tiny| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|31.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_base_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_base_en.md new file mode 100644 index 00000000000000..165075633a300c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_base_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - ELECTRA Base +author: John Snow Labs +name: onto_recognize_entities_electra_base +date: 2023-05-27 +tags: [open_source, english, onto_recognize_entities_electra_base, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_electra_base is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_base_en_4.4.2_3.2_1685202826895.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_base_en_4.4.2_3.2_1685202826895.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_electra_base', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_base", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.electra.base').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_electra_base', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_base", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.electra.base').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2088415920734405,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_electra_base| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|424.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_large_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_large_en.md new file mode 100644 index 00000000000000..e87af232ca890a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_large_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - ELECTRA Large +author: John Snow Labs +name: onto_recognize_entities_electra_large +date: 2023-05-27 +tags: [open_source, english, onto_recognize_entities_electra_large, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_electra_large is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_large_en_4.4.2_3.2_1685203170107.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_large_en_4.4.2_3.2_1685203170107.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('onto_recognize_entities_electra_large', lang = 'en') +annotations = pipeline.fullAnnotate("Hello from John Snow Labs!")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_large", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.large').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline('onto_recognize_entities_electra_large', lang = 'en') +annotations = pipeline.fullAnnotate("Hello from John Snow Labs!")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_large", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.large').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.264069110155105,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_electra_large| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.3 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_small_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_small_en.md new file mode 100644 index 00000000000000..2ce881cd270aa8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_small_en.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities OntoNotes pipeline - ELECTRA Small +author: John Snow Labs +name: onto_recognize_entities_electra_small +date: 2023-05-27 +tags: [open_source, english, onto_recognize_entities_electra_small, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_electra_small is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_small_en_4.4.2_3.2_1685202912935.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_electra_small_en_4.4.2_3.2_1685202912935.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_electra_small', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_small", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.electra.small').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_electra_small', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_small", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.electra.small').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2279076874256134,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_electra_small| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|66.3 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_lg_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_lg_en.md new file mode 100644 index 00000000000000..bc9cd1df9dce95 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_lg_en.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Onto Recognize Entities Lg +author: John Snow Labs +name: onto_recognize_entities_lg +date: 2023-05-27 +tags: [en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_lg is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entites. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_lg_en_4.4.2_3.2_1685188232491.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_lg_en_4.4.2_3.2_1685188232491.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("onto_recognize_entities_lg", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + + +{:.nlu-block} +```python +import nlu +nlu.load("en.ner.onto.lg").predict("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("onto_recognize_entities_lg", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.ner.onto.lg").predict("""I love johnsnowlabs! """) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_lg| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|2.5 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_sm_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_sm_en.md new file mode 100644 index 00000000000000..0f90874f95c6e7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_sm_en.md @@ -0,0 +1,121 @@ +--- +layout: model +title: Recognize Entities DL pipeline for English - Small +author: John Snow Labs +name: onto_recognize_entities_sm +date: 2023-05-27 +tags: [open_source, english, onto_recognize_entities_sm, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The onto_recognize_entities_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_sm_en_4.4.2_3.2_1685187842291.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/onto_recognize_entities_sm_en_4.4.2_3.2_1685187842291.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_sm', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("onto_recognize_entities_sm", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.sm').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('onto_recognize_entities_sm', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("onto_recognize_entities_sm", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.onto.sm').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2668800055980682,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|onto_recognize_entities_sm| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-pos_ud_bokmaal_nb.md b/docs/_posts/ahmedlone127/2023-05-27-pos_ud_bokmaal_nb.md new file mode 100644 index 00000000000000..9b67727f887766 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-pos_ud_bokmaal_nb.md @@ -0,0 +1,138 @@ +--- +layout: model +title: Part of Speech for Norwegian +author: John Snow Labs +name: pos_ud_bokmaal +date: 2023-05-27 +tags: [pos, norwegian, nb, open_source] +task: Part of Speech Tagging +language: nb +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model annotates the part of speech of tokens in a text. The parts of speech annotated include PRON (pronoun), CCONJ (coordinating conjunction), and 15 others. The part of speech model is useful for extracting the grammatical structure of a piece of text automatically. + +This model was trained using the dataset available at https://universaldependencies.org + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pos_ud_bokmaal_nb_4.4.2_3.2_1685205245330.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pos_ud_bokmaal_nb_4.4.2_3.2_1685205245330.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("pos") + +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos]) +light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text"))) +results = light_pipeline.fullAnnotate("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.") +``` +```scala + +val pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") + .setInputCols(Array("document", "token")) + .setOutputCol("pos") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos)) +val data = Seq("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python + +import nlu + +text = ["""Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene."""] +pos_df = nlu.load('nb.pos.ud_bokmaal').predict(text) +pos_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("pos") + +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos]) +light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text"))) +results = light_pipeline.fullAnnotate("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.") +``` +```scala +val pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") + .setInputCols(Array("document", "token")) + .setOutputCol("pos") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos)) +val data = Seq("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu + +text = ["""Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene."""] +pos_df = nlu.load('nb.pos.ud_bokmaal').predict(text) +pos_df +``` +
+ +## Results + +```bash +Results + + + +[Row(annotatorType='pos', begin=0, end=4, result='DET', metadata={'word': 'Annet'}), +Row(annotatorType='pos', begin=6, end=8, result='SCONJ', metadata={'word': 'enn'}), +Row(annotatorType='pos', begin=10, end=10, result='PART', metadata={'word': 'å'}), +Row(annotatorType='pos', begin=12, end=15, result='AUX', metadata={'word': 'være'}), +Row(annotatorType='pos', begin=17, end=22, result='NOUN', metadata={'word': 'kongen'}), +...] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pos_ud_bokmaal| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|nb| +|Size:|17.7 KB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- PerceptronModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_bert_en.md b/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_bert_en.md new file mode 100644 index 00000000000000..cc4cbd0d2a02e5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_bert_en.md @@ -0,0 +1,123 @@ +--- +layout: model +title: Recognize Entities DL pipeline for English - BERT +author: John Snow Labs +name: recognize_entities_bert +date: 2023-05-27 +tags: [open_source, english, recognize_entities_bert, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The recognize_entities_bert is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recognize_entities_bert_en_4.4.2_3.2_1685186799820.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recognize_entities_bert_en_4.4.2_3.2_1685186799820.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_bert', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("recognize_entities_bert", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.bert').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_bert', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("recognize_entities_bert", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner.bert').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.085488274693489,.,...]] | ['O', 'O', 'I-PER', 'I-PER', 'I-ORG', 'O'] | ['John Snow Labs'] || | document | sentence | token | embeddings | ner | entities | + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recognize_entities_bert| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|424.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- BertEmbeddings +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_dl_en.md b/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_dl_en.md new file mode 100644 index 00000000000000..895bf290c52ccb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_dl_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: Recognize Entities DL Pipeline for English +author: John Snow Labs +name: recognize_entities_dl +date: 2023-05-27 +tags: [open_source, english, recognize_entities_dl, pipeline, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The recognize_entities_dl is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps +and recognizes entities . +It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_en_4.4.2_3.2_1685186675683.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_en_4.4.2_3.2_1685186675683.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() + +``` +```scala + +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) + + +``` + +{:.nlu-block} +```python + +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner').predict(text) +result_df + +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipelinein +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'en') +annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "en") +val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) +``` + +{:.nlu-block} +```python +import nlu +text = [""Hello from John Snow Labs ! ""] +result_df = nlu.load('en.ner').predict(text) +result_df +``` +
+ +## Results + +```bash +Results + + +| | document | sentence | token | embeddings | ner | entities | +|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:---------------------------------------------------|:------------------------------| +| 0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[0.2668800055980682,.,...]] | ['B-ORG', 'I-ORG', 'I-ORG', 'I-ORG', 'I-ORG', 'O'] | ['Hello from John Snow Labs'] | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recognize_entities_dl| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|166.7 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_dl_fa.md b/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_dl_fa.md new file mode 100644 index 00000000000000..e6fadd932f60c2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_dl_fa.md @@ -0,0 +1,169 @@ +--- +layout: model +title: Explain Document DL Pipeline for Farsi/Persian +author: John Snow Labs +name: recognize_entities_dl +date: 2023-05-27 +tags: [pipeline, ner, fa, open_source] +task: Named Entity Recognition +language: fa +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The explain_document_dl is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities . It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_fa_4.4.2_3.2_1685203554740.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recognize_entities_dl_fa_4.4.2_3.2_1685203554740.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'fa') + +annotations = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")[0] + +annotations.keys() +``` +```scala + +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "fa") + +val result = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")(0) +``` + +{:.nlu-block} +```python + +import nlu + +text = ["""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند"""] + +result_df = nlu.load('fa.recognize_entities_dl').predict(text) + +result_df +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline + +pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'fa') + +annotations = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")[0] + +annotations.keys() +``` +```scala +val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "fa") + +val result = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")(0) +``` + +{:.nlu-block} +```python +import nlu + +text = ["""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند"""] + +result_df = nlu.load('fa.recognize_entities_dl').predict(text) + +result_df +``` +
+ +## Results + +```bash +Results + + + +| | document | sentence | token | clean_tokens | lemma | pos | embeddings | ner | entities | +|---:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------|:---------------|:---------|:------|:-------------|:------|:---------------------| +| 0 | "به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند | "به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند | " | " | " | PUNCT | " | O | خبرنگار ایرنا | +| 1 | | | به | گزارش | به | ADP | به | O | محمد قمی | +| 2 | | | گزارش | خبرنگار | گزارش | NOUN | گزارش | O | پاکدشت | +| 3 | | | خبرنگار | ایرنا | خبرنگار | NOUN | خبرنگار | B-ORG | علی‌اکبر موسوی خوئینی | +| 4 | | | ایرنا | ، | ایرنا | PROPN | ایرنا | I-ORG | شمس‌الدین وهابی | +| 5 | | | ، | اساس | ؛ | PUNCT | ، | O | تهران | +| 6 | | | بر | تصمیم | بر | ADP | بر | O | | +| 7 | | | اساس | این | اساس | NOUN | اساس | O | | +| 8 | | | تصمیم | مجمع | تصمیم | NOUN | تصمیم | O | | +| 9 | | | این | ، | این | DET | این | O | | +| 10 | | | مجمع | محمد | مجمع | NOUN | مجمع | O | | +| 11 | | | ، | قمی | ؛ | PUNCT | ، | O | | +| 12 | | | محمد | نماینده | محمد | PROPN | محمد | B-PER | | +| 13 | | | قمی | پاکدشت | قمی | PROPN | قمی | I-PER | | +| 14 | | | نماینده | عنوان | نماینده | NOUN | نماینده | O | | +| 15 | | | مردم | رئیس | مردم | NOUN | مردم | O | | +| 16 | | | پاکدشت | علی‌اکبر | پاکدشت | PROPN | پاکدشت | B-LOC | | +| 17 | | | به | موسوی | به | ADP | به | O | | +| 18 | | | عنوان | خوئینی | عنوان | NOUN | عنوان | O | | +| 19 | | | رئیس | شمس‌الدین | رئیس | NOUN | رئیس | O | | +| 20 | | | و | وهابی | او | CCONJ | و | O | | +| 21 | | | علی‌اکبر | نمایندگان | علی‌اکبر | PROPN | علی‌اکبر | B-PER | | +| 22 | | | موسوی | تهران | موسوی | PROPN | موسوی | I-PER | | +| 23 | | | خوئینی | عنوان | خوئینی | PROPN | خوئینی | I-PER | | +| 24 | | | و | نواب | او | CCONJ | و | O | | +| 25 | | | شمس‌الدین | رئیس | شمس‌الدین | PROPN | شمس‌الدین | B-PER | | +| 26 | | | وهابی | انتخاب | وهابی | PROPN | وهابی | I-PER | | +| 27 | | | نمایندگان | | نماینده | NOUN | نمایندگان | O | | +| 28 | | | مردم | | مردم | NOUN | مردم | O | | +| 29 | | | تهران | | تهران | PROPN | تهران | B-LOC | | +| 30 | | | به | | به | ADP | به | O | | +| 31 | | | عنوان | | عنوان | NOUN | عنوان | O | | +| 32 | | | نواب | | نواب | NOUN | نواب | O | | +| 33 | | | رئیس | | رئیس | NOUN | رئیس | O | | +| 34 | | | انتخاب | | انتخاب | NOUN | انتخاب | O | | +| 35 | | | شدند | | کرد#کن | VERB | شدند | O | | + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recognize_entities_dl| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|fa| +|Size:|1.2 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- StopWordsCleaner +- LemmatizerModel +- PerceptronModel +- WordEmbeddingsModel +- NerDLModel +- NerConverter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-roberta_base_token_classifier_ontonotes_pipeline_en.md new file mode 100644 index 00000000000000..c6ca506e52958e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -0,0 +1,104 @@ +--- +layout: model +title: RoBERTa Base Ontonotes NER Pipeline +author: John Snow Labs +name: roberta_base_token_classifier_ontonotes_pipeline +date: 2023-05-27 +tags: [open_source, ner, token_classifier, roberta, ontonotes, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_base_token_classifier_ontonotes](https://nlp.johnsnowlabs.com/2021/09/26/roberta_base_token_classifier_ontonotes_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.2_1685210622981.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.2_1685210622981.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala + +val pipeline = new PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) +``` +
+ +## Results + +```bash +Results + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | +|November 2020 |DATE | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_base_token_classifier_ontonotes_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|456.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-roberta_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-roberta_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..72dcd995dc0142 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-roberta_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: RoBERTa Large CoNLL-03 NER Pipeline +author: John Snow Labs +name: roberta_large_token_classifier_conll03_pipeline +date: 2023-05-27 +tags: [open_source, ner, token_classifier, roberta, conll03, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/26/roberta_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_large_token_classifier_conll03_pipeline_en_4.4.2_3.2_1685210902187.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_large_token_classifier_conll03_pipeline_en_4.4.2_3.2_1685210902187.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.")) +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.3 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_bne_capitel_ner_pipeline_es.md b/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_bne_capitel_ner_pipeline_es.md new file mode 100644 index 00000000000000..81a467a8d3d26a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_bne_capitel_ner_pipeline_es.md @@ -0,0 +1,104 @@ +--- +layout: model +title: Spanish NER Pipeline +author: John Snow Labs +name: roberta_token_classifier_bne_capitel_ner_pipeline +date: 2023-05-27 +tags: [roberta, token_classifier, spanish, ner, es, open_source] +task: Named Entity Recognition +language: es +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_token_classifier_bne_capitel_ner_es](https://nlp.johnsnowlabs.com/2021/12/07/roberta_token_classifier_bne_capitel_ner_es.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_bne_capitel_ner_pipeline_es_4.4.2_3.2_1685208624816.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_bne_capitel_ner_pipeline_es_4.4.2_3.2_1685208624816.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +```scala + +val pipeline = new PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") + +pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") +``` +
+ +## Results + +```bash +Results + + + ++------------------------+---------+ +|chunk |ner_label| ++------------------------+---------+ +|Antonio |PER | +|fábrica de Mercedes-Benz|ORG | +|Madrid |LOC | ++------------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_token_classifier_bne_capitel_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|459.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_icelandic_ner_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_icelandic_ner_pipeline_is.md new file mode 100644 index 00000000000000..44c64073d378c7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_icelandic_ner_pipeline_is.md @@ -0,0 +1,106 @@ +--- +layout: model +title: Icelandic NER Pipeline +author: John Snow Labs +name: roberta_token_classifier_icelandic_ner_pipeline +date: 2023-05-27 +tags: [open_source, ner, token_classifier, roberta, icelandic, is] +task: Named Entity Recognition +language: is +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_token_classifier_icelandic_ner](https://nlp.johnsnowlabs.com/2021/12/06/roberta_token_classifier_icelandic_ner_is.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_icelandic_ner_pipeline_is_4.4.2_3.2_1685208776704.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_icelandic_ner_pipeline_is_4.4.2_3.2_1685208776704.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +```scala + +val pipeline = new PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +```scala +val pipeline = new PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") + +pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") +``` +
+ +## Results + +```bash +Results + + + ++----------------+------------+ +|chunk |ner_label | ++----------------+------------+ +|Peter Fergusson |Person | +|New York |Location | +|október 2011 |Date | +|Tesla Motor |Organization| +|100K $ |Money | ++----------------+------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_token_classifier_icelandic_ner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|is| +|Size:|457.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_timex_semeval_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_timex_semeval_pipeline_en.md new file mode 100644 index 00000000000000..28e0ea1c3e114f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_timex_semeval_pipeline_en.md @@ -0,0 +1,110 @@ +--- +layout: model +title: Pipeline to Detect Time-related Terminology +author: John Snow Labs +name: roberta_token_classifier_timex_semeval_pipeline +date: 2023-05-27 +tags: [timex, semeval, ner, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [roberta_token_classifier_timex_semeval](https://nlp.johnsnowlabs.com/2021/12/28/roberta_token_classifier_timex_semeval_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_timex_semeval_pipeline_en_4.4.2_3.2_1685206876256.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_token_classifier_timex_semeval_pipeline_en_4.4.2_3.2_1685206876256.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +timex_pipeline = PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +```scala + + +val timex_pipeline = new PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +timex_pipeline = PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +```scala +val timex_pipeline = new PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") + +timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") +``` +
+ +## Results + +```bash +Results + + + + ++-------+-----------------+ +|chunk |ner_label | ++-------+-----------------+ +|22:12C |Period | +|3 |Number | +|days |Calendar-Interval| +|Tuesday|Day-Of-Week | +|to |Between | +|Friday |Day-Of-Week | ++-------+-----------------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_token_classifier_timex_semeval_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|439.5 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- RoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-spellcheck_dl_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-spellcheck_dl_pipeline_en.md new file mode 100644 index 00000000000000..af1fc87cbdba9b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-spellcheck_dl_pipeline_en.md @@ -0,0 +1,112 @@ +--- +layout: model +title: Context Spell Checker Pipeline for English +author: John Snow Labs +name: spellcheck_dl_pipeline +date: 2023-05-27 +tags: [spellcheck, spell, spellcheck_pipeline, spelling_corrector, en, open_source] +task: Spell Check +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained spellchecker pipeline is built on the top of [spellcheck_dl](https://nlp.johnsnowlabs.com/2022/04/02/spellcheck_dl_en_2_4.html) model. This pipeline is for PySpark 2.4.x users with SparkNLP 3.4.2 and above. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/spellcheck_dl_pipeline_en_4.4.2_3.2_1685207812692.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/spellcheck_dl_pipeline_en_4.4.2_3.2_1685207812692.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +text = ["During the summer we have the best ueather.", "I have a black ueather jacket, so nice."] + +pipeline.annotate(text) +``` +```scala + + +val pipeline = new PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +val example = Array("During the summer we have the best ueather.", "I have a black ueather jacket, so nice.") + +pipeline.annotate(example) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +text = ["During the summer we have the best ueather.", "I have a black ueather jacket, so nice."] + +pipeline.annotate(text) +``` +```scala +val pipeline = new PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") + +val example = Array("During the summer we have the best ueather.", "I have a black ueather jacket, so nice.") + +pipeline.annotate(example) +``` +
+ +## Results + +```bash +Results + + + + +[{'checked': ['During', 'the', 'summer', 'we', 'have', 'the', 'best', 'weather', '.'], + 'document': ['During the summer we have the best ueather.'], + 'token': ['During', 'the', 'summer', 'we', 'have', 'the', 'best', 'ueather', '.']}, + + {'checked': ['I', 'have', 'a', 'black', 'leather', 'jacket', ',', 'so', 'nice', '.'], + 'document': ['I have a black ueather jacket, so nice.'], + 'token': ['I', 'have', 'a', 'black', 'ueather', 'jacket', ',', 'so', 'nice', '.']}] + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|spellcheck_dl_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|99.7 MB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- ContextSpellCheckerModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-text_cleaning_en.md b/docs/_posts/ahmedlone127/2023-05-27-text_cleaning_en.md new file mode 100644 index 00000000000000..07657f6ad771cc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-text_cleaning_en.md @@ -0,0 +1,80 @@ +--- +layout: model +title: Text Cleaning +author: John Snow Labs +name: text_cleaning +date: 2023-05-27 +tags: [en, open_source] +task: Text Classification +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The text_cleaning is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and cleans text. + It performs most of the common text processing tasks on your dataframe + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/text_cleaning_en_4.4.2_3.2_1685191397892.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/text_cleaning_en_4.4.2_3.2_1685191397892.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("text_cleaning", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from sparknlp.pretrained import PretrainedPipeline +pipeline = PretrainedPipeline("text_cleaning", "en") + +result = pipeline.annotate("""I love johnsnowlabs! """) +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|text_cleaning| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|944.5 KB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- NormalizerModel +- StopWordsCleaner +- LemmatizerModel +- TokenAssembler \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_base_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..6ce93076e23c62 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_base_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: XLM-RoBERTa Base, CoNLL-03 NER Pipeline +author: John Snow Labs +name: xlm_roberta_base_token_classifier_conll03_pipeline +date: 2023-05-27 +tags: [open_source, ner, token_classifier, xlm_roberta, conll03, xlm, base, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/10/03/xlm_roberta_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_conll03_pipeline_en_4.4.2_3.2_1685210026503.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_conll03_pipeline_en_4.4.2_3.2_1685210026503.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|851.9 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md new file mode 100644 index 00000000000000..8a480372275471 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: XLM-RoBERTa Base NER Pipeline +author: John Snow Labs +name: xlm_roberta_base_token_classifier_ontonotes_pipeline +date: 2023-05-27 +tags: [open_source, ner, token_classifier, xlm_roberta, ontonotes, xlm, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifier_ontonotes](https://nlp.johnsnowlabs.com/2021/10/03/xlm_roberta_base_token_classifier_ontonotes_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.2_1685209772272.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_token_classifier_ontonotes_pipeline_en_4.4.2_3.2_1685209772272.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") + +pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | +|November 2020 |DATE | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_base_token_classifier_ontonotes_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|858.4 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_conll03_pipeline_de.md b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_conll03_pipeline_de.md new file mode 100644 index 00000000000000..bf1d04ed8366d6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_conll03_pipeline_de.md @@ -0,0 +1,104 @@ +--- +layout: model +title: NER Pipeline for German +author: John Snow Labs +name: xlm_roberta_large_token_classifier_conll03_pipeline +date: 2023-05-27 +tags: [german, roberta, xlm, ner, conll03, de, open_source] +task: Named Entity Recognition +language: de +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_large_token_classifier_conll03_de](https://nlp.johnsnowlabs.com/2021/12/25/xlm_roberta_large_token_classifier_conll03_de.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_conll03_pipeline_de_4.4.2_3.2_1685208423606.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_conll03_pipeline_de_4.4.2_3.2_1685208423606.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +```scala + +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") + +pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") +``` +
+ +## Results + +```bash +Results + + + ++----------------------+---------+ +|chunk |ner_label| ++----------------------+---------+ +|Ibser |PER | +|ASK Ebreichsdorf |ORG | +|Admira Wacker Mödling |ORG | ++----------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|de| +|Size:|1.8 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md new file mode 100644 index 00000000000000..78657d8c33e3ed --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md @@ -0,0 +1,104 @@ +--- +layout: model +title: NER Pipeline for 10 High Resourced Languages +author: John Snow Labs +name: xlm_roberta_large_token_classifier_hrl_pipeline +date: 2023-05-27 +tags: [arabic, german, english, spanish, french, italian, latvian, dutch, portuguese, chinese, xlm, roberta, ner, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlm_roberta_large_token_classifier_hrl](https://nlp.johnsnowlabs.com/2021/12/26/xlm_roberta_large_token_classifier_hrl_xx.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_hrl_pipeline_xx_4.4.2_3.2_1685207642320.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_hrl_pipeline_xx_4.4.2_3.2_1685207642320.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +```scala + +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +```scala +val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") + +pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") +``` +
+ +## Results + +```bash +Results + + + ++---------------------------+---------+ +|chunk |ner_label| ++---------------------------+---------+ +|الرياض |LOC | +|فيصل بن بندر بن عبد العزيز |PER | +|الرياض |LOC | ++---------------------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_large_token_classifier_hrl_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|1.8 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md new file mode 100644 index 00000000000000..b585db60590ce2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md @@ -0,0 +1,104 @@ +--- +layout: model +title: NER Pipeline for 10 African Languages +author: John Snow Labs +name: xlm_roberta_large_token_classifier_masakhaner_pipeline +date: 2023-05-27 +tags: [masakhaner, african, xlm_roberta, multilingual, pipeline, amharic, hausa, igbo, kinyarwanda, luganda, swahilu, wolof, yoruba, nigerian, pidgin, xx, open_source] +task: Named Entity Recognition +language: xx +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on [xlm_roberta_large_token_classifier_masakhaner](https://nlp.johnsnowlabs.com/2021/12/06/xlm_roberta_large_token_classifier_masakhaner_xx.html) ner model which is imported from `HuggingFace`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_masakhaner_pipeline_xx_4.4.2_3.2_1685205905144.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_token_classifier_masakhaner_pipeline_xx_4.4.2_3.2_1685205905144.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +masakhaner_pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +```scala + +val masakhaner_pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +val masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +masakhaner_pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +```scala +val masakhaner_pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") + +val masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") +``` +
+ +## Results + +```bash +Results + + + ++----------------+---------+ +|chunk |ner_label| ++----------------+---------+ +|አህመድ ቫንዳ |PER | +|ከ3-10-2000 ጀምሮ|DATE | +|በአዲስ አበባ |LOC | ++----------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_large_token_classifier_masakhaner_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|1.8 GB| + +## Included Models + +- DocumentAssembler +- SentenceDetector +- TokenizerModel +- XlmRoBertaForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-xlnet_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-xlnet_base_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..09b33d78c6bd29 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-xlnet_base_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: XLNet Base CoNLL-03 NER Pipeline +author: John Snow Labs +name: xlnet_base_token_classifier_conll03_pipeline +date: 2023-05-27 +tags: [ner, english, xlnet, base, token_classification, en, open_source] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlnet_base_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/28/xlnet_base_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlnet_base_token_classifier_conll03_pipeline_en_4.4.2_3.2_1685209539181.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlnet_base_token_classifier_conll03_pipeline_en_4.4.2_3.2_1685209539181.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PER | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlnet_base_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|438.6 MB| + +## Included Models + +- DocumentAssembler +- SentenceDetectorDLModel +- TokenizerModel +- XlnetForTokenClassification +- NerConverter +- Finisher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-05-27-xlnet_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-xlnet_large_token_classifier_conll03_pipeline_en.md new file mode 100644 index 00000000000000..6db5dbe6f40b79 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-05-27-xlnet_large_token_classifier_conll03_pipeline_en.md @@ -0,0 +1,103 @@ +--- +layout: model +title: XLNet Large CoNLL-03 NER Pipeline +author: John Snow Labs +name: xlnet_large_token_classifier_conll03_pipeline +date: 2023-05-27 +tags: [open_source, ner, token_classifier, xlnet, conll03, large, en] +task: Named Entity Recognition +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.2 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This pretrained pipeline is built on the top of [xlnet_large_token_classifier_conll03](https://nlp.johnsnowlabs.com/2021/09/28/xlnet_large_token_classifier_conll03_en.html) model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlnet_large_token_classifier_conll03_pipeline_en_4.4.2_3.2_1685210106219.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlnet_large_token_classifier_conll03_pipeline_en_4.4.2_3.2_1685210106219.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +pipeline = PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala + + +val pipeline = new PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +pipeline = PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +```scala +val pipeline = new PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") + +pipeline.annotate("My name is John and I work at John Snow Labs.") +``` +
+ +## Results + +```bash +Results + + + + ++--------------+---------+ +|chunk |ner_label| ++--------------+---------+ +|John |PERSON | +|John Snow Labs|ORG | ++--------------+---------+ + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlnet_large_token_classifier_conll03_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|19.0 KB| + +## Included Models + +- DocumentAssembler +- TokenizerModel +- NormalizerModel \ No newline at end of file From 4d64d1b78aab35a27d0a968c08d1e0de07affed9 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Sun, 28 May 2023 21:54:13 +0700 Subject: [PATCH 15/55] 2023-05-28-longformer_base_english_legal_en (#13838) * Add model 2023-05-28-longformer_base_english_legal_en * Update 2023-05-28-longformer_base_english_legal_en.md --------- Co-authored-by: Mary-Sci Co-authored-by: Merve Ertas Uslu <67653613+Mary-Sci@users.noreply.github.com> --- ...-05-28-longformer_base_english_legal_en.md | 97 +++++++++++++++++++ 1 file changed, 97 insertions(+) create mode 100644 docs/_posts/Mary-Sci/2023-05-28-longformer_base_english_legal_en.md diff --git a/docs/_posts/Mary-Sci/2023-05-28-longformer_base_english_legal_en.md b/docs/_posts/Mary-Sci/2023-05-28-longformer_base_english_legal_en.md new file mode 100644 index 00000000000000..88ff5b470c932d --- /dev/null +++ b/docs/_posts/Mary-Sci/2023-05-28-longformer_base_english_legal_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English Legal Longformer Base Embeddings Model +author: John Snow Labs +name: longformer_base_english_legal +date: 2023-05-28 +tags: [en, longformerformaskedlm, transformer, open_source, legal, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: LongformerEmbeddings +article_header: +type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Legal Longformer Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `legal-longformer-base` is a English model originally trained by `lexlms`. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/longformer_base_english_legal_en_4.4.2_3.0_1685282124579.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/longformer_base_english_legal_en_4.4.2_3.0_1685282124579.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +documentAssembler = DocumentAssembler() \ + .setInputCols("text") \ + .setOutputCols("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = LongformerEmbeddings.pretrained("longformer_base_english_legal","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = LongformerEmbeddings.pretrained("longformer_base_english_legal","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|longformer_base_english_legal| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|561.6 MB| +|Case sensitive:|true| +|Max sentence length:|4096| + +## References + +https://huggingface.co/lexlms/legal-longformer-base From 02a9afbacdde144d6af8ebcc671210d2da10a5d0 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Mon, 29 May 2023 14:02:30 +0700 Subject: [PATCH 16/55] 2023-05-28-xlm_longformer_base_english_legal_en (#13839) * Add model 2023-05-28-xlm_longformer_base_english_legal_en * Update 2023-05-28-xlm_longformer_base_english_legal_en.md * Add model 2023-05-28-longformer_large_english_legal_en * Update 2023-05-28-longformer_large_english_legal_en.md --------- Co-authored-by: Mary-Sci Co-authored-by: Merve Ertas Uslu <67653613+Mary-Sci@users.noreply.github.com> --- ...05-28-longformer_large_english_legal_en.md | 97 +++++++++++++++++++ ...28-xlm_longformer_base_english_legal_en.md | 97 +++++++++++++++++++ 2 files changed, 194 insertions(+) create mode 100644 docs/_posts/Mary-Sci/2023-05-28-longformer_large_english_legal_en.md create mode 100644 docs/_posts/Mary-Sci/2023-05-28-xlm_longformer_base_english_legal_en.md diff --git a/docs/_posts/Mary-Sci/2023-05-28-longformer_large_english_legal_en.md b/docs/_posts/Mary-Sci/2023-05-28-longformer_large_english_legal_en.md new file mode 100644 index 00000000000000..1892e2e093f980 --- /dev/null +++ b/docs/_posts/Mary-Sci/2023-05-28-longformer_large_english_legal_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English Legal Longformer Large Embeddings Model +author: John Snow Labs +name: longformer_large_english_legal +date: 2023-05-28 +tags: [en, longformerformaskedlm, transformer, open_source, legal, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: LongformerEmbeddings +article_header: +type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Legal Longformer Large Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `legal-longformer-large` is a English model originally trained by `lexlms`. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/longformer_large_english_legal_en_4.4.2_3.0_1685289330980.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/longformer_large_english_legal_en_4.4.2_3.0_1685289330980.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +documentAssembler = DocumentAssembler() \ + .setInputCols("text") \ + .setOutputCols("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = LongformerEmbeddings.pretrained("longformer_large_english_legal","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = LongformerEmbeddings.pretrained("longformer_large_english_legal","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|longformer_large_english_legal| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.6 GB| +|Case sensitive:|true| +|Max sentence length:|4096| + +## References + +https://huggingface.co/lexlms/legal-longformer-large diff --git a/docs/_posts/Mary-Sci/2023-05-28-xlm_longformer_base_english_legal_en.md b/docs/_posts/Mary-Sci/2023-05-28-xlm_longformer_base_english_legal_en.md new file mode 100644 index 00000000000000..de8f8f11dc3e5b --- /dev/null +++ b/docs/_posts/Mary-Sci/2023-05-28-xlm_longformer_base_english_legal_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English Legal XLM-Longformer Base Embeddings Model +author: John Snow Labs +name: xlm_longformer_base_english_legal +date: 2023-05-28 +tags: [en, longformerformaskedlm, transformer, open_source, legal, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 4.4.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: LongformerEmbeddings +article_header: +type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Legal XLM-Longformer Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `legal-xlm-longformer-base` is a English model originally trained by `joelito`. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_longformer_base_english_legal_en_4.4.2_3.0_1685286936656.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_longformer_base_english_legal_en_4.4.2_3.0_1685286936656.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +documentAssembler = DocumentAssembler() \ + .setInputCols("text") \ + .setOutputCols("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = LongformerEmbeddings.pretrained("xlm_longformer_base_english_legal","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = LongformerEmbeddings.pretrained("xlm_longformer_base_english_legal","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_longformer_base_english_legal| +|Compatibility:|Spark NLP 4.4.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|788.6 MB| +|Case sensitive:|true| +|Max sentence length:|4096| + +## References + +https://huggingface.co/joelito/legal-xlm-longformer-base From d05407461343b96bac87ff743f571c26f6e8d180 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Thu, 22 Jun 2023 02:33:54 +0700 Subject: [PATCH 17/55] 2023-06-21-bert_embeddings_distil_clinical_en (#13861) * Add model 2023-06-21-bert_embeddings_distil_clinical_en * Add model 2023-06-21-bert_embeddings_carlbert_webex_mlm_spatial_en * Add model 2023-06-21-bert_embeddings_chemical_uncased_finetuned_cust_c2_en * Add model 2023-06-21-bert_embeddings_lsg16k_Italian_Legal_it * Add model 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2023-06-21-ms_bluebert_base_uncased_en * Add model 2023-06-21-bert_embeddings_bert_base_qarib60_860k_ar * fixing wrong spark version and removing tensorflow --------- Co-authored-by: ahmedlone127 Co-authored-by: MaziyarPanahi --- ...23-06-21-bert_base_uncased_contracts_en.md | 149 +++++++++++++++++ .../2023-06-21-bert_embeddings_ARBERT_ar.md | 149 +++++++++++++++++ ...21-bert_embeddings_AraBertMo_base_V1_ar.md | 149 +++++++++++++++++ ...6-21-bert_embeddings_Ara_DialectBERT_ar.md | 149 +++++++++++++++++ ...-06-21-bert_embeddings_COVID_SciBERT_en.md | 149 +++++++++++++++++ ...023-06-21-bert_embeddings_DarijaBERT_ar.md | 149 +++++++++++++++++ ...-06-21-bert_embeddings_FinancialBERT_en.md | 149 +++++++++++++++++ ...06-21-bert_embeddings_German_MedBERT_de.md | 149 +++++++++++++++++ ...-06-21-bert_embeddings_InCaseLawBERT_en.md | 135 +++++++++++++++ ...23-06-21-bert_embeddings_InLegalBERT_en.md | 135 +++++++++++++++ .../2023-06-21-bert_embeddings_MARBERT_ar.md | 149 +++++++++++++++++ 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docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_small_generator_ko.md create mode 100644 docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_kr_electra_generator_ko.md create mode 100644 docs/_posts/ahmedlone127/2023-06-21-legalectra_base_es.md create mode 100644 docs/_posts/ahmedlone127/2023-06-21-legalectra_small_es.md create mode 100644 docs/_posts/ahmedlone127/2023-06-21-ms_bluebert_base_uncased_en.md diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_base_uncased_contracts_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_base_uncased_contracts_en.md new file mode 100644 index 00000000000000..2b1a76c0a31802 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_base_uncased_contracts_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English Legal Contracts BertEmbeddings model (Base, Uncased) +author: John Snow Labs +name: bert_base_uncased_contracts +date: 2023-06-21 +tags: [open_source, bert, embeddings, finance, contracts, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Word Embeddings model, trained on legal contracts, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-uncased-contracts` is a English model originally trained by `nlpaueb`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_contracts_en_5.0.0_3.0_1687337099443.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_contracts_en_5.0.0_3.0_1687337099443.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_base_uncased_contracts","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP."]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_base_uncased_contracts","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP.").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.bert.contracts.uncased_base").predict("""I love Spark NLP.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_base_uncased_contracts","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP."]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_base_uncased_contracts","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP.").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.bert.contracts.uncased_base").predict("""I love Spark NLP.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_contracts| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|407.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_ARBERT_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_ARBERT_ar.md new file mode 100644 index 00000000000000..a8bde5a9373131 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_ARBERT_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (ARBERT model) +author: John Snow Labs +name: bert_embeddings_ARBERT +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `ARBERT` is a Arabic model orginally trained by `UBC-NLP`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_ARBERT_ar_5.0.0_3.0_1687368387135.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_ARBERT_ar_5.0.0_3.0_1687368387135.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_ARBERT","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_ARBERT","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.arbert").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_ARBERT","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_ARBERT","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.arbert").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_ARBERT| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|605.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_AraBertMo_base_V1_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_AraBertMo_base_V1_ar.md new file mode 100644 index 00000000000000..507d80ddf48d81 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_AraBertMo_base_V1_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (from Ebtihal) +author: John Snow Labs +name: bert_embeddings_AraBertMo_base_V1 +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `AraBertMo_base_V1` is a Arabic model orginally trained by `Ebtihal`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_AraBertMo_base_V1_ar_5.0.0_3.0_1687367402700.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_AraBertMo_base_V1_ar_5.0.0_3.0_1687367402700.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_AraBertMo_base_V1","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_AraBertMo_base_V1","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.AraBertMo_base_V1").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_AraBertMo_base_V1","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_AraBertMo_base_V1","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.AraBertMo_base_V1").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_AraBertMo_base_V1| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|407.8 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_Ara_DialectBERT_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_Ara_DialectBERT_ar.md new file mode 100644 index 00000000000000..aa25c8bbbd15f0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_Ara_DialectBERT_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (from MutazYoune) +author: John Snow Labs +name: bert_embeddings_Ara_DialectBERT +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `Ara_DialectBERT` is a Arabic model orginally trained by `MutazYoune`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_Ara_DialectBERT_ar_5.0.0_3.0_1687367717615.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_Ara_DialectBERT_ar_5.0.0_3.0_1687367717615.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_Ara_DialectBERT","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_Ara_DialectBERT","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.Ara_DialectBERT").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_Ara_DialectBERT","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_Ara_DialectBERT","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.Ara_DialectBERT").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_Ara_DialectBERT| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|406.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_COVID_SciBERT_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_COVID_SciBERT_en.md new file mode 100644 index 00000000000000..1156a182032942 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_COVID_SciBERT_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English Bert Embeddings (from lordtt13) +author: John Snow Labs +name: bert_embeddings_COVID_SciBERT +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `COVID-SciBERT` is a English model orginally trained by `lordtt13`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_COVID_SciBERT_en_5.0.0_3.0_1687368450114.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_COVID_SciBERT_en_5.0.0_3.0_1687368450114.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_COVID_SciBERT","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_COVID_SciBERT","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.COVID_SciBERT").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_COVID_SciBERT","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_COVID_SciBERT","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.COVID_SciBERT").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_COVID_SciBERT| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|412.4 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_DarijaBERT_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_DarijaBERT_ar.md new file mode 100644 index 00000000000000..019db2765f56c4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_DarijaBERT_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (from Kamel) +author: John Snow Labs +name: bert_embeddings_DarijaBERT +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `DarijaBERT` is a Arabic model orginally trained by `Kamel`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_DarijaBERT_ar_5.0.0_3.0_1687367582690.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_DarijaBERT_ar_5.0.0_3.0_1687367582690.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_DarijaBERT","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_DarijaBERT","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.DarijaBERT").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_DarijaBERT","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_DarijaBERT","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.DarijaBERT").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_DarijaBERT| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|551.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_FinancialBERT_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_FinancialBERT_en.md new file mode 100644 index 00000000000000..f209c346ba5ecc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_FinancialBERT_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English Financial Bert Embeddings +author: John Snow Labs +name: bert_embeddings_FinancialBERT +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `FinancialBERT` is a English Financial model orginally trained on a very large corpus of financial texts including Earnings Calls, Corporate reports, Bloomberg News, TRC2-financial. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_FinancialBERT_en_5.0.0_3.0_1687368067375.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_FinancialBERT_en_5.0.0_3.0_1687368067375.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_FinancialBERT","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_FinancialBERT","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.FinancialBERT").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_FinancialBERT","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_FinancialBERT","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.FinancialBERT").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_FinancialBERT| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|409.4 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_German_MedBERT_de.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_German_MedBERT_de.md new file mode 100644 index 00000000000000..6f9f54bac44943 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_German_MedBERT_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German Medical Bert Embeddings +author: John Snow Labs +name: bert_embeddings_German_MedBERT +date: 2023-06-21 +tags: [bert, embeddings, de, open_source, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained German Medical Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `German-MedBERT` is a German model orginally trained by `smanjil`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_German_MedBERT_de_5.0.0_3.0_1687367757622.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_German_MedBERT_de_5.0.0_3.0_1687367757622.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_German_MedBERT","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_German_MedBERT","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.medbert").predict("""Ich liebe Funken NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_German_MedBERT","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_German_MedBERT","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.medbert").predict("""Ich liebe Funken NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_German_MedBERT| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|de| +|Size:|406.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_InCaseLawBERT_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_InCaseLawBERT_en.md new file mode 100644 index 00000000000000..df9c431d1607c0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_InCaseLawBERT_en.md @@ -0,0 +1,135 @@ +--- +layout: model +title: English BERT Embeddings (from law-ai) +author: John Snow Labs +name: bert_embeddings_InCaseLawBERT +date: 2023-06-21 +tags: [bert, en, embeddings, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BERT Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `InCaseLawBERT` is a English model originally trained by `law-ai`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_InCaseLawBERT_en_5.0.0_3.0_1687336500304.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_InCaseLawBERT_en_5.0.0_3.0_1687336500304.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_InCaseLawBERT","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_InCaseLawBERT","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_InCaseLawBERT","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_InCaseLawBERT","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_InCaseLawBERT| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|406.8 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_InLegalBERT_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_InLegalBERT_en.md new file mode 100644 index 00000000000000..2a5c249b1099dc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_InLegalBERT_en.md @@ -0,0 +1,135 @@ +--- +layout: model +title: Legal English BERT Embeddings (from law-ai) +author: John Snow Labs +name: bert_embeddings_InLegalBERT +date: 2023-06-21 +tags: [bert, en, embeddings, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BERT Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `InLegalBERT` is a English model originally trained by `law-ai`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_InLegalBERT_en_5.0.0_3.0_1687336959265.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_InLegalBERT_en_5.0.0_3.0_1687336959265.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_InLegalBERT","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_InLegalBERT","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_InLegalBERT","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_InLegalBERT","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_InLegalBERT| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|407.2 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_MARBERT_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_MARBERT_ar.md new file mode 100644 index 00000000000000..df8e6020ce97b2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_MARBERT_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (MARBERT model) +author: John Snow Labs +name: bert_embeddings_MARBERT +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `MARBERT` is a Arabic model orginally trained by `UBC-NLP`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_MARBERT_ar_5.0.0_3.0_1687367317123.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_MARBERT_ar_5.0.0_3.0_1687367317123.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_MARBERT","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_MARBERT","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.MARBERT").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_MARBERT","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_MARBERT","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.MARBERT").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_MARBERT| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|608.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_MARBERTv2_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_MARBERTv2_ar.md new file mode 100644 index 00000000000000..26d222b5c236ee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_MARBERTv2_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (MARBERT model v2) +author: John Snow Labs +name: bert_embeddings_MARBERTv2 +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `MARBERTv2` is a Arabic model orginally trained by `UBC-NLP`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_MARBERTv2_ar_5.0.0_3.0_1687354749271.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_MARBERTv2_ar_5.0.0_3.0_1687354749271.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_MARBERTv2","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_MARBERTv2","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.MARBERTv2").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_MARBERTv2","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_MARBERTv2","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.MARBERTv2").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_MARBERTv2| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|606.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_agriculture_bert_uncased_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_agriculture_bert_uncased_en.md new file mode 100644 index 00000000000000..0f8dd6b3d732db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_agriculture_bert_uncased_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English Bert Embeddings (Base, Uncased, Agriculture) +author: John Snow Labs +name: bert_embeddings_agriculture_bert_uncased +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `agriculture-bert-uncased` is a English model orginally trained by `recobo`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_agriculture_bert_uncased_en_5.0.0_3.0_1687368891491.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_agriculture_bert_uncased_en_5.0.0_3.0_1687368891491.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_agriculture_bert_uncased","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_agriculture_bert_uncased","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.agriculture_bert_uncased").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_agriculture_bert_uncased","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_agriculture_bert_uncased","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.agriculture_bert_uncased").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_agriculture_bert_uncased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|409.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_alberti_bert_base_multilingual_cased_es.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_alberti_bert_base_multilingual_cased_es.md new file mode 100644 index 00000000000000..334f6947d0078e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_alberti_bert_base_multilingual_cased_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish Bert Embeddings (from flax-community) +author: John Snow Labs +name: bert_embeddings_alberti_bert_base_multilingual_cased +date: 2023-06-21 +tags: [bert, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `alberti-bert-base-multilingual-cased` is a Spanish model orginally trained by `flax-community`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_alberti_bert_base_multilingual_cased_es_5.0.0_3.0_1687368551885.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_alberti_bert_base_multilingual_cased_es_5.0.0_3.0_1687368551885.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_alberti_bert_base_multilingual_cased","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_alberti_bert_base_multilingual_cased","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.alberti_bert_base_multilingual_cased").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_alberti_bert_base_multilingual_cased","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_alberti_bert_base_multilingual_cased","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.alberti_bert_base_multilingual_cased").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_alberti_bert_base_multilingual_cased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|664.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_arabert_c19_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_arabert_c19_ar.md new file mode 100644 index 00000000000000..d94b26d12cfe68 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_arabert_c19_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (Arabert model, Covid-19) +author: John Snow Labs +name: bert_embeddings_arabert_c19 +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `arabert_c19` is a Arabic model orginally trained by `moha`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_arabert_c19_ar_5.0.0_3.0_1687369343067.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_arabert_c19_ar_5.0.0_3.0_1687369343067.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_arabert_c19","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_arabert_c19","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.arabert_c19").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_arabert_c19","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_arabert_c19","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.arabert_c19").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_arabert_c19| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|504.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bangla_bert_base_bn.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bangla_bert_base_bn.md new file mode 100644 index 00000000000000..05609a11fcf313 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bangla_bert_base_bn.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Bangla Bert Embeddings +author: John Snow Labs +name: bert_embeddings_bangla_bert_base +date: 2023-06-21 +tags: [bert, embeddings, bn, open_source, onnx] +task: Embeddings +language: bn +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bangla-bert-base` is a Bangla model orginally trained by `sagorsarker`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bangla_bert_base_bn_5.0.0_3.0_1687370097955.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bangla_bert_base_bn_5.0.0_3.0_1687370097955.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bangla_bert_base","bn") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["আমি স্পার্ক এনএলপি ভালোবাসি"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bangla_bert_base","bn") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("আমি স্পার্ক এনএলপি ভালোবাসি").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("bn.embed.bangala_bert").predict("""আমি স্পার্ক এনএলপি ভালোবাসি""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bangla_bert_base","bn") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["আমি স্পার্ক এনএলপি ভালোবাসি"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bangla_bert_base","bn") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("আমি স্পার্ক এনএলপি ভালোবাসি").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("bn.embed.bangala_bert").predict("""আমি স্পার্ক এনএলপি ভালোবাসি""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bangla_bert_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|bn| +|Size:|614.7 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bangla_bert_bn.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bangla_bert_bn.md new file mode 100644 index 00000000000000..27aac09a7bc754 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bangla_bert_bn.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Bangla Bert Embeddings (from Kowsher) +author: John Snow Labs +name: bert_embeddings_bangla_bert +date: 2023-06-21 +tags: [bert, embeddings, bn, open_source, onnx] +task: Embeddings +language: bn +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bangla-bert` is a Bangla model orginally trained by `Kowsher`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bangla_bert_bn_5.0.0_3.0_1687369015466.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bangla_bert_bn_5.0.0_3.0_1687369015466.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bangla_bert","bn") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["আমি স্পার্ক এনএলপি ভালোবাসি"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bangla_bert","bn") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("আমি স্পার্ক এনএলপি ভালোবাসি").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("bn.embed.bangla_bert").predict("""আমি স্পার্ক এনএলপি ভালোবাসি""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bangla_bert","bn") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["আমি স্পার্ক এনএলপি ভালোবাসি"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bangla_bert","bn") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("আমি স্পার্ক এনএলপি ভালোবাসি").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("bn.embed.bangla_bert").predict("""আমি স্পার্ক এনএলপি ভালোবাসি""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bangla_bert| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|bn| +|Size:|612.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_base_uncased_issues_128_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_base_uncased_issues_128_en.md new file mode 100644 index 00000000000000..5679c83687a692 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_base_uncased_issues_128_en.md @@ -0,0 +1,141 @@ +--- +layout: model +title: English Bert Embeddings Cased model (from antoinev17) +author: John Snow Labs +name: bert_embeddings_base_uncased_issues_128 +date: 2023-06-21 +tags: [open_source, bert, bert_embeddings, bertformaskedlm, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-uncased-issues-128` is a English model originally trained by `antoinev17 +`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_base_uncased_issues_128_en_5.0.0_3.0_1687336183958.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_base_uncased_issues_128_en_5.0.0_3.0_1687336183958.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_base_uncased_issues_128","ko") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_base_uncased_issues_128","ko") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_base_uncased_issues_128","ko") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_base_uncased_issues_128","ko") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_base_uncased_issues_128| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|407.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_5lang_cased_es.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_5lang_cased_es.md new file mode 100644 index 00000000000000..99f607f5c1530b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_5lang_cased_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish Bert Embeddings (from amine) +author: John Snow Labs +name: bert_embeddings_bert_base_5lang_cased +date: 2023-06-21 +tags: [bert, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-5lang-cased` is a Spanish model orginally trained by `amine`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_5lang_cased_es_5.0.0_3.0_1687370074087.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_5lang_cased_es_5.0.0_3.0_1687370074087.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_5lang_cased","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_5lang_cased","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bert_base_5lang_cased").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_5lang_cased","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_5lang_cased","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bert_base_5lang_cased").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_5lang_cased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|461.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabert_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabert_ar.md new file mode 100644 index 00000000000000..e8151fef004624 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabert_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (Base, Arabert Model) +author: John Snow Labs +name: bert_embeddings_bert_base_arabert +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-arabert` is a Arabic model orginally trained by `aubmindlab`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabert_ar_5.0.0_3.0_1687370767272.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabert_ar_5.0.0_3.0_1687370767272.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabert","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabert","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabert").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabert","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabert","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabert").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_arabert| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|504.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv01_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv01_ar.md new file mode 100644 index 00000000000000..b0ffc23c7e5de1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv01_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (Base, Arabert Model, v01) +author: John Snow Labs +name: bert_embeddings_bert_base_arabertv01 +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-arabertv01` is a Arabic model orginally trained by `aubmindlab`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabertv01_ar_5.0.0_3.0_1687370107542.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabertv01_ar_5.0.0_3.0_1687370107542.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv01","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv01","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabertv01").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv01","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv01","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabertv01").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_arabertv01| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|505.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv02_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv02_ar.md new file mode 100644 index 00000000000000..00a46a50c9de69 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv02_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (Base, Arabert Model, v02) +author: John Snow Labs +name: bert_embeddings_bert_base_arabertv02 +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-arabertv02` is a Arabic model orginally trained by `aubmindlab`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabertv02_ar_5.0.0_3.0_1687369054270.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabertv02_ar_5.0.0_3.0_1687369054270.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv02","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv02","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabertv02").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv02","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv02","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabertv02").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_arabertv02| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|505.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv02_twitter_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv02_twitter_ar.md new file mode 100644 index 00000000000000..527a150609fdb3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv02_twitter_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (Base, Arabert Model, v02, Twitter) +author: John Snow Labs +name: bert_embeddings_bert_base_arabertv02_twitter +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-arabertv02-twitter` is a Arabic model orginally trained by `aubmindlab`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabertv02_twitter_ar_5.0.0_3.0_1687367879067.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabertv02_twitter_ar_5.0.0_3.0_1687367879067.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv02_twitter","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv02_twitter","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabertv02_twitter").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv02_twitter","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv02_twitter","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabertv02_twitter").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_arabertv02_twitter| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|505.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv2_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv2_ar.md new file mode 100644 index 00000000000000..28aa7881be3bad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv2_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (Base, Arabert Model, v2) +author: John Snow Labs +name: bert_embeddings_bert_base_arabertv2 +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-arabertv2` is a Arabic model orginally trained by `aubmindlab`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabertv2_ar_5.0.0_3.0_1687366696592.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabertv2_ar_5.0.0_3.0_1687366696592.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv2","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv2","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabertv2").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv2","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv2","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabertv2").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_arabertv2| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|504.8 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_ar.md new file mode 100644 index 00000000000000..ec1f8a7ed29f13 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (Base) +author: John Snow Labs +name: bert_embeddings_bert_base_arabic +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-arabic` is a Arabic model orginally trained by `asafaya`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabic_ar_5.0.0_3.0_1687367514433.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabic_ar_5.0.0_3.0_1687367514433.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabic").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabic").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_arabic| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|412.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_mix_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_mix_ar.md new file mode 100644 index 00000000000000..2db1ef256735f2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_mix_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (Base, DA-CA-MSA variants) +author: John Snow Labs +name: bert_embeddings_bert_base_arabic_camelbert_mix +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-arabic-camelbert-mix` is a Arabic model orginally trained by `CAMeL-Lab`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabic_camelbert_mix_ar_5.0.0_3.0_1687366836156.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabic_camelbert_mix_ar_5.0.0_3.0_1687366836156.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_mix","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_mix","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabic_camelbert_mix").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_mix","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_mix","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabic_camelbert_mix").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_arabic_camelbert_mix| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|406.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_ar.md new file mode 100644 index 00000000000000..97bcdf7edd7fe2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (Base, MSA dataset) +author: John Snow Labs +name: bert_embeddings_bert_base_arabic_camelbert_msa +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-arabic-camelbert-msa` is a Arabic model orginally trained by `CAMeL-Lab`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabic_camelbert_msa_ar_5.0.0_3.0_1687355261025.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabic_camelbert_msa_ar_5.0.0_3.0_1687355261025.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabic_camelbert_msa").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabic_camelbert_msa").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_arabic_camelbert_msa| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|406.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_eighth_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_eighth_ar.md new file mode 100644 index 00000000000000..6d2176403c972d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_eighth_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (Base, Trained on an eighth of the full MSA dataset) +author: John Snow Labs +name: bert_embeddings_bert_base_arabic_camelbert_msa_eighth +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-arabic-camelbert-msa-eighth` is a Arabic model orginally trained by `CAMeL-Lab`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabic_camelbert_msa_eighth_ar_5.0.0_3.0_1687366398028.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabic_camelbert_msa_eighth_ar_5.0.0_3.0_1687366398028.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_eighth","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_eighth","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabic_camelbert_msa_eighth").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_eighth","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_eighth","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabic_camelbert_msa_eighth").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_arabic_camelbert_msa_eighth| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|406.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_half_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_half_ar.md new file mode 100644 index 00000000000000..1ef43767d97397 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_half_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (Base, Trained on a half of the full MSA dataset) +author: John Snow Labs +name: bert_embeddings_bert_base_arabic_camelbert_msa_half +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-arabic-camelbert-msa-half` is a Arabic model orginally trained by `CAMeL-Lab`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabic_camelbert_msa_half_ar_5.0.0_3.0_1687355081033.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabic_camelbert_msa_half_ar_5.0.0_3.0_1687355081033.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_half","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_half","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabic_camelbert_msa_half").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_half","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_half","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabic_camelbert_msa_half").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_arabic_camelbert_msa_half| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|406.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_quarter_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_quarter_ar.md new file mode 100644 index 00000000000000..622c54ebe635f9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_quarter_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (Base, Trained on a quarter of the full MSA dataset) +author: John Snow Labs +name: bert_embeddings_bert_base_arabic_camelbert_msa_quarter +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-arabic-camelbert-msa-quarter` is a Arabic model orginally trained by `CAMeL-Lab`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabic_camelbert_msa_quarter_ar_5.0.0_3.0_1687366524279.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabic_camelbert_msa_quarter_ar_5.0.0_3.0_1687366524279.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_quarter","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_quarter","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabic_camelbert_msa_quarter").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_quarter","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_quarter","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabic_camelbert_msa_quarter").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_arabic_camelbert_msa_quarter| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|406.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_sixteenth_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_sixteenth_ar.md new file mode 100644 index 00000000000000..efc6980941ceab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_sixteenth_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (Base, Trained on a sixteenth of the full MSA dataset) +author: John Snow Labs +name: bert_embeddings_bert_base_arabic_camelbert_msa_sixteenth +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-arabic-camelbert-msa-sixteenth` is a Arabic model orginally trained by `CAMeL-Lab`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabic_camelbert_msa_sixteenth_ar_5.0.0_3.0_1687366813331.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_arabic_camelbert_msa_sixteenth_ar_5.0.0_3.0_1687366813331.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_sixteenth","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_sixteenth","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabic_camelbert_msa_sixteenth").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_sixteenth","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_sixteenth","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_arabic_camelbert_msa_sixteenth").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_arabic_camelbert_msa_sixteenth| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|406.4 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_cased_pt_lenerbr_pt.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_cased_pt_lenerbr_pt.md new file mode 100644 index 00000000000000..ae5920ac1cb795 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_cased_pt_lenerbr_pt.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Portuguese Legal Bert Embeddings (Cased) +author: John Snow Labs +name: bert_embeddings_bert_base_cased_pt_lenerbr +date: 2023-06-21 +tags: [bert, embeddings, pt, open_source, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Legal Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-cased-pt-lenerbr` is a Portuguese model orginally trained by `pierreguillou`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_cased_pt_lenerbr_pt_5.0.0_3.0_1687354957150.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_cased_pt_lenerbr_pt_5.0.0_3.0_1687354957150.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_cased_pt_lenerbr","pt") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Eu amo Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_cased_pt_lenerbr","pt") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Eu amo Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.embed.bert_base_cased_pt_lenerbr").predict("""Eu amo Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_cased_pt_lenerbr","pt") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Eu amo Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_cased_pt_lenerbr","pt") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Eu amo Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("pt.embed.bert_base_cased_pt_lenerbr").predict("""Eu amo Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_cased_pt_lenerbr| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|pt| +|Size:|405.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_german_cased_oldvocab_de.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_german_cased_oldvocab_de.md new file mode 100644 index 00000000000000..1f985340872ac9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_german_cased_oldvocab_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German Bert Embeddings (Base, Cased, Old Vocabulary) +author: John Snow Labs +name: bert_embeddings_bert_base_german_cased_oldvocab +date: 2023-06-21 +tags: [bert, embeddings, de, open_source, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-german-cased-oldvocab` is a German model orginally trained by `deepset`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_german_cased_oldvocab_de_5.0.0_3.0_1687355117712.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_german_cased_oldvocab_de_5.0.0_3.0_1687355117712.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_german_cased_oldvocab","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_german_cased_oldvocab","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.bert_base_german_cased_oldvocab").predict("""Ich liebe Funken NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_german_cased_oldvocab","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_german_cased_oldvocab","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.bert_base_german_cased_oldvocab").predict("""Ich liebe Funken NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_german_cased_oldvocab| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|de| +|Size:|406.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_german_uncased_de.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_german_uncased_de.md new file mode 100644 index 00000000000000..91fa66ad63e5d8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_german_uncased_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German Bert Embeddings +author: John Snow Labs +name: bert_embeddings_bert_base_german_uncased +date: 2023-06-21 +tags: [bert, embeddings, de, open_source, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-german-uncased` is a German model orginally trained by `dbmdz`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_german_uncased_de_5.0.0_3.0_1687366506395.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_german_uncased_de_5.0.0_3.0_1687366506395.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_german_uncased","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_german_uncased","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.bert_base_german_uncased").predict("""Ich liebe Funken NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_german_uncased","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_german_uncased","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.bert_base_german_uncased").predict("""Ich liebe Funken NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_german_uncased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|de| +|Size:|409.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_gl_cased_pt.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_gl_cased_pt.md new file mode 100644 index 00000000000000..5b67a75c4e936e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_gl_cased_pt.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Portuguese Bert Embeddings (Base, Cased) +author: John Snow Labs +name: bert_embeddings_bert_base_gl_cased +date: 2023-06-21 +tags: [bert, embeddings, pt, open_source, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-gl-cased` is a Portuguese model orginally trained by `marcosgg`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_gl_cased_pt_5.0.0_3.0_1687367086939.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_gl_cased_pt_5.0.0_3.0_1687367086939.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_gl_cased","pt") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Eu amo Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_gl_cased","pt") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Eu amo Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.embed.bert_base_gl_cased").predict("""Eu amo Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_gl_cased","pt") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Eu amo Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_gl_cased","pt") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Eu amo Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("pt.embed.bert_base_gl_cased").predict("""Eu amo Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_gl_cased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|pt| +|Size:|664.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_historical_german_rw_cased_de.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_historical_german_rw_cased_de.md new file mode 100644 index 00000000000000..4626d471479c49 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_historical_german_rw_cased_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German Bert Embeddings (from redewiedergabe) +author: John Snow Labs +name: bert_embeddings_bert_base_historical_german_rw_cased +date: 2023-06-21 +tags: [bert, embeddings, de, open_source, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-historical-german-rw-cased` is a German model orginally trained by `redewiedergabe`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_historical_german_rw_cased_de_5.0.0_3.0_1687366604668.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_historical_german_rw_cased_de_5.0.0_3.0_1687366604668.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_historical_german_rw_cased","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_historical_german_rw_cased","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.bert_base_historical_german_rw_cased").predict("""Ich liebe Funken NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_historical_german_rw_cased","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_historical_german_rw_cased","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.bert_base_historical_german_rw_cased").predict("""Ich liebe Funken NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_historical_german_rw_cased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|de| +|Size:|406.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_italian_xxl_cased_it.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_italian_xxl_cased_it.md new file mode 100644 index 00000000000000..ab7513a407cb6c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_italian_xxl_cased_it.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Italian Bert Embeddings (Cased) +author: John Snow Labs +name: bert_embeddings_bert_base_italian_xxl_cased +date: 2023-06-21 +tags: [bert, embeddings, it, open_source, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-italian-xxl-cased` is a Italian model orginally trained by `dbmdz`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_italian_xxl_cased_it_5.0.0_3.0_1687367037078.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_italian_xxl_cased_it_5.0.0_3.0_1687367037078.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_italian_xxl_cased","it") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_italian_xxl_cased","it") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Adoro Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("it.embed.bert_base_italian_xxl_cased").predict("""Adoro Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_italian_xxl_cased","it") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_italian_xxl_cased","it") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Adoro Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("it.embed.bert_base_italian_xxl_cased").predict("""Adoro Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_italian_xxl_cased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|it| +|Size:|412.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_italian_xxl_uncased_it.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_italian_xxl_uncased_it.md new file mode 100644 index 00000000000000..8f1b62389ff59c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_italian_xxl_uncased_it.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Italian Bert Embeddings (Uncased) +author: John Snow Labs +name: bert_embeddings_bert_base_italian_xxl_uncased +date: 2023-06-21 +tags: [bert, embeddings, it, open_source, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-italian-xxl-uncased` is a Italian model orginally trained by `dbmdz`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_italian_xxl_uncased_it_5.0.0_3.0_1687366606479.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_italian_xxl_uncased_it_5.0.0_3.0_1687366606479.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_italian_xxl_uncased","it") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_italian_xxl_uncased","it") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Adoro Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("it.embed.bert_base_italian_xxl_uncased").predict("""Adoro Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_italian_xxl_uncased","it") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_italian_xxl_uncased","it") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Adoro Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("it.embed.bert_base_italian_xxl_uncased").predict("""Adoro Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_italian_xxl_uncased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|it| +|Size:|412.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_ko.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_ko.md new file mode 100644 index 00000000000000..586c43ffa392b7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_ko.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Korean Bert Embeddings +author: John Snow Labs +name: bert_embeddings_bert_base +date: 2023-06-21 +tags: [bert, embeddings, ko, open_source, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base` is a Korean model orginally trained by `klue`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_ko_5.0.0_3.0_1687371079238.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_ko_5.0.0_3.0_1687371079238.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base","ko") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base","ko") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ko.embed.bert").predict("""나는 Spark NLP를 좋아합니다""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base","ko") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base","ko") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ko.embed.bert").predict("""나는 Spark NLP를 좋아합니다""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ko| +|Size:|412.4 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_finetuned_peticoes_pt.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_finetuned_peticoes_pt.md new file mode 100644 index 00000000000000..1977eade89c812 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_finetuned_peticoes_pt.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Legal Portuguese Embeddings (Base, Petitions) +author: John Snow Labs +name: bert_embeddings_bert_base_portuguese_cased_finetuned_peticoes +date: 2023-06-21 +tags: [bert, embeddings, pt, open_source, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-portuguese-cased-finetuned-peticoes` is a Portuguese model orginally trained by `Luciano`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_portuguese_cased_finetuned_peticoes_pt_5.0.0_3.0_1687371316772.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_portuguese_cased_finetuned_peticoes_pt_5.0.0_3.0_1687371316772.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_portuguese_cased_finetuned_peticoes","pt") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Eu amo Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_portuguese_cased_finetuned_peticoes","pt") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Eu amo Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.embed.bert_base_portuguese_cased_finetuned_peticoes").predict("""Eu amo Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_portuguese_cased_finetuned_peticoes","pt") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Eu amo Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_portuguese_cased_finetuned_peticoes","pt") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Eu amo Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("pt.embed.bert_base_portuguese_cased_finetuned_peticoes").predict("""Eu amo Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_portuguese_cased_finetuned_peticoes| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|pt| +|Size:|405.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_finetuned_tcu_acordaos_pt.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_finetuned_tcu_acordaos_pt.md new file mode 100644 index 00000000000000..51c244361ef7c6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_finetuned_tcu_acordaos_pt.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Legal Portuguese Embeddings (Base, Agreements) +author: John Snow Labs +name: bert_embeddings_bert_base_portuguese_cased_finetuned_tcu_acordaos +date: 2023-06-21 +tags: [bert, embeddings, pt, open_source, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-portuguese-cased-finetuned-tcu-acordaos` is a Portuguese model orginally trained by `Luciano`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_portuguese_cased_finetuned_tcu_acordaos_pt_5.0.0_3.0_1687371364352.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_portuguese_cased_finetuned_tcu_acordaos_pt_5.0.0_3.0_1687371364352.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_portuguese_cased_finetuned_tcu_acordaos","pt") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Eu amo Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_portuguese_cased_finetuned_tcu_acordaos","pt") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Eu amo Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.embed.bert_base_portuguese_cased_finetuned_tcu_acordaos").predict("""Eu amo Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_portuguese_cased_finetuned_tcu_acordaos","pt") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Eu amo Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_portuguese_cased_finetuned_tcu_acordaos","pt") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Eu amo Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("pt.embed.bert_base_portuguese_cased_finetuned_tcu_acordaos").predict("""Eu amo Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_portuguese_cased_finetuned_tcu_acordaos| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|pt| +|Size:|405.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_pt.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_pt.md new file mode 100644 index 00000000000000..7e8a494e05a9ea --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_pt.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Portuguese Bert Embeddings (Base) +author: John Snow Labs +name: bert_embeddings_bert_base_portuguese_cased +date: 2023-06-21 +tags: [bert, embeddings, pt, open_source, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-portuguese-cased` is a Portuguese model orginally trained by `neuralmind`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_portuguese_cased_pt_5.0.0_3.0_1687371699306.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_portuguese_cased_pt_5.0.0_3.0_1687371699306.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_portuguese_cased","pt") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Eu amo Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_portuguese_cased","pt") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Eu amo Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.embed.bert_base_portuguese_cased").predict("""Eu amo Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_portuguese_cased","pt") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Eu amo Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_portuguese_cased","pt") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Eu amo Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("pt.embed.bert_base_portuguese_cased").predict("""Eu amo Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_portuguese_cased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|pt| +|Size:|405.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib60_1790k_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib60_1790k_ar.md new file mode 100644 index 00000000000000..82419381d32b36 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib60_1790k_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (Base, 1790k Iterations) +author: John Snow Labs +name: bert_embeddings_bert_base_qarib60_1790k +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-qarib60_1790k` is a Arabic model orginally trained by `qarib`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_qarib60_1790k_ar_5.0.0_3.0_1687371740065.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_qarib60_1790k_ar_5.0.0_3.0_1687371740065.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_qarib60_1790k","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_qarib60_1790k","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_qarib60_1790k").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_qarib60_1790k","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_qarib60_1790k","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_qarib60_1790k").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_qarib60_1790k| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|504.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib60_860k_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib60_860k_ar.md new file mode 100644 index 00000000000000..b4cba476c77437 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib60_860k_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (Base, 860k Iterations) +author: John Snow Labs +name: bert_embeddings_bert_base_qarib60_860k +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-qarib60_860k` is a Arabic model orginally trained by `qarib`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_qarib60_860k_ar_5.0.0_3.0_1687373057769.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_qarib60_860k_ar_5.0.0_3.0_1687373057769.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_qarib60_860k","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_qarib60_860k","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_qarib60_860k").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_qarib60_860k","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_qarib60_860k","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_qarib60_860k").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_qarib60_860k| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|504.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib_ar.md new file mode 100644 index 00000000000000..1af0625cf15067 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (Base) +author: John Snow Labs +name: bert_embeddings_bert_base_qarib +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-qarib` is a Arabic model orginally trained by `qarib`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_qarib_ar_5.0.0_3.0_1687372513972.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_qarib_ar_5.0.0_3.0_1687372513972.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_qarib","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_qarib","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_qarib").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_qarib","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_qarib","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_base_qarib").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_qarib| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|504.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_dstc9_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_dstc9_en.md new file mode 100644 index 00000000000000..082e97e5cb0c38 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_dstc9_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English Bert Embeddings (from wilsontam) +author: John Snow Labs +name: bert_embeddings_bert_base_uncased_dstc9 +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-uncased-dstc9` is a English model orginally trained by `wilsontam`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_uncased_dstc9_en_5.0.0_3.0_1687372017097.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_uncased_dstc9_en_5.0.0_3.0_1687372017097.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_uncased_dstc9","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_uncased_dstc9","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.bert_base_uncased_dstc9").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_uncased_dstc9","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_uncased_dstc9","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.bert_base_uncased_dstc9").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_uncased_dstc9| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|407.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_mnli_sparse_70_unstructured_no_classifier_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_mnli_sparse_70_unstructured_no_classifier_en.md new file mode 100644 index 00000000000000..ec8779ce8600e9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_mnli_sparse_70_unstructured_no_classifier_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English Bert Embeddings (Base, Uncased, Unstructured, Without Classifier Layer) +author: John Snow Labs +name: bert_embeddings_bert_base_uncased_mnli_sparse_70_unstructured_no_classifier +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-uncased-mnli-sparse-70-unstructured-no-classifier` is a English model orginally trained by `Intel`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_uncased_mnli_sparse_70_unstructured_no_classifier_en_5.0.0_3.0_1687372422470.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_uncased_mnli_sparse_70_unstructured_no_classifier_en_5.0.0_3.0_1687372422470.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_uncased_mnli_sparse_70_unstructured_no_classifier","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_uncased_mnli_sparse_70_unstructured_no_classifier","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.bert_base_uncased_mnli_sparse_70_unstructured_no_classifier").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_uncased_mnli_sparse_70_unstructured_no_classifier","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_uncased_mnli_sparse_70_unstructured_no_classifier","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.bert_base_uncased_mnli_sparse_70_unstructured_no_classifier").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_uncased_mnli_sparse_70_unstructured_no_classifier| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|225.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_sparse_70_unstructured_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_sparse_70_unstructured_en.md new file mode 100644 index 00000000000000..0d1e90b869920a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_sparse_70_unstructured_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English Bert Embeddings (Base, Uncased, Unstructured) +author: John Snow Labs +name: bert_embeddings_bert_base_uncased_sparse_70_unstructured +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-base-uncased-sparse-70-unstructured` is a English model orginally trained by `Intel`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_uncased_sparse_70_unstructured_en_5.0.0_3.0_1687372619550.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_base_uncased_sparse_70_unstructured_en_5.0.0_3.0_1687372619550.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_uncased_sparse_70_unstructured","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_uncased_sparse_70_unstructured","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.bert_base_uncased_sparse_70_unstructured").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_uncased_sparse_70_unstructured","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_uncased_sparse_70_unstructured","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.bert_base_uncased_sparse_70_unstructured").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_base_uncased_sparse_70_unstructured| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|225.8 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_kor_base_ko.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_kor_base_ko.md new file mode 100644 index 00000000000000..cdf41dcad66847 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_kor_base_ko.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Korean Bert Embeddings (from kykim) +author: John Snow Labs +name: bert_embeddings_bert_kor_base +date: 2023-06-21 +tags: [bert, embeddings, ko, open_source, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-kor-base` is a Korean model orginally trained by `kykim`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_kor_base_ko_5.0.0_3.0_1687369025243.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_kor_base_ko_5.0.0_3.0_1687369025243.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_kor_base","ko") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_kor_base","ko") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ko.embed.bert_kor_base").predict("""나는 Spark NLP를 좋아합니다""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_kor_base","ko") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_kor_base","ko") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ko.embed.bert_kor_base").predict("""나는 Spark NLP를 좋아합니다""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_kor_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ko| +|Size:|441.2 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_medium_arabic_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_medium_arabic_ar.md new file mode 100644 index 00000000000000..e4ab0e46ec7075 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_medium_arabic_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (Medium) +author: John Snow Labs +name: bert_embeddings_bert_medium_arabic +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-medium-arabic` is a Arabic model orginally trained by `asafaya`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_medium_arabic_ar_5.0.0_3.0_1687370471346.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_medium_arabic_ar_5.0.0_3.0_1687370471346.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_medium_arabic","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_medium_arabic","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_medium_arabic").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_medium_arabic","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_medium_arabic","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_medium_arabic").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_medium_arabic| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|157.2 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_mini_arabic_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_mini_arabic_ar.md new file mode 100644 index 00000000000000..d4f0e3cce03c7e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_mini_arabic_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (Mini) +author: John Snow Labs +name: bert_embeddings_bert_mini_arabic +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-mini-arabic` is a Arabic model orginally trained by `asafaya`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_mini_arabic_ar_5.0.0_3.0_1687370518080.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_mini_arabic_ar_5.0.0_3.0_1687370518080.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_mini_arabic","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_mini_arabic","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_mini_arabic").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_mini_arabic","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_mini_arabic","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.bert_mini_arabic").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_mini_arabic| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|43.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_political_election2020_twitter_mlm_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_political_election2020_twitter_mlm_en.md new file mode 100644 index 00000000000000..ce6a900f6c10fe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_political_election2020_twitter_mlm_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English Bert Embeddings (from kornosk) +author: John Snow Labs +name: bert_embeddings_bert_political_election2020_twitter_mlm +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bert-political-election2020-twitter-mlm` is a English model orginally trained by `kornosk`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_political_election2020_twitter_mlm_en_5.0.0_3.0_1687370471142.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bert_political_election2020_twitter_mlm_en_5.0.0_3.0_1687370471142.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_political_election2020_twitter_mlm","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_political_election2020_twitter_mlm","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.bert_political_election2020_twitter_mlm").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_political_election2020_twitter_mlm","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_political_election2020_twitter_mlm","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.bert_political_election2020_twitter_mlm").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bert_political_election2020_twitter_mlm| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|407.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_beto_gn_base_cased_es.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_beto_gn_base_cased_es.md new file mode 100644 index 00000000000000..a0e6b0632c7e43 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_beto_gn_base_cased_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish Bert Embeddings (from mmaguero) +author: John Snow Labs +name: bert_embeddings_beto_gn_base_cased +date: 2023-06-21 +tags: [bert, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `beto-gn-base-cased` is a Spanish model orginally trained by `mmaguero`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_beto_gn_base_cased_es_5.0.0_3.0_1687370922012.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_beto_gn_base_cased_es_5.0.0_3.0_1687370922012.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_beto_gn_base_cased","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_beto_gn_base_cased","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.beto_gn_base_cased").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_beto_gn_base_cased","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_beto_gn_base_cased","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.beto_gn_base_cased").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_beto_gn_base_cased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|408.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bioclinicalbert_finetuned_covid_papers_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bioclinicalbert_finetuned_covid_papers_en.md new file mode 100644 index 00000000000000..259fb891ba3560 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bioclinicalbert_finetuned_covid_papers_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English BERT Embeddings Cased model (from mrm8488) +author: John Snow Labs +name: bert_embeddings_bioclinicalbert_finetuned_covid_papers +date: 2023-06-21 +tags: [en, open_source, bert, embeddings, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BERT Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bioclinicalBERT-finetuned-covid-papers` is a English model originally trained by `mrm8488`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bioclinicalbert_finetuned_covid_papers_en_5.0.0_3.0_1687337369326.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bioclinicalbert_finetuned_covid_papers_en_5.0.0_3.0_1687337369326.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bioclinicalbert_finetuned_covid_papers","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bioclinicalbert_finetuned_covid_papers","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.bert.covid_bio_clinical.finetuned").predict("""PUT YOUR STRING HERE""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_bioclinicalbert_finetuned_covid_papers","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_bioclinicalbert_finetuned_covid_papers","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.bert.covid_bio_clinical.finetuned").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bioclinicalbert_finetuned_covid_papers| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.2 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_carlbert_webex_mlm_spatial_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_carlbert_webex_mlm_spatial_en.md new file mode 100644 index 00000000000000..2f13e1e7ba8bd4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_carlbert_webex_mlm_spatial_en.md @@ -0,0 +1,140 @@ +--- +layout: model +title: English Bert Embeddings Cased model (from aditeyabaral) +author: John Snow Labs +name: bert_embeddings_carlbert_webex_mlm_spatial +date: 2023-06-21 +tags: [open_source, bert, bert_embeddings, bertformaskedlm, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `carlbert-webex-mlm-spatial` is a English model originally trained by `aditeyabaral`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_carlbert_webex_mlm_spatial_en_5.0.0_3.0_1687334153231.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_carlbert_webex_mlm_spatial_en_5.0.0_3.0_1687334153231.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_carlbert_webex_mlm_spatial","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark-NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_carlbert_webex_mlm_spatial","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark-NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_carlbert_webex_mlm_spatial","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark-NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_carlbert_webex_mlm_spatial","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark-NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_carlbert_webex_mlm_spatial| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|403.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chefberto_italian_cased_it.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chefberto_italian_cased_it.md new file mode 100644 index 00000000000000..1e2661ff475de6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chefberto_italian_cased_it.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Italian Embeddings (Base, Recipees) +author: John Snow Labs +name: bert_embeddings_chefberto_italian_cased +date: 2023-06-21 +tags: [bert, embeddings, it, open_source, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `chefberto-italian-cased` is a Italian model orginally trained by `vinhood`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_chefberto_italian_cased_it_5.0.0_3.0_1687371210449.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_chefberto_italian_cased_it_5.0.0_3.0_1687371210449.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_chefberto_italian_cased","it") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_chefberto_italian_cased","it") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Adoro Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("it.embed.chefberto_italian_cased").predict("""Adoro Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_chefberto_italian_cased","it") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_chefberto_italian_cased","it") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Adoro Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("it.embed.chefberto_italian_cased").predict("""Adoro Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_chefberto_italian_cased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|it| +|Size:|412.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_bert_uncased_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_bert_uncased_en.md new file mode 100644 index 00000000000000..8b4dcbb11e39e4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_bert_uncased_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English Bert Embeddings (Base, Uncased, Chemical) +author: John Snow Labs +name: bert_embeddings_chemical_bert_uncased +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `chemical-bert-uncased` is a English model orginally trained by `recobo`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_chemical_bert_uncased_en_5.0.0_3.0_1687370963306.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_chemical_bert_uncased_en_5.0.0_3.0_1687370963306.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_chemical_bert_uncased","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_chemical_bert_uncased","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.chemical_bert_uncased").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_chemical_bert_uncased","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_chemical_bert_uncased","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.chemical_bert_uncased").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_chemical_bert_uncased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|409.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_uncased_finetuned_cust_c1_cust_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_uncased_finetuned_cust_c1_cust_en.md new file mode 100644 index 00000000000000..1d3affe542dc5c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_uncased_finetuned_cust_c1_cust_en.md @@ -0,0 +1,140 @@ +--- +layout: model +title: English Bert Embeddings Cased model (from Shafin) +author: John Snow Labs +name: bert_embeddings_chemical_uncased_finetuned_cust_c1_cust +date: 2023-06-21 +tags: [open_source, bert, bert_embeddings, bertformaskedlm, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `chemical-bert-uncased-finetuned-cust-c1-cust` is a English model originally trained by `Shafin`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_chemical_uncased_finetuned_cust_c1_cust_en_5.0.0_3.0_1687335830911.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_chemical_uncased_finetuned_cust_c1_cust_en_5.0.0_3.0_1687335830911.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_chemical_uncased_finetuned_cust_c1_cust","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_chemical_uncased_finetuned_cust_c1_cust","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_chemical_uncased_finetuned_cust_c1_cust","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_chemical_uncased_finetuned_cust_c1_cust","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_chemical_uncased_finetuned_cust_c1_cust| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|409.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_uncased_finetuned_cust_c2_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_uncased_finetuned_cust_c2_en.md new file mode 100644 index 00000000000000..02fdd7d0894952 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_uncased_finetuned_cust_c2_en.md @@ -0,0 +1,140 @@ +--- +layout: model +title: English Bert Embeddings Cased model (from Shafin) +author: John Snow Labs +name: bert_embeddings_chemical_uncased_finetuned_cust_c2 +date: 2023-06-21 +tags: [open_source, bert, bert_embeddings, bertformaskedlm, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `chemical-bert-uncased-finetuned-cust-c2` is a English model originally trained by `shafin`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_chemical_uncased_finetuned_cust_c2_en_5.0.0_3.0_1687335658105.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_chemical_uncased_finetuned_cust_c2_en_5.0.0_3.0_1687335658105.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_chemical_uncased_finetuned_cust_c2","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_chemical_uncased_finetuned_cust_c2","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_chemical_uncased_finetuned_cust_c2","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_chemical_uncased_finetuned_cust_c2","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_chemical_uncased_finetuned_cust_c2| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|409.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_childes_bert_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_childes_bert_en.md new file mode 100644 index 00000000000000..60503fbe2496d8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_childes_bert_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English Bert Embeddings (from smeylan) +author: John Snow Labs +name: bert_embeddings_childes_bert +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `childes-bert` is a English model orginally trained by `smeylan`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_childes_bert_en_5.0.0_3.0_1687371245330.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_childes_bert_en_5.0.0_3.0_1687371245330.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_childes_bert","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_childes_bert","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.childes_bert").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_childes_bert","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_childes_bert","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.childes_bert").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_childes_bert| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|407.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_clinical_pubmed_bert_base_128_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_clinical_pubmed_bert_base_128_en.md new file mode 100644 index 00000000000000..ecc100c7def6f7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_clinical_pubmed_bert_base_128_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Clinical English Bert Embeddings (Base, 128 dimension) +author: John Snow Labs +name: bert_embeddings_clinical_pubmed_bert_base_128 +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `clinical-pubmed-bert-base-128` is a English model orginally trained by `Tsubasaz`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_clinical_pubmed_bert_base_128_en_5.0.0_3.0_1687342663053.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_clinical_pubmed_bert_base_128_en_5.0.0_3.0_1687342663053.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_clinical_pubmed_bert_base_128","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_clinical_pubmed_bert_base_128","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.clinical_pubmed_bert_base_128").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_clinical_pubmed_bert_base_128","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_clinical_pubmed_bert_base_128","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.clinical_pubmed_bert_base_128").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_clinical_pubmed_bert_base_128| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|408.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_clinical_pubmed_bert_base_512_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_clinical_pubmed_bert_base_512_en.md new file mode 100644 index 00000000000000..2c83ed64e84a41 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_clinical_pubmed_bert_base_512_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Clinical English Bert Embeddings (Base, 512 dimension) +author: John Snow Labs +name: bert_embeddings_clinical_pubmed_bert_base_512 +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `clinical-pubmed-bert-base-512` is a English model orginally trained by `Tsubasaz`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_clinical_pubmed_bert_base_512_en_5.0.0_3.0_1687341838471.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_clinical_pubmed_bert_base_512_en_5.0.0_3.0_1687341838471.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_clinical_pubmed_bert_base_512","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_clinical_pubmed_bert_base_512","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.clinical_pubmed_bert_base_512").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_clinical_pubmed_bert_base_512","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_clinical_pubmed_bert_base_512","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.clinical_pubmed_bert_base_512").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_clinical_pubmed_bert_base_512| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|408.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_crosloengual_bert_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_crosloengual_bert_en.md new file mode 100644 index 00000000000000..89a6839a95ee56 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_crosloengual_bert_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Multilingual (Croatian, Slovenian, English) Bert Embeddings (Base) +author: John Snow Labs +name: bert_embeddings_crosloengual_bert +date: 2023-06-21 +tags: [bert, embeddings, en, hr, sl, xx, multilingual, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `crosloengual-bert` is a English model orginally trained by `EMBEDDIA`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_crosloengual_bert_en_5.0.0_3.0_1687341501117.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_crosloengual_bert_en_5.0.0_3.0_1687341501117.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_crosloengual_bert","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_crosloengual_bert","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.crosloengual_bert").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_crosloengual_bert","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_crosloengual_bert","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.crosloengual_bert").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_crosloengual_bert| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|463.4 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dbert_ko.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dbert_ko.md new file mode 100644 index 00000000000000..0c6f71e2a0ddb4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dbert_ko.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Korean Bert Embeddings (from deeq) +author: John Snow Labs +name: bert_embeddings_dbert +date: 2023-06-21 +tags: [bert, embeddings, ko, open_source, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `dbert` is a Korean model orginally trained by `deeq`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_dbert_ko_5.0.0_3.0_1687341138674.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_dbert_ko_5.0.0_3.0_1687341138674.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_dbert","ko") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_dbert","ko") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ko.embed.dbert").predict("""나는 Spark NLP를 좋아합니다""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_dbert","ko") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_dbert","ko") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ko.embed.dbert").predict("""나는 Spark NLP를 좋아합니다""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_dbert| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ko| +|Size:|421.2 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_deberta_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_deberta_base_uncased_en.md new file mode 100644 index 00000000000000..22fae6bd9c819c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_deberta_base_uncased_en.md @@ -0,0 +1,153 @@ +--- +layout: model +title: English BertForMaskedLM Base Uncased model (from mlcorelib) +author: John Snow Labs +name: bert_embeddings_deberta_base_uncased +date: 2023-06-21 +tags: [en, open_source, bert_embeddings, bertformaskedlm, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForMaskedLM model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `deberta-base-uncased` is a English model originally trained by `mlcorelib`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_deberta_base_uncased_en_5.0.0_3.0_1687341134871.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_deberta_base_uncased_en_5.0.0_3.0_1687341134871.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +bert_loaded = BertEmbeddings.pretrained("bert_embeddings_deberta_base_uncased","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, bert_loaded]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val bert_loaded = BertEmbeddings.pretrained("bert_embeddings_deberta_base_uncased","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, bert_loaded)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.deberta_base_uncased").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +bert_loaded = BertEmbeddings.pretrained("bert_embeddings_deberta_base_uncased","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, bert_loaded]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val bert_loaded = BertEmbeddings.pretrained("bert_embeddings_deberta_base_uncased","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, bert_loaded)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.deberta_base_uncased").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_deberta_base_uncased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|407.2 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_distil_clinical_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_distil_clinical_en.md new file mode 100644 index 00000000000000..e29412f032a148 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_distil_clinical_en.md @@ -0,0 +1,140 @@ +--- +layout: model +title: English Bert Embeddings Cased model (from nlpie) +author: John Snow Labs +name: bert_embeddings_distil_clinical +date: 2023-06-21 +tags: [open_source, bert, bert_embeddings, bertformaskedlm, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `distil-clinicalbert` is a English model originally trained by `nlpie`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_distil_clinical_en_5.0.0_3.0_1687334036385.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_distil_clinical_en_5.0.0_3.0_1687334036385.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_distil_clinical","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark-NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_distil_clinical","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark-NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_distil_clinical","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark-NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_distil_clinical","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark-NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_distil_clinical| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|244.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_passage_encoder_allqa_base_es.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_passage_encoder_allqa_base_es.md new file mode 100644 index 00000000000000..622e96cea211e0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_passage_encoder_allqa_base_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish Bert Embeddings (Base, Pasage, Allqa) +author: John Snow Labs +name: bert_embeddings_dpr_spanish_passage_encoder_allqa_base +date: 2023-06-21 +tags: [bert, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `dpr-spanish-passage_encoder-allqa-base` is a Spanish model orginally trained by `IIC`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_dpr_spanish_passage_encoder_allqa_base_es_5.0.0_3.0_1687341854288.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_dpr_spanish_passage_encoder_allqa_base_es_5.0.0_3.0_1687341854288.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_passage_encoder_allqa_base","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_passage_encoder_allqa_base","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.dpr_spanish_passage_encoder_allqa_base").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_passage_encoder_allqa_base","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_passage_encoder_allqa_base","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.dpr_spanish_passage_encoder_allqa_base").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_dpr_spanish_passage_encoder_allqa_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|409.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_passage_encoder_squades_base_es.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_passage_encoder_squades_base_es.md new file mode 100644 index 00000000000000..5b4101b177a992 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_passage_encoder_squades_base_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish Bert Embeddings (Base, Pasage, Squades) +author: John Snow Labs +name: bert_embeddings_dpr_spanish_passage_encoder_squades_base +date: 2023-06-21 +tags: [bert, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `dpr-spanish-passage_encoder-squades-base` is a Spanish model orginally trained by `IIC`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_dpr_spanish_passage_encoder_squades_base_es_5.0.0_3.0_1687341276775.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_dpr_spanish_passage_encoder_squades_base_es_5.0.0_3.0_1687341276775.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_passage_encoder_squades_base","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_passage_encoder_squades_base","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.dpr_spanish_passage_encoder_squades_base").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_passage_encoder_squades_base","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_passage_encoder_squades_base","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.dpr_spanish_passage_encoder_squades_base").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_dpr_spanish_passage_encoder_squades_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|409.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_question_encoder_allqa_base_es.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_question_encoder_allqa_base_es.md new file mode 100644 index 00000000000000..fbf26507302d99 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_question_encoder_allqa_base_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish Bert Embeddings (Base, Question, Allqa) +author: John Snow Labs +name: bert_embeddings_dpr_spanish_question_encoder_allqa_base +date: 2023-06-21 +tags: [bert, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `dpr-spanish-question_encoder-allqa-base` is a Spanish model orginally trained by `IIC`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_dpr_spanish_question_encoder_allqa_base_es_5.0.0_3.0_1687340961201.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_dpr_spanish_question_encoder_allqa_base_es_5.0.0_3.0_1687340961201.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_question_encoder_allqa_base","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_question_encoder_allqa_base","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.dpr_spanish_question_encoder_allqa_base").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_question_encoder_allqa_base","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_question_encoder_allqa_base","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.dpr_spanish_question_encoder_allqa_base").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_dpr_spanish_question_encoder_allqa_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|409.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_question_encoder_squades_base_es.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_question_encoder_squades_base_es.md new file mode 100644 index 00000000000000..537f94ae494528 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_question_encoder_squades_base_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish Bert Embeddings (Base, Question, Squades) +author: John Snow Labs +name: bert_embeddings_dpr_spanish_question_encoder_squades_base +date: 2023-06-21 +tags: [bert, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `dpr-spanish-question_encoder-squades-base` is a Spanish model orginally trained by `IIC`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_dpr_spanish_question_encoder_squades_base_es_5.0.0_3.0_1687341460131.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_dpr_spanish_question_encoder_squades_base_es_5.0.0_3.0_1687341460131.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_question_encoder_squades_base","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_question_encoder_squades_base","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.dpr_spanish_question_encoder_squades_base").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_question_encoder_squades_base","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_question_encoder_squades_base","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.dpr_spanish_question_encoder_squades_base").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_dpr_spanish_question_encoder_squades_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|409.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dziribert_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dziribert_ar.md new file mode 100644 index 00000000000000..4a1f66ee841cec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dziribert_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (from alger-ia) +author: John Snow Labs +name: bert_embeddings_dziribert +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `dziribert` is a Arabic model orginally trained by `alger-ia`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_dziribert_ar_5.0.0_3.0_1687341113062.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_dziribert_ar_5.0.0_3.0_1687341113062.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_dziribert","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_dziribert","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.dziribert").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_dziribert","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_dziribert","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.dziribert").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_dziribert| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|462.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_false_positives_scancode_bert_base_uncased_L8_1_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_false_positives_scancode_bert_base_uncased_L8_1_en.md new file mode 100644 index 00000000000000..8bee08b62d0c53 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_false_positives_scancode_bert_base_uncased_L8_1_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English Bert Embeddings (Uncased) +author: John Snow Labs +name: bert_embeddings_false_positives_scancode_bert_base_uncased_L8_1 +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `false-positives-scancode-bert-base-uncased-L8-1` is a English model orginally trained by `ayansinha`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_false_positives_scancode_bert_base_uncased_L8_1_en_5.0.0_3.0_1687340166023.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_false_positives_scancode_bert_base_uncased_L8_1_en_5.0.0_3.0_1687340166023.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_false_positives_scancode_bert_base_uncased_L8_1","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_false_positives_scancode_bert_base_uncased_L8_1","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.false_positives_scancode_bert_base_uncased_L8_1").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_false_positives_scancode_bert_base_uncased_L8_1","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_false_positives_scancode_bert_base_uncased_L8_1","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.false_positives_scancode_bert_base_uncased_L8_1").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_false_positives_scancode_bert_base_uncased_L8_1| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|407.2 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_finbert_pretrain_yiyanghkust_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_finbert_pretrain_yiyanghkust_en.md new file mode 100644 index 00000000000000..a8f6efb7ae0030 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_finbert_pretrain_yiyanghkust_en.md @@ -0,0 +1,153 @@ +--- +layout: model +title: Financial English Bert Embeddings (Base, Communication texts) +author: John Snow Labs +name: bert_embeddings_finbert_pretrain_yiyanghkust +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Financial English Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `finbert-pretrain-yiyanghkust` is a English model orginally available in Hugging Face as `yiyanghkust/finbert-pretrain`. It was trained on the following datasets: + +- Corporate Reports 10-K & 10-Q: 2.5B tokens +- Earnings Call Transcripts: 1.3B tokens +- Analyst Reports: 1.1B tokens + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_finbert_pretrain_yiyanghkust_en_5.0.0_3.0_1687340890257.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_finbert_pretrain_yiyanghkust_en_5.0.0_3.0_1687340890257.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_finbert_pretrain_yiyanghkust","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_finbert_pretrain_yiyanghkust","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.finbert_pretrain_yiyanghkust").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_finbert_pretrain_yiyanghkust","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_finbert_pretrain_yiyanghkust","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.finbert_pretrain_yiyanghkust").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_finbert_pretrain_yiyanghkust| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|409.4 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_finest_bert_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_finest_bert_en.md new file mode 100644 index 00000000000000..e01933d053546d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_finest_bert_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Multilingual (Finnish, Estonian, English) Bert Embeddings (Base) +author: John Snow Labs +name: bert_embeddings_finest_bert +date: 2023-06-21 +tags: [bert, embeddings, fi, et, en, xx, multilingual, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `finest-bert` is a English model orginally trained by `EMBEDDIA`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_finest_bert_en_5.0.0_3.0_1687339089124.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_finest_bert_en_5.0.0_3.0_1687339089124.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_finest_bert","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_finest_bert","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.finest_bert").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_finest_bert","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_finest_bert","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.finest_bert").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_finest_bert| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|535.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_gbert_base_de.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_gbert_base_de.md new file mode 100644 index 00000000000000..f7c53a48bd8905 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_gbert_base_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German Bert Embeddings (Base, Cased) +author: John Snow Labs +name: bert_embeddings_gbert_base +date: 2023-06-21 +tags: [bert, embeddings, de, open_source, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `gbert-base` is a German model orginally trained by `deepset`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_gbert_base_de_5.0.0_3.0_1687339723694.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_gbert_base_de_5.0.0_3.0_1687339723694.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_gbert_base","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_gbert_base","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.gbert_base").predict("""Ich liebe Funken NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_gbert_base","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_gbert_base","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.gbert_base").predict("""Ich liebe Funken NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_gbert_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|de| +|Size:|409.7 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_german_financial_statements_bert_de.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_german_financial_statements_bert_de.md new file mode 100644 index 00000000000000..bd29b725b81ae5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_german_financial_statements_bert_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German Financial Bert Word Embeddings +author: John Snow Labs +name: bert_embeddings_german_financial_statements_bert +date: 2023-06-21 +tags: [bert, embeddings, de, open_source, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Financial Bert Word Embeddings model, trained on German Financial Statements. Uploaded to Hugging Face, adapted and imported into Spark NLP. `german-financial-statements-bert` is a German Financial model orginally trained upon 100,000 natural language annual financial statements. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_german_financial_statements_bert_de_5.0.0_3.0_1687339007310.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_german_financial_statements_bert_de_5.0.0_3.0_1687339007310.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_german_financial_statements_bert","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_german_financial_statements_bert","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.german_financial_statements_bert").predict("""Ich liebe Funken NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_german_financial_statements_bert","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_german_financial_statements_bert","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.german_financial_statements_bert").predict("""Ich liebe Funken NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_german_financial_statements_bert| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|de| +|Size:|406.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_hateBERT_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_hateBERT_en.md new file mode 100644 index 00000000000000..82f3c85d2247d3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_hateBERT_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English Bert Embeddings (from GroNLP) +author: John Snow Labs +name: bert_embeddings_hateBERT +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `hateBERT` is a English model orginally trained by `GroNLP`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_hateBERT_en_5.0.0_3.0_1687340123478.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_hateBERT_en_5.0.0_3.0_1687340123478.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_hateBERT","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_hateBERT","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.hateBERT").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_hateBERT","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_hateBERT","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.hateBERT").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_hateBERT| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|406.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_hseBert_it_cased_it.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_hseBert_it_cased_it.md new file mode 100644 index 00000000000000..0ac43ca8f34236 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_hseBert_it_cased_it.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Italian Bert Embeddings (from bullmount) +author: John Snow Labs +name: bert_embeddings_hseBert_it_cased +date: 2023-06-21 +tags: [bert, embeddings, it, open_source, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `hseBert-it-cased` is a Italian model orginally trained by `bullmount`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_hseBert_it_cased_it_5.0.0_3.0_1687340783377.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_hseBert_it_cased_it_5.0.0_3.0_1687340783377.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_hseBert_it_cased","it") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_hseBert_it_cased","it") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Adoro Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("it.embed.hseBert_it_cased").predict("""Adoro Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_hseBert_it_cased","it") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_hseBert_it_cased","it") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Adoro Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("it.embed.hseBert_it_cased").predict("""Adoro Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_hseBert_it_cased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|it| +|Size:|409.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_indic_transformers_hi_bert_hi.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_indic_transformers_hi_bert_hi.md new file mode 100644 index 00000000000000..cb1d065f6341ec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_indic_transformers_hi_bert_hi.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Hindi Bert Embeddings +author: John Snow Labs +name: bert_embeddings_indic_transformers_hi_bert +date: 2023-06-21 +tags: [bert, embeddings, hi, open_source, onnx] +task: Embeddings +language: hi +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `indic-transformers-hi-bert` is a Hindi model orginally trained by `neuralspace-reverie`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_indic_transformers_hi_bert_hi_5.0.0_3.0_1687339963111.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_indic_transformers_hi_bert_hi_5.0.0_3.0_1687339963111.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_indic_transformers_hi_bert","hi") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["मुझे स्पार्क एनएलपी पसंद है"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_indic_transformers_hi_bert","hi") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("मुझे स्पार्क एनएलपी पसंद है").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("hi.embed.indic_transformers_hi_bert").predict("""मुझे स्पार्क एनएलपी पसंद है""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_indic_transformers_hi_bert","hi") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["मुझे स्पार्क एनएलपी पसंद है"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_indic_transformers_hi_bert","hi") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("मुझे स्पार्क एनएलपी पसंद है").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("hi.embed.indic_transformers_hi_bert").predict("""मुझे स्पार्क एनएलपी पसंद है""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_indic_transformers_hi_bert| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|hi| +|Size:|609.2 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_indic_transformers_te_bert_te.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_indic_transformers_te_bert_te.md new file mode 100644 index 00000000000000..286777564e9003 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_indic_transformers_te_bert_te.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Telugu Bert Embeddings (from neuralspace-reverie) +author: John Snow Labs +name: bert_embeddings_indic_transformers_te_bert +date: 2023-06-21 +tags: [bert, embeddings, te, open_source, onnx] +task: Embeddings +language: te +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `indic-transformers-te-bert` is a Telugu model orginally trained by `neuralspace-reverie`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_indic_transformers_te_bert_te_5.0.0_3.0_1687340459352.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_indic_transformers_te_bert_te_5.0.0_3.0_1687340459352.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_indic_transformers_te_bert","te") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_indic_transformers_te_bert","te") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("te.embed.indic_transformers_te_bert").predict("""నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_indic_transformers_te_bert","te") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_indic_transformers_te_bert","te") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("te.embed.indic_transformers_te_bert").predict("""నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_indic_transformers_te_bert| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|te| +|Size:|609.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_javanese_bert_small_imdb_jv.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_javanese_bert_small_imdb_jv.md new file mode 100644 index 00000000000000..9f834ccdc83fa0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_javanese_bert_small_imdb_jv.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Javanese Bert Embeddings (Small, Imdb) +author: John Snow Labs +name: bert_embeddings_javanese_bert_small_imdb +date: 2023-06-21 +tags: [bert, embeddings, jv, open_source, onnx] +task: Embeddings +language: jv +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `javanese-bert-small-imdb` is a Javanese model orginally trained by `w11wo`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_javanese_bert_small_imdb_jv_5.0.0_3.0_1687341195384.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_javanese_bert_small_imdb_jv_5.0.0_3.0_1687341195384.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_javanese_bert_small_imdb","jv") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_javanese_bert_small_imdb","jv") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("jv.embed.javanese_bert_small_imdb").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_javanese_bert_small_imdb","jv") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_javanese_bert_small_imdb","jv") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("jv.embed.javanese_bert_small_imdb").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_javanese_bert_small_imdb| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|jv| +|Size:|407.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_javanese_bert_small_jv.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_javanese_bert_small_jv.md new file mode 100644 index 00000000000000..92986039f002be --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_javanese_bert_small_jv.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Javanese Bert Embeddings (Small, Wikipedia) +author: John Snow Labs +name: bert_embeddings_javanese_bert_small +date: 2023-06-21 +tags: [bert, embeddings, jv, open_source, onnx] +task: Embeddings +language: jv +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `javanese-bert-small` is a Javanese model orginally trained by `w11wo`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_javanese_bert_small_jv_5.0.0_3.0_1687339377809.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_javanese_bert_small_jv_5.0.0_3.0_1687339377809.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_javanese_bert_small","jv") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_javanese_bert_small","jv") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("jv.embed.javanese_bert_small").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_javanese_bert_small","jv") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_javanese_bert_small","jv") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("jv.embed.javanese_bert_small").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_javanese_bert_small| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|jv| +|Size:|407.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_jobbert_base_cased_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_jobbert_base_cased_en.md new file mode 100644 index 00000000000000..ac620f546f44ce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_jobbert_base_cased_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English BERT Embeddings (from jjzha) +author: John Snow Labs +name: bert_embeddings_jobbert_base_cased +date: 2023-06-21 +tags: [bert, en, embeddings, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BERT Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `jobbert-base-cased ` is a English model originally trained by `jjzha`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_jobbert_base_cased_en_5.0.0_3.0_1687336524220.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_jobbert_base_cased_en_5.0.0_3.0_1687336524220.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_jobbert_base_cased","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_jobbert_base_cased","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.bert.cased_base").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_jobbert_base_cased","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_jobbert_base_cased","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.bert.cased_base").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_jobbert_base_cased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|402.2 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_legal_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_legal_bert_base_uncased_en.md new file mode 100644 index 00000000000000..a490d46ea569a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_legal_bert_base_uncased_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Legal English Bert Embeddings (Base, Uncased) +author: John Snow Labs +name: bert_embeddings_legal_bert_base_uncased +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, legal, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Legal Pretrained Bert Embeddings model, trained with uncased text, uploaded to Hugging Face, adapted and imported into Spark NLP. `legal-bert-base-uncased` is a English model orginally trained by `nlpaueb`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_legal_bert_base_uncased_en_5.0.0_3.0_1687341978829.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_legal_bert_base_uncased_en_5.0.0_3.0_1687341978829.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_legal_bert_base_uncased","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_legal_bert_base_uncased","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.legal_bert_base_uncased").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_legal_bert_base_uncased","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_legal_bert_base_uncased","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.legal_bert_base_uncased").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_legal_bert_base_uncased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|407.2 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_legalbert_adept_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_legalbert_adept_en.md new file mode 100644 index 00000000000000..5dc84ccc68d550 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_legalbert_adept_en.md @@ -0,0 +1,135 @@ +--- +layout: model +title: English Legal BERT Embeddings +author: John Snow Labs +name: bert_embeddings_legalbert_adept +date: 2023-06-21 +tags: [bert, en, english, embeddings, transformer, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BERT Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `legalbert-adept` is a English model originally trained by `hatemestinbejaia`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_legalbert_adept_en_5.0.0_3.0_1687335917569.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_legalbert_adept_en_5.0.0_3.0_1687335917569.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + documentAssembler = nlp.DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = nlp.Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = nlp.BertEmbeddings.pretrained("bert_embeddings_legalbert_adept","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = nlp.Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_legalbert_adept","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = nlp.DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = nlp.Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = nlp.BertEmbeddings.pretrained("bert_embeddings_legalbert_adept","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = nlp.Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_legalbert_adept","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_legalbert_adept| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|407.2 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_lic_class_scancode_bert_base_cased_L32_1_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_lic_class_scancode_bert_base_cased_L32_1_en.md new file mode 100644 index 00000000000000..6e0309525b771a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_lic_class_scancode_bert_base_cased_L32_1_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English Bert Embeddings (Cased) +author: John Snow Labs +name: bert_embeddings_lic_class_scancode_bert_base_cased_L32_1 +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `lic-class-scancode-bert-base-cased-L32-1` is a English model orginally trained by `ayansinha`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_lic_class_scancode_bert_base_cased_L32_1_en_5.0.0_3.0_1687351576851.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_lic_class_scancode_bert_base_cased_L32_1_en_5.0.0_3.0_1687351576851.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_lic_class_scancode_bert_base_cased_L32_1","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_lic_class_scancode_bert_base_cased_L32_1","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.lic_class_scancode_bert_base_cased_L32_1").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_lic_class_scancode_bert_base_cased_L32_1","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_lic_class_scancode_bert_base_cased_L32_1","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.lic_class_scancode_bert_base_cased_L32_1").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_lic_class_scancode_bert_base_cased_L32_1| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|403.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_lsg16k_Italian_Legal_it.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_lsg16k_Italian_Legal_it.md new file mode 100644 index 00000000000000..022feacea52d3c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_lsg16k_Italian_Legal_it.md @@ -0,0 +1,135 @@ +--- +layout: model +title: English Legal BERT Embeddings +author: John Snow Labs +name: bert_embeddings_lsg16k_Italian_Legal +date: 2023-06-21 +tags: [longformer, it, italian, embeddings, transformer, open_source, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BERT Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `lsg16k-Italian-Legal-BERT` is a Italian model originally trained by `dlicari`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_lsg16k_Italian_Legal_it_5.0.0_3.0_1687335744395.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_lsg16k_Italian_Legal_it_5.0.0_3.0_1687335744395.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + documentAssembler = nlp.DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = nlp.Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = nlp.BertEmbeddings.pretrained("bert_embeddings_lsg16k_Italian_Legal","it") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = nlp.Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_lsg16k_Italian_Legal","it") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Adoro Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = nlp.DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = nlp.Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = nlp.BertEmbeddings.pretrained("bert_embeddings_lsg16k_Italian_Legal","it") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = nlp.Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_lsg16k_Italian_Legal","it") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Adoro Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_lsg16k_Italian_Legal| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|it| +|Size:|454.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_marathi_bert_mr.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_marathi_bert_mr.md new file mode 100644 index 00000000000000..6b39af7927411f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_marathi_bert_mr.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Marathi Bert Embeddings +author: John Snow Labs +name: bert_embeddings_marathi_bert +date: 2023-06-21 +tags: [bert, embeddings, mr, open_source, onnx] +task: Embeddings +language: mr +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `marathi-bert` is a Marathi model orginally trained by `l3cube-pune`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_marathi_bert_mr_5.0.0_3.0_1687350857061.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_marathi_bert_mr_5.0.0_3.0_1687350857061.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_marathi_bert","mr") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["मला स्पार्क एनएलपी आवडते"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_marathi_bert","mr") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("मला स्पार्क एनएलपी आवडते").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("mr.embed.marathi_bert").predict("""मला स्पार्क एनएलपी आवडते""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_marathi_bert","mr") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["मला स्पार्क एनएलपी आवडते"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_marathi_bert","mr") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("मला स्पार्क एनएलपी आवडते").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("mr.embed.marathi_bert").predict("""मला स्पार्क एनएलपी आवडते""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_marathi_bert| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|mr| +|Size:|665.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_mbert_ar_c19_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_mbert_ar_c19_ar.md new file mode 100644 index 00000000000000..3d6a26c549f735 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_mbert_ar_c19_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (Mbert model, Covid-19) +author: John Snow Labs +name: bert_embeddings_mbert_ar_c19 +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `mbert_ar_c19` is a Arabic model orginally trained by `moha`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_mbert_ar_c19_ar_5.0.0_3.0_1687351164607.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_mbert_ar_c19_ar_5.0.0_3.0_1687351164607.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_mbert_ar_c19","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_mbert_ar_c19","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.mbert_ar_c19").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_mbert_ar_c19","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_mbert_ar_c19","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.mbert_ar_c19").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_mbert_ar_c19| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|624.7 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_multi_dialect_bert_base_arabic_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_multi_dialect_bert_base_arabic_ar.md new file mode 100644 index 00000000000000..bc40d7959ba191 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_multi_dialect_bert_base_arabic_ar.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Arabic Bert Embeddings (from bashar-talafha) +author: John Snow Labs +name: bert_embeddings_multi_dialect_bert_base_arabic +date: 2023-06-21 +tags: [bert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `multi-dialect-bert-base-arabic` is a Arabic model orginally trained by `bashar-talafha`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_multi_dialect_bert_base_arabic_ar_5.0.0_3.0_1687351229326.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_multi_dialect_bert_base_arabic_ar_5.0.0_3.0_1687351229326.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_multi_dialect_bert_base_arabic","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_multi_dialect_bert_base_arabic","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.multi_dialect_bert_base_arabic").predict("""أنا أحب شرارة NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_multi_dialect_bert_base_arabic","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_multi_dialect_bert_base_arabic","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.multi_dialect_bert_base_arabic").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_multi_dialect_bert_base_arabic| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|411.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_netbert_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_netbert_en.md new file mode 100644 index 00000000000000..2edd51e298a1d8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_netbert_en.md @@ -0,0 +1,153 @@ +--- +layout: model +title: English BertForMaskedLM Cased model (from antoinelouis) +author: John Snow Labs +name: bert_embeddings_netbert +date: 2023-06-21 +tags: [en, open_source, bert_embeddings, bertformaskedlm, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForMaskedLM model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `netbert` is a English model originally trained by `antoinelouis`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_netbert_en_5.0.0_3.0_1687351022341.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_netbert_en_5.0.0_3.0_1687351022341.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +bert_loaded = BertEmbeddings.pretrained("bert_embeddings_netbert","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, bert_loaded]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val bert_loaded = BertEmbeddings.pretrained("bert_embeddings_netbert","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, bert_loaded)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.netbert").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +bert_loaded = BertEmbeddings.pretrained("bert_embeddings_netbert","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, bert_loaded]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val bert_loaded = BertEmbeddings.pretrained("bert_embeddings_netbert","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, bert_loaded)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.netbert").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_netbert| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|403.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_olm_base_uncased_oct_2022_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_olm_base_uncased_oct_2022_en.md new file mode 100644 index 00000000000000..3566124863b301 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_olm_base_uncased_oct_2022_en.md @@ -0,0 +1,140 @@ +--- +layout: model +title: English Bert Embeddings Cased model (from Tristan) +author: John Snow Labs +name: bert_embeddings_olm_base_uncased_oct_2022 +date: 2023-06-21 +tags: [open_source, bert, bert_embeddings, bertformaskedlm, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `olm-bert-base-uncased-oct-2022` is a English model originally trained by `Tristan`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_olm_base_uncased_oct_2022_en_5.0.0_3.0_1687336305222.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_olm_base_uncased_oct_2022_en_5.0.0_3.0_1687336305222.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_olm_base_uncased_oct_2022","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_olm_base_uncased_oct_2022","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_olm_base_uncased_oct_2022","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_olm_base_uncased_oct_2022","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_olm_base_uncased_oct_2022| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|464.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_pretrain_ko.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_pretrain_ko.md new file mode 100644 index 00000000000000..0308fcbcd07582 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_pretrain_ko.md @@ -0,0 +1,140 @@ +--- +layout: model +title: Korean Bert Embeddings Cased model (from onlydj96) +author: John Snow Labs +name: bert_embeddings_pretrain +date: 2023-06-21 +tags: [open_source, bert, bert_embeddings, bertformaskedlm, ko, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert_pretrain` is a Korean model originally trained by `onlydj96`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_pretrain_ko_5.0.0_3.0_1687336252702.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_pretrain_ko_5.0.0_3.0_1687336252702.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_pretrain","ko") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_pretrain","ko") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_pretrain","ko") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_pretrain","ko") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_pretrain| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ko| +|Size:|412.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_psych_search_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_psych_search_en.md new file mode 100644 index 00000000000000..f12d896dcbc372 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_psych_search_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English Bert Embeddings (from nlp4good) +author: John Snow Labs +name: bert_embeddings_psych_search +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `psych-search` is a English model orginally trained by `nlp4good`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_psych_search_en_5.0.0_3.0_1687350768319.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_psych_search_en_5.0.0_3.0_1687350768319.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_psych_search","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_psych_search","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.psych_search").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_psych_search","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_psych_search","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.psych_search").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_psych_search| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|409.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_scibert_scivocab_finetuned_cord19_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_scibert_scivocab_finetuned_cord19_en.md new file mode 100644 index 00000000000000..1e641bcf5d79b3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_scibert_scivocab_finetuned_cord19_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English BERT Embeddings Cased model (from mrm8488) +author: John Snow Labs +name: bert_embeddings_scibert_scivocab_finetuned_cord19 +date: 2023-06-21 +tags: [en, open_source, bert, embeddings, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BERT Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `scibert_scivocab-finetuned-CORD19` is a English model originally trained by `mrm8488`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_scibert_scivocab_finetuned_cord19_en_5.0.0_3.0_1687336817133.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_scibert_scivocab_finetuned_cord19_en_5.0.0_3.0_1687336817133.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_scibert_scivocab_finetuned_cord19","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_scibert_scivocab_finetuned_cord19","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.scibert.cord19_scibert.finetuned").predict("""PUT YOUR STRING HERE""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_scibert_scivocab_finetuned_cord19","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_scibert_scivocab_finetuned_cord19","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.scibert.cord19_scibert.finetuned").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_scibert_scivocab_finetuned_cord19| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sec_bert_base_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sec_bert_base_en.md new file mode 100644 index 00000000000000..8e1158bbb801c8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sec_bert_base_en.md @@ -0,0 +1,154 @@ +--- +layout: model +title: Financial English BERT Embeddings (Base) +author: John Snow Labs +name: bert_embeddings_sec_bert_base +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, financial, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Financial Pretrained BERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `sec-bert-base` is a English model orginally trained by `nlpaueb`. This is the reference base model, what means it uses the same architecture as BERT-BASE trained on financial documents. + +If you are interested in Financial Embeddings, take a look also at these two models: + +- [sec-num](https://nlp.johnsnowlabs.com/2022/04/12/bert_embeddings_sec_bert_num_en_3_0.html): Same as this base model but we replace every number token with a [NUM] pseudo-token handling all numeric expressions in a uniform manner, disallowing their fragmentation). +- [sec-shape](https://nlp.johnsnowlabs.com/2022/04/12/bert_embeddings_sec_bert_sh_en_3_0.html): Same as this base model but we replace numbers with pseudo-tokens that represent the number’s shape, so numeric expressions (of known shapes) are no longer fragmented, e.g., '53.2' becomes '[XX.X]' and '40,200.5' becomes '[XX,XXX.X]'. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_sec_bert_base_en_5.0.0_3.0_1687339042219.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_sec_bert_base_en_5.0.0_3.0_1687339042219.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_sec_bert_base","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_sec_bert_base","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.sec_bert_base").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_sec_bert_base","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_sec_bert_base","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.sec_bert_base").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_sec_bert_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|406.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sec_bert_sh_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sec_bert_sh_en.md new file mode 100644 index 00000000000000..0958df101c9131 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sec_bert_sh_en.md @@ -0,0 +1,155 @@ +--- +layout: model +title: Financial English BERT Embeddings (Number shape masking) +author: John Snow Labs +name: bert_embeddings_sec_bert_sh +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, financial, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Financial BERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `sec-bert-shape` is a English model orginally trained by `nlpaueb`.This model is the same as Bert Base but we replace numbers with pseudo-tokens that represent the number’s shape, so numeric expressions (of known shapes) are no longer fragmented, e.g., '53.2' becomes '[XX.X]' and '40,200.5' becomes '[XX,XXX.X]'. + +If you are interested in Financial Embeddings, take a look also at these two models: + +- [sec-base](https://nlp.johnsnowlabs.com/2022/04/12/bert_embeddings_sec_bert_base_en_3_0.html): Same as BERT Base but trained with financial documents. +- [sec-num](https://nlp.johnsnowlabs.com/2022/04/12/bert_embeddings_sec_bert_num_en_3_0.html): Same as Bert sec-base but we replace every number token with a [NUM] pseudo-token handling all numeric expressions in a uniform manner, disallowing their fragmentation). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_sec_bert_sh_en_5.0.0_3.0_1687339128341.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_sec_bert_sh_en_5.0.0_3.0_1687339128341.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_sec_bert_sh","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_sec_bert_sh","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.sec_bert_sh").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_sec_bert_sh","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_sec_bert_sh","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.sec_bert_sh").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_sec_bert_sh| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|406.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sikubert_zh.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sikubert_zh.md new file mode 100644 index 00000000000000..ce8ab67067aa3f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sikubert_zh.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Chinese Bert Embeddings (Siku Quanshu corpus) +author: John Snow Labs +name: bert_embeddings_sikubert +date: 2023-06-21 +tags: [bert, embeddings, zh, open_source, onnx] +task: Embeddings +language: zh +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `sikubert` is a Chinese model orginally trained by `SIKU-BERT`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_sikubert_zh_5.0.0_3.0_1687343740087.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_sikubert_zh_5.0.0_3.0_1687343740087.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_sikubert","zh") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_sikubert","zh") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("zh.embed.sikubert").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_sikubert","zh") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_sikubert","zh") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("zh.embed.sikubert").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_sikubert| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|zh| +|Size:|406.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sikuroberta_zh.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sikuroberta_zh.md new file mode 100644 index 00000000000000..68d128c492b7cd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sikuroberta_zh.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Chinese Bert Embeddings (from SIKU-BERT) +author: John Snow Labs +name: bert_embeddings_sikuroberta +date: 2023-06-21 +tags: [bert, embeddings, zh, open_source, onnx] +task: Embeddings +language: zh +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `sikuroberta` is a Chinese model orginally trained by `SIKU-BERT`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_sikuroberta_zh_5.0.0_3.0_1687343322944.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_sikuroberta_zh_5.0.0_3.0_1687343322944.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_sikuroberta","zh") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_sikuroberta","zh") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("zh.embed.sikuroberta").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_sikuroberta","zh") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_sikuroberta","zh") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("zh.embed.sikuroberta").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_sikuroberta| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|zh| +|Size:|405.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_telugu_bertu_te.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_telugu_bertu_te.md new file mode 100644 index 00000000000000..a521a954fb4236 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_telugu_bertu_te.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Telugu Bert Embeddings +author: John Snow Labs +name: bert_embeddings_telugu_bertu +date: 2023-06-21 +tags: [bert, embeddings, te, open_source, onnx] +task: Embeddings +language: te +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `telugu_bertu` is a Telugu model orginally trained by `kuppuluri`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_telugu_bertu_te_5.0.0_3.0_1687343021533.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_telugu_bertu_te_5.0.0_3.0_1687343021533.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_telugu_bertu","te") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_telugu_bertu","te") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("te.embed.telugu_bertu").predict("""నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_telugu_bertu","te") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_telugu_bertu","te") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("te.embed.telugu_bertu").predict("""నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_telugu_bertu| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|te| +|Size:|412.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_wineberto_italian_cased_it.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_wineberto_italian_cased_it.md new file mode 100644 index 00000000000000..64d487ee4850f5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_wineberto_italian_cased_it.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Italian Embeddings (Base, Wines description) +author: John Snow Labs +name: bert_embeddings_wineberto_italian_cased +date: 2023-06-21 +tags: [bert, embeddings, it, open_source, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `wineberto-italian-cased` is a Italian model orginally trained by `vinhood`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_wineberto_italian_cased_it_5.0.0_3.0_1687343289463.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_wineberto_italian_cased_it_5.0.0_3.0_1687343289463.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_wineberto_italian_cased","it") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_wineberto_italian_cased","it") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Adoro Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("it.embed.wineberto_italian_cased").predict("""Adoro Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_wineberto_italian_cased","it") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_wineberto_italian_cased","it") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Adoro Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("it.embed.wineberto_italian_cased").predict("""Adoro Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_wineberto_italian_cased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|it| +|Size:|412.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_wobert_chinese_plus_zh.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_wobert_chinese_plus_zh.md new file mode 100644 index 00000000000000..698131409af3a5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_wobert_chinese_plus_zh.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Chinese Bert Embeddings (from qinluo) +author: John Snow Labs +name: bert_embeddings_wobert_chinese_plus +date: 2023-06-21 +tags: [bert, embeddings, zh, open_source, onnx] +task: Embeddings +language: zh +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `wobert-chinese-plus` is a Chinese model orginally trained by `qinluo`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_wobert_chinese_plus_zh_5.0.0_3.0_1687343185496.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_wobert_chinese_plus_zh_5.0.0_3.0_1687343185496.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_wobert_chinese_plus","zh") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_wobert_chinese_plus","zh") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("zh.embed.wobert_chinese_plus").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_embeddings_wobert_chinese_plus","zh") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_embeddings_wobert_chinese_plus","zh") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("zh.embed.wobert_chinese_plus").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_wobert_chinese_plus| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|zh| +|Size:|464.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_sentence_embeddings_financial_de.md b/docs/_posts/ahmedlone127/2023-06-21-bert_sentence_embeddings_financial_de.md new file mode 100644 index 00000000000000..13d2898c521b50 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_sentence_embeddings_financial_de.md @@ -0,0 +1,151 @@ +--- +layout: model +title: German Financial Bert Word Embeddings +author: John Snow Labs +name: bert_sentence_embeddings_financial +date: 2023-06-21 +tags: [bert, embeddings, de, open_source, financial, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Although in the name of the model you will see the word `sentence`, this is a Word Embeddings Model. + +Financial Pretrained BERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `german-financial-statements-bert` is a German model orginally trained by `fabianrausch`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sentence_embeddings_financial_de_5.0.0_3.0_1687338810949.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sentence_embeddings_financial_de_5.0.0_3.0_1687338810949.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_sentence_embeddings_financial","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark-NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_sentence_embeddings_financial","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark-NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.bert.finance").predict("""Ich liebe Spark-NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bert_sentence_embeddings_financial","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark-NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bert_sentence_embeddings_financial","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark-NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.bert.finance").predict("""Ich liebe Spark-NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sentence_embeddings_financial| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|de| +|Size:|406.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_all_pt.md b/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_all_pt.md new file mode 100644 index 00000000000000..a38274560def80 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_all_pt.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Clinical Portuguese Bert Embeddings (Biomedical and Clinical) +author: John Snow Labs +name: biobert_embeddings_all +date: 2023-06-21 +tags: [biobert, embeddings, pt, open_source, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BioBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `biobertpt-all` is a Portuguese model orginally trained by `pucpr`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biobert_embeddings_all_pt_5.0.0_3.0_1687342387740.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biobert_embeddings_all_pt_5.0.0_3.0_1687342387740.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("biobert_embeddings_all","pt") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Odeio o cancro"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("biobert_embeddings_all","pt") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Odeio o cancro").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.embed.gs_all").predict("""Odeio o cancro""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("biobert_embeddings_all","pt") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Odeio o cancro"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("biobert_embeddings_all","pt") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Odeio o cancro").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("pt.embed.gs_all").predict("""Odeio o cancro""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biobert_embeddings_all| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|pt| +|Size:|664.8 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_biomedical_pt.md b/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_biomedical_pt.md new file mode 100644 index 00000000000000..b86fe840106cfa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_biomedical_pt.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Clinical Portuguese Bert Embeddiongs (Biomedical) +author: John Snow Labs +name: biobert_embeddings_biomedical +date: 2023-06-21 +tags: [biobert, embeddings, pt, open_source, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BioBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `biobertpt-bio` is a Portuguese model orginally trained by `pucpr`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biobert_embeddings_biomedical_pt_5.0.0_3.0_1687343400949.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biobert_embeddings_biomedical_pt_5.0.0_3.0_1687343400949.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("biobert_embeddings_biomedical","pt") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Odeio o cancro"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("biobert_embeddings_biomedical","pt") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Odeio o cancro").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.embed.gs_biomedical").predict("""Odeio o cancro""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("biobert_embeddings_biomedical","pt") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Odeio o cancro"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("biobert_embeddings_biomedical","pt") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Odeio o cancro").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("pt.embed.gs_biomedical").predict("""Odeio o cancro""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biobert_embeddings_biomedical| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|pt| +|Size:|665.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_clinical_pt.md b/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_clinical_pt.md new file mode 100644 index 00000000000000..d8a80c64c56b34 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_clinical_pt.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Clinical Portuguese Bert Embeddiongs (Clinical) +author: John Snow Labs +name: biobert_embeddings_clinical +date: 2023-06-21 +tags: [biobert, embeddings, pt, open_source, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BioBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `biobertpt-clin` is a Portuguese model orginally trained by `pucpr`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biobert_embeddings_clinical_pt_5.0.0_3.0_1687342893170.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biobert_embeddings_clinical_pt_5.0.0_3.0_1687342893170.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("biobert_embeddings_clinical","pt") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Odeio o cancro"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("biobert_embeddings_clinical","pt") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Odeio o cancro").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.embed.gs_clinical").predict("""Odeio o cancro""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("biobert_embeddings_clinical","pt") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Odeio o cancro"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("biobert_embeddings_clinical","pt") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Odeio o cancro").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("pt.embed.gs_clinical").predict("""Odeio o cancro""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biobert_embeddings_clinical| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|pt| +|Size:|665.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-biobert_pubmed_base_cased_v1.2_en.md b/docs/_posts/ahmedlone127/2023-06-21-biobert_pubmed_base_cased_v1.2_en.md new file mode 100644 index 00000000000000..794836f2f29942 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-biobert_pubmed_base_cased_v1.2_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: BioBERT Embeddings (Pubmed) +author: John Snow Labs +name: biobert_pubmed_base_cased_v1.2 +date: 2023-06-21 +tags: [bert, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is the v1.2 of [biobert_pubmed_base_cased](https://nlp.johnsnowlabs.com/2020/09/19/biobert_pubmed_base_cased.html) model and contains pre-trained weights of BioBERT, a language representation model for biomedical domain, especially designed for biomedical text mining tasks such as biomedical named entity recognition, relation extraction, question answering, etc. The details are described in the paper "[BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://arxiv.org/abs/1901.08746v2)". + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biobert_pubmed_base_cased_v1.2_en_5.0.0_3.0_1687336480762.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biobert_pubmed_base_cased_v1.2_en_5.0.0_3.0_1687336480762.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("biobert_pubmed_base_cased_v1.2","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I hate cancer"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("biobert_pubmed_base_cased_v1.2","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I hate cancer").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.biobert.pubmed.cased_base").predict("""I hate cancer""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("biobert_pubmed_base_cased_v1.2","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I hate cancer"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("biobert_pubmed_base_cased_v1.2","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I hate cancer").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.biobert.pubmed.cased_base").predict("""I hate cancer""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biobert_pubmed_base_cased_v1.2| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|403.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_0_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_0_cased_generator_de.md new file mode 100644 index 00000000000000..eb920c7ffb15b9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_0_cased_generator_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German Electra Embeddings (from stefan-it) +author: John Snow Labs +name: electra_embeddings_electra_base_gc4_64k_0_cased_generator +date: 2023-06-21 +tags: [de, open_source, electra, embeddings, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-base-gc4-64k-0-cased-generator` is a German model orginally trained by `stefan-it`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_0_cased_generator_de_5.0.0_3.0_1687338403600.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_0_cased_generator_de_5.0.0_3.0_1687338403600.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_0_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_0_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_64d").predict("""Ich liebe Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_0_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_0_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_64d").predict("""Ich liebe Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_base_gc4_64k_0_cased_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|221.4 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_1000000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_1000000_cased_generator_de.md new file mode 100644 index 00000000000000..d6613cc7e8cf70 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_1000000_cased_generator_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German Electra Embeddings (from stefan-it) +author: John Snow Labs +name: electra_embeddings_electra_base_gc4_64k_1000000_cased_generator +date: 2023-06-21 +tags: [de, open_source, electra, embeddings, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-base-gc4-64k-1000000-cased-generator` is a German model orginally trained by `stefan-it`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_1000000_cased_generator_de_5.0.0_3.0_1687337566476.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_1000000_cased_generator_de_5.0.0_3.0_1687337566476.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_1000000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_1000000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_1000000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_1000000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_1000000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_1000000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_base_gc4_64k_1000000_cased_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|222.2 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_100000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_100000_cased_generator_de.md new file mode 100644 index 00000000000000..b1110b5c50cebd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_100000_cased_generator_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German Electra Embeddings (from stefan-it) +author: John Snow Labs +name: electra_embeddings_electra_base_gc4_64k_100000_cased_generator +date: 2023-06-21 +tags: [de, open_source, electra, embeddings, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-base-gc4-64k-100000-cased-generator` is a German model orginally trained by `stefan-it`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_100000_cased_generator_de_5.0.0_3.0_1687337430315.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_100000_cased_generator_de_5.0.0_3.0_1687337430315.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_100000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_100000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_100000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_100000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_100000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_100000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_base_gc4_64k_100000_cased_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|222.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_200000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_200000_cased_generator_de.md new file mode 100644 index 00000000000000..e90872b5cf0574 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_200000_cased_generator_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German Electra Embeddings (from stefan-it) +author: John Snow Labs +name: electra_embeddings_electra_base_gc4_64k_200000_cased_generator +date: 2023-06-21 +tags: [de, open_source, electra, embeddings, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-base-gc4-64k-200000-cased-generator` is a German model orginally trained by `stefan-it`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_200000_cased_generator_de_5.0.0_3.0_1687337323809.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_200000_cased_generator_de_5.0.0_3.0_1687337323809.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_200000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_200000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_200000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_200000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_200000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_200000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_base_gc4_64k_200000_cased_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|222.2 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_300000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_300000_cased_generator_de.md new file mode 100644 index 00000000000000..e9a244fe395167 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_300000_cased_generator_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German Electra Embeddings (from stefan-it) +author: John Snow Labs +name: electra_embeddings_electra_base_gc4_64k_300000_cased_generator +date: 2023-06-21 +tags: [de, open_source, electra, embeddings, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-base-gc4-64k-300000-cased-generator` is a German model orginally trained by `stefan-it`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_300000_cased_generator_de_5.0.0_3.0_1687337742127.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_300000_cased_generator_de_5.0.0_3.0_1687337742127.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_300000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_300000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_300000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_300000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_300000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_300000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_base_gc4_64k_300000_cased_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|222.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_400000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_400000_cased_generator_de.md new file mode 100644 index 00000000000000..cf463da7d3ede6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_400000_cased_generator_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German Electra Embeddings (from stefan-it) +author: John Snow Labs +name: electra_embeddings_electra_base_gc4_64k_400000_cased_generator +date: 2023-06-21 +tags: [de, open_source, electra, embeddings, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-base-gc4-64k-400000-cased-generator` is a German model orginally trained by `stefan-it`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_400000_cased_generator_de_5.0.0_3.0_1687338531671.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_400000_cased_generator_de_5.0.0_3.0_1687338531671.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_400000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_400000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_400000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_400000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_400000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_400000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_base_gc4_64k_400000_cased_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|222.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_500000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_500000_cased_generator_de.md new file mode 100644 index 00000000000000..0e0368cae00d7c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_500000_cased_generator_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German Electra Embeddings (from stefan-it) +author: John Snow Labs +name: electra_embeddings_electra_base_gc4_64k_500000_cased_generator +date: 2023-06-21 +tags: [de, open_source, electra, embeddings, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-base-gc4-64k-500000-cased-generator` is a German model orginally trained by `stefan-it`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_500000_cased_generator_de_5.0.0_3.0_1687337310787.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_500000_cased_generator_de_5.0.0_3.0_1687337310787.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_500000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_500000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_500000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_500000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_500000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_500000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_base_gc4_64k_500000_cased_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|222.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_600000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_600000_cased_generator_de.md new file mode 100644 index 00000000000000..3f63d4ca68b519 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_600000_cased_generator_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German Electra Embeddings (from stefan-it) +author: John Snow Labs +name: electra_embeddings_electra_base_gc4_64k_600000_cased_generator +date: 2023-06-21 +tags: [de, open_source, electra, embeddings, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-base-gc4-64k-600000-cased-generator` is a German model orginally trained by `stefan-it`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_600000_cased_generator_de_5.0.0_3.0_1687338289447.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_600000_cased_generator_de_5.0.0_3.0_1687338289447.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_600000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_600000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_600000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_600000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_600000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_600000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_base_gc4_64k_600000_cased_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|222.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_700000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_700000_cased_generator_de.md new file mode 100644 index 00000000000000..6ce95cc170b433 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_700000_cased_generator_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German Electra Embeddings (from stefan-it) +author: John Snow Labs +name: electra_embeddings_electra_base_gc4_64k_700000_cased_generator +date: 2023-06-21 +tags: [de, open_source, electra, embeddings, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-base-gc4-64k-700000-cased-generator` is a German model orginally trained by `stefan-it`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_700000_cased_generator_de_5.0.0_3.0_1687336559193.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_700000_cased_generator_de_5.0.0_3.0_1687336559193.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_700000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_700000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_700000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_700000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_700000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_700000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_base_gc4_64k_700000_cased_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|222.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_800000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_800000_cased_generator_de.md new file mode 100644 index 00000000000000..b3e7e29f1c1fe9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_800000_cased_generator_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German Electra Embeddings (from stefan-it) +author: John Snow Labs +name: electra_embeddings_electra_base_gc4_64k_800000_cased_generator +date: 2023-06-21 +tags: [de, open_source, electra, embeddings, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-base-gc4-64k-800000-cased-generator` is a German model orginally trained by `stefan-it`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_800000_cased_generator_de_5.0.0_3.0_1687336668760.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_800000_cased_generator_de_5.0.0_3.0_1687336668760.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_800000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_800000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_800000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_800000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_800000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_800000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_base_gc4_64k_800000_cased_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|222.2 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_900000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_900000_cased_generator_de.md new file mode 100644 index 00000000000000..9e2a0a0531c231 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_900000_cased_generator_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German Electra Embeddings (from stefan-it) +author: John Snow Labs +name: electra_embeddings_electra_base_gc4_64k_900000_cased_generator +date: 2023-06-21 +tags: [de, open_source, electra, embeddings, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-base-gc4-64k-900000-cased-generator` is a German model orginally trained by `stefan-it`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_900000_cased_generator_de_5.0.0_3.0_1687336789214.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_gc4_64k_900000_cased_generator_de_5.0.0_3.0_1687336789214.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_900000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_900000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_900000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_900000_cased_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_900000_cased_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.cased_base_gc4_64k_900000.by_stefan_it").predict("""Ich liebe Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_base_gc4_64k_900000_cased_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|222.2 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_generator_en.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_generator_en.md new file mode 100644 index 00000000000000..e83a14d6f6ba62 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_generator_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English Electra Embeddings (from google) +author: John Snow Labs +name: electra_embeddings_electra_base_generator +date: 2023-06-21 +tags: [en, open_source, electra, embeddings, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-base-generator` is a English model orginally trained by `google`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_generator_en_5.0.0_3.0_1687337315482.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_generator_en_5.0.0_3.0_1687337315482.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_generator","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_generator","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.electra.base").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_generator","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_generator","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.electra.base").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_base_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|125.7 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_italian_xxl_cased_generator_it.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_italian_xxl_cased_generator_it.md new file mode 100644 index 00000000000000..b650a25e829416 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_italian_xxl_cased_generator_it.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Italian Electra Embeddings (from dbmdz) +author: John Snow Labs +name: electra_embeddings_electra_base_italian_xxl_cased_generator +date: 2023-06-21 +tags: [it, open_source, electra, embeddings, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-base-italian-xxl-cased-generator` is a Italian model orginally trained by `dbmdz`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_italian_xxl_cased_generator_it_5.0.0_3.0_1687337384147.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_italian_xxl_cased_generator_it_5.0.0_3.0_1687337384147.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_italian_xxl_cased_generator","it") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_italian_xxl_cased_generator","it") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Adoro Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("it.embed.electra.cased_xxl_base").predict("""Adoro Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_italian_xxl_cased_generator","it") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_italian_xxl_cased_generator","it") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Adoro Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("it.embed.electra.cased_xxl_base").predict("""Adoro Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_base_italian_xxl_cased_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|it| +|Size:|127.4 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_turkish_mc4_cased_generator_tr.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_turkish_mc4_cased_generator_tr.md new file mode 100644 index 00000000000000..e41b06ae521f40 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_turkish_mc4_cased_generator_tr.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Turkish Electra Embeddings (from dbmdz) +author: John Snow Labs +name: electra_embeddings_electra_base_turkish_mc4_cased_generator +date: 2023-06-21 +tags: [tr, open_source, electra, embeddings, onnx] +task: Embeddings +language: tr +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-base-turkish-mc4-cased-generator` is a Turkish model orginally trained by `dbmdz`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_turkish_mc4_cased_generator_tr_5.0.0_3.0_1687337596423.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_turkish_mc4_cased_generator_tr_5.0.0_3.0_1687337596423.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_turkish_mc4_cased_generator","tr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Spark NLP'yi seviyorum"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_turkish_mc4_cased_generator","tr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Spark NLP'yi seviyorum").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("tr.embed.electra.cased_base").predict("""Spark NLP'yi seviyorum""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_turkish_mc4_cased_generator","tr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Spark NLP'yi seviyorum"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_turkish_mc4_cased_generator","tr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Spark NLP'yi seviyorum").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("tr.embed.electra.cased_base").predict("""Spark NLP'yi seviyorum""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_base_turkish_mc4_cased_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|tr| +|Size:|129.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_turkish_mc4_uncased_generator_tr.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_turkish_mc4_uncased_generator_tr.md new file mode 100644 index 00000000000000..41859cc555c6a2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_turkish_mc4_uncased_generator_tr.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Turkish Electra Embeddings (from dbmdz) +author: John Snow Labs +name: electra_embeddings_electra_base_turkish_mc4_uncased_generator +date: 2023-06-21 +tags: [tr, open_source, electra, embeddings, onnx] +task: Embeddings +language: tr +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-base-turkish-mc4-uncased-generator` is a Turkish model orginally trained by `dbmdz`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_turkish_mc4_uncased_generator_tr_5.0.0_3.0_1687337246703.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_base_turkish_mc4_uncased_generator_tr_5.0.0_3.0_1687337246703.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_turkish_mc4_uncased_generator","tr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Spark NLP'yi seviyorum"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_turkish_mc4_uncased_generator","tr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Spark NLP'yi seviyorum").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("tr.embed.electra.uncased_base").predict("""Spark NLP'yi seviyorum""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_turkish_mc4_uncased_generator","tr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Spark NLP'yi seviyorum"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_turkish_mc4_uncased_generator","tr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Spark NLP'yi seviyorum").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("tr.embed.electra.uncased_base").predict("""Spark NLP'yi seviyorum""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_base_turkish_mc4_uncased_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|tr| +|Size:|130.0 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_large_generator_en.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_large_generator_en.md new file mode 100644 index 00000000000000..bbfb7f281e7a49 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_large_generator_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English Electra Embeddings (from google) +author: John Snow Labs +name: electra_embeddings_electra_large_generator +date: 2023-06-21 +tags: [en, open_source, electra, embeddings, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-large-generator` is a English model orginally trained by `google`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_large_generator_en_5.0.0_3.0_1687337805375.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_large_generator_en_5.0.0_3.0_1687337805375.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_large_generator","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_large_generator","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.electra.large").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_large_generator","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_large_generator","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.electra.large").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_large_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|191.2 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_small_generator_en.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_small_generator_en.md new file mode 100644 index 00000000000000..dabe96a7d7b5a2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_small_generator_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English Electra Embeddings (from google) +author: John Snow Labs +name: electra_embeddings_electra_small_generator +date: 2023-06-21 +tags: [en, open_source, electra, embeddings, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-small-generator` is a English model orginally trained by `google`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_small_generator_en_5.0.0_3.0_1687337729115.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_small_generator_en_5.0.0_3.0_1687337729115.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_small_generator","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_small_generator","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.electra.small").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_small_generator","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_small_generator","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.electra.small").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_small_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|50.8 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_small_japanese_generator_ja.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_small_japanese_generator_ja.md new file mode 100644 index 00000000000000..29d87ba99e8341 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_small_japanese_generator_ja.md @@ -0,0 +1,135 @@ +--- +layout: model +title: Japanese Electra Embeddings (from Cinnamon) +author: John Snow Labs +name: electra_embeddings_electra_small_japanese_generator +date: 2023-06-21 +tags: [ja, open_source, electra, embeddings, onnx] +task: Embeddings +language: ja +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-small-japanese-generator` is a Japanese model orginally trained by `Cinnamon`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_small_japanese_generator_ja_5.0.0_3.0_1687338737717.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_small_japanese_generator_ja_5.0.0_3.0_1687338737717.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_small_japanese_generator","ja") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Spark NLPが大好きです"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_small_japanese_generator","ja") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Spark NLPが大好きです").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_small_japanese_generator","ja") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Spark NLPが大好きです"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_small_japanese_generator","ja") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Spark NLPが大好きです").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_small_japanese_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|ja| +|Size:|51.7 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_base_cased_generator_tl.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_base_cased_generator_tl.md new file mode 100644 index 00000000000000..84aeba9befe4a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_base_cased_generator_tl.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Tagalog Electra Embeddings (from jcblaise) +author: John Snow Labs +name: electra_embeddings_electra_tagalog_base_cased_generator +date: 2023-06-21 +tags: [tl, open_source, electra, embeddings, onnx] +task: Embeddings +language: tl +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-tagalog-base-cased-generator` is a Tagalog model orginally trained by `jcblaise`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_tagalog_base_cased_generator_tl_5.0.0_3.0_1687338660491.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_tagalog_base_cased_generator_tl_5.0.0_3.0_1687338660491.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_base_cased_generator","tl") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Mahilig ako sa Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_base_cased_generator","tl") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Mahilig ako sa Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("tl.embed.electra.cased_base").predict("""Mahilig ako sa Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_base_cased_generator","tl") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Mahilig ako sa Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_base_cased_generator","tl") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Mahilig ako sa Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("tl.embed.electra.cased_base").predict("""Mahilig ako sa Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_tagalog_base_cased_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|tl| +|Size:|129.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_base_uncased_generator_tl.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_base_uncased_generator_tl.md new file mode 100644 index 00000000000000..e48ebc2ee91dfd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_base_uncased_generator_tl.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Tagalog Electra Embeddings (from jcblaise) +author: John Snow Labs +name: electra_embeddings_electra_tagalog_base_uncased_generator +date: 2023-06-21 +tags: [tl, open_source, electra, embeddings, onnx] +task: Embeddings +language: tl +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-tagalog-base-uncased-generator` is a Tagalog model orginally trained by `jcblaise`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_tagalog_base_uncased_generator_tl_5.0.0_3.0_1687338703736.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_tagalog_base_uncased_generator_tl_5.0.0_3.0_1687338703736.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_base_uncased_generator","tl") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Mahilig ako sa Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_base_uncased_generator","tl") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Mahilig ako sa Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("tl.embed.electra.uncased_base").predict("""Mahilig ako sa Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_base_uncased_generator","tl") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Mahilig ako sa Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_base_uncased_generator","tl") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Mahilig ako sa Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("tl.embed.electra.uncased_base").predict("""Mahilig ako sa Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_tagalog_base_uncased_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|tl| +|Size:|129.9 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_small_cased_generator_tl.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_small_cased_generator_tl.md new file mode 100644 index 00000000000000..df146d8836dce7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_small_cased_generator_tl.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Tagalog Electra Embeddings (from jcblaise) +author: John Snow Labs +name: electra_embeddings_electra_tagalog_small_cased_generator +date: 2023-06-21 +tags: [tl, open_source, electra, embeddings, onnx] +task: Embeddings +language: tl +edition: Spark NLP 5.0.0 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-tagalog-small-cased-generator` is a Tagalog model orginally trained by `jcblaise`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_tagalog_small_cased_generator_tl_5.0.0_3.0_1687338628903.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_tagalog_small_cased_generator_tl_5.0.0_3.0_1687338628903.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_small_cased_generator","tl") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Mahilig ako sa Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_small_cased_generator","tl") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Mahilig ako sa Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("tl.embed.electra.cased_small").predict("""Mahilig ako sa Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_small_cased_generator","tl") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Mahilig ako sa Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_small_cased_generator","tl") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Mahilig ako sa Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("tl.embed.electra.cased_small").predict("""Mahilig ako sa Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_tagalog_small_cased_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|tl| +|Size:|18.2 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_small_uncased_generator_tl.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_small_uncased_generator_tl.md new file mode 100644 index 00000000000000..58cea57ca00683 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_small_uncased_generator_tl.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Tagalog Electra Embeddings (from jcblaise) +author: John Snow Labs +name: electra_embeddings_electra_tagalog_small_uncased_generator +date: 2023-06-21 +tags: [tl, open_source, electra, embeddings, onnx] +task: Embeddings +language: tl +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electra-tagalog-small-uncased-generator` is a Tagalog model orginally trained by `jcblaise`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_tagalog_small_uncased_generator_tl_5.0.0_3.0_1687338586547.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electra_tagalog_small_uncased_generator_tl_5.0.0_3.0_1687338586547.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_small_uncased_generator","tl") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Mahilig ako sa Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_small_uncased_generator","tl") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Mahilig ako sa Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("tl.embed.electra.uncased_small").predict("""Mahilig ako sa Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_small_uncased_generator","tl") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Mahilig ako sa Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_small_uncased_generator","tl") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Mahilig ako sa Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("tl.embed.electra.uncased_small").predict("""Mahilig ako sa Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electra_tagalog_small_uncased_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|tl| +|Size:|18.2 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electricidad_base_generator_es.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electricidad_base_generator_es.md new file mode 100644 index 00000000000000..be35b303a82993 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electricidad_base_generator_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish Electra Uncased Embeddings (Oscar dataset) +author: John Snow Labs +name: electra_embeddings_electricidad_base_generator +date: 2023-06-21 +tags: [es, open_source, electra, embeddings, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `electricidad-base-generator` is a Spanish model orginally trained by `mrm8488`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_electricidad_base_generator_es_5.0.0_3.0_1687337686007.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_electricidad_base_generator_es_5.0.0_3.0_1687337686007.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electricidad_base_generator","es") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Amo Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electricidad_base_generator","es") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Amo Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.electra.base").predict("""Amo Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_electricidad_base_generator","es") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Amo Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_electricidad_base_generator","es") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Amo Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.electra.base").predict("""Amo Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_electricidad_base_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|es| +|Size:|126.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_finance_koelectra_base_generator_ko.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_finance_koelectra_base_generator_ko.md new file mode 100644 index 00000000000000..af1b52acd3ac9e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_finance_koelectra_base_generator_ko.md @@ -0,0 +1,135 @@ +--- +layout: model +title: Korean Electra Embeddings (from krevas) +author: John Snow Labs +name: electra_embeddings_finance_koelectra_base_generator +date: 2023-06-21 +tags: [ko, open_source, electra, embeddings, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Financial Korean Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `finance-koelectra-base-generator` is a Korean model orginally trained by `krevas`. This is a Base model. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_finance_koelectra_base_generator_ko_5.0.0_3.0_1687337679070.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_finance_koelectra_base_generator_ko_5.0.0_3.0_1687337679070.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_finance_koelectra_base_generator","ko") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_finance_koelectra_base_generator","ko") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_finance_koelectra_base_generator","ko") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_finance_koelectra_base_generator","ko") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_finance_koelectra_base_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|ko| +|Size:|129.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_finance_koelectra_small_generator_ko.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_finance_koelectra_small_generator_ko.md new file mode 100644 index 00000000000000..76acc791540c7c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_finance_koelectra_small_generator_ko.md @@ -0,0 +1,135 @@ +--- +layout: model +title: Korean Electra Embeddings (from krevas) +author: John Snow Labs +name: electra_embeddings_finance_koelectra_small_generator +date: 2023-06-21 +tags: [ko, open_source, electra, embeddings, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Financial Korean Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `finance-koelectra-small-generator` is a Korean model orginally trained by `krevas`. This is a small (sm) version. Other bigger versions are available. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_finance_koelectra_small_generator_ko_5.0.0_3.0_1687338677896.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_finance_koelectra_small_generator_ko_5.0.0_3.0_1687338677896.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_finance_koelectra_small_generator","ko") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_finance_koelectra_small_generator","ko") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_finance_koelectra_small_generator","ko") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_finance_koelectra_small_generator","ko") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_finance_koelectra_small_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|ko| +|Size:|51.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_gelectra_base_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_gelectra_base_generator_de.md new file mode 100644 index 00000000000000..35d0fddb181a54 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_gelectra_base_generator_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German Electra Embeddings (from deepset) +author: John Snow Labs +name: electra_embeddings_gelectra_base_generator +date: 2023-06-21 +tags: [de, open_source, electra, embeddings, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `gelectra-base-generator` is a German model orginally trained by `deepset`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_gelectra_base_generator_de_5.0.0_3.0_1687338626775.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_gelectra_base_generator_de_5.0.0_3.0_1687338626775.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_gelectra_base_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_gelectra_base_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.base").predict("""Ich liebe Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_gelectra_base_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_gelectra_base_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.base").predict("""Ich liebe Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_gelectra_base_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|127.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_gelectra_large_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_gelectra_large_generator_de.md new file mode 100644 index 00000000000000..6d2e16d4eeefb6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_gelectra_large_generator_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German Electra Embeddings (from deepset) +author: John Snow Labs +name: electra_embeddings_gelectra_large_generator +date: 2023-06-21 +tags: [de, open_source, electra, embeddings, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `gelectra-large-generator` is a German model orginally trained by `deepset`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_gelectra_large_generator_de_5.0.0_3.0_1687338033613.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_gelectra_large_generator_de_5.0.0_3.0_1687338033613.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_gelectra_large_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_gelectra_large_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.large").predict("""Ich liebe Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_gelectra_large_generator","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_gelectra_large_generator","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.electra.large").predict("""Ich liebe Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_gelectra_large_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|193.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_generator_ko.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_generator_ko.md new file mode 100644 index 00000000000000..53e791d787729c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_generator_ko.md @@ -0,0 +1,135 @@ +--- +layout: model +title: Korean Electra Embeddings (from monologg) +author: John Snow Labs +name: electra_embeddings_koelectra_base_generator +date: 2023-06-21 +tags: [ko, open_source, electra, embeddings, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `koelectra-base-generator` is a Korean model orginally trained by `monologg`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_koelectra_base_generator_ko_5.0.0_3.0_1687337873576.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_koelectra_base_generator_ko_5.0.0_3.0_1687337873576.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_base_generator","ko") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_base_generator","ko") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_base_generator","ko") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_base_generator","ko") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_koelectra_base_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|ko| +|Size:|130.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_v2_generator_ko.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_v2_generator_ko.md new file mode 100644 index 00000000000000..94addd53290ea6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_v2_generator_ko.md @@ -0,0 +1,135 @@ +--- +layout: model +title: Korean Electra Embeddings (from monologg) +author: John Snow Labs +name: electra_embeddings_koelectra_base_v2_generator +date: 2023-06-21 +tags: [ko, open_source, electra, embeddings, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `koelectra-base-v2-generator` is a Korean model orginally trained by `monologg`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_koelectra_base_v2_generator_ko_5.0.0_3.0_1687337792559.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_koelectra_base_v2_generator_ko_5.0.0_3.0_1687337792559.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_base_v2_generator","ko") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_base_v2_generator","ko") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_base_v2_generator","ko") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_base_v2_generator","ko") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_koelectra_base_v2_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|ko| +|Size:|129.7 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_v3_generator_ko.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_v3_generator_ko.md new file mode 100644 index 00000000000000..d08cb9f7252863 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_v3_generator_ko.md @@ -0,0 +1,135 @@ +--- +layout: model +title: Korean Electra Embeddings (from monologg) +author: John Snow Labs +name: electra_embeddings_koelectra_base_v3_generator +date: 2023-06-21 +tags: [ko, open_source, electra, embeddings, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `koelectra-base-v3-generator` is a Korean model orginally trained by `monologg`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_koelectra_base_v3_generator_ko_5.0.0_3.0_1687337798528.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_koelectra_base_v3_generator_ko_5.0.0_3.0_1687337798528.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_base_v3_generator","ko") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_base_v3_generator","ko") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_base_v3_generator","ko") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_base_v3_generator","ko") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_koelectra_base_v3_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|ko| +|Size:|137.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_small_generator_ko.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_small_generator_ko.md new file mode 100644 index 00000000000000..e323b62a1bbec4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_small_generator_ko.md @@ -0,0 +1,135 @@ +--- +layout: model +title: Korean Electra Embeddings (from monologg) +author: John Snow Labs +name: electra_embeddings_koelectra_small_generator +date: 2023-06-21 +tags: [ko, open_source, electra, embeddings, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `koelectra-small-generator` is a Korean model orginally trained by `monologg`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_koelectra_small_generator_ko_5.0.0_3.0_1687338723919.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_koelectra_small_generator_ko_5.0.0_3.0_1687338723919.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_small_generator","ko") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_small_generator","ko") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_small_generator","ko") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_small_generator","ko") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_koelectra_small_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|ko| +|Size:|51.7 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_kr_electra_generator_ko.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_kr_electra_generator_ko.md new file mode 100644 index 00000000000000..0aeb0d295a40c1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_kr_electra_generator_ko.md @@ -0,0 +1,135 @@ +--- +layout: model +title: Korean Electra Embeddings (from snunlp) +author: John Snow Labs +name: electra_embeddings_kr_electra_generator +date: 2023-06-21 +tags: [ko, open_source, electra, embeddings, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `KR-ELECTRA-generator` is a Korean model orginally trained by `snunlp`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/electra_embeddings_kr_electra_generator_ko_5.0.0_3.0_1687338860027.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/electra_embeddings_kr_electra_generator_ko_5.0.0_3.0_1687338860027.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_kr_electra_generator","ko") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_kr_electra_generator","ko") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("electra_embeddings_kr_electra_generator","ko") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("electra_embeddings_kr_electra_generator","ko") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|electra_embeddings_kr_electra_generator| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|ko| +|Size:|124.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-legalectra_base_es.md b/docs/_posts/ahmedlone127/2023-06-21-legalectra_base_es.md new file mode 100644 index 00000000000000..ed972bedaa7edf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-legalectra_base_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish Legal Electra Word Embeddings Base model +author: John Snow Labs +name: legalectra_base +date: 2023-06-21 +tags: [open_source, legalectra, embeddings, electra, legal, es, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Spanish Legal Word Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `legalectra-base-spanish` is a English model originally trained by `mrm8488`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legalectra_base_es_5.0.0_3.0_1687336669896.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legalectra_base_es_5.0.0_3.0_1687336669896.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +electra = BertEmbeddings.pretrained("legalectra_base","es") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, electra]) + +data = spark.createDataFrame([["Amo a Spark NLP."]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val electra = BertEmbeddings.pretrained("legalectra_base","es") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, electra)) + +val data = Seq("Amo a Spark NLP.").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bert.base_legal").predict("""Amo a Spark NLP.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +electra = BertEmbeddings.pretrained("legalectra_base","es") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, electra]) + +data = spark.createDataFrame([["Amo a Spark NLP."]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val electra = BertEmbeddings.pretrained("legalectra_base","es") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, electra)) + +val data = Seq("Amo a Spark NLP.").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bert.base_legal").predict("""Amo a Spark NLP.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legalectra_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|408.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-legalectra_small_es.md b/docs/_posts/ahmedlone127/2023-06-21-legalectra_small_es.md new file mode 100644 index 00000000000000..a2fb304397a6a0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-legalectra_small_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish Electra Legal Word Embeddings Small model +author: John Snow Labs +name: legalectra_small +date: 2023-06-21 +tags: [open_source, legalectra, embeddings, electra, legal, small, es, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Spanish Legal Word Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `legalectra-small-spanish` is a English model originally trained by `mrm8488`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legalectra_small_es_5.0.0_3.0_1687336489949.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legalectra_small_es_5.0.0_3.0_1687336489949.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +electra = BertEmbeddings.pretrained("legalectra_small","es") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, electra]) + +data = spark.createDataFrame([["Amo a Spark NLP."]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val electra = BertEmbeddings.pretrained("legalectra_small","es") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, electra)) + +val data = Seq("Amo a Spark NLP.").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bert.small_legal").predict("""Amo a Spark NLP.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +electra = BertEmbeddings.pretrained("legalectra_small","es") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, electra]) + +data = spark.createDataFrame([["Amo a Spark NLP."]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val electra = BertEmbeddings.pretrained("legalectra_small","es") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, electra)) + +val data = Seq("Amo a Spark NLP.").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bert.small_legal").predict("""Amo a Spark NLP.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legalectra_small| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|51.2 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-21-ms_bluebert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-06-21-ms_bluebert_base_uncased_en.md new file mode 100644 index 00000000000000..bf7539ce60805d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-21-ms_bluebert_base_uncased_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: MS-BERT base model (uncased) +author: John Snow Labs +name: ms_bluebert_base_uncased +date: 2023-06-21 +tags: [embeddings, bert, open_source, en, clinical, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is trained by taking BlueBert as the base model, and training on dataset contained approximately 75,000 clinical notes, for about 5000 patients, totaling to over 35.7 million words. These notes were collected from patients who visited St. Michael's Hospital MS Clinic between 2015 to 2019. The notes contained a variety of information pertaining to a neurological exam. For example, a note can contain information on the patient's condition, their progress over time and diagnosis. + +BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: + +Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. +Next sentence prediction (NSP): the models concatenate two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ms_bluebert_base_uncased_en_5.0.0_3.0_1687372625112.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ms_bluebert_base_uncased_en_5.0.0_3.0_1687372625112.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = BertEmbeddings.pretrained("ms_bluebert_base_uncased", "en") \ + .setInputCols(["sentence", "token"]) \ + .setOutputCol("embeddings") + +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = BertEmbeddings.pretrained("ms_bluebert_base_uncased", "en") + .setInputCols("sentence", "token") + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` +
+ +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = BertEmbeddings.pretrained("ms_bluebert_base_uncased", "en") \ + .setInputCols(["sentence", "token"]) \ + .setOutputCol("embeddings") + +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = BertEmbeddings.pretrained("ms_bluebert_base_uncased", "en") + .setInputCols("sentence", "token") + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` +
+ +## Results + +```bash +Results + + +Generates 768 dimensional embeddings per token + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ms_bluebert_base_uncased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|408.2 MB| +|Case sensitive:|false| + +## References + +https://huggingface.co/NLP4H/ms_bert \ No newline at end of file From 43ab7941aa3c8acba5c6edf19ea1762fd2c24f0c Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Mon, 26 Jun 2023 21:39:21 +0700 Subject: [PATCH 18/55] 2023-06-26-distilbert_embeddings_finetuned_sarcasm_classification_en (#13867) * Add model 2023-06-26-distilbert_embeddings_finetuned_sarcasm_classification_en * Add model 2023-06-26-distilbert_embeddings_distilbert_base_indonesian_id * Add model 2023-06-26-distilbert_embeddings_BERTino_it * Add model 2023-06-26-distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa_en * Add model 2023-06-26-distilbert_embeddings_malaysian_distilbert_small_ms * Add model 2023-06-26-distilbert_embeddings_distilbert_fa_zwnj_base_fa * Add model 2023-06-26-distilbert_embeddings_javanese_distilbert_small_jv * Add model 2023-06-26-distilbert_embeddings_javanese_distilbert_small_imdb_jv * Add model 2023-06-26-distilbert_embeddings_indic_transformers_hi_distilbert_hi * Add model 2023-06-26-distilbert_embeddings_marathi_distilbert_mr * Add model 2023-06-26-distilbert_embeddings_indic_transformers_bn_distilbert_bn * Add model 2023-06-26-distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa_en * Add model 2023-06-26-deberta_embeddings_xsmall_dapt_scientific_papers_pubmed_en * Add model 2023-06-26-deberta_embeddings_spm_vie_vie * Add model 2023-06-26-deberta_embeddings_vie_small_vie * Add model 2023-06-26-deberta_embeddings_tapt_nbme_v3_base_en * Add model 2023-06-26-deberta_embeddings_erlangshen_v2_chinese_sentencepiece_zh * Add model 2023-06-26-deberta_v3_xsmall_en * Add model 2023-06-26-deberta_embeddings_mlm_test_en * Add model 2023-06-26-deberta_v3_small_en * Add model 2023-06-26-roberta_base_swiss_legal_gsw --------- Co-authored-by: ahmedlone127 --- ..._erlangshen_v2_chinese_sentencepiece_zh.md | 140 ++++++++++++++++ ...23-06-26-deberta_embeddings_mlm_test_en.md | 140 ++++++++++++++++ ...23-06-26-deberta_embeddings_spm_vie_vie.md | 140 ++++++++++++++++ ...deberta_embeddings_tapt_nbme_v3_base_en.md | 140 ++++++++++++++++ ...-06-26-deberta_embeddings_vie_small_vie.md | 140 ++++++++++++++++ ...xsmall_dapt_scientific_papers_pubmed_en.md | 140 ++++++++++++++++ .../2023-06-26-deberta_v3_small_en.md | 100 ++++++++++++ .../2023-06-26-deberta_v3_xsmall_en.md | 102 ++++++++++++ ...-06-26-distilbert_embeddings_BERTino_it.md | 149 ++++++++++++++++++ ...mbeddings_distilbert_base_indonesian_id.md | 149 ++++++++++++++++++ ...ased_sparse_85_unstructured_pruneofa_en.md | 149 ++++++++++++++++++ ...ased_sparse_90_unstructured_pruneofa_en.md | 149 ++++++++++++++++++ ...t_embeddings_distilbert_fa_zwnj_base_fa.md | 149 ++++++++++++++++++ ...ngs_finetuned_sarcasm_classification_en.md | 149 ++++++++++++++++++ ...ngs_indic_transformers_bn_distilbert_bn.md | 149 ++++++++++++++++++ ...ngs_indic_transformers_hi_distilbert_hi.md | 149 ++++++++++++++++++ ...dings_javanese_distilbert_small_imdb_jv.md | 149 ++++++++++++++++++ ...embeddings_javanese_distilbert_small_jv.md | 149 ++++++++++++++++++ ...mbeddings_malaysian_distilbert_small_ms.md | 149 ++++++++++++++++++ ...ilbert_embeddings_marathi_distilbert_mr.md | 149 ++++++++++++++++++ ...2023-06-26-roberta_base_swiss_legal_gsw.md | 80 ++++++++++ 21 files changed, 2910 insertions(+) create mode 100644 docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_erlangshen_v2_chinese_sentencepiece_zh.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_mlm_test_en.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_spm_vie_vie.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_tapt_nbme_v3_base_en.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_vie_small_vie.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_xsmall_dapt_scientific_papers_pubmed_en.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-deberta_v3_small_en.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-deberta_v3_xsmall_en.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_BERTino_it.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_indonesian_id.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa_en.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa_en.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_fa_zwnj_base_fa.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_finetuned_sarcasm_classification_en.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_indic_transformers_bn_distilbert_bn.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_indic_transformers_hi_distilbert_hi.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_javanese_distilbert_small_imdb_jv.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_javanese_distilbert_small_jv.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_malaysian_distilbert_small_ms.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_marathi_distilbert_mr.md create mode 100644 docs/_posts/ahmedlone127/2023-06-26-roberta_base_swiss_legal_gsw.md diff --git a/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_erlangshen_v2_chinese_sentencepiece_zh.md b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_erlangshen_v2_chinese_sentencepiece_zh.md new file mode 100644 index 00000000000000..169e9a31db0eb5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_erlangshen_v2_chinese_sentencepiece_zh.md @@ -0,0 +1,140 @@ +--- +layout: model +title: Chinese Deberta Embeddings Cased model (from IDEA-CCNL) +author: John Snow Labs +name: deberta_embeddings_erlangshen_v2_chinese_sentencepiece +date: 2023-06-26 +tags: [open_source, deberta, deberta_embeddings, debertav2formaskedlm, zh, onnx] +task: Embeddings +language: zh +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DeBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DebertaV2ForMaskedLM model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `Erlangshen-DeBERTa-v2-186M-Chinese-SentencePiece` is a Chinese model originally trained by `IDEA-CCNL`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/deberta_embeddings_erlangshen_v2_chinese_sentencepiece_zh_5.0.0_3.0_1687781761029.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_embeddings_erlangshen_v2_chinese_sentencepiece_zh_5.0.0_3.0_1687781761029.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_erlangshen_v2_chinese_sentencepiece","zh") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark-NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_erlangshen_v2_chinese_sentencepiece","zh") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark-NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_erlangshen_v2_chinese_sentencepiece","zh") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark-NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_erlangshen_v2_chinese_sentencepiece","zh") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark-NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|deberta_embeddings_erlangshen_v2_chinese_sentencepiece| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|zh| +|Size:|443.8 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_mlm_test_en.md b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_mlm_test_en.md new file mode 100644 index 00000000000000..2ee4b096c3fc6c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_mlm_test_en.md @@ -0,0 +1,140 @@ +--- +layout: model +title: English Deberta Embeddings model (from domenicrosati) +author: John Snow Labs +name: deberta_embeddings_mlm_test +date: 2023-06-26 +tags: [deberta, open_source, deberta_embeddings, debertav2formaskedlm, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DeBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DebertaEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `deberta-mlm-test` is a English model originally trained by `domenicrosati`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/deberta_embeddings_mlm_test_en_5.0.0_3.0_1687782209221.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_embeddings_mlm_test_en_5.0.0_3.0_1687782209221.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_mlm_test","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_mlm_test","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_mlm_test","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_mlm_test","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|deberta_embeddings_mlm_test| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|265.4 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_spm_vie_vie.md b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_spm_vie_vie.md new file mode 100644 index 00000000000000..8f303ff4ccea17 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_spm_vie_vie.md @@ -0,0 +1,140 @@ +--- +layout: model +title: Vietnamese Deberta Embeddings model (from hieule) +author: John Snow Labs +name: deberta_embeddings_spm_vie +date: 2023-06-26 +tags: [deberta, open_source, deberta_embeddings, debertav2formaskedlm, vie, onnx] +task: Embeddings +language: vie +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DeBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DebertaEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `spm-vie-deberta` is a Vietnamese model originally trained by `hieule`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/deberta_embeddings_spm_vie_vie_5.0.0_3.0_1687780843112.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_embeddings_spm_vie_vie_5.0.0_3.0_1687780843112.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_spm_vie","vie") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_spm_vie","vie") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_spm_vie","vie") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_spm_vie","vie") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|deberta_embeddings_spm_vie| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|vie| +|Size:|289.7 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_tapt_nbme_v3_base_en.md b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_tapt_nbme_v3_base_en.md new file mode 100644 index 00000000000000..30f49fc915d1a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_tapt_nbme_v3_base_en.md @@ -0,0 +1,140 @@ +--- +layout: model +title: English Deberta Embeddings model (from ZZ99) +author: John Snow Labs +name: deberta_embeddings_tapt_nbme_v3_base +date: 2023-06-26 +tags: [deberta, open_source, deberta_embeddings, debertav2formaskedlm, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DeBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DebertaEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `tapt_nbme_deberta_v3_base` is a English model originally trained by `ZZ99`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/deberta_embeddings_tapt_nbme_v3_base_en_5.0.0_3.0_1687780869777.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_embeddings_tapt_nbme_v3_base_en_5.0.0_3.0_1687780869777.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_tapt_nbme_v3_base","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_tapt_nbme_v3_base","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_tapt_nbme_v3_base","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_tapt_nbme_v3_base","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|deberta_embeddings_tapt_nbme_v3_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|687.5 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_vie_small_vie.md b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_vie_small_vie.md new file mode 100644 index 00000000000000..6b33b485b4900c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_vie_small_vie.md @@ -0,0 +1,140 @@ +--- +layout: model +title: Vietnamese Deberta Embeddings model (from binhquoc) +author: John Snow Labs +name: deberta_embeddings_vie_small +date: 2023-06-26 +tags: [deberta, open_source, deberta_embeddings, debertav2formaskedlm, vie, onnx] +task: Embeddings +language: vie +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DeBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DebertaEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `vie-deberta-small` is a Vietnamese model originally trained by `binhquoc`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/deberta_embeddings_vie_small_vie_5.0.0_3.0_1687780922709.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_embeddings_vie_small_vie_5.0.0_3.0_1687780922709.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_vie_small","vie") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_vie_small","vie") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_vie_small","vie") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_vie_small","vie") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|deberta_embeddings_vie_small| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|vie| +|Size:|277.4 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_xsmall_dapt_scientific_papers_pubmed_en.md b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_xsmall_dapt_scientific_papers_pubmed_en.md new file mode 100644 index 00000000000000..50107e47b2c704 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_xsmall_dapt_scientific_papers_pubmed_en.md @@ -0,0 +1,140 @@ +--- +layout: model +title: English Deberta Embeddings model (from domenicrosati) +author: John Snow Labs +name: deberta_embeddings_xsmall_dapt_scientific_papers_pubmed +date: 2023-06-26 +tags: [deberta, open_source, deberta_embeddings, debertav2formaskedlm, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DeBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DebertaEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `deberta-xsmall-dapt-scientific-papers-pubmed` is a English model originally trained by `domenicrosati`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/deberta_embeddings_xsmall_dapt_scientific_papers_pubmed_en_5.0.0_3.0_1687780385270.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_embeddings_xsmall_dapt_scientific_papers_pubmed_en_5.0.0_3.0_1687780385270.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_xsmall_dapt_scientific_papers_pubmed","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_xsmall_dapt_scientific_papers_pubmed","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_xsmall_dapt_scientific_papers_pubmed","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_xsmall_dapt_scientific_papers_pubmed","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|deberta_embeddings_xsmall_dapt_scientific_papers_pubmed| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|244.9 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-deberta_v3_small_en.md b/docs/_posts/ahmedlone127/2023-06-26-deberta_v3_small_en.md new file mode 100644 index 00000000000000..4f13393720b21f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-deberta_v3_small_en.md @@ -0,0 +1,100 @@ +--- +layout: model +title: DeBERTa small model +author: John Snow Labs +name: deberta_v3_small +date: 2023-06-26 +tags: [en, english, embeddings, deberta, v3, small, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DeBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The DeBERTa model was proposed in [[https://arxiv.org/abs/2006.03654 DeBERTa: Decoding-enhanced BERT with Disentangled Attention]] by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google’s BERT model released in 2018 and Facebook’s RoBERTa model released in 2019. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/deberta_v3_small_en_5.0.0_3.0_1687783064877.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_v3_small_en_5.0.0_3.0_1687783064877.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DeBertaEmbeddings.pretrained("deberta_v3_small", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = DeBertaEmbeddings.pretrained("deberta_v3_small", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.deberta_v3_small").predict("""Put your text here.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DeBertaEmbeddings.pretrained("deberta_v3_small", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = DeBertaEmbeddings.pretrained("deberta_v3_small", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.deberta_v3_small").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|deberta_v3_small| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|334.1 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## Benchmarking + +```bash +Benchmarking +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-deberta_v3_xsmall_en.md b/docs/_posts/ahmedlone127/2023-06-26-deberta_v3_xsmall_en.md new file mode 100644 index 00000000000000..9f3baaee954b9e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-deberta_v3_xsmall_en.md @@ -0,0 +1,102 @@ +--- +layout: model +title: DeBERTa xsmall model +author: John Snow Labs +name: deberta_v3_xsmall +date: 2023-06-26 +tags: [en, english, embeddings, deberta, xsmall, v3, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DeBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The DeBERTa model was proposed in [[https://arxiv.org/abs/2006.03654 DeBERTa: Decoding-enhanced BERT with Disentangled Attention]] by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google’s BERT model released in 2018 and Facebook’s RoBERTa model released in 2019. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/deberta_v3_xsmall_en_5.0.0_3.0_1687782011152.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_v3_xsmall_en_5.0.0_3.0_1687782011152.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DeBertaEmbeddings.pretrained("deberta_v3_xsmall", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") + +``` +```scala +val embeddings = DeBertaEmbeddings.pretrained("deberta_v3_xsmall", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") + +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.deberta_v3_xsmall").predict("""Put your text here.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DeBertaEmbeddings.pretrained("deberta_v3_xsmall", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = DeBertaEmbeddings.pretrained("deberta_v3_xsmall", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.deberta_v3_xsmall").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|deberta_v3_xsmall| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|167.8 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## Benchmarking + +```bash +Benchmarking +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_BERTino_it.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_BERTino_it.md new file mode 100644 index 00000000000000..7233e7dd8045ec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_BERTino_it.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Italian DistilBERT Embeddings +author: John Snow Labs +name: distilbert_embeddings_BERTino +date: 2023-06-26 +tags: [distilbert, embeddings, it, open_source, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `BERTino` is a Italian model orginally trained by `indigo-ai`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_BERTino_it_5.0.0_3.0_1687777390566.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_BERTino_it_5.0.0_3.0_1687777390566.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_BERTino","it") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_BERTino","it") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Adoro Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("it.embed.BERTino").predict("""Adoro Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_BERTino","it") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_BERTino","it") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Adoro Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("it.embed.BERTino").predict("""Adoro Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_BERTino| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|it| +|Size:|253.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_indonesian_id.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_indonesian_id.md new file mode 100644 index 00000000000000..4e1fad3954642c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_indonesian_id.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Indonesian DistilBERT Embeddings +author: John Snow Labs +name: distilbert_embeddings_distilbert_base_indonesian +date: 2023-06-26 +tags: [distilbert, embeddings, id, open_source, onnx] +task: Embeddings +language: id +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `distilbert-base-indonesian` is a Indonesian model orginally trained by `cahya`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_distilbert_base_indonesian_id_5.0.0_3.0_1687777360898.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_distilbert_base_indonesian_id_5.0.0_3.0_1687777360898.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_indonesian","id") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Saya suka percikan NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_indonesian","id") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Saya suka percikan NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("id.embed.distilbert").predict("""Saya suka percikan NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_indonesian","id") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Saya suka percikan NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_indonesian","id") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Saya suka percikan NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("id.embed.distilbert").predict("""Saya suka percikan NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_distilbert_base_indonesian| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|id| +|Size:|253.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa_en.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa_en.md new file mode 100644 index 00000000000000..9202845526f1ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English DistilBERT Embeddings (%85 sparse) +author: John Snow Labs +name: distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa +date: 2023-06-26 +tags: [distilbert, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `distilbert-base-uncased-sparse-85-unstructured-pruneofa` is a English model orginally trained by `Intel`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa_en_5.0.0_3.0_1687777999251.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa_en_5.0.0_3.0_1687777999251.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilbert_base_uncased_sparse_85_unstructured_pruneofa").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilbert_base_uncased_sparse_85_unstructured_pruneofa").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|132.5 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa_en.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa_en.md new file mode 100644 index 00000000000000..d172172048e1b6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English DistilBERT Embeddings (%90 sparse) +author: John Snow Labs +name: distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa +date: 2023-06-26 +tags: [distilbert, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `distilbert-base-uncased-sparse-90-unstructured-pruneofa` is a English model orginally trained by `Intel`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa_en_5.0.0_3.0_1687778292303.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa_en_5.0.0_3.0_1687778292303.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilbert_base_uncased_sparse_90_unstructured_pruneofa").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilbert_base_uncased_sparse_90_unstructured_pruneofa").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|123.3 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_fa_zwnj_base_fa.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_fa_zwnj_base_fa.md new file mode 100644 index 00000000000000..6e1c4d47a4a93b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_fa_zwnj_base_fa.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Persian DistilBERT Embeddings (from HooshvareLab) +author: John Snow Labs +name: distilbert_embeddings_distilbert_fa_zwnj_base +date: 2023-06-26 +tags: [distilbert, embeddings, fa, open_source, onnx] +task: Embeddings +language: fa +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `distilbert-fa-zwnj-base` is a Persian model orginally trained by `HooshvareLab`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_distilbert_fa_zwnj_base_fa_5.0.0_3.0_1687778060683.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_distilbert_fa_zwnj_base_fa_5.0.0_3.0_1687778060683.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_fa_zwnj_base","fa") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["من عاشق جرقه NLP هستم"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_fa_zwnj_base","fa") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("من عاشق جرقه NLP هستم").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("fa.embed.distilbert_fa_zwnj_base").predict("""من عاشق جرقه NLP هستم""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_fa_zwnj_base","fa") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["من عاشق جرقه NLP هستم"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_fa_zwnj_base","fa") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("من عاشق جرقه NLP هستم").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fa.embed.distilbert_fa_zwnj_base").predict("""من عاشق جرقه NLP هستم""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_distilbert_fa_zwnj_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|fa| +|Size:|282.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_finetuned_sarcasm_classification_en.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_finetuned_sarcasm_classification_en.md new file mode 100644 index 00000000000000..7df586e990947a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_finetuned_sarcasm_classification_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English DistilBERT Embeddings Cased model (from mrm8488) +author: John Snow Labs +name: distilbert_embeddings_finetuned_sarcasm_classification +date: 2023-06-26 +tags: [open_source, distilbert, embeddings, sarcasm, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBERT Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `distilbert_embeddings_finetuned_sarcasm_classification` is a English model originally trained by `mrm8488`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_finetuned_sarcasm_classification_en_5.0.0_3.0_1687777366459.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_finetuned_sarcasm_classification_en_5.0.0_3.0_1687777366459.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_finetuned_sarcasm_classification","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE."]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_finetuned_sarcasm_classification","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("PUT YOUR STRING HERE.").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distil_bert.finetuned").predict("""PUT YOUR STRING HERE.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_finetuned_sarcasm_classification","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE."]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_finetuned_sarcasm_classification","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("PUT YOUR STRING HERE.").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distil_bert.finetuned").predict("""PUT YOUR STRING HERE.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_finetuned_sarcasm_classification| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_indic_transformers_bn_distilbert_bn.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_indic_transformers_bn_distilbert_bn.md new file mode 100644 index 00000000000000..750a960889bb4e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_indic_transformers_bn_distilbert_bn.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Bangla DistilBERT Embeddings (from neuralspace-reverie) +author: John Snow Labs +name: distilbert_embeddings_indic_transformers_bn_distilbert +date: 2023-06-26 +tags: [distilbert, embeddings, bn, open_source, onnx] +task: Embeddings +language: bn +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `indic-transformers-bn-distilbert` is a Bangla model orginally trained by `neuralspace-reverie`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_indic_transformers_bn_distilbert_bn_5.0.0_3.0_1687778001310.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_indic_transformers_bn_distilbert_bn_5.0.0_3.0_1687778001310.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_indic_transformers_bn_distilbert","bn") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["আমি স্পার্ক এনএলপি ভালোবাসি"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_indic_transformers_bn_distilbert","bn") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("আমি স্পার্ক এনএলপি ভালোবাসি").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("bn.embed.indic_transformers_bn_distilbert").predict("""আমি স্পার্ক এনএলপি ভালোবাসি""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_indic_transformers_bn_distilbert","bn") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["আমি স্পার্ক এনএলপি ভালোবাসি"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_indic_transformers_bn_distilbert","bn") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("আমি স্পার্ক এনএলপি ভালোবাসি").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("bn.embed.indic_transformers_bn_distilbert").predict("""আমি স্পার্ক এনএলপি ভালোবাসি""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_indic_transformers_bn_distilbert| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|bn| +|Size:|248.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_indic_transformers_hi_distilbert_hi.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_indic_transformers_hi_distilbert_hi.md new file mode 100644 index 00000000000000..709aae71d5d5cb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_indic_transformers_hi_distilbert_hi.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Hindi DistilBERT Embeddings (from neuralspace-reverie) +author: John Snow Labs +name: distilbert_embeddings_indic_transformers_hi_distilbert +date: 2023-06-26 +tags: [distilbert, embeddings, hi, open_source, onnx] +task: Embeddings +language: hi +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `indic-transformers-hi-distilbert` is a Hindi model orginally trained by `neuralspace-reverie`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_indic_transformers_hi_distilbert_hi_5.0.0_3.0_1687778274733.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_indic_transformers_hi_distilbert_hi_5.0.0_3.0_1687778274733.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_indic_transformers_hi_distilbert","hi") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["मुझे स्पार्क एनएलपी पसंद है"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_indic_transformers_hi_distilbert","hi") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("मुझे स्पार्क एनएलपी पसंद है").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("hi.embed.indic_transformers_hi_distilbert").predict("""मुझे स्पार्क एनएलपी पसंद है""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_indic_transformers_hi_distilbert","hi") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["मुझे स्पार्क एनएलपी पसंद है"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_indic_transformers_hi_distilbert","hi") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("मुझे स्पार्क एनएलपी पसंद है").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("hi.embed.indic_transformers_hi_distilbert").predict("""मुझे स्पार्क एनएलपी पसंद है""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_indic_transformers_hi_distilbert| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|hi| +|Size:|247.2 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_javanese_distilbert_small_imdb_jv.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_javanese_distilbert_small_imdb_jv.md new file mode 100644 index 00000000000000..9ddef8bf45a2fc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_javanese_distilbert_small_imdb_jv.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Javanese DistilBERT Embeddings (Small, Imdb) +author: John Snow Labs +name: distilbert_embeddings_javanese_distilbert_small_imdb +date: 2023-06-26 +tags: [distilbert, embeddings, jv, open_source, onnx] +task: Embeddings +language: jv +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `javanese-distilbert-small-imdb` is a Javanese model orginally trained by `w11wo`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_javanese_distilbert_small_imdb_jv_5.0.0_3.0_1687778138415.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_javanese_distilbert_small_imdb_jv_5.0.0_3.0_1687778138415.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_javanese_distilbert_small_imdb","jv") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_javanese_distilbert_small_imdb","jv") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("jv.embed.javanese_distilbert_small_imdb").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_javanese_distilbert_small_imdb","jv") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_javanese_distilbert_small_imdb","jv") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("jv.embed.javanese_distilbert_small_imdb").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_javanese_distilbert_small_imdb| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|jv| +|Size:|247.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_javanese_distilbert_small_jv.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_javanese_distilbert_small_jv.md new file mode 100644 index 00000000000000..15b0dcb0b86cda --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_javanese_distilbert_small_jv.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Javanese DistilBERT Embeddings (Small, Wikipedia) +author: John Snow Labs +name: distilbert_embeddings_javanese_distilbert_small +date: 2023-06-26 +tags: [distilbert, embeddings, jv, open_source, onnx] +task: Embeddings +language: jv +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `javanese-distilbert-small` is a Javanese model orginally trained by `w11wo`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_javanese_distilbert_small_jv_5.0.0_3.0_1687778132742.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_javanese_distilbert_small_jv_5.0.0_3.0_1687778132742.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_javanese_distilbert_small","jv") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_javanese_distilbert_small","jv") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("jv.embed.distilbert").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_javanese_distilbert_small","jv") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_javanese_distilbert_small","jv") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("jv.embed.distilbert").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_javanese_distilbert_small| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|jv| +|Size:|247.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_malaysian_distilbert_small_ms.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_malaysian_distilbert_small_ms.md new file mode 100644 index 00000000000000..9092f73999f3c2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_malaysian_distilbert_small_ms.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Malay DistilBERT Embeddings (from w11wo) +author: John Snow Labs +name: distilbert_embeddings_malaysian_distilbert_small +date: 2023-06-26 +tags: [distilbert, embeddings, ms, open_source, onnx] +task: Embeddings +language: ms +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `malaysian-distilbert-small` is a Malay model orginally trained by `w11wo`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_malaysian_distilbert_small_ms_5.0.0_3.0_1687777995710.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_malaysian_distilbert_small_ms_5.0.0_3.0_1687777995710.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_malaysian_distilbert_small","ms") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Saya suka Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_malaysian_distilbert_small","ms") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Saya suka Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ms.embed.distilbert").predict("""Saya suka Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_malaysian_distilbert_small","ms") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Saya suka Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_malaysian_distilbert_small","ms") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Saya suka Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ms.embed.distilbert").predict("""Saya suka Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_malaysian_distilbert_small| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ms| +|Size:|248.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_marathi_distilbert_mr.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_marathi_distilbert_mr.md new file mode 100644 index 00000000000000..9ca015f1047ab5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_marathi_distilbert_mr.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Marathi DistilBERT Embeddings (from DarshanDeshpande) +author: John Snow Labs +name: distilbert_embeddings_marathi_distilbert +date: 2023-06-26 +tags: [distilbert, embeddings, mr, open_source, onnx] +task: Embeddings +language: mr +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `marathi-distilbert` is a Marathi model orginally trained by `DarshanDeshpande`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_marathi_distilbert_mr_5.0.0_3.0_1687778299122.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_marathi_distilbert_mr_5.0.0_3.0_1687778299122.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_marathi_distilbert","mr") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["मला स्पार्क एनएलपी आवडते"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_marathi_distilbert","mr") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("मला स्पार्क एनएलपी आवडते").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("mr.embed.distilbert").predict("""मला स्पार्क एनएलपी आवडते""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_marathi_distilbert","mr") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["मला स्पार्क एनएलपी आवडते"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_marathi_distilbert","mr") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("मला स्पार्क एनएलपी आवडते").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("mr.embed.distilbert").predict("""मला स्पार्क एनएलपी आवडते""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_marathi_distilbert| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|mr| +|Size:|247.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-26-roberta_base_swiss_legal_gsw.md b/docs/_posts/ahmedlone127/2023-06-26-roberta_base_swiss_legal_gsw.md new file mode 100644 index 00000000000000..8cc4701ee197fc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-26-roberta_base_swiss_legal_gsw.md @@ -0,0 +1,80 @@ +--- +layout: model +title: Swiss Legal Roberta Embeddings +author: John Snow Labs +name: roberta_base_swiss_legal +date: 2023-06-26 +tags: [gsw, swiss, embeddings, transformer, open_source, legal, onnx] +task: Embeddings +language: gsw +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Legal Roberta Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `legal-swiss-roberta-base` is a Swiss model originally trained by `joelito`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_base_swiss_legal_gsw_5.0.0_3.0_1687788882271.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_base_swiss_legal_gsw_5.0.0_3.0_1687788882271.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +sentence_embeddings = RoBertaEmbeddings.pretrained("roberta_base_swiss_legal", "gsw")\ + .setInputCols(["sentence"])\ + .setOutputCol("embeddings") +``` +```scala +val sentence_embeddings = RoBertaEmbeddings.pretrained("roberta_base_swiss_legal", "gsw") + .setInputCols("sentence") + .setOutputCol("embeddings")) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +sentence_embeddings = RoBertaEmbeddings.pretrained("roberta_base_swiss_legal", "gsw")\ + .setInputCols(["sentence"])\ + .setOutputCol("embeddings") +``` +```scala +val sentence_embeddings = RoBertaEmbeddings.pretrained("roberta_base_swiss_legal", "gsw") + .setInputCols("sentence") + .setOutputCol("embeddings")) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_base_swiss_legal| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|gsw| +|Size:|692.1 MB| +|Case sensitive:|true| \ No newline at end of file From 7cde44f60849d92eeafd9433a0f06324a850b8bc Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Tue, 27 Jun 2023 21:23:58 +0700 Subject: [PATCH 19/55] 2023-06-27-roberta_embeddings_robertinh_gl (#13868) * Add model 2023-06-27-roberta_embeddings_robertinh_gl * Add model 2023-06-27-roberta_embeddings_roberta_base_wechsel_german_de * Add model 2023-06-27-roberta_embeddings_roberta_base_russian_v0_ru * Add model 2023-06-27-roberta_embeddings_ruperta_base_finetuned_spa_constitution_en * Add model 2023-06-27-roberta_embeddings_robasqu_eu * Add model 2023-06-27-roberta_embeddings_roberta_ko_small_ko * Add model 2023-06-27-roberta_embeddings_hindi_hi * Add model 2023-06-27-roberta_embeddings_sundanese_roberta_base_su * Add model 2023-06-27-roberta_embeddings_roberta_pubmed_en * Add model 2023-06-27-roberta_embeddings_distilroberta_base_climate_f_en * Add model 2023-06-27-roberta_embeddings_roberta_urdu_small_ur * Add model 2023-06-27-roberta_embeddings_BR_BERTo_pt * Add model 2023-06-27-roberta_embeddings_distilroberta_base_climate_d_s_en * Add model 2023-06-27-roberta_embeddings_distilroberta_base_climate_d_en * Add model 2023-06-27-roberta_embeddings_ukr_roberta_base_uk * Add model 2023-06-27-roberta_embeddings_roberta_base_wechsel_french_fr * Add model 2023-06-27-roberta_embeddings_Bible_roberta_base_en * Add model 2023-06-27-roberta_embeddings_bertin_roberta_large_spanish_es * Add model 2023-06-27-roberta_embeddings_roberta_base_wechsel_chinese_zh * Add model 2023-06-27-roberta_embeddings_bertin_roberta_base_spanish_es * Add model 2023-06-27-roberta_embeddings_bertin_base_gaussian_es * Add model 2023-06-27-roberta_embeddings_bertin_base_random_exp_512seqlen_es * Add model 2023-06-27-roberta_embeddings_RuPERTa_base_es * Add model 2023-06-27-roberta_embeddings_roberta_base_bne_es * Add model 2023-06-27-roberta_embeddings_bertin_base_stepwise_exp_512seqlen_es * Add model 2023-06-27-roberta_embeddings_MedRoBERTa.nl_nl * Add model 2023-06-27-roberta_embeddings_bertin_base_random_es * Add model 2023-06-27-roberta_embeddings_RoBERTalex_es * Add model 2023-06-27-roberta_embeddings_SecRoBERTa_en * Add model 2023-06-27-roberta_embeddings_KanBERTo_kn * Add model 2023-06-27-roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title_en * Add model 2023-06-27-roberta_embeddings_MedRoBERTa.nl_nl * Add model 2023-06-27-roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies_en * Add model 2023-06-27-roberta_embeddings_bertin_base_stepwise_es * Add model 2023-06-27-roberta_embeddings_KanBERTo_kn * Add model 2023-06-27-roberta_embeddings_bertin_base_gaussian_exp_512seqlen_es * Add model 2023-06-27-roberta_embeddings_mlm_spanish_roberta_base_es * Add model 2023-06-27-roberta_embeddings_KNUBert_kn * Add model 2023-06-27-roberta_embeddings_javanese_roberta_small_jv * Add model 2023-06-27-roberta_embeddings_indonesian_roberta_base_id * Add model 2023-06-27-roberta_embeddings_indic_transformers_hi_roberta_hi * Add model 2023-06-27-roberta_embeddings_indo_roberta_small_id * Add model 2023-06-27-roberta_embeddings_fairlex_scotus_minilm_en * Add model 2023-06-27-roberta_embeddings_indic_transformers_te_roberta_te * Add model 2023-06-27-roberta_embeddings_javanese_roberta_small_imdb_jv * Add model 2023-06-27-roberta_embeddings_jurisbert_es * Add model 2023-06-27-roberta_embeddings_roberta_base_indonesian_522M_id * Add model 2023-06-27-roberta_embeddings_fairlex_ecthr_minilm_en * Add model 2023-06-27-roberta_embeddings_muppet_roberta_base_en --------- Co-authored-by: ahmedlone127 --- 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+supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `BR_BERTo` is a Portuguese model orginally trained by `rdenadai`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_BR_BERTo_pt_5.0.0_3.0_1687869764918.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_BR_BERTo_pt_5.0.0_3.0_1687869764918.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_BR_BERTo","pt") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Eu amo Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_BR_BERTo","pt") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Eu amo Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("pt.embed.BR_BERTo").predict("""Eu amo Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_BR_BERTo","pt") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Eu amo Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_BR_BERTo","pt") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Eu amo Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("pt.embed.BR_BERTo").predict("""Eu amo Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_BR_BERTo| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|pt| +|Size:|634.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_Bible_roberta_base_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_Bible_roberta_base_en.md new file mode 100644 index 00000000000000..ae2c8f33cc9d83 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_Bible_roberta_base_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English RoBERTa Embeddings (from abhi1nandy2) +author: John Snow Labs +name: roberta_embeddings_Bible_roberta_base +date: 2023-06-27 +tags: [roberta, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `Bible-roberta-base` is a English model orginally trained by `abhi1nandy2`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_Bible_roberta_base_en_5.0.0_3.0_1687870518003.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_Bible_roberta_base_en_5.0.0_3.0_1687870518003.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_Bible_roberta_base","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_Bible_roberta_base","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.Bible_roberta_base").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_Bible_roberta_base","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_Bible_roberta_base","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.Bible_roberta_base").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_Bible_roberta_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|465.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_KNUBert_kn.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_KNUBert_kn.md new file mode 100644 index 00000000000000..f5810aa56beba4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_KNUBert_kn.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Kannada RoBERTa Embeddings (from Chakita) +author: John Snow Labs +name: roberta_embeddings_KNUBert +date: 2023-06-27 +tags: [roberta, embeddings, kn, open_source, onnx] +task: Embeddings +language: kn +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `KNUBert` is a Kannada model orginally trained by `Chakita`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_KNUBert_kn_5.0.0_3.0_1687873446149.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_KNUBert_kn_5.0.0_3.0_1687873446149.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_KNUBert","kn") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["ನಾನು ಸ್ಪಾರ್ಕ್ ಎನ್ಎಲ್ಪಿ ಪ್ರೀತಿಸುತ್ತೇನೆ"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_KNUBert","kn") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("ನಾನು ಸ್ಪಾರ್ಕ್ ಎನ್ಎಲ್ಪಿ ಪ್ರೀತಿಸುತ್ತೇನೆ").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("kn.embed.KNUBert").predict("""ನಾನು ಸ್ಪಾರ್ಕ್ ಎನ್ಎಲ್ಪಿ ಪ್ರೀತಿಸುತ್ತೇನೆ""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_KNUBert","kn") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["ನಾನು ಸ್ಪಾರ್ಕ್ ಎನ್ಎಲ್ಪಿ ಪ್ರೀತಿಸುತ್ತೇನೆ"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_KNUBert","kn") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("ನಾನು ಸ್ಪಾರ್ಕ್ ಎನ್ಎಲ್ಪಿ ಪ್ರೀತಿಸುತ್ತೇನೆ").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("kn.embed.KNUBert").predict("""ನಾನು ಸ್ಪಾರ್ಕ್ ಎನ್ಎಲ್ಪಿ ಪ್ರೀತಿಸುತ್ತೇನೆ""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_KNUBert| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|kn| +|Size:|311.8 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_KanBERTo_kn.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_KanBERTo_kn.md new file mode 100644 index 00000000000000..3b7e500c1b61cf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_KanBERTo_kn.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Kannada RoBERTa Embeddings (from Naveen-k) +author: John Snow Labs +name: roberta_embeddings_KanBERTo +date: 2023-06-27 +tags: [roberta, embeddings, kn, open_source, onnx] +task: Embeddings +language: kn +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `KanBERTo` is a Kannada model orginally trained by `Naveen-k`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_KanBERTo_kn_5.0.0_3.0_1687872951235.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_KanBERTo_kn_5.0.0_3.0_1687872951235.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_KanBERTo","kn") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["ನಾನು ಸ್ಪಾರ್ಕ್ ಎನ್ಎಲ್ಪಿ ಪ್ರೀತಿಸುತ್ತೇನೆ"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_KanBERTo","kn") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("ನಾನು ಸ್ಪಾರ್ಕ್ ಎನ್ಎಲ್ಪಿ ಪ್ರೀತಿಸುತ್ತೇನೆ").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("kn.embed.KanBERTo").predict("""ನಾನು ಸ್ಪಾರ್ಕ್ ಎನ್ಎಲ್ಪಿ ಪ್ರೀತಿಸುತ್ತೇನೆ""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_KanBERTo","kn") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["ನಾನು ಸ್ಪಾರ್ಕ್ ಎನ್ಎಲ್ಪಿ ಪ್ರೀತಿಸುತ್ತೇನೆ"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_KanBERTo","kn") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("ನಾನು ಸ್ಪಾರ್ಕ್ ಎನ್ಎಲ್ಪಿ ಪ್ರೀತಿಸುತ್ತೇನೆ").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("kn.embed.KanBERTo").predict("""ನಾನು ಸ್ಪಾರ್ಕ್ ಎನ್ಎಲ್ಪಿ ಪ್ರೀತಿಸುತ್ತೇನೆ""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_KanBERTo| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|kn| +|Size:|311.8 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_MedRoBERTa.nl_nl.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_MedRoBERTa.nl_nl.md new file mode 100644 index 00000000000000..954d50c138bddb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_MedRoBERTa.nl_nl.md @@ -0,0 +1,135 @@ +--- +layout: model +title: Dutch RoBERTa Embeddings (from CLTL) +author: John Snow Labs +name: roberta_embeddings_MedRoBERTa.nl +date: 2023-06-27 +tags: [roberta, embeddings, nl, open_source, onnx] +task: Embeddings +language: nl +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `MedRoBERTa.nl` is a Dutch model orginally trained by `CLTL`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_MedRoBERTa.nl_nl_5.0.0_3.0_1687872706120.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_MedRoBERTa.nl_nl_5.0.0_3.0_1687872706120.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_MedRoBERTa.nl","nl") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ik hou van vonk nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_MedRoBERTa.nl","nl") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ik hou van vonk nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_MedRoBERTa.nl","nl") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ik hou van vonk nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_MedRoBERTa.nl","nl") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ik hou van vonk nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_MedRoBERTa.nl| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|nl| +|Size:|469.7 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_RoBERTalex_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_RoBERTalex_es.md new file mode 100644 index 00000000000000..6db5ee04644354 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_RoBERTalex_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish Legal RoBERTa Embeddings +author: John Snow Labs +name: roberta_embeddings_RoBERTalex +date: 2023-06-27 +tags: [roberta, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +RoBERTa Legal Embeddings, trained by `PlanTL-GOB-ES`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_RoBERTalex_es_5.0.0_3.0_1687872189190.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_RoBERTalex_es_5.0.0_3.0_1687872189190.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_RoBERTalex","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_RoBERTalex","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.RoBERTalex").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_RoBERTalex","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_RoBERTalex","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.RoBERTalex").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_RoBERTalex| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|298.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_RuPERTa_base_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_RuPERTa_base_es.md new file mode 100644 index 00000000000000..70dbfe309f033b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_RuPERTa_base_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish RoBERTa Embeddings (from mrm8488) +author: John Snow Labs +name: roberta_embeddings_RuPERTa_base +date: 2023-06-27 +tags: [roberta, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `RuPERTa-base` is a Spanish model orginally trained by `mrm8488`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_RuPERTa_base_es_5.0.0_3.0_1687871363701.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_RuPERTa_base_es_5.0.0_3.0_1687871363701.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_RuPERTa_base","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_RuPERTa_base","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.RuPERTa_base").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_RuPERTa_base","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_RuPERTa_base","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.RuPERTa_base").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_RuPERTa_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|470.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_SecRoBERTa_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_SecRoBERTa_en.md new file mode 100644 index 00000000000000..d5ad2b1a0d58e3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_SecRoBERTa_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English RoBERTa Embeddings (from jackaduma) +author: John Snow Labs +name: roberta_embeddings_SecRoBERTa +date: 2023-06-27 +tags: [roberta, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `SecRoBERTa` is a English model orginally trained by `jackaduma`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_SecRoBERTa_en_5.0.0_3.0_1687872365192.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_SecRoBERTa_en_5.0.0_3.0_1687872365192.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_SecRoBERTa","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_SecRoBERTa","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.SecRoBERTa").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_SecRoBERTa","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_SecRoBERTa","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.SecRoBERTa").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_SecRoBERTa| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|311.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_gaussian_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_gaussian_es.md new file mode 100644 index 00000000000000..6920c29287a315 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_gaussian_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish RoBERTa Embeddings (Base, Gaussian Function) +author: John Snow Labs +name: roberta_embeddings_bertin_base_gaussian +date: 2023-06-27 +tags: [roberta, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bertin-base-gaussian` is a Spanish model orginally trained by `bertin-project`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_bertin_base_gaussian_es_5.0.0_3.0_1687871224647.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_bertin_base_gaussian_es_5.0.0_3.0_1687871224647.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_gaussian","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_gaussian","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bertin_base_gaussian").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_gaussian","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_gaussian","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bertin_base_gaussian").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_bertin_base_gaussian| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|231.7 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_gaussian_exp_512seqlen_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_gaussian_exp_512seqlen_es.md new file mode 100644 index 00000000000000..0dbfd7bbf1f936 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_gaussian_exp_512seqlen_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish RoBERTa Embeddings (Base, Using Sequence Length 512) +author: John Snow Labs +name: roberta_embeddings_bertin_base_gaussian_exp_512seqlen +date: 2023-06-27 +tags: [roberta, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bertin-base-gaussian-exp-512seqlen` is a Spanish model orginally trained by `bertin-project`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_bertin_base_gaussian_exp_512seqlen_es_5.0.0_3.0_1687872969913.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_bertin_base_gaussian_exp_512seqlen_es_5.0.0_3.0_1687872969913.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_gaussian_exp_512seqlen","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_gaussian_exp_512seqlen","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bertin_base_gaussian_exp_512seqlen").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_gaussian_exp_512seqlen","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_gaussian_exp_512seqlen","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bertin_base_gaussian_exp_512seqlen").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_bertin_base_gaussian_exp_512seqlen| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|231.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_random_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_random_es.md new file mode 100644 index 00000000000000..9653c8545ae5ba --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_random_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish RoBERTa Embeddings (Base, Random Sampling) +author: John Snow Labs +name: roberta_embeddings_bertin_base_random +date: 2023-06-27 +tags: [roberta, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bertin-base-random` is a Spanish model orginally trained by `bertin-project`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_bertin_base_random_es_5.0.0_3.0_1687872147035.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_bertin_base_random_es_5.0.0_3.0_1687872147035.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_random","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_random","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bertin_base_random").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_random","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_random","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bertin_base_random").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_bertin_base_random| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|231.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_random_exp_512seqlen_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_random_exp_512seqlen_es.md new file mode 100644 index 00000000000000..cba118e80030f6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_random_exp_512seqlen_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish RoBERTa Embeddings (Base, Random Sampling, Using Sequence Length 512) +author: John Snow Labs +name: roberta_embeddings_bertin_base_random_exp_512seqlen +date: 2023-06-27 +tags: [roberta, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bertin-base-random-exp-512seqlen` is a Spanish model orginally trained by `bertin-project`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_bertin_base_random_exp_512seqlen_es_5.0.0_3.0_1687871241466.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_bertin_base_random_exp_512seqlen_es_5.0.0_3.0_1687871241466.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_random_exp_512seqlen","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_random_exp_512seqlen","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bertin_base_random_exp_512seqlen").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_random_exp_512seqlen","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_random_exp_512seqlen","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bertin_base_random_exp_512seqlen").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_bertin_base_random_exp_512seqlen| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|227.7 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_stepwise_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_stepwise_es.md new file mode 100644 index 00000000000000..b668d63c59d89c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_stepwise_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish RoBERTa Embeddings (Base, Stepwise) +author: John Snow Labs +name: roberta_embeddings_bertin_base_stepwise +date: 2023-06-27 +tags: [roberta, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bertin-base-stepwise` is a Spanish model orginally trained by `bertin-project`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_bertin_base_stepwise_es_5.0.0_3.0_1687872843723.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_bertin_base_stepwise_es_5.0.0_3.0_1687872843723.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_stepwise","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_stepwise","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bertin_base_stepwise").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_stepwise","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_stepwise","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bertin_base_stepwise").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_bertin_base_stepwise| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|231.7 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_stepwise_exp_512seqlen_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_stepwise_exp_512seqlen_es.md new file mode 100644 index 00000000000000..ef023c27d100e6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_stepwise_exp_512seqlen_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish RoBERTa Embeddings (Base, Stepwise, Using Sequence Length 512) +author: John Snow Labs +name: roberta_embeddings_bertin_base_stepwise_exp_512seqlen +date: 2023-06-27 +tags: [roberta, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bertin-base-stepwise-exp-512seqlen` is a Spanish model orginally trained by `bertin-project`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_bertin_base_stepwise_exp_512seqlen_es_5.0.0_3.0_1687872063840.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_bertin_base_stepwise_exp_512seqlen_es_5.0.0_3.0_1687872063840.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_stepwise_exp_512seqlen","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_stepwise_exp_512seqlen","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bertin_base_stepwise_exp_512seqlen").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_stepwise_exp_512seqlen","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_stepwise_exp_512seqlen","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bertin_base_stepwise_exp_512seqlen").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_bertin_base_stepwise_exp_512seqlen| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|232.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_roberta_base_spanish_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_roberta_base_spanish_es.md new file mode 100644 index 00000000000000..462366bdad47ef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_roberta_base_spanish_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish RoBERTa Embeddings (Bertin Base) +author: John Snow Labs +name: roberta_embeddings_bertin_roberta_base_spanish +date: 2023-06-27 +tags: [roberta, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model for Spanish Language, trained within the Bertin project. Other non-base Bertin models can be found [here](https://nlp.johnsnowlabs.com/models?q=bertin). The model was uploaded to Hugging Face, adapted and imported into Spark NLP. `bertin-roberta-base-spanish` is a Spanish model orginally trained by `bertin-project`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_bertin_roberta_base_spanish_es_5.0.0_3.0_1687871077280.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_bertin_roberta_base_spanish_es_5.0.0_3.0_1687871077280.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_roberta_base_spanish","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_roberta_base_spanish","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bertin_roberta_base_spanish").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_roberta_base_spanish","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_roberta_base_spanish","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bertin_roberta_base_spanish").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_bertin_roberta_base_spanish| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|462.2 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_roberta_large_spanish_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_roberta_large_spanish_es.md new file mode 100644 index 00000000000000..1a10a0441cc6d9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_roberta_large_spanish_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish RoBERTa Embeddings (from flax-community) +author: John Snow Labs +name: roberta_embeddings_bertin_roberta_large_spanish +date: 2023-06-27 +tags: [roberta, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `bertin-roberta-large-spanish` is a Spanish model orginally trained by `flax-community`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_bertin_roberta_large_spanish_es_5.0.0_3.0_1687870695333.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_bertin_roberta_large_spanish_es_5.0.0_3.0_1687870695333.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_roberta_large_spanish","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_roberta_large_spanish","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bertin_roberta_large_spanish").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_roberta_large_spanish","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_roberta_large_spanish","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.bertin_roberta_large_spanish").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_bertin_roberta_large_spanish| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|230.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_d_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_d_en.md new file mode 100644 index 00000000000000..235066a9fad86d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_d_en.md @@ -0,0 +1,151 @@ +--- +layout: model +title: English RoBERTa Embeddings (Sampling strategy 'div select') +author: John Snow Labs +name: roberta_embeddings_distilroberta_base_climate_d +date: 2023-06-27 +tags: [roberta, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `distilroberta-base-climate-d` is a English model orginally trained by `climatebert`. + +Sampling strategy d: As expressed in the author's paper [here](https://arxiv.org/pdf/2110.12010.pdf), d is "div select", meaning 70% of the most diverse sentences of one of the corpus was used, discarding the rest. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_distilroberta_base_climate_d_en_5.0.0_3.0_1687870130521.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_distilroberta_base_climate_d_en_5.0.0_3.0_1687870130521.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_climate_d","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_climate_d","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilroberta_base_climate_d").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_climate_d","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_climate_d","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilroberta_base_climate_d").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_distilroberta_base_climate_d| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|307.4 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_d_s_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_d_s_en.md new file mode 100644 index 00000000000000..bd8c33572f99c2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_d_s_en.md @@ -0,0 +1,151 @@ +--- +layout: model +title: English RoBERTa Embeddings (Mixed sampling strategy) +author: John Snow Labs +name: roberta_embeddings_distilroberta_base_climate_d_s +date: 2023-06-27 +tags: [roberta, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `distilroberta-base-climate-d-s` is a English model orginally trained by `climatebert`. + +Sampling strategy ds:As expressed in the author's paper [here](https://arxiv.org/pdf/2110.12010.pdf), ds is "div select + sim select", meaning 70% of the biggest composite scaled score diverse+sim was used, discarding the rest. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_distilroberta_base_climate_d_s_en_5.0.0_3.0_1687869802826.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_distilroberta_base_climate_d_s_en_5.0.0_3.0_1687869802826.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_climate_d_s","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_climate_d_s","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilroberta_base_climate_d_s").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_climate_d_s","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_climate_d_s","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilroberta_base_climate_d_s").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_distilroberta_base_climate_d_s| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|307.4 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_f_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_f_en.md new file mode 100644 index 00000000000000..1c3db397a2a509 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_f_en.md @@ -0,0 +1,151 @@ +--- +layout: model +title: English RoBERTa Embeddings (Sampling strategy 'full select') +author: John Snow Labs +name: roberta_embeddings_distilroberta_base_climate_f +date: 2023-06-27 +tags: [roberta, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `distilroberta-base-climate-f` is a English model orginally trained by `climatebert`. + +Sampling strategy f: As expressed in the author's paper [here](https://arxiv.org/pdf/2110.12010.pdf), f is "full select" strategy, meaning all sentences from all corpora where selected. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_distilroberta_base_climate_f_en_5.0.0_3.0_1687869647532.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_distilroberta_base_climate_f_en_5.0.0_3.0_1687869647532.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_climate_f","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_climate_f","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilroberta_base_climate_f").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_climate_f","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_climate_f","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilroberta_base_climate_f").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_distilroberta_base_climate_f| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|307.4 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title_en.md new file mode 100644 index 00000000000000..f9a2af7472f6ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English RoBERTa Embeddings (Base, Titles) +author: John Snow Labs +name: roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title +date: 2023-06-27 +tags: [roberta, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `distilroberta-base-finetuned-jira-qt-issue-title` is a English model orginally trained by `ietz`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title_en_5.0.0_3.0_1687872655212.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title_en_5.0.0_3.0_1687872655212.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilroberta_base_finetuned_jira_qt_issue_title").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilroberta_base_finetuned_jira_qt_issue_title").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|306.4 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies_en.md new file mode 100644 index 00000000000000..b9661e848333d1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English RoBERTa Embeddings (Base, Titles, Bodies) +author: John Snow Labs +name: roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies +date: 2023-06-27 +tags: [roberta, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `distilroberta-base-finetuned-jira-qt-issue-titles-and-bodies` is a English model orginally trained by `ietz`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies_en_5.0.0_3.0_1687872819451.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies_en_5.0.0_3.0_1687872819451.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|306.4 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_fairlex_ecthr_minilm_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_fairlex_ecthr_minilm_en.md new file mode 100644 index 00000000000000..5a32da11d9de7d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_fairlex_ecthr_minilm_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English RoBERTa Embeddings (ECtHR dataset) +author: John Snow Labs +name: roberta_embeddings_fairlex_ecthr_minilm +date: 2023-06-27 +tags: [roberta, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `fairlex-ecthr-minilm` is a English model orginally trained by `coastalcph`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_fairlex_ecthr_minilm_en_5.0.0_3.0_1687874692287.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_fairlex_ecthr_minilm_en_5.0.0_3.0_1687874692287.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_fairlex_ecthr_minilm","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_fairlex_ecthr_minilm","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.fairlex_ecthr_minilm").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_fairlex_ecthr_minilm","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_fairlex_ecthr_minilm","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.fairlex_ecthr_minilm").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_fairlex_ecthr_minilm| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|114.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_fairlex_scotus_minilm_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_fairlex_scotus_minilm_en.md new file mode 100644 index 00000000000000..773d7bcf2c57d3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_fairlex_scotus_minilm_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English RoBERTa Embeddings (SCOTUS dataset) +author: John Snow Labs +name: roberta_embeddings_fairlex_scotus_minilm +date: 2023-06-27 +tags: [roberta, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `fairlex-scotus-minilm` is a English model orginally trained by `coastalcph`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_fairlex_scotus_minilm_en_5.0.0_3.0_1687873942052.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_fairlex_scotus_minilm_en_5.0.0_3.0_1687873942052.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_fairlex_scotus_minilm","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_fairlex_scotus_minilm","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.fairlex_scotus_minilm").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_fairlex_scotus_minilm","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_fairlex_scotus_minilm","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.fairlex_scotus_minilm").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_fairlex_scotus_minilm| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|114.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_hindi_hi.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_hindi_hi.md new file mode 100644 index 00000000000000..01ad5f075cbd15 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_hindi_hi.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Hindi RoBERTa Embeddings Cased model (from mrm8488) +author: John Snow Labs +name: roberta_embeddings_hindi +date: 2023-06-27 +tags: [hi, open_source, roberta, embeddings, onnx] +task: Embeddings +language: hi +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `HindiBERTa` is a Hindi model originally trained by `mrm8488`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_hindi_hi_5.0.0_3.0_1687869215499.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_hindi_hi_5.0.0_3.0_1687869215499.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_hindi","hi") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_hindi","hi") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("hi.embed.roberta").predict("""PUT YOUR STRING HERE""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_hindi","hi") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_hindi","hi") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("hi.embed.roberta").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_hindi| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|hi| +|Size:|311.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indic_transformers_hi_roberta_hi.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indic_transformers_hi_roberta_hi.md new file mode 100644 index 00000000000000..f69f15963140ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indic_transformers_hi_roberta_hi.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Hindi RoBERTa Embeddings (from neuralspace-reverie) +author: John Snow Labs +name: roberta_embeddings_indic_transformers_hi_roberta +date: 2023-06-27 +tags: [roberta, embeddings, hi, open_source, onnx] +task: Embeddings +language: hi +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `indic-transformers-hi-roberta` is a Hindi model orginally trained by `neuralspace-reverie`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_indic_transformers_hi_roberta_hi_5.0.0_3.0_1687873778793.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_indic_transformers_hi_roberta_hi_5.0.0_3.0_1687873778793.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indic_transformers_hi_roberta","hi") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["मुझे स्पार्क एनएलपी पसंद है"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indic_transformers_hi_roberta","hi") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("मुझे स्पार्क एनएलपी पसंद है").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("hi.embed.indic_transformers_hi_roberta").predict("""मुझे स्पार्क एनएलपी पसंद है""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indic_transformers_hi_roberta","hi") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["मुझे स्पार्क एनएलपी पसंद है"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indic_transformers_hi_roberta","hi") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("मुझे स्पार्क एनएलपी पसंद है").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("hi.embed.indic_transformers_hi_roberta").predict("""मुझे स्पार्क एनएलपी पसंद है""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_indic_transformers_hi_roberta| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|hi| +|Size:|311.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indic_transformers_te_roberta_te.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indic_transformers_te_roberta_te.md new file mode 100644 index 00000000000000..08583c9e8b8960 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indic_transformers_te_roberta_te.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Telugu RoBERTa Embeddings (from neuralspace-reverie) +author: John Snow Labs +name: roberta_embeddings_indic_transformers_te_roberta +date: 2023-06-27 +tags: [roberta, embeddings, te, open_source, onnx] +task: Embeddings +language: te +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `indic-transformers-te-roberta` is a Telugu model orginally trained by `neuralspace-reverie`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_indic_transformers_te_roberta_te_5.0.0_3.0_1687874468330.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_indic_transformers_te_roberta_te_5.0.0_3.0_1687874468330.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indic_transformers_te_roberta","te") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indic_transformers_te_roberta","te") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("te.embed.indic_transformers_te_roberta").predict("""నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indic_transformers_te_roberta","te") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indic_transformers_te_roberta","te") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("te.embed.indic_transformers_te_roberta").predict("""నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_indic_transformers_te_roberta| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|te| +|Size:|312.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indo_roberta_small_id.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indo_roberta_small_id.md new file mode 100644 index 00000000000000..6331c54d43f671 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indo_roberta_small_id.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Indonesian RoBERTa Embeddings (from w11wo) +author: John Snow Labs +name: roberta_embeddings_indo_roberta_small +date: 2023-06-27 +tags: [roberta, embeddings, id, open_source, onnx] +task: Embeddings +language: id +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `indo-roberta-small` is a Indonesian model orginally trained by `w11wo`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_indo_roberta_small_id_5.0.0_3.0_1687873872173.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_indo_roberta_small_id_5.0.0_3.0_1687873872173.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indo_roberta_small","id") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Saya suka percikan NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indo_roberta_small","id") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Saya suka percikan NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("id.embed.indo_roberta_small").predict("""Saya suka percikan NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indo_roberta_small","id") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Saya suka percikan NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indo_roberta_small","id") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Saya suka percikan NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("id.embed.indo_roberta_small").predict("""Saya suka percikan NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_indo_roberta_small| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|id| +|Size:|311.4 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indonesian_roberta_base_id.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indonesian_roberta_base_id.md new file mode 100644 index 00000000000000..77ff6a04ce597b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indonesian_roberta_base_id.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Indonesian RoBERTa Embeddings (Base) +author: John Snow Labs +name: roberta_embeddings_indonesian_roberta_base +date: 2023-06-27 +tags: [roberta, embeddings, id, open_source, onnx] +task: Embeddings +language: id +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `indonesian-roberta-base` is a Indonesian model orginally trained by `flax-community`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_indonesian_roberta_base_id_5.0.0_3.0_1687873627160.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_indonesian_roberta_base_id_5.0.0_3.0_1687873627160.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indonesian_roberta_base","id") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Saya suka percikan NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indonesian_roberta_base","id") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Saya suka percikan NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("id.embed.indonesian_roberta_base").predict("""Saya suka percikan NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indonesian_roberta_base","id") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Saya suka percikan NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indonesian_roberta_base","id") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Saya suka percikan NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("id.embed.indonesian_roberta_base").predict("""Saya suka percikan NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_indonesian_roberta_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|id| +|Size:|465.4 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_javanese_roberta_small_imdb_jv.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_javanese_roberta_small_imdb_jv.md new file mode 100644 index 00000000000000..19408b12a0bed8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_javanese_roberta_small_imdb_jv.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Javanese RoBERTa Embeddings (Small, IMDB Movie Review) +author: John Snow Labs +name: roberta_embeddings_javanese_roberta_small_imdb +date: 2023-06-27 +tags: [roberta, embeddings, jv, open_source, onnx] +task: Embeddings +language: jv +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `javanese-roberta-small-imdb` is a Javanese model orginally trained by `w11wo`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_javanese_roberta_small_imdb_jv_5.0.0_3.0_1687874553810.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_javanese_roberta_small_imdb_jv_5.0.0_3.0_1687874553810.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_javanese_roberta_small_imdb","jv") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_javanese_roberta_small_imdb","jv") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("jv.embed.javanese_roberta_small_imdb").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_javanese_roberta_small_imdb","jv") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_javanese_roberta_small_imdb","jv") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("jv.embed.javanese_roberta_small_imdb").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_javanese_roberta_small_imdb| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|jv| +|Size:|465.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_javanese_roberta_small_jv.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_javanese_roberta_small_jv.md new file mode 100644 index 00000000000000..1da0c11ee775fd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_javanese_roberta_small_jv.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Javanese RoBERTa Embeddings (Small, Javanese Wikipedia) +author: John Snow Labs +name: roberta_embeddings_javanese_roberta_small +date: 2023-06-27 +tags: [roberta, embeddings, jv, open_source, onnx] +task: Embeddings +language: jv +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `javanese-roberta-small` is a Javanese model orginally trained by `w11wo`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_javanese_roberta_small_jv_5.0.0_3.0_1687873541316.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_javanese_roberta_small_jv_5.0.0_3.0_1687873541316.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_javanese_roberta_small","jv") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_javanese_roberta_small","jv") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("jv.embed.javanese_roberta_small").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_javanese_roberta_small","jv") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_javanese_roberta_small","jv") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("jv.embed.javanese_roberta_small").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_javanese_roberta_small| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|jv| +|Size:|465.8 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_jurisbert_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_jurisbert_es.md new file mode 100644 index 00000000000000..685198ff9883b0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_jurisbert_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish RoBERTa Embeddings (from scjnugacj) +author: John Snow Labs +name: roberta_embeddings_jurisbert +date: 2023-06-27 +tags: [roberta, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `jurisbert` is a Spanish model orginally trained by `scjnugacj`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_jurisbert_es_5.0.0_3.0_1687874582910.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_jurisbert_es_5.0.0_3.0_1687874582910.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_jurisbert","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_jurisbert","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.jurisbert").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_jurisbert","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_jurisbert","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.jurisbert").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_jurisbert| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|463.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_mlm_spanish_roberta_base_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_mlm_spanish_roberta_base_es.md new file mode 100644 index 00000000000000..0ad5d20ef3fe54 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_mlm_spanish_roberta_base_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish RoBERTa Embeddings (from MMG) +author: John Snow Labs +name: roberta_embeddings_mlm_spanish_roberta_base +date: 2023-06-27 +tags: [roberta, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `mlm-spanish-roberta-base` is a Spanish model orginally trained by `MMG`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_mlm_spanish_roberta_base_es_5.0.0_3.0_1687873365386.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_mlm_spanish_roberta_base_es_5.0.0_3.0_1687873365386.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_mlm_spanish_roberta_base","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_mlm_spanish_roberta_base","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.mlm_spanish_roberta_base").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_mlm_spanish_roberta_base","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_mlm_spanish_roberta_base","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.mlm_spanish_roberta_base").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_mlm_spanish_roberta_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|470.8 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_muppet_roberta_base_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_muppet_roberta_base_en.md new file mode 100644 index 00000000000000..238181eea3e7a3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_muppet_roberta_base_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English RoBERTa Embeddings (Base, Wikipedia and Bookcorpus datasets) +author: John Snow Labs +name: roberta_embeddings_muppet_roberta_base +date: 2023-06-27 +tags: [roberta, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `muppet-roberta-base` is a English model orginally trained by `facebook`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_muppet_roberta_base_en_5.0.0_3.0_1687874715051.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_muppet_roberta_base_en_5.0.0_3.0_1687874715051.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_muppet_roberta_base","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_muppet_roberta_base","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.muppet_roberta_base").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_muppet_roberta_base","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_muppet_roberta_base","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.muppet_roberta_base").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_muppet_roberta_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|298.4 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_robasqu_eu.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_robasqu_eu.md new file mode 100644 index 00000000000000..90ed07b322abfb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_robasqu_eu.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Basque RoBERTa Embeddings Cased model (from mrm8488) +author: John Snow Labs +name: roberta_embeddings_robasqu +date: 2023-06-27 +tags: [eu, open_source, roberta, embeddings, onnx] +task: Embeddings +language: eu +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `RoBasquERTa` is a Basque model originally trained by `mrm8488`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_robasqu_eu_5.0.0_3.0_1687868888738.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_robasqu_eu_5.0.0_3.0_1687868888738.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_robasqu","eu") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_robasqu","eu") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("eu.embed.roberta").predict("""PUT YOUR STRING HERE""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_robasqu","eu") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_robasqu","eu") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("eu.embed.roberta").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_robasqu| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|eu| +|Size:|310.4 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_bne_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_bne_es.md new file mode 100644 index 00000000000000..4412023121038a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_bne_es.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Spanish RoBERTa Embeddings (Base) +author: John Snow Labs +name: roberta_embeddings_roberta_base_bne +date: 2023-06-27 +tags: [roberta, embeddings, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `roberta-base-bne` is a Spanish model orginally trained by `PlanTL-GOB-ES`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_roberta_base_bne_es_5.0.0_3.0_1687871414251.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_roberta_base_bne_es_5.0.0_3.0_1687871414251.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_bne","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_bne","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.roberta_base_bne").predict("""Me encanta chispa nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_bne","es") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_bne","es") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Me encanta chispa nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("es.embed.roberta_base_bne").predict("""Me encanta chispa nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_roberta_base_bne| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|es| +|Size:|295.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_indonesian_522M_id.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_indonesian_522M_id.md new file mode 100644 index 00000000000000..30a5a85951db7d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_indonesian_522M_id.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Indonesian RoBERTa Embeddings (from cahya) +author: John Snow Labs +name: roberta_embeddings_roberta_base_indonesian_522M +date: 2023-06-27 +tags: [roberta, embeddings, id, open_source, onnx] +task: Embeddings +language: id +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `roberta-base-indonesian-522M` is a Indonesian model orginally trained by `cahya`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_roberta_base_indonesian_522M_id_5.0.0_3.0_1687874649406.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_roberta_base_indonesian_522M_id_5.0.0_3.0_1687874649406.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_indonesian_522M","id") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Saya suka percikan NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_indonesian_522M","id") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Saya suka percikan NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("id.embed.roberta_base_indonesian_522M").predict("""Saya suka percikan NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_indonesian_522M","id") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Saya suka percikan NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_indonesian_522M","id") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Saya suka percikan NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("id.embed.roberta_base_indonesian_522M").predict("""Saya suka percikan NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_roberta_base_indonesian_522M| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|id| +|Size:|470.7 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_russian_v0_ru.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_russian_v0_ru.md new file mode 100644 index 00000000000000..5d49a37350de0f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_russian_v0_ru.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Russian RoBERTa Embeddings (from blinoff) +author: John Snow Labs +name: roberta_embeddings_roberta_base_russian_v0 +date: 2023-06-27 +tags: [roberta, embeddings, ru, open_source, onnx] +task: Embeddings +language: ru +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `roberta-base-russian-v0` is a Russian model orginally trained by `blinoff`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_roberta_base_russian_v0_ru_5.0.0_3.0_1687868830424.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_roberta_base_russian_v0_ru_5.0.0_3.0_1687868830424.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_russian_v0","ru") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Я люблю искра NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_russian_v0","ru") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Я люблю искра NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ru.embed.roberta_base_russian_v0").predict("""Я люблю искра NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_russian_v0","ru") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Я люблю искра NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_russian_v0","ru") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Я люблю искра NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ru.embed.roberta_base_russian_v0").predict("""Я люблю искра NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_roberta_base_russian_v0| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ru| +|Size:|465.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_chinese_zh.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_chinese_zh.md new file mode 100644 index 00000000000000..6b0bc0e8bcf727 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_chinese_zh.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Chinese RoBERTa Embeddings (from benjamin) +author: John Snow Labs +name: roberta_embeddings_roberta_base_wechsel_chinese +date: 2023-06-27 +tags: [roberta, embeddings, zh, open_source, onnx] +task: Embeddings +language: zh +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `roberta-base-wechsel-chinese` is a Chinese model orginally trained by `benjamin`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_roberta_base_wechsel_chinese_zh_5.0.0_3.0_1687871022810.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_roberta_base_wechsel_chinese_zh_5.0.0_3.0_1687871022810.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_wechsel_chinese","zh") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["我喜欢Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_wechsel_chinese","zh") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("我喜欢Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("zh.embed.roberta_base_wechsel_chinese").predict("""我喜欢Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_wechsel_chinese","zh") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["我喜欢Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_wechsel_chinese","zh") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("我喜欢Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("zh.embed.roberta_base_wechsel_chinese").predict("""我喜欢Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_roberta_base_wechsel_chinese| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|zh| +|Size:|466.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_french_fr.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_french_fr.md new file mode 100644 index 00000000000000..61dc5c17eb026f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_french_fr.md @@ -0,0 +1,149 @@ +--- +layout: model +title: French RoBERTa Embeddings (from benjamin) +author: John Snow Labs +name: roberta_embeddings_roberta_base_wechsel_french +date: 2023-06-27 +tags: [roberta, embeddings, fr, open_source, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `roberta-base-wechsel-french` is a French model orginally trained by `benjamin`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_roberta_base_wechsel_french_fr_5.0.0_3.0_1687870303923.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_roberta_base_wechsel_french_fr_5.0.0_3.0_1687870303923.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_wechsel_french","fr") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark Nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_wechsel_french","fr") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark Nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.roberta_base_wechsel_french").predict("""J'adore Spark Nlp""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_wechsel_french","fr") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark Nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_wechsel_french","fr") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark Nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.roberta_base_wechsel_french").predict("""J'adore Spark Nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_roberta_base_wechsel_french| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|fr| +|Size:|465.7 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_german_de.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_german_de.md new file mode 100644 index 00000000000000..f1c52bdc3df88e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_german_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German RoBERTa Embeddings (from benjamin) +author: John Snow Labs +name: roberta_embeddings_roberta_base_wechsel_german +date: 2023-06-27 +tags: [roberta, embeddings, de, open_source, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `roberta-base-wechsel-german` is a German model orginally trained by `benjamin`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_roberta_base_wechsel_german_de_5.0.0_3.0_1687868753727.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_roberta_base_wechsel_german_de_5.0.0_3.0_1687868753727.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_wechsel_german","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_wechsel_german","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.roberta_base_wechsel_german").predict("""Ich liebe Funken NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_wechsel_german","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_wechsel_german","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.roberta_base_wechsel_german").predict("""Ich liebe Funken NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_roberta_base_wechsel_german| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|de| +|Size:|465.7 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_ko_small_ko.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_ko_small_ko.md new file mode 100644 index 00000000000000..12b377d5c0d070 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_ko_small_ko.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Korean RoBERTa Embeddings (from lassl) +author: John Snow Labs +name: roberta_embeddings_roberta_ko_small +date: 2023-06-27 +tags: [roberta, embeddings, ko, open_source, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `roberta-ko-small` is a Korean model orginally trained by `lassl`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_roberta_ko_small_ko_5.0.0_3.0_1687868994337.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_roberta_ko_small_ko_5.0.0_3.0_1687868994337.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_ko_small","ko") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_ko_small","ko") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ko.embed.roberta_ko_small").predict("""나는 Spark NLP를 좋아합니다""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_ko_small","ko") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_ko_small","ko") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ko.embed.roberta_ko_small").predict("""나는 Spark NLP를 좋아합니다""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_roberta_ko_small| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ko| +|Size:|86.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_pubmed_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_pubmed_en.md new file mode 100644 index 00000000000000..e3a64ba4f52a29 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_pubmed_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English RoBERTa Embeddings (Base, Biomarkers/Carcinoma/Clinical Trial) +author: John Snow Labs +name: roberta_embeddings_roberta_pubmed +date: 2023-06-27 +tags: [roberta, embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `roberta-pubmed` is a English model orginally trained by `raynardj`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_roberta_pubmed_en_5.0.0_3.0_1687869550742.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_roberta_pubmed_en_5.0.0_3.0_1687869550742.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_pubmed","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_pubmed","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.roberta_pubmed").predict("""I love Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_pubmed","en") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_pubmed","en") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.roberta_pubmed").predict("""I love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_roberta_pubmed| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|466.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_urdu_small_ur.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_urdu_small_ur.md new file mode 100644 index 00000000000000..8f5a603c1976a8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_urdu_small_ur.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Urdu RoBERTa Embeddings +author: John Snow Labs +name: roberta_embeddings_roberta_urdu_small +date: 2023-06-27 +tags: [roberta, embeddings, ur, open_source, onnx] +task: Embeddings +language: ur +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `roberta-urdu-small` is a Urdu model orginally trained by `urduhack`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_roberta_urdu_small_ur_5.0.0_3.0_1687869693441.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_roberta_urdu_small_ur_5.0.0_3.0_1687869693441.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_urdu_small","ur") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["مجھے سپارک این ایل پی سے محبت ہے"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_urdu_small","ur") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("مجھے سپارک این ایل پی سے محبت ہے").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("ur.embed.roberta_urdu_small").predict("""مجھے سپارک این ایل پی سے محبت ہے""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_urdu_small","ur") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["مجھے سپارک این ایل پی سے محبت ہے"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_urdu_small","ur") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("مجھے سپارک این ایل پی سے محبت ہے").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ur.embed.roberta_urdu_small").predict("""مجھے سپارک این ایل پی سے محبت ہے""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_roberta_urdu_small| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ur| +|Size:|471.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_robertinh_gl.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_robertinh_gl.md new file mode 100644 index 00000000000000..d587c92f3a8366 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_robertinh_gl.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Galician RoBERTa Embeddings Cased model (from mrm8488) +author: John Snow Labs +name: roberta_embeddings_robertinh +date: 2023-06-27 +tags: [gl, open_source, roberta, embeddings, onnx] +task: Embeddings +language: gl +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `RoBERTinha` is a Galician model originally trained by `mrm8488`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_robertinh_gl_5.0.0_3.0_1687868721484.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_robertinh_gl_5.0.0_3.0_1687868721484.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_robertinh","gl") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_robertinh","gl") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("gl.embed.roberta").predict("""PUT YOUR STRING HERE""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_robertinh","gl") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_robertinh","gl") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("gl.embed.roberta").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_robertinh| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|gl| +|Size:|311.7 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_ruperta_base_finetuned_spa_constitution_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_ruperta_base_finetuned_spa_constitution_en.md new file mode 100644 index 00000000000000..2b84c9374f5630 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_ruperta_base_finetuned_spa_constitution_en.md @@ -0,0 +1,149 @@ +--- +layout: model +title: English RoBERTa Embeddings Base Cased model (from mrm8488) +author: John Snow Labs +name: roberta_embeddings_ruperta_base_finetuned_spa_constitution +date: 2023-06-27 +tags: [en, open_source, roberta, embeddings, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `RuPERTa-base-finetuned-spa-constitution` is a English model originally trained by `mrm8488`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_ruperta_base_finetuned_spa_constitution_en_5.0.0_3.0_1687868848699.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_ruperta_base_finetuned_spa_constitution_en_5.0.0_3.0_1687868848699.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_ruperta_base_finetuned_spa_constitution","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_ruperta_base_finetuned_spa_constitution","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.roberta.base_finetuned").predict("""PUT YOUR STRING HERE""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_ruperta_base_finetuned_spa_constitution","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_ruperta_base_finetuned_spa_constitution","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.roberta.base_finetuned").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_ruperta_base_finetuned_spa_constitution| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|469.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_sundanese_roberta_base_su.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_sundanese_roberta_base_su.md new file mode 100644 index 00000000000000..7585bf0774d018 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_sundanese_roberta_base_su.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Sundanese RoBERTa Embeddings (from w11wo) +author: John Snow Labs +name: roberta_embeddings_sundanese_roberta_base +date: 2023-06-27 +tags: [roberta, embeddings, su, open_source, onnx] +task: Embeddings +language: su +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `sundanese-roberta-base` is a Sundanese model orginally trained by `w11wo`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_sundanese_roberta_base_su_5.0.0_3.0_1687869405297.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_sundanese_roberta_base_su_5.0.0_3.0_1687869405297.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_sundanese_roberta_base","su") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Abdi bogoh Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_sundanese_roberta_base","su") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Abdi bogoh Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("su.embed.sundanese_roberta_base").predict("""Abdi bogoh Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_sundanese_roberta_base","su") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Abdi bogoh Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_sundanese_roberta_base","su") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Abdi bogoh Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("su.embed.sundanese_roberta_base").predict("""Abdi bogoh Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_sundanese_roberta_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|su| +|Size:|465.6 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_ukr_roberta_base_uk.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_ukr_roberta_base_uk.md new file mode 100644 index 00000000000000..00a9d4227090f5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_ukr_roberta_base_uk.md @@ -0,0 +1,149 @@ +--- +layout: model +title: Ukrainian RoBERTa Embeddings +author: John Snow Labs +name: roberta_embeddings_ukr_roberta_base +date: 2023-06-27 +tags: [roberta, embeddings, uk, open_source, onnx] +task: Embeddings +language: uk +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `ukr-roberta-base` is a Ukrainian model orginally trained by `youscan`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_embeddings_ukr_roberta_base_uk_5.0.0_3.0_1687870224676.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_embeddings_ukr_roberta_base_uk_5.0.0_3.0_1687870224676.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_ukr_roberta_base","uk") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Я люблю Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_ukr_roberta_base","uk") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Я люблю Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("uk.embed.ukr_roberta_base").predict("""Я люблю Spark NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_ukr_roberta_base","uk") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Я люблю Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_ukr_roberta_base","uk") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Я люблю Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("uk.embed.ukr_roberta_base").predict("""Я люблю Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_embeddings_ukr_roberta_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|uk| +|Size:|471.3 MB| +|Case sensitive:|true| \ No newline at end of file From ced98b67fe01e6a707f2aa74adcf85d8788e8859 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Fri, 30 Jun 2023 13:47:09 +0700 Subject: [PATCH 20/55] Add model 2023-06-29-xlmroberta_embeddings_paraphrase_mpnet_base_v2_xx (#13872) Co-authored-by: Damla-Gurbaz --- ..._embeddings_paraphrase_mpnet_base_v2_xx.md | 100 ++++++++++++++++++ 1 file changed, 100 insertions(+) create mode 100644 docs/_posts/Damla-Gurbaz/2023-06-29-xlmroberta_embeddings_paraphrase_mpnet_base_v2_xx.md diff --git a/docs/_posts/Damla-Gurbaz/2023-06-29-xlmroberta_embeddings_paraphrase_mpnet_base_v2_xx.md b/docs/_posts/Damla-Gurbaz/2023-06-29-xlmroberta_embeddings_paraphrase_mpnet_base_v2_xx.md new file mode 100644 index 00000000000000..55a0e813ca93d5 --- /dev/null +++ b/docs/_posts/Damla-Gurbaz/2023-06-29-xlmroberta_embeddings_paraphrase_mpnet_base_v2_xx.md @@ -0,0 +1,100 @@ +--- +layout: model +title: Multilingual XLMRoBerta Embeddings Cased Model +author: John Snow Labs +name: xlmroberta_embeddings_paraphrase_mpnet_base_v2 +date: 2023-06-29 +tags: [xx, embeddings, xlmroberta, open_source, transformer, tensorflow] +task: Embeddings +language: xx +edition: Spark NLP 4.4.4 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: XlmRoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained XLMRoberta Embeddings model is a multilingual embedding model adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlmroberta_embeddings_paraphrase_mpnet_base_v2_xx_4.4.4_3.0_1688073546075.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlmroberta_embeddings_paraphrase_mpnet_base_v2_xx_4.4.4_3.0_1688073546075.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = XlmRoBertaEmbeddings.pretrained("xlmroberta_embeddings_paraphrase_mpnet_base_v2","xx") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, + tokenizer, + embeddings]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = XlmRoBertaEmbeddings.pretrained("xlmroberta_embeddings_paraphrase_mpnet_base_v2", "xx") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, + tokenizer, + embeddings)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlmroberta_embeddings_paraphrase_mpnet_base_v2| +|Compatibility:|Spark NLP 4.4.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|1.0 GB| +|Case sensitive:|true| + +## References + +https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2 \ No newline at end of file From dfaabd40fe7e726b2eb7c77a6d25f9a71c3c0ece Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Sat, 1 Jul 2023 20:06:03 +0700 Subject: [PATCH 21/55] 2023-06-08-instructor_base_en (#13850) * Add model 2023-06-08-instructor_base_en * Update 2023-06-08-instructor_base_en.md * Add model 2023-06-21-e5_base_v2_en * Add model 2023-06-21-e5_base_en * Add model 2023-06-21-e5_large_v2_en * Add model 2023-06-21-e5_large_en * Add model 2023-06-21-e5_small_v2_en * Add model 2023-06-21-e5_small_en * Add model 2023-06-21-instructor_large_en --------- Co-authored-by: prabod Co-authored-by: Maziyar Panahi --- .../prabod/2023-06-08-instructor_base_en.md | 75 +++++++++++++++++++ docs/_posts/prabod/2023-06-21-e5_base_en.md | 71 ++++++++++++++++++ .../_posts/prabod/2023-06-21-e5_base_v2_en.md | 68 +++++++++++++++++ docs/_posts/prabod/2023-06-21-e5_large_en.md | 71 ++++++++++++++++++ .../prabod/2023-06-21-e5_large_v2_en.md | 71 ++++++++++++++++++ docs/_posts/prabod/2023-06-21-e5_small_en.md | 71 ++++++++++++++++++ .../prabod/2023-06-21-e5_small_v2_en.md | 71 ++++++++++++++++++ .../prabod/2023-06-21-instructor_large_en.md | 74 ++++++++++++++++++ 8 files changed, 572 insertions(+) create mode 100644 docs/_posts/prabod/2023-06-08-instructor_base_en.md create mode 100644 docs/_posts/prabod/2023-06-21-e5_base_en.md create mode 100644 docs/_posts/prabod/2023-06-21-e5_base_v2_en.md create mode 100644 docs/_posts/prabod/2023-06-21-e5_large_en.md create mode 100644 docs/_posts/prabod/2023-06-21-e5_large_v2_en.md create mode 100644 docs/_posts/prabod/2023-06-21-e5_small_en.md create mode 100644 docs/_posts/prabod/2023-06-21-e5_small_v2_en.md create mode 100644 docs/_posts/prabod/2023-06-21-instructor_large_en.md diff --git a/docs/_posts/prabod/2023-06-08-instructor_base_en.md b/docs/_posts/prabod/2023-06-08-instructor_base_en.md new file mode 100644 index 00000000000000..01d6754fa28473 --- /dev/null +++ b/docs/_posts/prabod/2023-06-08-instructor_base_en.md @@ -0,0 +1,75 @@ +--- +layout: model +title: Instructor Base Sentence Embeddings +author: John Snow Labs +name: instructor_base +date: 2023-06-08 +tags: [instructor, sentence_embeddings, t5, text_semantic_similarity, text_reranking, sentence_similarity, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: InstructorEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Instructor👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) by simply providing the task instruction, without any finetuning. Instructor👨‍ achieves sota on 70 diverse embedding tasks. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/instructor_base_en_5.0.0_3.0_1686224519068.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/instructor_base_en_5.0.0_3.0_1686224519068.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +instruction = InstructorEmbeddings.pretrained("instructor_base","en") \ + .setInstruction("Instruction here: ") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + + pipeline = Pipeline().setStages([document_assembler, instruction]) +``` +```scala + val embeddings = InstructorEmbeddings + .pretrained("instructor_base","en") + .setInstruction("Instruction here: ") + .setInputCols(Array("document")) + .setOutputCol("instructor") + + val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|instructor_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[instructor]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/hkunlp/instructor-base diff --git a/docs/_posts/prabod/2023-06-21-e5_base_en.md b/docs/_posts/prabod/2023-06-21-e5_base_en.md new file mode 100644 index 00000000000000..d81452cf903590 --- /dev/null +++ b/docs/_posts/prabod/2023-06-21-e5_base_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: E5 Base Sentence Embeddings +author: John Snow Labs +name: e5_base +date: 2023-06-21 +tags: [en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_base_en_5.0.0_3.0_1687350215936.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_base_en_5.0.0_3.0_1687350215936.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_base","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_base","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|260.5 MB| + +## References + +https://huggingface.co/intfloat/e5-base \ No newline at end of file diff --git a/docs/_posts/prabod/2023-06-21-e5_base_v2_en.md b/docs/_posts/prabod/2023-06-21-e5_base_v2_en.md new file mode 100644 index 00000000000000..140496bade1a70 --- /dev/null +++ b/docs/_posts/prabod/2023-06-21-e5_base_v2_en.md @@ -0,0 +1,68 @@ +--- +layout: model +title: E5 Base v2 Sentence Embeddings +author: John Snow Labs +name: e5_base_v2 +date: 2023-06-21 +tags: [e5, sentence_embeddings, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.4 +supported: true +engine: tensorflow +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_base_v2_en_5.0.0_3.4_1687349803929.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_base_v2_en_5.0.0_3.4_1687349803929.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_base_v2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_base_v2","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") + +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_base_v2| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|260.6 MB| \ No newline at end of file diff --git a/docs/_posts/prabod/2023-06-21-e5_large_en.md b/docs/_posts/prabod/2023-06-21-e5_large_en.md new file mode 100644 index 00000000000000..e1bd6b18e30107 --- /dev/null +++ b/docs/_posts/prabod/2023-06-21-e5_large_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: E5 Large Sentence Embeddings +author: John Snow Labs +name: e5_large +date: 2023-06-21 +tags: [en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_large_en_5.0.0_3.0_1687350762773.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_large_en_5.0.0_3.0_1687350762773.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_large","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_large","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_large| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|799.1 MB| + +## References + +https://huggingface.co/intfloat/e5-large \ No newline at end of file diff --git a/docs/_posts/prabod/2023-06-21-e5_large_v2_en.md b/docs/_posts/prabod/2023-06-21-e5_large_v2_en.md new file mode 100644 index 00000000000000..10b99644a9dfac --- /dev/null +++ b/docs/_posts/prabod/2023-06-21-e5_large_v2_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: E5 Large V2 Sentence Embeddings +author: John Snow Labs +name: e5_large_v2 +date: 2023-06-21 +tags: [en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_large_v2_en_5.0.0_3.0_1687350498606.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_large_v2_en_5.0.0_3.0_1687350498606.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_large_v2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_large_v2","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_large_v2| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|799.1 MB| + +## References + +https://huggingface.co/intfloat/e5-large-v2 \ No newline at end of file diff --git a/docs/_posts/prabod/2023-06-21-e5_small_en.md b/docs/_posts/prabod/2023-06-21-e5_small_en.md new file mode 100644 index 00000000000000..018b4754b15d5e --- /dev/null +++ b/docs/_posts/prabod/2023-06-21-e5_small_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: E5 Small Sentence Embeddings +author: John Snow Labs +name: e5_small +date: 2023-06-21 +tags: [en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_small_en_5.0.0_3.0_1687351055229.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_small_en_5.0.0_3.0_1687351055229.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_small","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_small","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_small| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|80.9 MB| + +## References + +https://huggingface.co/intfloat/e5-small \ No newline at end of file diff --git a/docs/_posts/prabod/2023-06-21-e5_small_v2_en.md b/docs/_posts/prabod/2023-06-21-e5_small_v2_en.md new file mode 100644 index 00000000000000..4f7b015718f5c1 --- /dev/null +++ b/docs/_posts/prabod/2023-06-21-e5_small_v2_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: E5 Small V2 Sentence Embeddings +author: John Snow Labs +name: e5_small_v2 +date: 2023-06-21 +tags: [en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_small_v2_en_5.0.0_3.0_1687350926144.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_small_v2_en_5.0.0_3.0_1687350926144.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_small_v2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_small_v2","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_small_v2| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|80.9 MB| + +## References + +https://huggingface.co/intfloat/e5-small-v2 \ No newline at end of file diff --git a/docs/_posts/prabod/2023-06-21-instructor_large_en.md b/docs/_posts/prabod/2023-06-21-instructor_large_en.md new file mode 100644 index 00000000000000..fb040c3cf37918 --- /dev/null +++ b/docs/_posts/prabod/2023-06-21-instructor_large_en.md @@ -0,0 +1,74 @@ +--- +layout: model +title: Instructor Large Sentence Embeddings +author: John Snow Labs +name: instructor_large +date: 2023-06-21 +tags: [en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: InstructorEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Instructor👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) by simply providing the task instruction, without any finetuning. Instructor👨‍ achieves sota on 70 diverse embedding tasks. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/instructor_large_en_5.0.0_3.0_1687351199226.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/instructor_large_en_5.0.0_3.0_1687351199226.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +instruction = InstructorEmbeddings.pretrained("instructor_large","en") \ + .setInstruction("Instruction here: ") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, instruction]) +``` +```scala + val embeddings = InstructorEmbeddings + .pretrained("instructor_large","en") + .setInstruction("Instruction here: ") + .setInputCols(Array("document")) + .setOutputCol("instructor") + val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|instructor_large| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[instructor]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/hkunlp/instructor-large \ No newline at end of file From 59113cdf3af52632b24414cbde6ec2b377bf7a40 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Sat, 1 Jul 2023 20:06:13 +0700 Subject: [PATCH 22/55] 2023-06-28-roberta_base_en (#13871) * Add model 2023-06-28-roberta_base_en * Add model 2023-06-28-roberta_base_opt_en * Add model 2023-06-28-roberta_base_quantized_en * Add model 2023-06-28-small_bert_L2_768_en * Add model 2023-06-28-small_bert_L2_768_opt_en * Add model 2023-06-28-small_bert_L2_768_quantized_en * Add model 2023-06-28-distilbert_base_cased_en * Add model 2023-06-28-distilbert_base_cased_opt_en * Add model 2023-06-28-distilbert_base_cased_quantized_en * Add model 2023-06-28-deberta_v3_base_en * Add model 2023-06-28-deberta_v3_base_opt_en * Add model 2023-06-28-deberta_v3_base_quantized_en * Add model 2023-06-28-distilbert_base_uncased_en * Add model 2023-06-28-distilbert_base_uncased_opt_en * Add model 2023-06-28-distilbert_base_uncased_quantized_en * Add model 2023-06-28-distilbert_base_multilingual_cased_xx * Add model 2023-06-28-distilbert_base_multilingual_cased_xx * Add model 2023-06-28-distilbert_base_multilingual_cased_opt_xx * Add model 2023-06-28-distilbert_base_multilingual_cased_quantized_xx * Add model 2023-06-28-distilbert_embeddings_distilbert_base_german_cased_de * Add model 2023-06-28-distilbert_embeddings_distilbert_base_german_cased_opt_de * Add model 2023-06-28-distilbert_embeddings_distilbert_base_german_cased_quantized_de * Add model 2023-06-29-bert_base_cased_en * Add model 2023-06-29-bert_base_cased_opt_en * Add model 2023-06-29-bert_base_cased_quantized_en --------- Co-authored-by: ahmedlone127 --- .../2023-06-28-deberta_v3_base_en.md | 100 ++++++++++++ .../2023-06-28-deberta_v3_base_opt_en.md | 100 ++++++++++++ ...2023-06-28-deberta_v3_base_quantized_en.md | 100 ++++++++++++ .../2023-06-28-distilbert_base_cased_en.md | 118 ++++++++++++++ ...2023-06-28-distilbert_base_cased_opt_en.md | 118 ++++++++++++++ ...6-28-distilbert_base_cased_quantized_en.md | 118 ++++++++++++++ ...stilbert_base_multilingual_cased_opt_xx.md | 117 ++++++++++++++ ...rt_base_multilingual_cased_quantized_xx.md | 117 ++++++++++++++ ...8-distilbert_base_multilingual_cased_xx.md | 117 ++++++++++++++ .../2023-06-28-distilbert_base_uncased_en.md | 118 ++++++++++++++ ...23-06-28-distilbert_base_uncased_opt_en.md | 118 ++++++++++++++ ...28-distilbert_base_uncased_quantized_en.md | 118 ++++++++++++++ ...eddings_distilbert_base_german_cased_de.md | 149 ++++++++++++++++++ ...ngs_distilbert_base_german_cased_opt_de.md | 149 ++++++++++++++++++ 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a/docs/_posts/ahmedlone127/2023-06-28-deberta_v3_base_en.md b/docs/_posts/ahmedlone127/2023-06-28-deberta_v3_base_en.md new file mode 100644 index 00000000000000..e67736379a0df8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-deberta_v3_base_en.md @@ -0,0 +1,100 @@ +--- +layout: model +title: DeBERTa base model +author: John Snow Labs +name: deberta_v3_base +date: 2023-06-28 +tags: [en, english, open_source, embeddings, deberta, v3, base, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DeBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The DeBERTa model was proposed in [[https://arxiv.org/abs/2006.03654 DeBERTa: Decoding-enhanced BERT with Disentangled Attention]] by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google’s BERT model released in 2018 and Facebook’s RoBERTa model released in 2019. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/deberta_v3_base_en_5.0.0_3.0_1687957496351.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_v3_base_en_5.0.0_3.0_1687957496351.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.deberta_v3_base").predict("""Put your text here.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.deberta_v3_base").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|deberta_v3_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|435.2 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## Benchmarking + +```bash +Benchmarking +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-deberta_v3_base_opt_en.md b/docs/_posts/ahmedlone127/2023-06-28-deberta_v3_base_opt_en.md new file mode 100644 index 00000000000000..68d625e906f51c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-deberta_v3_base_opt_en.md @@ -0,0 +1,100 @@ +--- +layout: model +title: DeBERTa base model +author: John Snow Labs +name: deberta_v3_base_opt +date: 2023-06-28 +tags: [en, english, open_source, embeddings, deberta, v3, base, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DeBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The DeBERTa model was proposed in [[https://arxiv.org/abs/2006.03654 DeBERTa: Decoding-enhanced BERT with Disentangled Attention]] by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google’s BERT model released in 2018 and Facebook’s RoBERTa model released in 2019. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/deberta_v3_base_opt_en_5.0.0_3.0_1687958380723.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_v3_base_opt_en_5.0.0_3.0_1687958380723.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.deberta_v3_base").predict("""Put your text here.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.deberta_v3_base").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|deberta_v3_base_opt| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|469.3 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## Benchmarking + +```bash +Benchmarking +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-deberta_v3_base_quantized_en.md b/docs/_posts/ahmedlone127/2023-06-28-deberta_v3_base_quantized_en.md new file mode 100644 index 00000000000000..6457327a744a22 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-deberta_v3_base_quantized_en.md @@ -0,0 +1,100 @@ +--- +layout: model +title: DeBERTa base model +author: John Snow Labs +name: deberta_v3_base_quantized +date: 2023-06-28 +tags: [en, english, open_source, embeddings, deberta, v3, base, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DeBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +The DeBERTa model was proposed in [[https://arxiv.org/abs/2006.03654 DeBERTa: Decoding-enhanced BERT with Disentangled Attention]] by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google’s BERT model released in 2018 and Facebook’s RoBERTa model released in 2019. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/deberta_v3_base_quantized_en_5.0.0_3.0_1687958846162.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_v3_base_quantized_en_5.0.0_3.0_1687958846162.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.deberta_v3_base").predict("""Put your text here.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.deberta_v3_base").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|deberta_v3_base_quantized| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|310.7 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## Benchmarking + +```bash +Benchmarking +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_cased_en.md b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_cased_en.md new file mode 100644 index 00000000000000..2a6be63cb79d9b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_cased_en.md @@ -0,0 +1,118 @@ +--- +layout: model +title: DistilBERT base model (cased) +author: John Snow Labs +name: distilbert_base_cased +date: 2023-06-28 +tags: [distilbert, en, english, open_source, embeddings, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-cased). It was introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation). This model is cased: it does make a difference between english and English. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_cased_en_5.0.0_3.0_1687955596708.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_cased_en_5.0.0_3.0_1687955596708.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DistilBertEmbeddings.pretrained("distilbert_base_cased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = DistilBertEmbeddings.pretrained("distilbert_base_cased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilbert").predict("""Put your text here.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DistilBertEmbeddings.pretrained("distilbert_base_cased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = DistilBertEmbeddings.pretrained("distilbert_base_cased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilbert").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_cased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|243.8 MB| +|Case sensitive:|true| + +## References + +[https://huggingface.co/distilbert-base-cased](https://huggingface.co/distilbert-base-cased) + +## Benchmarking + +```bash +Benchmarking + + +When fine-tuned on downstream tasks, this model achieves the following results: + +Glue test results: + +| Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | +|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| +| | 81.5 | 87.8 | 88.2 | 90.4 | 47.2 | 85.5 | 85.6 | 60.6 | + + +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_cased_opt_en.md b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_cased_opt_en.md new file mode 100644 index 00000000000000..fb694045a0f578 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_cased_opt_en.md @@ -0,0 +1,118 @@ +--- +layout: model +title: DistilBERT base model (cased) +author: John Snow Labs +name: distilbert_base_cased_opt +date: 2023-06-28 +tags: [distilbert, en, english, open_source, embeddings, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-cased). It was introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation). This model is cased: it does make a difference between english and English. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_cased_opt_en_5.0.0_3.0_1687955659414.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_cased_opt_en_5.0.0_3.0_1687955659414.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DistilBertEmbeddings.pretrained("distilbert_base_cased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = DistilBertEmbeddings.pretrained("distilbert_base_cased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilbert").predict("""Put your text here.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DistilBertEmbeddings.pretrained("distilbert_base_cased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = DistilBertEmbeddings.pretrained("distilbert_base_cased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilbert").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_cased_opt| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|243.8 MB| +|Case sensitive:|true| + +## References + +[https://huggingface.co/distilbert-base-cased](https://huggingface.co/distilbert-base-cased) + +## Benchmarking + +```bash +Benchmarking + + +When fine-tuned on downstream tasks, this model achieves the following results: + +Glue test results: + +| Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | +|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| +| | 81.5 | 87.8 | 88.2 | 90.4 | 47.2 | 85.5 | 85.6 | 60.6 | + + +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_cased_quantized_en.md b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_cased_quantized_en.md new file mode 100644 index 00000000000000..8ced215668327e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_cased_quantized_en.md @@ -0,0 +1,118 @@ +--- +layout: model +title: DistilBERT base model (cased) +author: John Snow Labs +name: distilbert_base_cased_quantized +date: 2023-06-28 +tags: [distilbert, en, english, open_source, embeddings, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-cased). It was introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation). This model is cased: it does make a difference between english and English. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_cased_quantized_en_5.0.0_3.0_1687955697660.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_cased_quantized_en_5.0.0_3.0_1687955697660.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DistilBertEmbeddings.pretrained("distilbert_base_cased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = DistilBertEmbeddings.pretrained("distilbert_base_cased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilbert").predict("""Put your text here.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DistilBertEmbeddings.pretrained("distilbert_base_cased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = DistilBertEmbeddings.pretrained("distilbert_base_cased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilbert").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_cased_quantized| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|111.1 MB| +|Case sensitive:|true| + +## References + +[https://huggingface.co/distilbert-base-cased](https://huggingface.co/distilbert-base-cased) + +## Benchmarking + +```bash +Benchmarking + + +When fine-tuned on downstream tasks, this model achieves the following results: + +Glue test results: + +| Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | +|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| +| | 81.5 | 87.8 | 88.2 | 90.4 | 47.2 | 85.5 | 85.6 | 60.6 | + + +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_multilingual_cased_opt_xx.md b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_multilingual_cased_opt_xx.md new file mode 100644 index 00000000000000..f7e2051a4eae33 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_multilingual_cased_opt_xx.md @@ -0,0 +1,117 @@ +--- +layout: model +title: DistilBERT base multilingual model (cased) +author: John Snow Labs +name: distilbert_base_multilingual_cased_opt +date: 2023-06-28 +tags: [distilbert, embeddings, xx, multilingual, open_source, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is a distilled version of the [BERT base multilingual model](bert-base-multilingual-cased). The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation). This model is cased: it does make a difference between english and English. The model is trained on the concatenation of Wikipedia in 104 different languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages). + +The model has 6 layers, 768 dimension,s and 12 heads, totalizing 134M parameters (compared to 177M parameters for mBERT-base). On average DistilmBERT is twice as fast as mBERT-base. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_multilingual_cased_opt_xx_5.0.0_3.0_1687985561229.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_multilingual_cased_opt_xx_5.0.0_3.0_1687985561229.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DistilBertEmbeddings.pretrained("distilbert_base_multilingual_cased", "xx") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = DistilBertEmbeddings.pretrained("distilbert_base_multilingual_cased", "xx") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("xx.embed.distilbert").predict("""Put your text here.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DistilBertEmbeddings.pretrained("distilbert_base_multilingual_cased", "xx") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = DistilBertEmbeddings.pretrained("distilbert_base_multilingual_cased", "xx") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("xx.embed.distilbert").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_multilingual_cased_opt| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|505.4 MB| +|Case sensitive:|true| + +## References + +[https://huggingface.co/distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) + +## Benchmarking + +```bash +Benchmarking + + +| Model | English | Spanish | Chinese | German | Arabic | Urdu | +| :---: | :---: | :---: | :---: | :---: | :---: | :---:| +| mBERT base cased (computed) | 82.1 | 74.6 | 69.1 | 72.3 | 66.4 | 58.5 | +| mBERT base uncased (reported)| 81.4 | 74.3 | 63.8 | 70.5 | 62.1 | 58.3 | +| DistilmBERT | 78.2 | 69.1 | 64.0 | 66.3 | 59.1 | 54.7 | + +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_multilingual_cased_quantized_xx.md b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_multilingual_cased_quantized_xx.md new file mode 100644 index 00000000000000..909e419bb06c8c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_multilingual_cased_quantized_xx.md @@ -0,0 +1,117 @@ +--- +layout: model +title: DistilBERT base multilingual model (cased) +author: John Snow Labs +name: distilbert_base_multilingual_cased_quantized +date: 2023-06-28 +tags: [distilbert, embeddings, xx, multilingual, open_source, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is a distilled version of the [BERT base multilingual model](bert-base-multilingual-cased). The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation). This model is cased: it does make a difference between english and English. The model is trained on the concatenation of Wikipedia in 104 different languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages). + +The model has 6 layers, 768 dimension,s and 12 heads, totalizing 134M parameters (compared to 177M parameters for mBERT-base). On average DistilmBERT is twice as fast as mBERT-base. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_multilingual_cased_quantized_xx_5.0.0_3.0_1687985753267.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_multilingual_cased_quantized_xx_5.0.0_3.0_1687985753267.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DistilBertEmbeddings.pretrained("distilbert_base_multilingual_cased", "xx") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = DistilBertEmbeddings.pretrained("distilbert_base_multilingual_cased", "xx") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("xx.embed.distilbert").predict("""Put your text here.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DistilBertEmbeddings.pretrained("distilbert_base_multilingual_cased", "xx") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = DistilBertEmbeddings.pretrained("distilbert_base_multilingual_cased", "xx") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("xx.embed.distilbert").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_multilingual_cased_quantized| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|371.7 MB| +|Case sensitive:|true| + +## References + +[https://huggingface.co/distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) + +## Benchmarking + +```bash +Benchmarking + + +| Model | English | Spanish | Chinese | German | Arabic | Urdu | +| :---: | :---: | :---: | :---: | :---: | :---: | :---:| +| mBERT base cased (computed) | 82.1 | 74.6 | 69.1 | 72.3 | 66.4 | 58.5 | +| mBERT base uncased (reported)| 81.4 | 74.3 | 63.8 | 70.5 | 62.1 | 58.3 | +| DistilmBERT | 78.2 | 69.1 | 64.0 | 66.3 | 59.1 | 54.7 | + +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_multilingual_cased_xx.md b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_multilingual_cased_xx.md new file mode 100644 index 00000000000000..24e2b11a218e68 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_multilingual_cased_xx.md @@ -0,0 +1,117 @@ +--- +layout: model +title: DistilBERT base multilingual model (cased) +author: John Snow Labs +name: distilbert_base_multilingual_cased +date: 2023-06-28 +tags: [distilbert, embeddings, xx, multilingual, open_source, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is a distilled version of the [BERT base multilingual model](bert-base-multilingual-cased). The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation). This model is cased: it does make a difference between english and English. The model is trained on the concatenation of Wikipedia in 104 different languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages). + +The model has 6 layers, 768 dimension,s and 12 heads, totalizing 134M parameters (compared to 177M parameters for mBERT-base). On average DistilmBERT is twice as fast as mBERT-base. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_multilingual_cased_xx_5.0.0_3.0_1687985402696.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_multilingual_cased_xx_5.0.0_3.0_1687985402696.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DistilBertEmbeddings.pretrained("distilbert_base_multilingual_cased", "xx") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = DistilBertEmbeddings.pretrained("distilbert_base_multilingual_cased", "xx") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("xx.embed.distilbert").predict("""Put your text here.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DistilBertEmbeddings.pretrained("distilbert_base_multilingual_cased", "xx") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = DistilBertEmbeddings.pretrained("distilbert_base_multilingual_cased", "xx") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("xx.embed.distilbert").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_multilingual_cased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|505.4 MB| +|Case sensitive:|true| + +## References + +[https://huggingface.co/distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) + +## Benchmarking + +```bash +Benchmarking + + +| Model | English | Spanish | Chinese | German | Arabic | Urdu | +| :---: | :---: | :---: | :---: | :---: | :---: | :---:| +| mBERT base cased (computed) | 82.1 | 74.6 | 69.1 | 72.3 | 66.4 | 58.5 | +| mBERT base uncased (reported)| 81.4 | 74.3 | 63.8 | 70.5 | 62.1 | 58.3 | +| DistilmBERT | 78.2 | 69.1 | 64.0 | 66.3 | 59.1 | 54.7 | + +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_uncased_en.md new file mode 100644 index 00000000000000..ee169a452abca5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_uncased_en.md @@ -0,0 +1,118 @@ +--- +layout: model +title: DistilBERT base model (uncased) +author: John Snow Labs +name: distilbert_base_uncased +date: 2023-06-28 +tags: [distilbert, en, english, embeddings, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-cased). It was introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation). This model is uncased: it does not make a difference between english and English. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_en_5.0.0_3.0_1687984618202.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_en_5.0.0_3.0_1687984618202.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DistilBertEmbeddings.pretrained("distilbert_base_uncased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = DistilBertEmbeddings.pretrained("distilbert_base_uncased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilbert.base.uncased").predict("""Put your text here.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DistilBertEmbeddings.pretrained("distilbert_base_uncased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = DistilBertEmbeddings.pretrained("distilbert_base_uncased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilbert.base.uncased").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| +|Case sensitive:|true| + +## References + +[https://huggingface.co/distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) + +## Benchmarking + +```bash +Benchmarking + + +When fine-tuned on downstream tasks, this model achieves the following results: + +Glue test results: + +| Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | +|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| +| | 82.2 | 88.5 | 89.2 | 91.3 | 51.3 | 85.8 | 87.5 | 59.9 | + + +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_uncased_opt_en.md b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_uncased_opt_en.md new file mode 100644 index 00000000000000..61491f8bda0547 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_uncased_opt_en.md @@ -0,0 +1,118 @@ +--- +layout: model +title: DistilBERT base model (uncased) +author: John Snow Labs +name: distilbert_base_uncased_opt +date: 2023-06-28 +tags: [distilbert, en, english, embeddings, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-cased). It was introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation). This model is uncased: it does not make a difference between english and English. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_opt_en_5.0.0_3.0_1687984673095.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_opt_en_5.0.0_3.0_1687984673095.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DistilBertEmbeddings.pretrained("distilbert_base_uncased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = DistilBertEmbeddings.pretrained("distilbert_base_uncased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilbert.base.uncased").predict("""Put your text here.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DistilBertEmbeddings.pretrained("distilbert_base_uncased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = DistilBertEmbeddings.pretrained("distilbert_base_uncased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilbert.base.uncased").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_opt| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| +|Case sensitive:|true| + +## References + +[https://huggingface.co/distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) + +## Benchmarking + +```bash +Benchmarking + + +When fine-tuned on downstream tasks, this model achieves the following results: + +Glue test results: + +| Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | +|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| +| | 82.2 | 88.5 | 89.2 | 91.3 | 51.3 | 85.8 | 87.5 | 59.9 | + + +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_uncased_quantized_en.md b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_uncased_quantized_en.md new file mode 100644 index 00000000000000..b5608cd91ac12f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_uncased_quantized_en.md @@ -0,0 +1,118 @@ +--- +layout: model +title: DistilBERT base model (uncased) +author: John Snow Labs +name: distilbert_base_uncased_quantized +date: 2023-06-28 +tags: [distilbert, en, english, embeddings, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-cased). It was introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation). This model is uncased: it does not make a difference between english and English. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_quantized_en_5.0.0_3.0_1687984715653.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_quantized_en_5.0.0_3.0_1687984715653.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DistilBertEmbeddings.pretrained("distilbert_base_uncased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = DistilBertEmbeddings.pretrained("distilbert_base_uncased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilbert.base.uncased").predict("""Put your text here.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = DistilBertEmbeddings.pretrained("distilbert_base_uncased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +``` +```scala +val embeddings = DistilBertEmbeddings.pretrained("distilbert_base_uncased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.distilbert.base.uncased").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_quantized| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|114.3 MB| +|Case sensitive:|true| + +## References + +[https://huggingface.co/distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) + +## Benchmarking + +```bash +Benchmarking + + +When fine-tuned on downstream tasks, this model achieves the following results: + +Glue test results: + +| Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | +|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| +| | 82.2 | 88.5 | 89.2 | 91.3 | 51.3 | 85.8 | 87.5 | 59.9 | + + +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_de.md b/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_de.md new file mode 100644 index 00000000000000..5ded15b165dcb2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German DistilBERT Embeddings +author: John Snow Labs +name: distilbert_embeddings_distilbert_base_german_cased +date: 2023-06-28 +tags: [distilbert, embeddings, de, open_source, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `distilbert-base-german-cased` is a German model orginally trained by HuggingFace. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_distilbert_base_german_cased_de_5.0.0_3.0_1687986069961.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_distilbert_base_german_cased_de_5.0.0_3.0_1687986069961.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_german_cased","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_german_cased","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.distilbert_base_german_cased").predict("""Ich liebe Funken NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_german_cased","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_german_cased","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.distilbert_base_german_cased").predict("""Ich liebe Funken NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_distilbert_base_german_cased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|de| +|Size:|250.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_opt_de.md b/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_opt_de.md new file mode 100644 index 00000000000000..aa7520c32d32f5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_opt_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German DistilBERT Embeddings +author: John Snow Labs +name: distilbert_embeddings_distilbert_base_german_cased_opt +date: 2023-06-28 +tags: [distilbert, embeddings, de, open_source, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `distilbert-base-german-cased` is a German model orginally trained by HuggingFace. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_distilbert_base_german_cased_opt_de_5.0.0_3.0_1687986127648.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_distilbert_base_german_cased_opt_de_5.0.0_3.0_1687986127648.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_german_cased","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_german_cased","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.distilbert_base_german_cased").predict("""Ich liebe Funken NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_german_cased","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_german_cased","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.distilbert_base_german_cased").predict("""Ich liebe Funken NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_distilbert_base_german_cased_opt| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|de| +|Size:|250.3 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_quantized_de.md b/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_quantized_de.md new file mode 100644 index 00000000000000..57b94449d930ed --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_quantized_de.md @@ -0,0 +1,149 @@ +--- +layout: model +title: German DistilBERT Embeddings +author: John Snow Labs +name: distilbert_embeddings_distilbert_base_german_cased_quantized +date: 2023-06-28 +tags: [distilbert, embeddings, de, open_source, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `distilbert-base-german-cased` is a German model orginally trained by HuggingFace. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_distilbert_base_german_cased_quantized_de_5.0.0_3.0_1687986166061.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_distilbert_base_german_cased_quantized_de_5.0.0_3.0_1687986166061.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_german_cased","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_german_cased","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.distilbert_base_german_cased").predict("""Ich liebe Funken NLP""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_german_cased","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_german_cased","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.distilbert_base_german_cased").predict("""Ich liebe Funken NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_distilbert_base_german_cased_quantized| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|de| +|Size:|115.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-roberta_base_en.md b/docs/_posts/ahmedlone127/2023-06-28-roberta_base_en.md new file mode 100644 index 00000000000000..9ef8061b84bf7c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-roberta_base_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: RoBERTa base model +author: John Snow Labs +name: roberta_base +date: 2023-06-28 +tags: [en, english, roberta, embeddings, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1907.11692) and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it makes a difference between english and English. + +RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. + +More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then runs the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. + +This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_base_en_5.0.0_3.0_1687951314039.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_base_en_5.0.0_3.0_1687951314039.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = RoBertaEmbeddings.pretrained("roberta_base", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = RoBertaEmbeddings.pretrained("roberta_base", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.roberta").predict("""Put your text here.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = RoBertaEmbeddings.pretrained("roberta_base", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = RoBertaEmbeddings.pretrained("roberta_base", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.roberta").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_base| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|298.2 MB| +|Case sensitive:|true| + +## References + +[https://huggingface.co/roberta-base](https://huggingface.co/roberta-base) + +## Benchmarking + +```bash +Benchmarking + + +When fine-tuned on downstream tasks, this model achieves the following results: + +Glue test results: + +| Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | +|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| +| | 87.6 | 91.9 | 92.8 | 94.8 | 63.6 | 91.2 | 90.2 | 78.7 | + + +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-roberta_base_opt_en.md b/docs/_posts/ahmedlone127/2023-06-28-roberta_base_opt_en.md new file mode 100644 index 00000000000000..53d45bb7101929 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-roberta_base_opt_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: RoBERTa base model +author: John Snow Labs +name: roberta_base_opt +date: 2023-06-28 +tags: [en, english, roberta, embeddings, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1907.11692) and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it makes a difference between english and English. + +RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. + +More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then runs the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. + +This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_base_opt_en_5.0.0_3.0_1687951622206.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_base_opt_en_5.0.0_3.0_1687951622206.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = RoBertaEmbeddings.pretrained("roberta_base", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = RoBertaEmbeddings.pretrained("roberta_base", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.roberta").predict("""Put your text here.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = RoBertaEmbeddings.pretrained("roberta_base", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = RoBertaEmbeddings.pretrained("roberta_base", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.roberta").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_base_opt| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|298.3 MB| +|Case sensitive:|true| + +## References + +[https://huggingface.co/roberta-base](https://huggingface.co/roberta-base) + +## Benchmarking + +```bash +Benchmarking + + +When fine-tuned on downstream tasks, this model achieves the following results: + +Glue test results: + +| Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | +|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| +| | 87.6 | 91.9 | 92.8 | 94.8 | 63.6 | 91.2 | 90.2 | 78.7 | + + +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-roberta_base_quantized_en.md b/docs/_posts/ahmedlone127/2023-06-28-roberta_base_quantized_en.md new file mode 100644 index 00000000000000..2459015ce7c014 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-roberta_base_quantized_en.md @@ -0,0 +1,122 @@ +--- +layout: model +title: RoBERTa base model +author: John Snow Labs +name: roberta_base_quantized +date: 2023-06-28 +tags: [en, english, roberta, embeddings, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: RoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1907.11692) and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it makes a difference between english and English. + +RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. + +More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then runs the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. + +This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_base_quantized_en_5.0.0_3.0_1687951753623.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_base_quantized_en_5.0.0_3.0_1687951753623.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = RoBertaEmbeddings.pretrained("roberta_base", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = RoBertaEmbeddings.pretrained("roberta_base", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.roberta").predict("""Put your text here.""") +``` + +
+ +{:.model-param} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = RoBertaEmbeddings.pretrained("roberta_base", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = RoBertaEmbeddings.pretrained("roberta_base", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.embed.roberta").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_base_quantized| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|153.6 MB| +|Case sensitive:|true| + +## References + +[https://huggingface.co/roberta-base](https://huggingface.co/roberta-base) + +## Benchmarking + +```bash +Benchmarking + + +When fine-tuned on downstream tasks, this model achieves the following results: + +Glue test results: + +| Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | +|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| +| | 87.6 | 91.9 | 92.8 | 94.8 | 63.6 | 91.2 | 90.2 | 78.7 | + + +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-small_bert_L2_768_en.md b/docs/_posts/ahmedlone127/2023-06-28-small_bert_L2_768_en.md new file mode 100644 index 00000000000000..81ffc14f7063a1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-small_bert_L2_768_en.md @@ -0,0 +1,136 @@ +--- +layout: model +title: Smaller BERT Embeddings (L-2_H-768_A-12) +author: John Snow Labs +name: small_bert_L2_768 +date: 2023-06-28 +tags: [open_source, embeddings, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This is one of the smaller BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962). The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_bert_L2_768_en_5.0.0_3.0_1687953630830.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_bert_L2_768_en_5.0.0_3.0_1687953630830.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+ +{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +... +embeddings = BertEmbeddings.pretrained("small_bert_L2_768", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text")) +result = pipeline_model.transform(spark.createDataFrame([['I love NLP']], ["text"])) +``` + +```scala +... +val embeddings = BertEmbeddings.pretrained("small_bert_L2_768", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +val data = Seq("I love NLP").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu + +text = ["I love NLP"] +embeddings_df = nlu.load('en.embed.bert.small_L2_768').predict(text, output_level='token') +embeddings_df +``` + +
+ +{:.h2_title} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +... +embeddings = BertEmbeddings.pretrained("small_bert_L2_768", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text")) +result = pipeline_model.transform(spark.createDataFrame([['I love NLP']], ["text"])) +``` +```scala +... +val embeddings = BertEmbeddings.pretrained("small_bert_L2_768", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +val data = Seq("I love NLP").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu + +text = ["I love NLP"] +embeddings_df = nlu.load('en.embed.bert.small_L2_768').predict(text, output_level='token') +embeddings_df +``` +
+ +## Results + +```bash +Results + + token en_embed_bert_small_L2_768_embeddings + + I [-0.2451338768005371, 0.40763044357299805, -0.... +love [-0.23793038725852966, -0.07403656840324402, -... +NLP [-0.864113450050354, -0.2902209758758545, 0.54... + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_bert_L2_768| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|141.9 MB| +|Case sensitive:|false| + +## References + +The model is imported from https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-768_A-12/1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-small_bert_L2_768_opt_en.md b/docs/_posts/ahmedlone127/2023-06-28-small_bert_L2_768_opt_en.md new file mode 100644 index 00000000000000..ebe871e2fc8481 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-small_bert_L2_768_opt_en.md @@ -0,0 +1,136 @@ +--- +layout: model +title: Smaller BERT Embeddings (L-2_H-768_A-12) +author: John Snow Labs +name: small_bert_L2_768_opt +date: 2023-06-28 +tags: [open_source, embeddings, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This is one of the smaller BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962). The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_bert_L2_768_opt_en_5.0.0_3.0_1687953671808.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_bert_L2_768_opt_en_5.0.0_3.0_1687953671808.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+ +{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +... +embeddings = BertEmbeddings.pretrained("small_bert_L2_768", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text")) +result = pipeline_model.transform(spark.createDataFrame([['I love NLP']], ["text"])) +``` + +```scala +... +val embeddings = BertEmbeddings.pretrained("small_bert_L2_768", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +val data = Seq("I love NLP").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu + +text = ["I love NLP"] +embeddings_df = nlu.load('en.embed.bert.small_L2_768').predict(text, output_level='token') +embeddings_df +``` + +
+ +{:.h2_title} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +... +embeddings = BertEmbeddings.pretrained("small_bert_L2_768", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text")) +result = pipeline_model.transform(spark.createDataFrame([['I love NLP']], ["text"])) +``` +```scala +... +val embeddings = BertEmbeddings.pretrained("small_bert_L2_768", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +val data = Seq("I love NLP").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu + +text = ["I love NLP"] +embeddings_df = nlu.load('en.embed.bert.small_L2_768').predict(text, output_level='token') +embeddings_df +``` +
+ +## Results + +```bash +Results + + token en_embed_bert_small_L2_768_embeddings + + I [-0.2451338768005371, 0.40763044357299805, -0.... +love [-0.23793038725852966, -0.07403656840324402, -... +NLP [-0.864113450050354, -0.2902209758758545, 0.54... + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_bert_L2_768_opt| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|141.9 MB| +|Case sensitive:|false| + +## References + +The model is imported from https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-768_A-12/1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-28-small_bert_L2_768_quantized_en.md b/docs/_posts/ahmedlone127/2023-06-28-small_bert_L2_768_quantized_en.md new file mode 100644 index 00000000000000..88d5059afbcd2d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-28-small_bert_L2_768_quantized_en.md @@ -0,0 +1,136 @@ +--- +layout: model +title: Smaller BERT Embeddings (L-2_H-768_A-12) +author: John Snow Labs +name: small_bert_L2_768_quantized +date: 2023-06-28 +tags: [open_source, embeddings, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This is one of the smaller BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962). The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_bert_L2_768_quantized_en_5.0.0_3.0_1687953706128.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_bert_L2_768_quantized_en_5.0.0_3.0_1687953706128.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+ +{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +... +embeddings = BertEmbeddings.pretrained("small_bert_L2_768", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text")) +result = pipeline_model.transform(spark.createDataFrame([['I love NLP']], ["text"])) +``` + +```scala +... +val embeddings = BertEmbeddings.pretrained("small_bert_L2_768", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +val data = Seq("I love NLP").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu + +text = ["I love NLP"] +embeddings_df = nlu.load('en.embed.bert.small_L2_768').predict(text, output_level='token') +embeddings_df +``` + +
+ +{:.h2_title} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +... +embeddings = BertEmbeddings.pretrained("small_bert_L2_768", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text")) +result = pipeline_model.transform(spark.createDataFrame([['I love NLP']], ["text"])) +``` +```scala +... +val embeddings = BertEmbeddings.pretrained("small_bert_L2_768", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +val data = Seq("I love NLP").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu + +text = ["I love NLP"] +embeddings_df = nlu.load('en.embed.bert.small_L2_768').predict(text, output_level='token') +embeddings_df +``` +
+ +## Results + +```bash +Results + + token en_embed_bert_small_L2_768_embeddings + + I [-0.2451338768005371, 0.40763044357299805, -0.... +love [-0.23793038725852966, -0.07403656840324402, -... +NLP [-0.864113450050354, -0.2902209758758545, 0.54... + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_bert_L2_768_quantized| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|97.4 MB| +|Case sensitive:|false| + +## References + +The model is imported from https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-768_A-12/1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-29-bert_base_cased_en.md b/docs/_posts/ahmedlone127/2023-06-29-bert_base_cased_en.md new file mode 100644 index 00000000000000..d83d5b747e5e33 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-29-bert_base_cased_en.md @@ -0,0 +1,137 @@ +--- +layout: model +title: BERT Embeddings (Base Cased) +author: John Snow Labs +name: bert_base_cased +date: 2023-06-29 +tags: [open_source, embeddings, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model contains a deep bidirectional transformer trained on Wikipedia and the BookCorpus. The details are described in the paper "[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)". + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_en_5.0.0_3.0_1688044252396.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_en_5.0.0_3.0_1688044252396.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+ +{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +... +embeddings = BertEmbeddings.pretrained("bert_base_cased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text")) +result = pipeline_model.transform(spark.createDataFrame([['I love NLP']], ["text"])) +``` + +```scala +... +val embeddings = BertEmbeddings.pretrained("bert_base_cased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +val data = Seq("I love NLP").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu + +text = ["I love NLP"] +embeddings_df = nlu.load('en.embed.bert.base_cased').predict(text, output_level='token') +embeddings_df +``` + +
+ +{:.h2_title} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +... +embeddings = BertEmbeddings.pretrained("bert_base_cased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text")) +result = pipeline_model.transform(spark.createDataFrame([['I love NLP']], ["text"])) +``` +```scala +... +val embeddings = BertEmbeddings.pretrained("bert_base_cased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +val data = Seq("I love NLP").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu + +text = ["I love NLP"] +embeddings_df = nlu.load('en.embed.bert.base_cased').predict(text, output_level='token') +embeddings_df +``` +
+ +## Results + +```bash +Results + + token en_embed_bert_base_cased_embeddings + + I [0.43879568576812744, -0.40160006284713745, 0.... + love [0.21737590432167053, -0.3865768313407898, -0.... + NLP [-0.16226479411125183, -0.053727392107248306, ... + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|403.6 MB| +|Case sensitive:|true| + +## References + +The model is imported from [https://tfhub.dev/google/bert_cased_L-12_H-768_A-12/1](https://tfhub.dev/google/bert_cased_L-12_H-768_A-12/1) \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-29-bert_base_cased_opt_en.md b/docs/_posts/ahmedlone127/2023-06-29-bert_base_cased_opt_en.md new file mode 100644 index 00000000000000..3a9083db719381 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-29-bert_base_cased_opt_en.md @@ -0,0 +1,137 @@ +--- +layout: model +title: BERT Embeddings (Base Cased) +author: John Snow Labs +name: bert_base_cased_opt +date: 2023-06-29 +tags: [open_source, embeddings, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model contains a deep bidirectional transformer trained on Wikipedia and the BookCorpus. The details are described in the paper "[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)". + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_opt_en_5.0.0_3.0_1688044364323.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_opt_en_5.0.0_3.0_1688044364323.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+ +{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +... +embeddings = BertEmbeddings.pretrained("bert_base_cased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text")) +result = pipeline_model.transform(spark.createDataFrame([['I love NLP']], ["text"])) +``` + +```scala +... +val embeddings = BertEmbeddings.pretrained("bert_base_cased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +val data = Seq("I love NLP").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu + +text = ["I love NLP"] +embeddings_df = nlu.load('en.embed.bert.base_cased').predict(text, output_level='token') +embeddings_df +``` + +
+ +{:.h2_title} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +... +embeddings = BertEmbeddings.pretrained("bert_base_cased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text")) +result = pipeline_model.transform(spark.createDataFrame([['I love NLP']], ["text"])) +``` +```scala +... +val embeddings = BertEmbeddings.pretrained("bert_base_cased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +val data = Seq("I love NLP").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu + +text = ["I love NLP"] +embeddings_df = nlu.load('en.embed.bert.base_cased').predict(text, output_level='token') +embeddings_df +``` +
+ +## Results + +```bash +Results + + token en_embed_bert_base_cased_embeddings + + I [0.43879568576812744, -0.40160006284713745, 0.... + love [0.21737590432167053, -0.3865768313407898, -0.... + NLP [-0.16226479411125183, -0.053727392107248306, ... + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_opt| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|403.7 MB| +|Case sensitive:|true| + +## References + +The model is imported from [https://tfhub.dev/google/bert_cased_L-12_H-768_A-12/1](https://tfhub.dev/google/bert_cased_L-12_H-768_A-12/1) \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-06-29-bert_base_cased_quantized_en.md b/docs/_posts/ahmedlone127/2023-06-29-bert_base_cased_quantized_en.md new file mode 100644 index 00000000000000..f477c9e22a3310 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-06-29-bert_base_cased_quantized_en.md @@ -0,0 +1,137 @@ +--- +layout: model +title: BERT Embeddings (Base Cased) +author: John Snow Labs +name: bert_base_cased_quantized +date: 2023-06-29 +tags: [open_source, embeddings, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model contains a deep bidirectional transformer trained on Wikipedia and the BookCorpus. The details are described in the paper "[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)". + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_quantized_en_5.0.0_3.0_1688044431004.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_quantized_en_5.0.0_3.0_1688044431004.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + +
+ +{% include programmingLanguageSelectScalaPythonNLU.html %} + +```python +... +embeddings = BertEmbeddings.pretrained("bert_base_cased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text")) +result = pipeline_model.transform(spark.createDataFrame([['I love NLP']], ["text"])) +``` + +```scala +... +val embeddings = BertEmbeddings.pretrained("bert_base_cased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +val data = Seq("I love NLP").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu + +text = ["I love NLP"] +embeddings_df = nlu.load('en.embed.bert.base_cased').predict(text, output_level='token') +embeddings_df +``` + +
+ +{:.h2_title} + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +... +embeddings = BertEmbeddings.pretrained("bert_base_cased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text")) +result = pipeline_model.transform(spark.createDataFrame([['I love NLP']], ["text"])) +``` +```scala +... +val embeddings = BertEmbeddings.pretrained("bert_base_cased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +val data = Seq("I love NLP").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu + +text = ["I love NLP"] +embeddings_df = nlu.load('en.embed.bert.base_cased').predict(text, output_level='token') +embeddings_df +``` +
+ +## Results + +```bash +Results + + token en_embed_bert_base_cased_embeddings + + I [0.43879568576812744, -0.40160006284713745, 0.... + love [0.21737590432167053, -0.3865768313407898, -0.... + NLP [-0.16226479411125183, -0.053727392107248306, ... + + + +{:.model-param} +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_quantized| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|139.5 MB| +|Case sensitive:|true| + +## References + +The model is imported from [https://tfhub.dev/google/bert_cased_L-12_H-768_A-12/1](https://tfhub.dev/google/bert_cased_L-12_H-768_A-12/1) \ No newline at end of file From 27840ed5308ee318662e749b1ad84b88b0166c6b Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Wed, 5 Jul 2023 22:46:14 +0700 Subject: [PATCH 23/55] Add model 2023-07-05-image_classifier_convnext_tiny_224_local_en (#13879) Co-authored-by: gadde5300 --- ...e_classifier_convnext_tiny_224_local_en.md | 75 +++++++++++++++++++ 1 file changed, 75 insertions(+) create mode 100644 docs/_posts/gadde5300/2023-07-05-image_classifier_convnext_tiny_224_local_en.md diff --git a/docs/_posts/gadde5300/2023-07-05-image_classifier_convnext_tiny_224_local_en.md b/docs/_posts/gadde5300/2023-07-05-image_classifier_convnext_tiny_224_local_en.md new file mode 100644 index 00000000000000..9d4e1557756535 --- /dev/null +++ b/docs/_posts/gadde5300/2023-07-05-image_classifier_convnext_tiny_224_local_en.md @@ -0,0 +1,75 @@ +--- +layout: model +title: ConvNextForImageClassification - image_classifier_convnext_tiny_224_local +author: John Snow Labs +name: image_classifier_convnext_tiny_224_local +date: 2023-07-05 +tags: [convnext, en, engligh, image_classification, imagenet, convolution, open_source, tensorflow] +task: Image Classification +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: ConvNextForImageClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained ConvNext model for Image Classification, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. + +The ConvNeXT model was proposed in A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/image_classifier_convnext_tiny_224_local_en_5.0.0_3.0_1688564243397.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/image_classifier_convnext_tiny_224_local_en_5.0.0_3.0_1688564243397.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +image_assembler = ImageAssembler()\ + .setInputCol("image")\ + .setOutputCol("image_assembler") + +imageClassifier = ConvNextForImageClassification.pretrained("image_classifier_convnext_tiny_224_local", "en")\ + .setInputCols("image_assembler")\ + .setOutputCol("class") + +pipeline = Pipeline(stages=[ + image_assembler, + imageClassifier, +]) + +pipelineModel = pipeline.fit(imageDF) + +pipelineDF = pipelineModel.transform(imageDF) +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|image_classifier_convnext_tiny_224_local| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[image_assembler]| +|Output Labels:|[class]| +|Language:|en| +|Size:|107.6 MB| \ No newline at end of file From 566b6ee369a2289912758e452a7e18ac452b8d44 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Tue, 18 Jul 2023 17:42:09 +0700 Subject: [PATCH 24/55] Add model 2023-07-06-quora_distilbert_multilingual_en (#13882) Co-authored-by: purulalwani --- ...-07-06-quora_distilbert_multilingual_en.md | 59 +++++++++++++++++++ 1 file changed, 59 insertions(+) create mode 100644 docs/_posts/purulalwani/2023-07-06-quora_distilbert_multilingual_en.md diff --git a/docs/_posts/purulalwani/2023-07-06-quora_distilbert_multilingual_en.md b/docs/_posts/purulalwani/2023-07-06-quora_distilbert_multilingual_en.md new file mode 100644 index 00000000000000..699f6458d53c74 --- /dev/null +++ b/docs/_posts/purulalwani/2023-07-06-quora_distilbert_multilingual_en.md @@ -0,0 +1,59 @@ +--- +layout: model +title: Embeddings For Similarity Search +author: purulalwani +name: quora_distilbert_multilingual +date: 2023-07-06 +tags: [en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.2 +supported: false +engine: tensorflow +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Copy of https://huggingface.co/sentence-transformers/quora-distilbert-multilingual + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/community.johnsnowlabs.com/purulalwani/quora_distilbert_multilingual_en_5.0.0_3.2_1688648417016.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://community.johnsnowlabs.com/purulalwani/quora_distilbert_multilingual_en_5.0.0_3.2_1688648417016.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +See -> https://huggingface.co/sentence-transformers/quora-distilbert-multilingual +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|quora_distilbert_multilingual| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Community| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|506.5 MB| +|Case sensitive:|false| \ No newline at end of file From d2464555e33e79ab26457988346b689e47855d45 Mon Sep 17 00:00:00 2001 From: ahmedlone127 Date: Tue, 18 Jul 2023 16:23:39 +0500 Subject: [PATCH 25/55] removed duplicated sections (#13885) --- .../2023-05-19-match_pattern_en.md | 9 ---- .../2023-05-20-analyze_sentiment_en.md | 32 ------------ .../2023-05-20-clean_pattern_en.md | 26 ---------- .../ahmedlone127/2023-05-20-clean_stop_en.md | 27 ---------- .../2023-05-20-entity_recognizer_lg_fr.md | 27 ---------- .../2023-05-20-entity_recognizer_md_fr.md | 27 ---------- .../2023-05-20-explain_document_dl_en.md | 27 ---------- .../2023-05-20-explain_document_md_de.md | 27 ---------- 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...oberta_embeddings_roberta_urdu_small_ur.md | 48 ------------------ ...3-06-27-roberta_embeddings_robertinh_gl.md | 48 ------------------ ...erta_base_finetuned_spa_constitution_en.md | 48 ------------------ ...ta_embeddings_sundanese_roberta_base_su.md | 48 ------------------ ...-roberta_embeddings_ukr_roberta_base_uk.md | 48 ------------------ .../2023-06-28-distilbert_base_cased_en.md | 22 -------- ...eddings_distilbert_base_german_cased_de.md | 48 ------------------ ...ngs_distilbert_base_german_cased_opt_de.md | 48 ------------------ ...stilbert_base_german_cased_quantized_de.md | 48 ------------------ 693 files changed, 20387 deletions(-) diff --git a/docs/_posts/ahmedlone127/2023-05-19-match_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-19-match_pattern_en.md index 4865e422088735..bfb5f90b83c92c 100644 --- a/docs/_posts/ahmedlone127/2023-05-19-match_pattern_en.md +++ b/docs/_posts/ahmedlone127/2023-05-19-match_pattern_en.md @@ -33,16 +33,7 @@ The match_pattern is a pretrained pipeline that we can use to process text with ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models") - result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-05-20-analyze_sentiment_en.md b/docs/_posts/ahmedlone127/2023-05-20-analyze_sentiment_en.md index 16f8004a7107ae..b7d0aa182eb859 100644 --- a/docs/_posts/ahmedlone127/2023-05-20-analyze_sentiment_en.md +++ b/docs/_posts/ahmedlone127/2023-05-20-analyze_sentiment_en.md @@ -34,39 +34,7 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} - -```python - -from sparknlp.pretrained import PretrainedPipeline - -pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en') - -result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") - - -``` -```scala - -import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline -val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en") - -val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") - -``` - -{:.nlu-block} -```python - -import nlu -text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""] -result_df = nlu.load('en.classify').predict(text) -result_df - -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-20-clean_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-20-clean_pattern_en.md index 87defa5ce1ce92..7c68325df1b011 100644 --- a/docs/_posts/ahmedlone127/2023-05-20-clean_pattern_en.md +++ b/docs/_posts/ahmedlone127/2023-05-20-clean_pattern_en.md @@ -34,33 +34,7 @@ The clean_pattern is a pretrained pipeline that we can use to process text with ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('clean_pattern', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() -``` -```scala - - -val pipeline = new PretrainedPipeline("clean_pattern", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.clean.pattern').predict(text) -result_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-20-clean_stop_en.md b/docs/_posts/ahmedlone127/2023-05-20-clean_stop_en.md index 2cafdec0c1da18..949f6dbcb9d8f1 100644 --- a/docs/_posts/ahmedlone127/2023-05-20-clean_stop_en.md +++ b/docs/_posts/ahmedlone127/2023-05-20-clean_stop_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('clean_stop', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("clean_stop", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.clean.stop').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_lg_fr.md b/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_lg_fr.md index 5786fc442e5e44..9ddbf5591d4d9d 100644 --- a/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_lg_fr.md +++ b/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_lg_fr.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'fr') -annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "fr") -val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Bonjour de John Snow Labs! ""] -result_df = nlu.load('fr.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_md_fr.md b/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_md_fr.md index 1ddb38d5994928..ed2b3c47d29a5d 100644 --- a/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_md_fr.md +++ b/docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_md_fr.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fr') -annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fr") -val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Bonjour de John Snow Labs! ""] -result_df = nlu.load('fr.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-20-explain_document_dl_en.md b/docs/_posts/ahmedlone127/2023-05-20-explain_document_dl_en.md index 2cb06faa2f8753..a904a73ca50847 100644 --- a/docs/_posts/ahmedlone127/2023-05-20-explain_document_dl_en.md +++ b/docs/_posts/ahmedlone127/2023-05-20-explain_document_dl_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline('explain_document_dl', lang = 'en') -annotations = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_dl", lang = "en") -val result = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = ["The Mona Lisa is an oil painting from the 16th century."] -result_df = nlu.load('en.explain.dl').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-20-explain_document_md_de.md b/docs/_posts/ahmedlone127/2023-05-20-explain_document_md_de.md index 716a8c7ef93bda..df429bdeeaa112 100644 --- a/docs/_posts/ahmedlone127/2023-05-20-explain_document_md_de.md +++ b/docs/_posts/ahmedlone127/2023-05-20-explain_document_md_de.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'de') -annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "de") -val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hallo aus John Snow Labs! ""] -result_df = nlu.load('de.explain.document').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-20-explain_document_md_fr.md b/docs/_posts/ahmedlone127/2023-05-20-explain_document_md_fr.md index 7242cd87aba84a..80db50d9153398 100644 --- a/docs/_posts/ahmedlone127/2023-05-20-explain_document_md_fr.md +++ b/docs/_posts/ahmedlone127/2023-05-20-explain_document_md_fr.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'fr') -annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "fr") -val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Bonjour de John Snow Labs! ""] -result_df = nlu.load('fr.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-20-explain_document_ml_en.md b/docs/_posts/ahmedlone127/2023-05-20-explain_document_ml_en.md index 710aa1f053ff23..a74f8c38d2ff18 100644 --- a/docs/_posts/ahmedlone127/2023-05-20-explain_document_ml_en.md +++ b/docs/_posts/ahmedlone127/2023-05-20-explain_document_ml_en.md @@ -34,33 +34,7 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline('explain_document_ml', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() -``` -```scala - - -val pipeline = new PretrainedPipeline("explain_document_ml", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.explain').predict(text) -result_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-20-match_datetime_en.md b/docs/_posts/ahmedlone127/2023-05-20-match_datetime_en.md index 3fcefb1d7eb349..c26ea7dd887e84 100644 --- a/docs/_posts/ahmedlone127/2023-05-20-match_datetime_en.md +++ b/docs/_posts/ahmedlone127/2023-05-20-match_datetime_en.md @@ -32,35 +32,7 @@ DateMatcher based on yyyy/MM/dd ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline_local = PretrainedPipeline('match_datetime') - -tres = pipeline_local.fullAnnotate(input_list)[0] -for dte in tres['date']: - sent = tres['sentence'][int(dte.metadata['sentence'])] - print (f'text/chunk {sent.result[dte.begin:dte.end+1]} | mapped_date: {dte.result}') -``` -```scala - -import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP -SparkNLP.version() - -val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. -He also visited and the day before, but at that time he was alone. -David again visited today with his colleagues. -He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") - -val pipeline = PretrainedPipeline("match_datetime", lang="en") - -val annotation = pipeline.transform(testData) - -annotation.show() -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-20-match_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-20-match_pattern_en.md index 055d160c31f8bf..0e48aa6ecbea9d 100644 --- a/docs/_posts/ahmedlone127/2023-05-20-match_pattern_en.md +++ b/docs/_posts/ahmedlone127/2023-05-20-match_pattern_en.md @@ -33,16 +33,7 @@ The match_pattern is a pretrained pipeline that we can use to process text with ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models") - result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-05-20-movies_sentiment_analysis_en.md b/docs/_posts/ahmedlone127/2023-05-20-movies_sentiment_analysis_en.md index 95b2ce6a9e50d9..fe6a089067f250 100644 --- a/docs/_posts/ahmedlone127/2023-05-20-movies_sentiment_analysis_en.md +++ b/docs/_posts/ahmedlone127/2023-05-20-movies_sentiment_analysis_en.md @@ -33,16 +33,7 @@ The movies_sentiment_analysis is a pretrained pipeline that we can use to proces ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("movies_sentiment_analysis", "en") -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-05-20-recognize_entities_bert_en.md b/docs/_posts/ahmedlone127/2023-05-20-recognize_entities_bert_en.md index 926f24a29a77c3..069a6fcfad8eec 100644 --- a/docs/_posts/ahmedlone127/2023-05-20-recognize_entities_bert_en.md +++ b/docs/_posts/ahmedlone127/2023-05-20-recognize_entities_bert_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('recognize_entities_bert', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("recognize_entities_bert", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.bert').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-20-recognize_entities_dl_en.md b/docs/_posts/ahmedlone127/2023-05-20-recognize_entities_dl_en.md index fd6967e6b74e1c..4487b2ba269aad 100644 --- a/docs/_posts/ahmedlone127/2023-05-20-recognize_entities_dl_en.md +++ b/docs/_posts/ahmedlone127/2023-05-20-recognize_entities_dl_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-albert_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-albert_base_token_classifier_conll03_pipeline_en.md index c67e8675e84d6b..6b0e05370f8273 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-albert_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-albert_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [albert_base_token_classifier_co ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-albert_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-albert_large_token_classifier_conll03_pipeline_en.md index d1d0a027101f98..b8021add1f0219 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-albert_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-albert_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [albert_large_token_classifier_c ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-albert_xlarge_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-albert_xlarge_token_classifier_conll03_pipeline_en.md index 1e109782e0404e..1231adcb2910fc 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-albert_xlarge_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-albert_xlarge_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [albert_xlarge_token_classifier_ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-analyze_sentimentdl_glove_imdb_en.md b/docs/_posts/ahmedlone127/2023-05-21-analyze_sentimentdl_glove_imdb_en.md index bb3533370321b9..e28d3e58a4d962 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-analyze_sentimentdl_glove_imdb_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-analyze_sentimentdl_glove_imdb_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("analyze_sentimentdl_glove_imdb", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.sentiment.glove').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-bert_sequence_classifier_age_news_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-bert_sequence_classifier_age_news_pipeline_en.md index 5d271a57a015e8..1b2d289850778c 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-bert_sequence_classifier_age_news_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-bert_sequence_classifier_age_news_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [bert_sequence_classifier_age_ne ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -news_pipeline = PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") - -news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") -``` -```scala - - -val news_pipeline = new PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") -news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-bert_sequence_classifier_trec_coarse_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-bert_sequence_classifier_trec_coarse_pipeline_en.md index 52cb640bdcccfa..262f0a71cba827 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-bert_sequence_classifier_trec_coarse_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-bert_sequence_classifier_trec_coarse_pipeline_en.md @@ -34,23 +34,7 @@ The TREC dataset for question classification consists of open-domain, fact-based ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -trec_pipeline = PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") - -trec_pipeline.annotate("Germany is the largest country in Europe economically.") -``` -```scala - - -val trec_pipeline = new PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") -trec_pipeline.annotate("Germany is the largest country in Europe economically.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md b/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md index 49ca3e37d26312..a5e2d2b07c75a4 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md +++ b/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_dutch_udl ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") - -pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") -``` -```scala - -val pipeline = new PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") -pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_hi_en_ner_pipeline_hi.md b/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_hi_en_ner_pipeline_hi.md index 0b61c71f04caf9..727f842171f4f4 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_hi_en_ner_pipeline_hi.md +++ b/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_hi_en_ner_pipeline_hi.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on [bert_token_classifier_hi_en_ner](https://n ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") - -pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") -``` -```scala - -val pipeline = new PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") -val pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_scandi_ner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_scandi_ner_pipeline_xx.md index e87e9642c6170b..62139e7aaf7e8c 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_scandi_ner_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-21-bert_token_classifier_scandi_ner_pipeline_xx.md @@ -32,20 +32,7 @@ This pretrained pipeline is built on [bert_token_classifier_scandi_ner](https:// ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -scandiner_pipeline = PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") -scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") -``` -```scala - -val scandiner_pipeline = new PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") - -val scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-distilbert_base_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-21-distilbert_base_token_classifier_masakhaner_pipeline_xx.md index d0bc40acb6ec8d..49b6b871c0b531 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-distilbert_base_token_classifier_masakhaner_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-21-distilbert_base_token_classifier_masakhaner_pipeline_xx.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [distilbert_base_token_classifie ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -masakhaner_pipeline = PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") - -masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") -``` -```scala - -val masakhaner_pipeline = new PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") -masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-distilbert_token_classifier_typo_detector_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-distilbert_token_classifier_typo_detector_pipeline_en.md index cfeb1b836428a1..498f3354ec4a19 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-distilbert_token_classifier_typo_detector_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-distilbert_token_classifier_typo_detector_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [distilbert_token_classifier_typ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") - -typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") -``` -```scala - - -val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") -typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-distilbert_token_classifier_typo_detector_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-21-distilbert_token_classifier_typo_detector_pipeline_is.md index f71f8d6fbd6c55..92700cc73669fa 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-distilbert_token_classifier_typo_detector_pipeline_is.md +++ b/docs/_posts/ahmedlone127/2023-05-21-distilbert_token_classifier_typo_detector_pipeline_is.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [distilbert_token_classifier_typ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") - -typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") -``` -```scala - -val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") -typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-distilroberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-distilroberta_base_token_classifier_ontonotes_pipeline_en.md index 4213fa76ba1ff2..7c3088631df4db 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-distilroberta_base_token_classifier_ontonotes_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-distilroberta_base_token_classifier_ontonotes_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [distilroberta_base_token_classi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") - -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -```scala - - -val pipeline = new PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_da.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_da.md index 8955e9f3dade76..d99e3609165658 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_da.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_da.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "da") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("da.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_de.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_de.md index cc54bb0dab8119..36e9fcacdab55b 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_de.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_de.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'de') -annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "de") -val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hallo aus John Snow Labs! ""] -result_df = nlu.load('de.ner.recognizer.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_es.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_es.md index 75475181299702..2b71e0b7426164 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_es.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_es.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "es") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("es.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_fi.md index 55ebf123c1df74..993586c78c5028 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_fi.md @@ -33,26 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "fi") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("fi.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_nl.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_nl.md index dff7cd9661e907..1c5d740b65f070 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_nl.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_nl.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "nl") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("nl.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_no.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_no.md index 8699ac760ad0f3..37a59b0796c4cf 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_no.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_no.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "no") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("no.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_pl.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_pl.md index b25eb321ead9c0..322cf40e2bc802 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_pl.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_pl.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "pl") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("pl.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_pt.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_pt.md index 141a06d8077ec9..80deb5025851cb 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_pt.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_pt.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "pt") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("pt.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_ru.md index fcf058e50dc668..39a1c27dfc0fe5 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_ru.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "ru") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("ru.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_sv.md index 7f3490502e1367..0a1a16fac9b1f8 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_lg_sv.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "sv") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("sv.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_da.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_da.md index 65da905933269c..9a242227e8641f 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_da.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_da.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_de.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_de.md index a9a6c2bdd55d90..3661a875c83343 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_de.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_de.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'de') -annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "de") -val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hallo aus John Snow Labs! ""] -result_df = nlu.load('de.ner.recognizer').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_es.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_es.md index 3529cfbc437fe0..4c8aaa9f5a64b1 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_es.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_fi.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_fi.md index e67072c399a524..1dcc4094f5f962 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_fi.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_no.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_no.md index 3f575cab2d81aa..f5a70f9e33b688 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_no.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_ru.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_ru.md index 0c1f23aa64fe70..73c4520e8fca99 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_sv.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_sv.md index a3be16bd99c74f..bb39d3e3d8c00e 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_md_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_da.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_da.md index 303ae0989da47d..7081936b631e6e 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_da.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_da.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_es.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_es.md index 481dbe7d2460cc..fd22b0474f3d2e 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_es.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_fi.md index 793d02168e2078..6af5cfc1cef5f4 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_fi.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_no.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_no.md index 40887242a00edb..84e09d742e4dca 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_no.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_ru.md index 2077b8eb5eaf1e..af79802db59e34 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_sv.md index 26691aca25af85..2063746c4cfcb9 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-21-entity_recognizer_sm_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_da.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_da.md index 8084d8e7b4fee4..0138947598d5b3 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_da.md +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_da.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_es.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_es.md index 440ff38002a317..6c3399bbfcba89 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_es.md +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_es.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_fi.md index dcf425acc83419..dabde7c5fd2afa 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_fi.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_no.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_no.md index 5a2c307dbda354..9ad3c6690fa89c 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_no.md +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_no.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_ru.md index a72929290d4ee7..a3e48901c38a09 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_ru.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_sv.md index 916e0c895d7905..e4d4e3628e32f1 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_lg_sv.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_da.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_da.md index 2ea14f5b2331cd..cfb05e3e471108 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_da.md +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_da.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_es.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_es.md index 2907d7a7c35f51..d67a50a8102529 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_es.md +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_fi.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_fi.md index 71b87ab1245b42..348e59f61e688e 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_fi.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_no.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_no.md index 8558af1efebade..02d37c89023037 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_no.md +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_ru.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_ru.md index c679540f9b0387..6ba65fd95a81b5 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_sv.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_sv.md index 0483eb82713997..e278d418a550e7 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_md_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_da.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_da.md index 9d60898c5ec6d1..4ac55e5f37dfdb 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_da.md +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_da.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_es.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_es.md index 9fb4075ae18c3c..29316f66d38b22 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_es.md +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_fi.md index 85d99ba6092594..fe2f1eaf97840a 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_fi.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_no.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_no.md index 21fbb60d5981b6..7d5627c90b13b3 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_no.md +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_ru.md index 38585aedaa18ff..d12e29fd113830 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_sv.md index ec8aec2971da3f..42da296e420cd1 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-21-explain_document_sm_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-longformer_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-longformer_base_token_classifier_conll03_pipeline_en.md index 23ff94d863572b..c400941b4d06b3 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-longformer_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-longformer_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [longformer_base_token_classifie ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-longformer_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-longformer_large_token_classifier_conll03_pipeline_en.md index 1172ee55ae06b5..fee32be7ddc148 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-longformer_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-longformer_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [longformer_large_token_classifi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-match_chunks_en.md b/docs/_posts/ahmedlone127/2023-05-21-match_chunks_en.md index 0e7bb1cabc639e..37d5b30f676a40 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-match_chunks_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-match_chunks_en.md @@ -32,41 +32,7 @@ The pipeline uses regex `
?/*+` ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline - -pipeline_local = PretrainedPipeline('match_chunks') - -result = pipeline_local.annotate("David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.") - -result['chunk'] -``` -```scala - -import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP - -SparkNLP.version() -val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") - -val pipeline = PretrainedPipeline("match_chunks", lang="en") - -val annotation = pipeline.transform(testData) - -annotation.show() -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.match.chunks").predict("""David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.""") -``` - -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-nerdl_fewnerd_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-nerdl_fewnerd_100d_pipeline_en.md index a400d3f53f1cfb..fdb8a8575de607 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-nerdl_fewnerd_100d_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-nerdl_fewnerd_100d_pipeline_en.md @@ -34,21 +34,7 @@ This pretrained pipeline is built on Few-NERD model and it detects : ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") - -fewnerd_pipeline.annotate("""The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).""") -``` -```scala - -val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") -val result = pipeline.fullAnnotate("The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).")(0) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-nerdl_fewnerd_subentity_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-nerdl_fewnerd_subentity_100d_pipeline_en.md index 6e5bda94df3f1b..f2354f3df356c0 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-nerdl_fewnerd_subentity_100d_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-nerdl_fewnerd_subentity_100d_pipeline_en.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on Few-NERD/inter public dataset and it extrac ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") - -fewnerd_pipeline.annotate("""12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.""") -``` -```scala - -val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") -val result = pipeline.fullAnnotate("12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.")(0) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-nerdl_restaurant_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-nerdl_restaurant_100d_pipeline_en.md index b0d7072f07eb69..7998bd9d1a518a 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-nerdl_restaurant_100d_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-nerdl_restaurant_100d_pipeline_en.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [nerdl_restaurant_100d](https:// ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -restaurant_pipeline = PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") - -restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") -``` -```scala - -val restaurant_pipeline = new PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") -restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_base_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_base_en.md index 87e96cf134ef92..a5c5ef53878afb 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_base_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_base_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_base', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_base", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.base').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_large_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_large_en.md index b922543e2b7a56..846f9fe71c573f 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_large_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_large_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_large', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_large", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.large').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_medium_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_medium_en.md index 3bc0840d862e29..55dec840aeb4b7 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_medium_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_medium_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_medium', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_medium", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.medium').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_mini_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_mini_en.md index 14b73191a94566..65282f7e045389 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_mini_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_mini_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_mini', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_mini", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.mini').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_small_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_small_en.md index d1cccd62c63532..35c56706a58ffc 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_small_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_small_en.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_small', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_small", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.small').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_tiny_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_tiny_en.md index 558a056a497f46..f6bf639757575b 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_tiny_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_bert_tiny_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_tiny', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_tiny", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.tiny').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_base_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_base_en.md index fe1c34d91f31e8..946c3f59fb77b7 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_base_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_base_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_electra_base', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_base", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.electra.base').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_large_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_large_en.md index ac1128949d45af..f0603b75a24ed2 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_large_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_large_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline('onto_recognize_entities_electra_large', lang = 'en') -annotations = pipeline.fullAnnotate("Hello from John Snow Labs!")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_large", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.large').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_small_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_small_en.md index 3dda6bffca3e74..7946e1c2b4e515 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_small_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_electra_small_en.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_electra_small', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_small", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.electra.small').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_lg_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_lg_en.md index ed9a31526cc8be..09b5d8473ac50a 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_lg_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_lg_en.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("onto_recognize_entities_lg", "en") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("en.ner.onto.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_sm_en.md b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_sm_en.md index 973ef3cece1364..22848bb5a595ca 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_sm_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-onto_recognize_entities_sm_en.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_sm', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_sm", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.sm').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-21-pos_ud_bokmaal_nb.md b/docs/_posts/ahmedlone127/2023-05-21-pos_ud_bokmaal_nb.md index 0d388eff34fad8..9678e18b957057 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-pos_ud_bokmaal_nb.md +++ b/docs/_posts/ahmedlone127/2023-05-21-pos_ud_bokmaal_nb.md @@ -34,39 +34,7 @@ This model was trained using the dataset available at https://universaldependenc ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("pos") - -nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos]) -light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text"))) -results = light_pipeline.fullAnnotate("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.") -``` -```scala - -val pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") - .setInputCols(Array("document", "token")) - .setOutputCol("pos") - -val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos)) -val data = Seq("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.").toDF("text") -val result = pipeline.fit(data).transform(data) -``` - -{:.nlu-block} -```python - -import nlu -text = ["""Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene."""] -pos_df = nlu.load('nb.pos.ud_bokmaal').predict(text) -pos_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-recognize_entities_dl_fa.md b/docs/_posts/ahmedlone127/2023-05-21-recognize_entities_dl_fa.md index 9e6cd9bc8b4e0a..4ee3fa20cb8b34 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-recognize_entities_dl_fa.md +++ b/docs/_posts/ahmedlone127/2023-05-21-recognize_entities_dl_fa.md @@ -32,37 +32,7 @@ The explain_document_dl is a pretrained pipeline that we can use to process text ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline - -pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'fa') - -annotations = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")[0] - -annotations.keys() -``` -```scala - -val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "fa") -val result = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")(0) -``` - -{:.nlu-block} -```python - -import nlu - -text = ["""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند"""] - -result_df = nlu.load('fa.recognize_entities_dl').predict(text) - -result_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-roberta_base_token_classifier_ontonotes_pipeline_en.md index 0778fc9a699292..dd1ac545aea6fe 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-roberta_base_token_classifier_ontonotes_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [roberta_base_token_classifier_o ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") - -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -```scala - -val pipeline = new PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-roberta_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-roberta_large_token_classifier_conll03_pipeline_en.md index 896a3bee6473e6..7a0f8c97829685 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-roberta_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-roberta_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [roberta_large_token_classifier_ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_bne_capitel_ner_pipeline_es.md b/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_bne_capitel_ner_pipeline_es.md index 43d61d82783359..96214182883b38 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_bne_capitel_ner_pipeline_es.md +++ b/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_bne_capitel_ner_pipeline_es.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [roberta_token_classifier_bne_ca ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") - -pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") -``` -```scala - -val pipeline = new PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") -pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_icelandic_ner_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_icelandic_ner_pipeline_is.md index 5636ff0f7d2ac6..1f694d0f8887f5 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_icelandic_ner_pipeline_is.md +++ b/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_icelandic_ner_pipeline_is.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [roberta_token_classifier_icelan ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") - -pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") -``` -```scala - -val pipeline = new PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") -pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_timex_semeval_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_timex_semeval_pipeline_en.md index f49f81cfbb1ea6..f7dfb7c4cebb80 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_timex_semeval_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-roberta_token_classifier_timex_semeval_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [roberta_token_classifier_timex_ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -timex_pipeline = PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") - -timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") -``` -```scala - - -val timex_pipeline = new PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") -timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-spellcheck_dl_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-spellcheck_dl_pipeline_en.md index 50b9658933ce7d..f217407e8318b7 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-spellcheck_dl_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-spellcheck_dl_pipeline_en.md @@ -32,27 +32,7 @@ This pretrained spellchecker pipeline is built on the top of [spellcheck_dl](htt ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") - -text = ["During the summer we have the best ueather.", "I have a black ueather jacket, so nice."] - -pipeline.annotate(text) -``` -```scala - - -val pipeline = new PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") -val example = Array("During the summer we have the best ueather.", "I have a black ueather jacket, so nice.") - -pipeline.annotate(example) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-text_cleaning_en.md b/docs/_posts/ahmedlone127/2023-05-21-text_cleaning_en.md index 2450fe258fcc9a..b81b2b80ac2b22 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-text_cleaning_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-text_cleaning_en.md @@ -33,16 +33,7 @@ The text_cleaning is a pretrained pipeline that we can use to process text with ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("text_cleaning", "en") -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_base_token_classifier_conll03_pipeline_en.md index 66efd285422515..ee5036f57a4ac9 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md index 61da217d710f2d..79f5241292024c 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") - -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_conll03_pipeline_de.md b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_conll03_pipeline_de.md index bb9753961cb0a7..9faccd3fc16704 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_conll03_pipeline_de.md +++ b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_conll03_pipeline_de.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_large_token_classif ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") - -pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") -``` -```scala - -val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") -pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md index 549d4b28e9709b..49e146c5ca5a42 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_large_token_classif ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") - -pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") -``` -```scala - -val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") -pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md index 4f77ba5205735f..f5bcba0621a95f 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-21-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on [xlm_roberta_large_token_classifier_masakha ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -masakhaner_pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") - -masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") -``` -```scala - -val masakhaner_pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") -val masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-xlnet_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-xlnet_base_token_classifier_conll03_pipeline_en.md index 74dfe5133cd1c1..b1406aa89850cf 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-xlnet_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-xlnet_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlnet_base_token_classifier_con ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-21-xlnet_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-21-xlnet_large_token_classifier_conll03_pipeline_en.md index 8fa9ae510aa17e..ac0910cc8e9009 100644 --- a/docs/_posts/ahmedlone127/2023-05-21-xlnet_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-21-xlnet_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlnet_large_token_classifier_co ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-albert_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-albert_base_token_classifier_conll03_pipeline_en.md index 15bc10dee1a55d..023b1c2b54cdac 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-albert_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-albert_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [albert_base_token_classifier_co ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-albert_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-albert_large_token_classifier_conll03_pipeline_en.md index 491f9477630b3a..4e65d749a60991 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-albert_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-albert_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [albert_large_token_classifier_c ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-albert_xlarge_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-albert_xlarge_token_classifier_conll03_pipeline_en.md index f941f83ee783e6..0ffc760b94d81d 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-albert_xlarge_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-albert_xlarge_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [albert_xlarge_token_classifier_ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-analyze_sentiment_en.md b/docs/_posts/ahmedlone127/2023-05-22-analyze_sentiment_en.md index b28842b096cb8f..60d3a66eb5ce9d 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-analyze_sentiment_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-analyze_sentiment_en.md @@ -34,39 +34,7 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} - -```python - -from sparknlp.pretrained import PretrainedPipeline - -pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en') - -result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") - - -``` -```scala - -import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline -val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en") - -val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") - -``` - -{:.nlu-block} -```python - -import nlu -text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""] -result_df = nlu.load('en.classify').predict(text) -result_df - -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-analyze_sentimentdl_glove_imdb_en.md b/docs/_posts/ahmedlone127/2023-05-22-analyze_sentimentdl_glove_imdb_en.md index 12dad070707716..7caae5228343f6 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-analyze_sentimentdl_glove_imdb_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-analyze_sentimentdl_glove_imdb_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("analyze_sentimentdl_glove_imdb", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.sentiment.glove').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-bert_sequence_classifier_age_news_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-bert_sequence_classifier_age_news_pipeline_en.md index 9a8e3fb42796b3..00e876b279f9b4 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-bert_sequence_classifier_age_news_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-bert_sequence_classifier_age_news_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [bert_sequence_classifier_age_ne ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -news_pipeline = PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") - -news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") -``` -```scala - - -val news_pipeline = new PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") -news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-bert_sequence_classifier_trec_coarse_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-bert_sequence_classifier_trec_coarse_pipeline_en.md index 42d4d0ad25c7db..388e8693da5e98 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-bert_sequence_classifier_trec_coarse_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-bert_sequence_classifier_trec_coarse_pipeline_en.md @@ -34,23 +34,7 @@ The TREC dataset for question classification consists of open-domain, fact-based ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -trec_pipeline = PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") - -trec_pipeline.annotate("Germany is the largest country in Europe economically.") -``` -```scala - - -val trec_pipeline = new PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") -trec_pipeline.annotate("Germany is the largest country in Europe economically.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md b/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md index e344aa5f0b4f91..dce7d90f75101e 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md +++ b/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_dutch_udl ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") - -pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") -``` -```scala - -val pipeline = new PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") -pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_hi_en_ner_pipeline_hi.md b/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_hi_en_ner_pipeline_hi.md index 0dbde13ed54557..2dea52a19b93a7 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_hi_en_ner_pipeline_hi.md +++ b/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_hi_en_ner_pipeline_hi.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on [bert_token_classifier_hi_en_ner](https://n ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") - -pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") -``` -```scala - -val pipeline = new PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") -val pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_scandi_ner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_scandi_ner_pipeline_xx.md index 96ecea3ee6a270..ba899a9e474383 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_scandi_ner_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-22-bert_token_classifier_scandi_ner_pipeline_xx.md @@ -32,20 +32,7 @@ This pretrained pipeline is built on [bert_token_classifier_scandi_ner](https:// ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -scandiner_pipeline = PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") -scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") -``` -```scala - -val scandiner_pipeline = new PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") - -val scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-clean_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-22-clean_pattern_en.md index d1a29a03fad8ab..19adf6f1233737 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-clean_pattern_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-clean_pattern_en.md @@ -34,33 +34,7 @@ The clean_pattern is a pretrained pipeline that we can use to process text with ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('clean_pattern', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() -``` -```scala - - -val pipeline = new PretrainedPipeline("clean_pattern", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.clean.pattern').predict(text) -result_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-clean_stop_en.md b/docs/_posts/ahmedlone127/2023-05-22-clean_stop_en.md index d3ce8b84318824..2e4ddf5910551a 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-clean_stop_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-clean_stop_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('clean_stop', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("clean_stop", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.clean.stop').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-distilbert_base_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-22-distilbert_base_token_classifier_masakhaner_pipeline_xx.md index b9e9ad4659bce5..8f5cbc8c1a479a 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-distilbert_base_token_classifier_masakhaner_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-22-distilbert_base_token_classifier_masakhaner_pipeline_xx.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [distilbert_base_token_classifie ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -masakhaner_pipeline = PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") - -masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") -``` -```scala - -val masakhaner_pipeline = new PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") -masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-distilbert_token_classifier_typo_detector_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-distilbert_token_classifier_typo_detector_pipeline_en.md index 68fd4a0067cb56..f1baf41eafd85a 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-distilbert_token_classifier_typo_detector_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-distilbert_token_classifier_typo_detector_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [distilbert_token_classifier_typ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") - -typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") -``` -```scala - - -val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") -typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-distilbert_token_classifier_typo_detector_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-22-distilbert_token_classifier_typo_detector_pipeline_is.md index 5264facb573761..a9c358bb015517 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-distilbert_token_classifier_typo_detector_pipeline_is.md +++ b/docs/_posts/ahmedlone127/2023-05-22-distilbert_token_classifier_typo_detector_pipeline_is.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [distilbert_token_classifier_typ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") - -typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") -``` -```scala - -val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") -typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-distilroberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-distilroberta_base_token_classifier_ontonotes_pipeline_en.md index 27235c5826115d..fbbcda43681094 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-distilroberta_base_token_classifier_ontonotes_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-distilroberta_base_token_classifier_ontonotes_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [distilroberta_base_token_classi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") - -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -```scala - - -val pipeline = new PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_da.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_da.md index 3db719c608008b..e5e389ac248f0f 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_da.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_da.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "da") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("da.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_de.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_de.md index e610fe29fc9466..d83f24ea8fde84 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_de.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_de.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'de') -annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "de") -val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hallo aus John Snow Labs! ""] -result_df = nlu.load('de.ner.recognizer.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_es.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_es.md index 3379529b426ecc..7b40c5992a64fc 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_es.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_es.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "es") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("es.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_fi.md index 19276be9edc20f..b1d5ba6252fba6 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_fi.md @@ -33,26 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "fi") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("fi.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_fr.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_fr.md index b0724b67df638f..6d5482fe230e86 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_fr.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_fr.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'fr') -annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "fr") -val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Bonjour de John Snow Labs! ""] -result_df = nlu.load('fr.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_nl.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_nl.md index a833bd49ae918d..cc03ff7f2e57c6 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_nl.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_nl.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "nl") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("nl.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_pl.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_pl.md index 9b7cfa38872a5e..de431938a6d564 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_pl.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_pl.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "pl") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("pl.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_pt.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_pt.md index b62f280417de8a..4b560929e248f1 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_pt.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_pt.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "pt") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("pt.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_ru.md index 592f8f3daee790..610bc6930f7097 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_ru.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "ru") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("ru.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_sv.md index ea42724fa41ac2..4d24f5823ec969 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_lg_sv.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "sv") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("sv.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_da.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_da.md index 250bce40234d48..453bd325ccbacb 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_da.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_da.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_de.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_de.md index f65fb8fc1941c5..f84017805ca409 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_de.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_de.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'de') -annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "de") -val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hallo aus John Snow Labs! ""] -result_df = nlu.load('de.ner.recognizer').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_es.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_es.md index 481e6272b3ea80..f49762c331985b 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_es.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_fi.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_fi.md index a136eb2862bb2a..2f49e226c74d32 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_fi.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_fr.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_fr.md index f1cc75df9acfff..ab5d71f95242e8 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_fr.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_fr.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fr') -annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fr") -val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Bonjour de John Snow Labs! ""] -result_df = nlu.load('fr.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_no.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_no.md index ce25923c56e400..53a78a0f6fa6eb 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_no.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_ru.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_ru.md index 99598085e1433e..e475a3407c8c83 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_sv.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_sv.md index d4099b83261023..900f51c5e137ed 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_md_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_da.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_da.md index 01a6ba6a68fa5f..e09667a5dba079 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_da.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_da.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_es.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_es.md index c034fd3a3a45f3..7d383507125bb6 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_es.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_fi.md index a31fd716168455..642498faf5473e 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_fi.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_no.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_no.md index 59ff12c659a006..cf937ac1b7f4ae 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_no.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_ru.md index d791648f8b3f4a..0fc73579028494 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_sv.md index be54bafe49fec0..dac74a420ff9b5 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-22-entity_recognizer_sm_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_dl_en.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_dl_en.md index 22525f8c0909a0..214aa7846e4dc7 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_dl_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_dl_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline('explain_document_dl', lang = 'en') -annotations = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_dl", lang = "en") -val result = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = ["The Mona Lisa is an oil painting from the 16th century."] -result_df = nlu.load('en.explain.dl').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_da.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_da.md index b763647ec89c5f..a79791781db4e3 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_da.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_da.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_es.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_es.md index 76a63466d126db..08106ad6c587b9 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_es.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_es.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_fi.md index 84e43520c89a9e..a721a749078cd9 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_fi.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_no.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_no.md index 58e772adc29637..902f7a21a48eda 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_no.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_no.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_ru.md index b323facb0ee86b..36c0ec6e88a21c 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_ru.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_sv.md index 0d05cd01531234..1cad18e958ed5e 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_lg_sv.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_da.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_da.md index d0511eec69f4f3..5fc96f5b076353 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_da.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_da.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_de.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_de.md index 7d8c9d76430d9d..59ef17f80c2bba 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_de.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_de.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'de') -annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "de") -val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hallo aus John Snow Labs! ""] -result_df = nlu.load('de.explain.document').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_es.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_es.md index dd22a740c2dade..f82936d3997551 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_es.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_fi.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_fi.md index 90c527826353da..4db986c415269b 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_fi.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_fr.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_fr.md index 914f74e1b22ebc..127f4e84947072 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_fr.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_fr.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'fr') -annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "fr") -val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Bonjour de John Snow Labs! ""] -result_df = nlu.load('fr.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_no.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_no.md index 5d10909e8fc22a..808da2a4fd7051 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_no.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_ru.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_ru.md index cbc9c302074826..042dba72cb5b43 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_sv.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_sv.md index 994633443fdff2..1abbbc466e56c1 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_md_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_ml_en.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_ml_en.md index c172b6ae4150d9..888875a101b20e 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_ml_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_ml_en.md @@ -34,33 +34,7 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline('explain_document_ml', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() -``` -```scala - - -val pipeline = new PretrainedPipeline("explain_document_ml", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.explain').predict(text) -result_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_da.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_da.md index 099cacbd81241c..2affd7493ff6b3 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_da.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_da.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_es.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_es.md index bd110694edd184..d49491a5616876 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_es.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_fi.md index 8d69d6805b998c..590230eb640bbb 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_fi.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_no.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_no.md index 9a66e31df60fac..100d65667cca69 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_no.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_ru.md index a81133879157b3..0cf0116a1bb190 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_sv.md index f401f38e1a3a09..27e5174316912a 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-22-explain_document_sm_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-longformer_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-longformer_base_token_classifier_conll03_pipeline_en.md index 3878cb8b993593..d0e9e735bcc0b8 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-longformer_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-longformer_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [longformer_base_token_classifie ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-longformer_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-longformer_large_token_classifier_conll03_pipeline_en.md index d22ba6ca506a8a..3a0765bd9fde78 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-longformer_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-longformer_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [longformer_large_token_classifi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-match_chunks_en.md b/docs/_posts/ahmedlone127/2023-05-22-match_chunks_en.md index cfb0fd4d59d7de..06c2b74410b28a 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-match_chunks_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-match_chunks_en.md @@ -32,41 +32,7 @@ The pipeline uses regex `
?/*+` ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline - -pipeline_local = PretrainedPipeline('match_chunks') - -result = pipeline_local.annotate("David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.") - -result['chunk'] -``` -```scala - -import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP - -SparkNLP.version() -val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") - -val pipeline = PretrainedPipeline("match_chunks", lang="en") - -val annotation = pipeline.transform(testData) - -annotation.show() -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.match.chunks").predict("""David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.""") -``` - -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-match_datetime_en.md b/docs/_posts/ahmedlone127/2023-05-22-match_datetime_en.md index 57932b42257f82..f80a11e7fa7852 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-match_datetime_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-match_datetime_en.md @@ -32,35 +32,7 @@ DateMatcher based on yyyy/MM/dd ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline_local = PretrainedPipeline('match_datetime') - -tres = pipeline_local.fullAnnotate(input_list)[0] -for dte in tres['date']: - sent = tres['sentence'][int(dte.metadata['sentence'])] - print (f'text/chunk {sent.result[dte.begin:dte.end+1]} | mapped_date: {dte.result}') -``` -```scala - -import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP -SparkNLP.version() - -val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. -He also visited and the day before, but at that time he was alone. -David again visited today with his colleagues. -He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") - -val pipeline = PretrainedPipeline("match_datetime", lang="en") - -val annotation = pipeline.transform(testData) - -annotation.show() -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-match_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-22-match_pattern_en.md index 4c55bc1f6ce271..481caef65b1f88 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-match_pattern_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-match_pattern_en.md @@ -33,16 +33,7 @@ The match_pattern is a pretrained pipeline that we can use to process text with ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models") - result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-05-22-movies_sentiment_analysis_en.md b/docs/_posts/ahmedlone127/2023-05-22-movies_sentiment_analysis_en.md index cc0ac4fc6f624c..560156c89d760a 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-movies_sentiment_analysis_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-movies_sentiment_analysis_en.md @@ -33,16 +33,7 @@ The movies_sentiment_analysis is a pretrained pipeline that we can use to proces ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("movies_sentiment_analysis", "en") -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-05-22-nerdl_fewnerd_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-nerdl_fewnerd_100d_pipeline_en.md index 9ca480fccdef9d..0b5111e8d0f896 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-nerdl_fewnerd_100d_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-nerdl_fewnerd_100d_pipeline_en.md @@ -34,21 +34,7 @@ This pretrained pipeline is built on Few-NERD model and it detects : ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") - -fewnerd_pipeline.annotate("""The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).""") -``` -```scala - -val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") -val result = pipeline.fullAnnotate("The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).")(0) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-nerdl_fewnerd_subentity_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-nerdl_fewnerd_subentity_100d_pipeline_en.md index 6337f0136a984d..86920d4eef13a9 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-nerdl_fewnerd_subentity_100d_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-nerdl_fewnerd_subentity_100d_pipeline_en.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on Few-NERD/inter public dataset and it extrac ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") - -fewnerd_pipeline.annotate("""12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.""") -``` -```scala - -val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") -val result = pipeline.fullAnnotate("12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.")(0) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-nerdl_restaurant_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-nerdl_restaurant_100d_pipeline_en.md index 15f6f43e5b8103..a40cc0735dda25 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-nerdl_restaurant_100d_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-nerdl_restaurant_100d_pipeline_en.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [nerdl_restaurant_100d](https:// ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -restaurant_pipeline = PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") - -restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") -``` -```scala - -val restaurant_pipeline = new PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") -restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_base_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_base_en.md index d63528bb521f24..9bf91e1036a342 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_base_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_base_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_base', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_base", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.base').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_large_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_large_en.md index f362f3d323a62b..7b6ec2cf54212f 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_large_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_large_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_large', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_large", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.large').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_medium_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_medium_en.md index 519d1c16b5c60a..6d53255765848b 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_medium_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_medium_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_medium', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_medium", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.medium').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_mini_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_mini_en.md index 9bb36244046f97..e3e30b745e284e 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_mini_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_mini_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_mini', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_mini", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.mini').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_small_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_small_en.md index 07de2dd5f53614..6fa55447acd74c 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_small_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_small_en.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_small', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_small", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.small').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_tiny_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_tiny_en.md index 1a722c34fa70bd..d7f03e348d7154 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_tiny_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_bert_tiny_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_tiny', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_tiny", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.tiny').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_base_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_base_en.md index 905ff995657472..31f126c38a1bac 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_base_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_base_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_electra_base', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_base", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.electra.base').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_large_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_large_en.md index 1e68b4e623705d..7fd51a3c25d637 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_large_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_large_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline('onto_recognize_entities_electra_large', lang = 'en') -annotations = pipeline.fullAnnotate("Hello from John Snow Labs!")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_large", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.large').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_small_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_small_en.md index 1177daf831daf2..4a212a1ced1461 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_small_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_electra_small_en.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_electra_small', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_small", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.electra.small').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_lg_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_lg_en.md index a03017071dfa89..b0832c47fbf9bc 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_lg_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_lg_en.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("onto_recognize_entities_lg", "en") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("en.ner.onto.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_sm_en.md b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_sm_en.md index 9895cd6398d85c..74528f8b336b37 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_sm_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-onto_recognize_entities_sm_en.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_sm', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_sm", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.sm').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-pos_ud_bokmaal_nb.md b/docs/_posts/ahmedlone127/2023-05-22-pos_ud_bokmaal_nb.md index e5c889d05ef3a7..690fbfa93c7bfe 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-pos_ud_bokmaal_nb.md +++ b/docs/_posts/ahmedlone127/2023-05-22-pos_ud_bokmaal_nb.md @@ -34,39 +34,7 @@ This model was trained using the dataset available at https://universaldependenc ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("pos") - -nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos]) -light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text"))) -results = light_pipeline.fullAnnotate("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.") -``` -```scala - -val pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") - .setInputCols(Array("document", "token")) - .setOutputCol("pos") - -val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos)) -val data = Seq("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.").toDF("text") -val result = pipeline.fit(data).transform(data) -``` - -{:.nlu-block} -```python - -import nlu -text = ["""Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene."""] -pos_df = nlu.load('nb.pos.ud_bokmaal').predict(text) -pos_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_bert_en.md b/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_bert_en.md index ac7290c2c2f439..ad32d266f1e076 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_bert_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_bert_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('recognize_entities_bert', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("recognize_entities_bert", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.bert').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_dl_en.md b/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_dl_en.md index ee25fc3f75a502..9c02e144ca8c26 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_dl_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_dl_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_dl_fa.md b/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_dl_fa.md index 722e3d58623246..51a551c4114919 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_dl_fa.md +++ b/docs/_posts/ahmedlone127/2023-05-22-recognize_entities_dl_fa.md @@ -32,37 +32,7 @@ The explain_document_dl is a pretrained pipeline that we can use to process text ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline - -pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'fa') - -annotations = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")[0] - -annotations.keys() -``` -```scala - -val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "fa") -val result = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")(0) -``` - -{:.nlu-block} -```python - -import nlu - -text = ["""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند"""] - -result_df = nlu.load('fa.recognize_entities_dl').predict(text) - -result_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-roberta_base_token_classifier_ontonotes_pipeline_en.md index 0ec192b82de752..5fab17c3b9af2b 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-roberta_base_token_classifier_ontonotes_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [roberta_base_token_classifier_o ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") - -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -```scala - -val pipeline = new PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-roberta_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-roberta_large_token_classifier_conll03_pipeline_en.md index a7d710875988c0..1ea8fa9a308f8d 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-roberta_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-roberta_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [roberta_large_token_classifier_ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_bne_capitel_ner_pipeline_es.md b/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_bne_capitel_ner_pipeline_es.md index ff1f64782cf1ee..3afc986f076815 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_bne_capitel_ner_pipeline_es.md +++ b/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_bne_capitel_ner_pipeline_es.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [roberta_token_classifier_bne_ca ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") - -pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") -``` -```scala - -val pipeline = new PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") -pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_icelandic_ner_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_icelandic_ner_pipeline_is.md index 61688280e98e16..9920b51285fa58 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_icelandic_ner_pipeline_is.md +++ b/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_icelandic_ner_pipeline_is.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [roberta_token_classifier_icelan ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") - -pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") -``` -```scala - -val pipeline = new PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") -pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_timex_semeval_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_timex_semeval_pipeline_en.md index 93eb4f1b9a89a0..846081aa6031da 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_timex_semeval_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-roberta_token_classifier_timex_semeval_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [roberta_token_classifier_timex_ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -timex_pipeline = PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") - -timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") -``` -```scala - - -val timex_pipeline = new PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") -timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-spellcheck_dl_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-spellcheck_dl_pipeline_en.md index 02d60cf8b49fa2..9d2b944659aaf8 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-spellcheck_dl_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-spellcheck_dl_pipeline_en.md @@ -32,27 +32,7 @@ This pretrained spellchecker pipeline is built on the top of [spellcheck_dl](htt ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") - -text = ["During the summer we have the best ueather.", "I have a black ueather jacket, so nice."] - -pipeline.annotate(text) -``` -```scala - - -val pipeline = new PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") -val example = Array("During the summer we have the best ueather.", "I have a black ueather jacket, so nice.") - -pipeline.annotate(example) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-text_cleaning_en.md b/docs/_posts/ahmedlone127/2023-05-22-text_cleaning_en.md index bd21c3c74648b7..191bb5e3da0837 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-text_cleaning_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-text_cleaning_en.md @@ -33,16 +33,7 @@ The text_cleaning is a pretrained pipeline that we can use to process text with ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("text_cleaning", "en") -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_base_token_classifier_conll03_pipeline_en.md index b01501066476f8..bd7c77c8df7002 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md index 9b0a4e00a3cbec..c62562d48e60ad 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") - -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_conll03_pipeline_de.md b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_conll03_pipeline_de.md index a5cde6f39d05a4..ee9bd6e7dc8675 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_conll03_pipeline_de.md +++ b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_conll03_pipeline_de.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_large_token_classif ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") - -pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") -``` -```scala - -val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") -pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md index b50cc0a119e8ce..33c74c86cfb92d 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_large_token_classif ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") - -pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") -``` -```scala - -val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") -pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md index 33594be3b84966..a0a6092a8e4336 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-22-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on [xlm_roberta_large_token_classifier_masakha ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -masakhaner_pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") - -masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") -``` -```scala - -val masakhaner_pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") -val masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-xlnet_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-xlnet_base_token_classifier_conll03_pipeline_en.md index e11013676e7752..9395b9b0450103 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-xlnet_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-xlnet_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlnet_base_token_classifier_con ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-22-xlnet_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-22-xlnet_large_token_classifier_conll03_pipeline_en.md index 58dc4fccd0d7f8..b4f98b17e2fbc3 100644 --- a/docs/_posts/ahmedlone127/2023-05-22-xlnet_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-22-xlnet_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlnet_large_token_classifier_co ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-24-analyze_sentiment_en.md b/docs/_posts/ahmedlone127/2023-05-24-analyze_sentiment_en.md index 5d9c83df977dbf..4b610cebf162cb 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-analyze_sentiment_en.md +++ b/docs/_posts/ahmedlone127/2023-05-24-analyze_sentiment_en.md @@ -34,39 +34,7 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} - -```python - -from sparknlp.pretrained import PretrainedPipeline - -pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en') - -result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") - - -``` -```scala - -import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline -val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en") - -val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") - -``` - -{:.nlu-block} -```python - -import nlu -text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""] -result_df = nlu.load('en.classify').predict(text) -result_df - -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-24-analyze_sentimentdl_glove_imdb_en.md b/docs/_posts/ahmedlone127/2023-05-24-analyze_sentimentdl_glove_imdb_en.md index b9f91f44783fce..8f0083c01c486b 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-analyze_sentimentdl_glove_imdb_en.md +++ b/docs/_posts/ahmedlone127/2023-05-24-analyze_sentimentdl_glove_imdb_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("analyze_sentimentdl_glove_imdb", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.sentiment.glove').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-clean_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-24-clean_pattern_en.md index b13244944aa964..7b07fd0486dbd5 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-clean_pattern_en.md +++ b/docs/_posts/ahmedlone127/2023-05-24-clean_pattern_en.md @@ -34,33 +34,7 @@ The clean_pattern is a pretrained pipeline that we can use to process text with ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('clean_pattern', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() -``` -```scala - - -val pipeline = new PretrainedPipeline("clean_pattern", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.clean.pattern').predict(text) -result_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-24-clean_stop_en.md b/docs/_posts/ahmedlone127/2023-05-24-clean_stop_en.md index 6068e038ad1ee6..0226561f3acd94 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-clean_stop_en.md +++ b/docs/_posts/ahmedlone127/2023-05-24-clean_stop_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('clean_stop', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("clean_stop", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.clean.stop').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_de.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_de.md index dd930bea9c5bf6..c4b97d494b207f 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_de.md +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_de.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'de') -annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "de") -val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hallo aus John Snow Labs! ""] -result_df = nlu.load('de.ner.recognizer.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_es.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_es.md index 51c2508bf37b4e..855f0fed257afa 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_es.md +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_es.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "es") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("es.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_fr.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_fr.md index 18c3447a08a0bc..c2d22843e89cba 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_fr.md +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_fr.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'fr') -annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "fr") -val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Bonjour de John Snow Labs! ""] -result_df = nlu.load('fr.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_nl.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_nl.md index 2c911b74026429..31f27d1708a88c 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_nl.md +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_nl.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "nl") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("nl.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_no.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_no.md index 4cefe051b56add..c8b7aab8a11595 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_no.md +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_no.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "no") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("no.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_pl.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_pl.md index 2955701471bfe1..193f16302af83e 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_pl.md +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_pl.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "pl") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("pl.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_pt.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_pt.md index 4b1cde8456ac3c..6a5dc77670e5b3 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_pt.md +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_pt.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "pt") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("pt.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_ru.md index 49dfad88bac238..066285352e686c 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_ru.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "ru") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("ru.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_sv.md index 2d7712cc130eec..7fdb92324adfbb 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_lg_sv.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "sv") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("sv.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_de.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_de.md index 2598aa24330417..e0413a8449c2a1 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_de.md +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_de.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'de') -annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "de") -val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hallo aus John Snow Labs! ""] -result_df = nlu.load('de.ner.recognizer').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_es.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_es.md index 16fc2ffd0cc20b..f5d3feb29f7b90 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_es.md +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_fr.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_fr.md index 72dac1583e9b59..6a8542f87337dd 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_fr.md +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_fr.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fr') -annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fr") -val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Bonjour de John Snow Labs! ""] -result_df = nlu.load('fr.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_no.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_no.md index 5e43ac4f67f878..d83b24fdd46076 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_no.md +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_ru.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_ru.md index daa6bd25d17025..ef852e7b641b21 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_sv.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_sv.md index e2153d5b120cfc..372841e0a9dd29 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_md_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_es.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_es.md index 53cfb3b430866a..525acf5f72da3f 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_es.md +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_no.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_no.md index 37a8591fe2ef78..b0ae7737cb368f 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_no.md +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_ru.md index 6ae2893777183f..5705b7a501cb86 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_sv.md index f7642a41d11171..bb3360f31b314b 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-24-entity_recognizer_sm_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_dl_en.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_dl_en.md index ce04a272f78d0b..373095bcd9ae01 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-explain_document_dl_en.md +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_dl_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline('explain_document_dl', lang = 'en') -annotations = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_dl", lang = "en") -val result = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = ["The Mona Lisa is an oil painting from the 16th century."] -result_df = nlu.load('en.explain.dl').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_es.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_es.md index 81358c6d035804..2ca4a444df90ba 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_es.md +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_es.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_no.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_no.md index 9702d232220434..927822969f8122 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_no.md +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_no.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_ru.md index 9fdae922e46e8a..64c7829a9140f0 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_ru.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_sv.md index 132f8cdbb495a2..147cc6720b1ea2 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_lg_sv.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_de.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_de.md index 3d4f4dc3ef291d..bf618f14deec66 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_de.md +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_de.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'de') -annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "de") -val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hallo aus John Snow Labs! ""] -result_df = nlu.load('de.explain.document').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_es.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_es.md index 0ce6e3cfe83274..0a4b97317d5dc5 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_es.md +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_fr.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_fr.md index a43059872d0343..a42c90e8fcb162 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_fr.md +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_fr.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'fr') -annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "fr") -val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Bonjour de John Snow Labs! ""] -result_df = nlu.load('fr.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_no.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_no.md index d0fc0624d92ee7..722979d39c9881 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_no.md +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_ru.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_ru.md index 761bb44e1825bd..d009a4bbde8940 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_sv.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_sv.md index 395727eadff20a..2e3085c84f1d20 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_md_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_ml_en.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_ml_en.md index c8c470caefcf24..4261fe36617030 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-explain_document_ml_en.md +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_ml_en.md @@ -34,33 +34,7 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline('explain_document_ml', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() -``` -```scala - - -val pipeline = new PretrainedPipeline("explain_document_ml", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.explain').predict(text) -result_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_es.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_es.md index edcce5d9a5a792..95b955ce51bcfb 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_es.md +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_no.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_no.md index 52a43cf866c269..6b300ca302d561 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_no.md +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_ru.md index 782cc68a2a9e61..b1c0f204de2528 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_sv.md index eb558461247667..04a0d72c777ffb 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-24-explain_document_sm_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-match_chunks_en.md b/docs/_posts/ahmedlone127/2023-05-24-match_chunks_en.md index bc4e550333f299..27edbcd4dcba6d 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-match_chunks_en.md +++ b/docs/_posts/ahmedlone127/2023-05-24-match_chunks_en.md @@ -32,41 +32,7 @@ The pipeline uses regex `
?/*+` ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline - -pipeline_local = PretrainedPipeline('match_chunks') - -result = pipeline_local.annotate("David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.") - -result['chunk'] -``` -```scala - -import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP - -SparkNLP.version() -val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") - -val pipeline = PretrainedPipeline("match_chunks", lang="en") - -val annotation = pipeline.transform(testData) - -annotation.show() -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.match.chunks").predict("""David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.""") -``` - -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-24-match_datetime_en.md b/docs/_posts/ahmedlone127/2023-05-24-match_datetime_en.md index 14a0f3958959fb..3d4f273132097c 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-match_datetime_en.md +++ b/docs/_posts/ahmedlone127/2023-05-24-match_datetime_en.md @@ -32,35 +32,7 @@ DateMatcher based on yyyy/MM/dd ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline_local = PretrainedPipeline('match_datetime') - -tres = pipeline_local.fullAnnotate(input_list)[0] -for dte in tres['date']: - sent = tres['sentence'][int(dte.metadata['sentence'])] - print (f'text/chunk {sent.result[dte.begin:dte.end+1]} | mapped_date: {dte.result}') -``` -```scala - -import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP -SparkNLP.version() - -val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. -He also visited and the day before, but at that time he was alone. -David again visited today with his colleagues. -He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") - -val pipeline = PretrainedPipeline("match_datetime", lang="en") - -val annotation = pipeline.transform(testData) - -annotation.show() -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-24-match_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-24-match_pattern_en.md index f6664a3ddd1cb7..05e23273179e6b 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-match_pattern_en.md +++ b/docs/_posts/ahmedlone127/2023-05-24-match_pattern_en.md @@ -33,16 +33,7 @@ The match_pattern is a pretrained pipeline that we can use to process text with ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models") - result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-05-24-movies_sentiment_analysis_en.md b/docs/_posts/ahmedlone127/2023-05-24-movies_sentiment_analysis_en.md index ffc88a8a21c0af..1e277044f1530a 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-movies_sentiment_analysis_en.md +++ b/docs/_posts/ahmedlone127/2023-05-24-movies_sentiment_analysis_en.md @@ -33,16 +33,7 @@ The movies_sentiment_analysis is a pretrained pipeline that we can use to proces ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("movies_sentiment_analysis", "en") -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-05-24-onto_recognize_entities_lg_en.md b/docs/_posts/ahmedlone127/2023-05-24-onto_recognize_entities_lg_en.md index c53073ddb80d2f..b2d21a19e91baa 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-onto_recognize_entities_lg_en.md +++ b/docs/_posts/ahmedlone127/2023-05-24-onto_recognize_entities_lg_en.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("onto_recognize_entities_lg", "en") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("en.ner.onto.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-24-onto_recognize_entities_sm_en.md b/docs/_posts/ahmedlone127/2023-05-24-onto_recognize_entities_sm_en.md index 61c9e4f510886b..250716e139ac16 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-onto_recognize_entities_sm_en.md +++ b/docs/_posts/ahmedlone127/2023-05-24-onto_recognize_entities_sm_en.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_sm', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_sm", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.sm').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-recognize_entities_bert_en.md b/docs/_posts/ahmedlone127/2023-05-24-recognize_entities_bert_en.md index b57803f6d35a4e..6b3629ad27c371 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-recognize_entities_bert_en.md +++ b/docs/_posts/ahmedlone127/2023-05-24-recognize_entities_bert_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('recognize_entities_bert', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("recognize_entities_bert", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.bert').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-recognize_entities_dl_en.md b/docs/_posts/ahmedlone127/2023-05-24-recognize_entities_dl_en.md index aad937ec1cdd60..2706b675fcf069 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-recognize_entities_dl_en.md +++ b/docs/_posts/ahmedlone127/2023-05-24-recognize_entities_dl_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-24-text_cleaning_en.md b/docs/_posts/ahmedlone127/2023-05-24-text_cleaning_en.md index df3850fb08eb8e..1fb49f48f29565 100644 --- a/docs/_posts/ahmedlone127/2023-05-24-text_cleaning_en.md +++ b/docs/_posts/ahmedlone127/2023-05-24-text_cleaning_en.md @@ -33,16 +33,7 @@ The text_cleaning is a pretrained pipeline that we can use to process text with ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("text_cleaning", "en") -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-05-25-albert_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-albert_base_token_classifier_conll03_pipeline_en.md index e2e1cc820d93d9..1a614340b5e4f1 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-albert_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-albert_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [albert_base_token_classifier_co ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-albert_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-albert_large_token_classifier_conll03_pipeline_en.md index 8e78af9a31809a..218612f0a2d072 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-albert_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-albert_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [albert_large_token_classifier_c ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-albert_xlarge_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-albert_xlarge_token_classifier_conll03_pipeline_en.md index 4ae7fcc69bac01..4a76a5ffdcb430 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-albert_xlarge_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-albert_xlarge_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [albert_xlarge_token_classifier_ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-analyze_sentiment_en.md b/docs/_posts/ahmedlone127/2023-05-25-analyze_sentiment_en.md index 8bdc4ac6e9d31a..0772d9c28e4f1c 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-analyze_sentiment_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-analyze_sentiment_en.md @@ -34,39 +34,7 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} - -```python - -from sparknlp.pretrained import PretrainedPipeline - -pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en') - -result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") - - -``` -```scala - -import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline -val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en") - -val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") - -``` - -{:.nlu-block} -```python - -import nlu -text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""] -result_df = nlu.load('en.classify').predict(text) -result_df - -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-analyze_sentimentdl_glove_imdb_en.md b/docs/_posts/ahmedlone127/2023-05-25-analyze_sentimentdl_glove_imdb_en.md index 2706b7b2ff19b3..161023da722d18 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-analyze_sentimentdl_glove_imdb_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-analyze_sentimentdl_glove_imdb_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("analyze_sentimentdl_glove_imdb", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.sentiment.glove').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-bert_sequence_classifier_age_news_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-bert_sequence_classifier_age_news_pipeline_en.md index e58bd487c8d55e..e261c7c911c9e2 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-bert_sequence_classifier_age_news_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-bert_sequence_classifier_age_news_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [bert_sequence_classifier_age_ne ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -news_pipeline = PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") - -news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") -``` -```scala - - -val news_pipeline = new PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") -news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-bert_sequence_classifier_trec_coarse_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-bert_sequence_classifier_trec_coarse_pipeline_en.md index a60580df832493..3bebd8be5a253a 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-bert_sequence_classifier_trec_coarse_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-bert_sequence_classifier_trec_coarse_pipeline_en.md @@ -34,23 +34,7 @@ The TREC dataset for question classification consists of open-domain, fact-based ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -trec_pipeline = PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") - -trec_pipeline.annotate("Germany is the largest country in Europe economically.") -``` -```scala - - -val trec_pipeline = new PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") -trec_pipeline.annotate("Germany is the largest country in Europe economically.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md b/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md index 334a754c8aadbe..0a5d83e6f0cc16 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md +++ b/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_dutch_udl ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") - -pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") -``` -```scala - -val pipeline = new PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") -pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_hi_en_ner_pipeline_hi.md b/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_hi_en_ner_pipeline_hi.md index cec6fc28e13e46..9658e818519183 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_hi_en_ner_pipeline_hi.md +++ b/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_hi_en_ner_pipeline_hi.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on [bert_token_classifier_hi_en_ner](https://n ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") - -pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") -``` -```scala - -val pipeline = new PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") -val pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_scandi_ner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_scandi_ner_pipeline_xx.md index 1d374a3449d63e..d62223e03a0a82 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_scandi_ner_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-25-bert_token_classifier_scandi_ner_pipeline_xx.md @@ -32,20 +32,7 @@ This pretrained pipeline is built on [bert_token_classifier_scandi_ner](https:// ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -scandiner_pipeline = PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") -scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") -``` -```scala - -val scandiner_pipeline = new PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") - -val scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-clean_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-25-clean_pattern_en.md index f8f32369a4eda7..3792288064faae 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-clean_pattern_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-clean_pattern_en.md @@ -34,33 +34,7 @@ The clean_pattern is a pretrained pipeline that we can use to process text with ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('clean_pattern', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() -``` -```scala - - -val pipeline = new PretrainedPipeline("clean_pattern", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.clean.pattern').predict(text) -result_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-clean_stop_en.md b/docs/_posts/ahmedlone127/2023-05-25-clean_stop_en.md index 0f2a945ce528cf..23a7966fc4f16d 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-clean_stop_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-clean_stop_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('clean_stop', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("clean_stop", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.clean.stop').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-distilbert_base_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-25-distilbert_base_token_classifier_masakhaner_pipeline_xx.md index 51bb9ddb9ce828..d48859e9a7b294 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-distilbert_base_token_classifier_masakhaner_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-25-distilbert_base_token_classifier_masakhaner_pipeline_xx.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [distilbert_base_token_classifie ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -masakhaner_pipeline = PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") - -masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") -``` -```scala - -val masakhaner_pipeline = new PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") -masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-distilbert_token_classifier_typo_detector_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-distilbert_token_classifier_typo_detector_pipeline_en.md index 16dcc217ef93ab..f99979eac55ad4 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-distilbert_token_classifier_typo_detector_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-distilbert_token_classifier_typo_detector_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [distilbert_token_classifier_typ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") - -typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") -``` -```scala - - -val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") -typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-distilbert_token_classifier_typo_detector_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-25-distilbert_token_classifier_typo_detector_pipeline_is.md index 02feb817995b9d..3efd91f556d73d 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-distilbert_token_classifier_typo_detector_pipeline_is.md +++ b/docs/_posts/ahmedlone127/2023-05-25-distilbert_token_classifier_typo_detector_pipeline_is.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [distilbert_token_classifier_typ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") - -typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") -``` -```scala - -val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") -typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-distilroberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-distilroberta_base_token_classifier_ontonotes_pipeline_en.md index 7d8c43b01b758d..f13b797bf40650 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-distilroberta_base_token_classifier_ontonotes_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-distilroberta_base_token_classifier_ontonotes_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [distilroberta_base_token_classi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") - -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -```scala - - -val pipeline = new PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_da.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_da.md index a914f9245e6972..59802a0bcbad8a 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_da.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_da.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "da") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("da.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_de.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_de.md index ac74fffbb10209..4f21140076ebaa 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_de.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_de.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'de') -annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "de") -val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hallo aus John Snow Labs! ""] -result_df = nlu.load('de.ner.recognizer.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_es.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_es.md index dcaf88d51af2a1..88a630747ad3cf 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_es.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_es.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "es") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("es.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fi.md index 013f16d4dbdb1c..eca405694552fd 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fi.md @@ -33,26 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "fi") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("fi.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fr.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fr.md index 9c4e73c2af609a..1be455c32ecb6b 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fr.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_fr.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'fr') -annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "fr") -val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Bonjour de John Snow Labs! ""] -result_df = nlu.load('fr.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_nl.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_nl.md index fd4314e0006bfc..c477ba006ebc41 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_nl.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_nl.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "nl") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("nl.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_no.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_no.md index 35fa6de66de6f0..7ee980b966fd63 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_no.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_no.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "no") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("no.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pl.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pl.md index 44db088ba2847b..7826ebab1b0bdf 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pl.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pl.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "pl") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("pl.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pt.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pt.md index 82b823e2afca0a..915503690ea2a8 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pt.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_pt.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "pt") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("pt.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_ru.md index a4c97678e65201..276a15804f031e 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_ru.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "ru") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("ru.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_sv.md index d7bb9221fbf93c..fe7caf70f6fc7f 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_lg_sv.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "sv") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("sv.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_da.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_da.md index 8aaa0181ed988d..cd0afbbbbae3cd 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_da.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_da.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_de.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_de.md index ad7bc5c4ca7a30..33c4398a889214 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_de.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_de.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'de') -annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "de") -val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hallo aus John Snow Labs! ""] -result_df = nlu.load('de.ner.recognizer').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_es.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_es.md index 83f9541829c9aa..6a72f30df6c8c9 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_es.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fi.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fi.md index fe05574fe2541a..29dbedc0154910 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fi.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fr.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fr.md index 3b4d8d57efb687..4970917de162da 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fr.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_fr.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fr') -annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fr") -val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Bonjour de John Snow Labs! ""] -result_df = nlu.load('fr.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_no.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_no.md index fd95d668ad2f48..eeaaafd113c28d 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_no.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_ru.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_ru.md index c05bbe85ae6a23..db13ad5878697a 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_sv.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_sv.md index 0d31ac7e45bec8..cfb85ccecf8843 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_md_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_da.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_da.md index b51c4758a0cf36..c5b677dd9af26a 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_da.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_da.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_es.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_es.md index d6b573223d9fb9..4c8bbeedc296cb 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_es.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_fi.md index b3a87b93f6edbd..3594c571a5ca26 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_fi.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_no.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_no.md index 123e862837174b..91c845f8ff6d25 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_no.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_ru.md index 0ed56858781ab1..bb4f4ec3b9367e 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_sv.md index 5d8cd94201d0f6..cf1c0d7f06c112 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-25-entity_recognizer_sm_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_dl_en.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_dl_en.md index 48ac092cf76157..789368cfc4405e 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_dl_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_dl_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline('explain_document_dl', lang = 'en') -annotations = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_dl", lang = "en") -val result = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = ["The Mona Lisa is an oil painting from the 16th century."] -result_df = nlu.load('en.explain.dl').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_da.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_da.md index 0ccf156f06db78..208b052cd92456 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_da.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_da.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_es.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_es.md index 60436844a502a7..beb2306b3c9768 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_es.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_es.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_fi.md index 159abee951a035..34b28453b2e8e7 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_fi.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_no.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_no.md index 43f95fdfc89ad0..220fe0f0c81668 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_no.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_no.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_ru.md index 4aef430d1f1da0..4b0702a4b41778 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_ru.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_sv.md index 61113b16764d0a..abce79b0904276 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_lg_sv.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_da.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_da.md index fbfeb598e42d42..f1dd80cebc9903 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_da.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_da.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_de.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_de.md index 61a671d663cc95..c756947dbfb475 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_de.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_de.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'de') -annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "de") -val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hallo aus John Snow Labs! ""] -result_df = nlu.load('de.explain.document').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_es.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_es.md index 750f307ec55d0e..571ddc9bc2892e 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_es.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fi.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fi.md index a5a183487a9fd5..539fba448f04cc 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fi.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fr.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fr.md index 4e3bd64d36f4b1..fb84245d482d7e 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fr.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_fr.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'fr') -annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "fr") -val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Bonjour de John Snow Labs! ""] -result_df = nlu.load('fr.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_no.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_no.md index ed848865a3f17e..96a44a3ed6efc0 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_no.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_ru.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_ru.md index 10741f307e1adf..f8f01b7898cf91 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_sv.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_sv.md index bd082756480915..02935afd61c137 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_md_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_ml_en.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_ml_en.md index ab3e62efb1a1d2..2090bf2deca223 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_ml_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_ml_en.md @@ -34,33 +34,7 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline('explain_document_ml', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() -``` -```scala - - -val pipeline = new PretrainedPipeline("explain_document_ml", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.explain').predict(text) -result_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_da.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_da.md index 3bdacc90f82667..1c1a6475d3d176 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_da.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_da.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_es.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_es.md index 38a2e76ff4e209..7372b1d7c3c882 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_es.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_fi.md index be1359fc083280..71c4c5ea468ed5 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_fi.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_no.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_no.md index 1e76fef347c49a..6a82aaa3758093 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_no.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_ru.md index 73af8d334fe53d..e48cc027987ec2 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_sv.md index 014650d0410a7b..9722bf8681fd0c 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-25-explain_document_sm_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-longformer_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-longformer_base_token_classifier_conll03_pipeline_en.md index 5aa26c0b319b5a..bcd2ee3b6fc21e 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-longformer_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-longformer_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [longformer_base_token_classifie ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-longformer_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-longformer_large_token_classifier_conll03_pipeline_en.md index 648159c17e83ac..64408b19d828db 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-longformer_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-longformer_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [longformer_large_token_classifi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-match_chunks_en.md b/docs/_posts/ahmedlone127/2023-05-25-match_chunks_en.md index 903012f4a225c2..3f9c07cc9200e3 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-match_chunks_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-match_chunks_en.md @@ -32,41 +32,7 @@ The pipeline uses regex `
?/*+` ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline - -pipeline_local = PretrainedPipeline('match_chunks') - -result = pipeline_local.annotate("David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.") - -result['chunk'] -``` -```scala - -import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP - -SparkNLP.version() -val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") - -val pipeline = PretrainedPipeline("match_chunks", lang="en") - -val annotation = pipeline.transform(testData) - -annotation.show() -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.match.chunks").predict("""David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.""") -``` - -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-match_datetime_en.md b/docs/_posts/ahmedlone127/2023-05-25-match_datetime_en.md index e5f01baffac33f..13303393ba9b17 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-match_datetime_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-match_datetime_en.md @@ -32,35 +32,7 @@ DateMatcher based on yyyy/MM/dd ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline_local = PretrainedPipeline('match_datetime') - -tres = pipeline_local.fullAnnotate(input_list)[0] -for dte in tres['date']: - sent = tres['sentence'][int(dte.metadata['sentence'])] - print (f'text/chunk {sent.result[dte.begin:dte.end+1]} | mapped_date: {dte.result}') -``` -```scala - -import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP -SparkNLP.version() - -val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. -He also visited and the day before, but at that time he was alone. -David again visited today with his colleagues. -He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") - -val pipeline = PretrainedPipeline("match_datetime", lang="en") - -val annotation = pipeline.transform(testData) - -annotation.show() -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-match_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-25-match_pattern_en.md index 67b720cb60cbc5..009754c8228acc 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-match_pattern_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-match_pattern_en.md @@ -33,16 +33,7 @@ The match_pattern is a pretrained pipeline that we can use to process text with ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models") - result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-05-25-movies_sentiment_analysis_en.md b/docs/_posts/ahmedlone127/2023-05-25-movies_sentiment_analysis_en.md index 1aa1e43fe345b0..d8980a4ca672bf 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-movies_sentiment_analysis_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-movies_sentiment_analysis_en.md @@ -33,16 +33,7 @@ The movies_sentiment_analysis is a pretrained pipeline that we can use to proces ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("movies_sentiment_analysis", "en") -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-05-25-nerdl_fewnerd_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-nerdl_fewnerd_100d_pipeline_en.md index 576e051d5bc96f..e42dce8104bd73 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-nerdl_fewnerd_100d_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-nerdl_fewnerd_100d_pipeline_en.md @@ -34,21 +34,7 @@ This pretrained pipeline is built on Few-NERD model and it detects : ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") - -fewnerd_pipeline.annotate("""The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).""") -``` -```scala - -val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") -val result = pipeline.fullAnnotate("The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).")(0) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-nerdl_fewnerd_subentity_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-nerdl_fewnerd_subentity_100d_pipeline_en.md index df56e314588370..3ca2a8bc9b489f 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-nerdl_fewnerd_subentity_100d_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-nerdl_fewnerd_subentity_100d_pipeline_en.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on Few-NERD/inter public dataset and it extrac ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") - -fewnerd_pipeline.annotate("""12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.""") -``` -```scala - -val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") -val result = pipeline.fullAnnotate("12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.")(0) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-nerdl_restaurant_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-nerdl_restaurant_100d_pipeline_en.md index 35b24d5bdc7092..83a1cb38617118 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-nerdl_restaurant_100d_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-nerdl_restaurant_100d_pipeline_en.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [nerdl_restaurant_100d](https:// ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -restaurant_pipeline = PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") - -restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") -``` -```scala - -val restaurant_pipeline = new PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") -restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_base_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_base_en.md index 4991fe05619320..0d6cece91ccb92 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_base_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_base_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_base', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_base", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.base').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_large_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_large_en.md index e34af1639fe2b7..c856a1a4aa5147 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_large_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_large_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_large', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_large", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.large').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_medium_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_medium_en.md index 5e5b33a34999f0..8493b81a06be01 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_medium_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_medium_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_medium', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_medium", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.medium').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_mini_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_mini_en.md index 18514bae287899..eeacbfa75c694a 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_mini_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_mini_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_mini', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_mini", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.mini').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_small_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_small_en.md index b6278456400e9e..585e1d24700bdd 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_small_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_small_en.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_small', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_small", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.small').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_tiny_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_tiny_en.md index 7efda03ad07cda..dac2005d116562 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_tiny_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_bert_tiny_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_tiny', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_tiny", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.tiny').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_base_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_base_en.md index b85ab4b633b442..905c5523afcb2d 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_base_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_base_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_electra_base', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_base", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.electra.base').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_large_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_large_en.md index 91b5c23ebbd365..13841dccd44d10 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_large_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_large_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline('onto_recognize_entities_electra_large', lang = 'en') -annotations = pipeline.fullAnnotate("Hello from John Snow Labs!")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_large", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.large').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_small_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_small_en.md index 8ccf11eed91884..e451c9d6991531 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_small_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_electra_small_en.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_electra_small', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_small", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.electra.small').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_lg_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_lg_en.md index 0425702cf6b0d8..9d6883feb15a08 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_lg_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_lg_en.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("onto_recognize_entities_lg", "en") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("en.ner.onto.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_sm_en.md b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_sm_en.md index 91b9700a46de0f..379c8723aba6d7 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_sm_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-onto_recognize_entities_sm_en.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_sm', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_sm", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.sm').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-pos_ud_bokmaal_nb.md b/docs/_posts/ahmedlone127/2023-05-25-pos_ud_bokmaal_nb.md index 29450208088378..229e3294938bda 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-pos_ud_bokmaal_nb.md +++ b/docs/_posts/ahmedlone127/2023-05-25-pos_ud_bokmaal_nb.md @@ -34,39 +34,7 @@ This model was trained using the dataset available at https://universaldependenc ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("pos") - -nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos]) -light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text"))) -results = light_pipeline.fullAnnotate("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.") -``` -```scala - -val pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") - .setInputCols(Array("document", "token")) - .setOutputCol("pos") - -val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos)) -val data = Seq("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.").toDF("text") -val result = pipeline.fit(data).transform(data) -``` - -{:.nlu-block} -```python - -import nlu -text = ["""Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene."""] -pos_df = nlu.load('nb.pos.ud_bokmaal').predict(text) -pos_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_bert_en.md b/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_bert_en.md index 404c38a15a40b7..d453db71ef8b29 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_bert_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_bert_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('recognize_entities_bert', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("recognize_entities_bert", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.bert').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_dl_en.md b/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_dl_en.md index 0c3dc5fcf95ca4..3af3b54ebd403b 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_dl_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_dl_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_dl_fa.md b/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_dl_fa.md index 67c967a63ba643..1b9faf990a29aa 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_dl_fa.md +++ b/docs/_posts/ahmedlone127/2023-05-25-recognize_entities_dl_fa.md @@ -32,37 +32,7 @@ The explain_document_dl is a pretrained pipeline that we can use to process text ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline - -pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'fa') - -annotations = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")[0] - -annotations.keys() -``` -```scala - -val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "fa") -val result = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")(0) -``` - -{:.nlu-block} -```python - -import nlu - -text = ["""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند"""] - -result_df = nlu.load('fa.recognize_entities_dl').predict(text) - -result_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-roberta_base_token_classifier_ontonotes_pipeline_en.md index 4d4c5e36796b92..2fb9ad5732ff43 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-roberta_base_token_classifier_ontonotes_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [roberta_base_token_classifier_o ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") - -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -```scala - -val pipeline = new PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-roberta_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-roberta_large_token_classifier_conll03_pipeline_en.md index 35380043fb7944..0931979b5d90ba 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-roberta_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-roberta_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [roberta_large_token_classifier_ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_bne_capitel_ner_pipeline_es.md b/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_bne_capitel_ner_pipeline_es.md index 26d479aef03036..f9ba55ec390af7 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_bne_capitel_ner_pipeline_es.md +++ b/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_bne_capitel_ner_pipeline_es.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [roberta_token_classifier_bne_ca ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") - -pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") -``` -```scala - -val pipeline = new PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") -pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_icelandic_ner_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_icelandic_ner_pipeline_is.md index fa8f705e0b014e..3b10c5444ac054 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_icelandic_ner_pipeline_is.md +++ b/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_icelandic_ner_pipeline_is.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [roberta_token_classifier_icelan ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") - -pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") -``` -```scala - -val pipeline = new PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") -pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_timex_semeval_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_timex_semeval_pipeline_en.md index 16f045eb439192..195f67e86bad6a 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_timex_semeval_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-roberta_token_classifier_timex_semeval_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [roberta_token_classifier_timex_ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -timex_pipeline = PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") - -timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") -``` -```scala - - -val timex_pipeline = new PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") -timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-spellcheck_dl_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-spellcheck_dl_pipeline_en.md index 16069df0f43f0c..223b7ef1633a55 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-spellcheck_dl_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-spellcheck_dl_pipeline_en.md @@ -32,27 +32,7 @@ This pretrained spellchecker pipeline is built on the top of [spellcheck_dl](htt ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") - -text = ["During the summer we have the best ueather.", "I have a black ueather jacket, so nice."] - -pipeline.annotate(text) -``` -```scala - - -val pipeline = new PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") -val example = Array("During the summer we have the best ueather.", "I have a black ueather jacket, so nice.") - -pipeline.annotate(example) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-text_cleaning_en.md b/docs/_posts/ahmedlone127/2023-05-25-text_cleaning_en.md index 52c450ba6f87c4..3401f567de7cb2 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-text_cleaning_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-text_cleaning_en.md @@ -33,16 +33,7 @@ The text_cleaning is a pretrained pipeline that we can use to process text with ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("text_cleaning", "en") -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_base_token_classifier_conll03_pipeline_en.md index 9de2e5ceda88cc..48b82243926956 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md index 3b4f28b8401058..d2753ee87ba850 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") - -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_conll03_pipeline_de.md b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_conll03_pipeline_de.md index a66592362aefc1..7d873291073ad6 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_conll03_pipeline_de.md +++ b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_conll03_pipeline_de.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_large_token_classif ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") - -pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") -``` -```scala - -val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") -pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md index 0644860ec4af4f..702ff0c9c5e3b3 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_large_token_classif ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") - -pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") -``` -```scala - -val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") -pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md index ef6310376136df..0213c1cab9a5bc 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-25-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on [xlm_roberta_large_token_classifier_masakha ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -masakhaner_pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") - -masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") -``` -```scala - -val masakhaner_pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") -val masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-xlnet_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-xlnet_base_token_classifier_conll03_pipeline_en.md index ab857ce93611fa..e251dfcc9cedc1 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-xlnet_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-xlnet_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlnet_base_token_classifier_con ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-25-xlnet_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-25-xlnet_large_token_classifier_conll03_pipeline_en.md index a7692a3b4a989f..8b1c8aaf7d1e88 100644 --- a/docs/_posts/ahmedlone127/2023-05-25-xlnet_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-25-xlnet_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlnet_large_token_classifier_co ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-albert_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-albert_base_token_classifier_conll03_pipeline_en.md index 78c671f91f89da..ddfc68a5bf7462 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-albert_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-albert_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [albert_base_token_classifier_co ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-albert_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-albert_large_token_classifier_conll03_pipeline_en.md index 4f4443a9414ff3..55a46993bb0838 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-albert_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-albert_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [albert_large_token_classifier_c ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-albert_xlarge_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-albert_xlarge_token_classifier_conll03_pipeline_en.md index 1cfd1f3b1eea22..55722ec014ba04 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-albert_xlarge_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-albert_xlarge_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [albert_xlarge_token_classifier_ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_age_news_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_age_news_pipeline_en.md index 7f4939e8c4dcf9..3bb66c51cb8d31 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_age_news_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_age_news_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [bert_sequence_classifier_age_ne ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -news_pipeline = PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") - -news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") -``` -```scala - - -val news_pipeline = new PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") -news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_trec_coarse_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_trec_coarse_pipeline_en.md index 3990534aee8c8b..ac200ce8945f6d 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_trec_coarse_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-bert_sequence_classifier_trec_coarse_pipeline_en.md @@ -34,23 +34,7 @@ The TREC dataset for question classification consists of open-domain, fact-based ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -trec_pipeline = PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") - -trec_pipeline.annotate("Germany is the largest country in Europe economically.") -``` -```scala - - -val trec_pipeline = new PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") -trec_pipeline.annotate("Germany is the largest country in Europe economically.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md b/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md index 76147872e1abee..4aafb4c2c2d37c 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md +++ b/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_dutch_udl ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") - -pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") -``` -```scala - -val pipeline = new PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") -pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_hi_en_ner_pipeline_hi.md b/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_hi_en_ner_pipeline_hi.md index bc5223db30914c..e8540e8ce3e798 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_hi_en_ner_pipeline_hi.md +++ b/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_hi_en_ner_pipeline_hi.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on [bert_token_classifier_hi_en_ner](https://n ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") - -pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") -``` -```scala - -val pipeline = new PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") -val pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_scandi_ner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_scandi_ner_pipeline_xx.md index e2f6ac52e6214b..4ed75b1a684b13 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_scandi_ner_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-26-bert_token_classifier_scandi_ner_pipeline_xx.md @@ -32,20 +32,7 @@ This pretrained pipeline is built on [bert_token_classifier_scandi_ner](https:// ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -scandiner_pipeline = PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") -scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") -``` -```scala - -val scandiner_pipeline = new PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") - -val scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-distilbert_base_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-26-distilbert_base_token_classifier_masakhaner_pipeline_xx.md index 9fb584b272e041..3ffabc3016f8dc 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-distilbert_base_token_classifier_masakhaner_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-26-distilbert_base_token_classifier_masakhaner_pipeline_xx.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [distilbert_base_token_classifie ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -masakhaner_pipeline = PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") - -masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") -``` -```scala - -val masakhaner_pipeline = new PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") -masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_en.md index 2892cf85cbea2a..fc6aec47a4ad75 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [distilbert_token_classifier_typ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") - -typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") -``` -```scala - - -val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") -typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_is.md index 050826db384303..adf0267d9e2d75 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_is.md +++ b/docs/_posts/ahmedlone127/2023-05-26-distilbert_token_classifier_typo_detector_pipeline_is.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [distilbert_token_classifier_typ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") - -typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") -``` -```scala - -val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") -typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-distilroberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-distilroberta_base_token_classifier_ontonotes_pipeline_en.md index 61a39c2b402af3..74cfd4179e7120 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-distilroberta_base_token_classifier_ontonotes_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-distilroberta_base_token_classifier_ontonotes_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [distilroberta_base_token_classi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") - -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -```scala - - -val pipeline = new PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-longformer_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-longformer_base_token_classifier_conll03_pipeline_en.md index 8d3a12f5bdae5a..09757e0d6e4895 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-longformer_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-longformer_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [longformer_base_token_classifie ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-longformer_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-longformer_large_token_classifier_conll03_pipeline_en.md index bb275a83e66ad3..52d9049bd4d0f4 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-longformer_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-longformer_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [longformer_large_token_classifi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_100d_pipeline_en.md index 27eca880193ab7..320fdaa9dd75ef 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_100d_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_100d_pipeline_en.md @@ -34,21 +34,7 @@ This pretrained pipeline is built on Few-NERD model and it detects : ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") - -fewnerd_pipeline.annotate("""The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).""") -``` -```scala - -val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") -val result = pipeline.fullAnnotate("The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).")(0) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_subentity_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_subentity_100d_pipeline_en.md index 5275f8a0281ee1..e3428ef238824c 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_subentity_100d_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-nerdl_fewnerd_subentity_100d_pipeline_en.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on Few-NERD/inter public dataset and it extrac ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") - -fewnerd_pipeline.annotate("""12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.""") -``` -```scala - -val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") -val result = pipeline.fullAnnotate("12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.")(0) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-nerdl_restaurant_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-nerdl_restaurant_100d_pipeline_en.md index 1cd9bd857b7dcb..46af091aebde31 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-nerdl_restaurant_100d_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-nerdl_restaurant_100d_pipeline_en.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [nerdl_restaurant_100d](https:// ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -restaurant_pipeline = PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") - -restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") -``` -```scala - -val restaurant_pipeline = new PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") -restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-pos_ud_bokmaal_nb.md b/docs/_posts/ahmedlone127/2023-05-26-pos_ud_bokmaal_nb.md index 5fef7a9e1b2912..c2702092f5b1d9 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-pos_ud_bokmaal_nb.md +++ b/docs/_posts/ahmedlone127/2023-05-26-pos_ud_bokmaal_nb.md @@ -34,39 +34,7 @@ This model was trained using the dataset available at https://universaldependenc ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("pos") - -nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos]) -light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text"))) -results = light_pipeline.fullAnnotate("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.") -``` -```scala - -val pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") - .setInputCols(Array("document", "token")) - .setOutputCol("pos") - -val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos)) -val data = Seq("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.").toDF("text") -val result = pipeline.fit(data).transform(data) -``` - -{:.nlu-block} -```python - -import nlu -text = ["""Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene."""] -pos_df = nlu.load('nb.pos.ud_bokmaal').predict(text) -pos_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-recognize_entities_dl_fa.md b/docs/_posts/ahmedlone127/2023-05-26-recognize_entities_dl_fa.md index f3050a29f18a85..b1adad1961fabb 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-recognize_entities_dl_fa.md +++ b/docs/_posts/ahmedlone127/2023-05-26-recognize_entities_dl_fa.md @@ -32,37 +32,7 @@ The explain_document_dl is a pretrained pipeline that we can use to process text ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline - -pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'fa') - -annotations = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")[0] - -annotations.keys() -``` -```scala - -val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "fa") -val result = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")(0) -``` - -{:.nlu-block} -```python - -import nlu - -text = ["""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند"""] - -result_df = nlu.load('fa.recognize_entities_dl').predict(text) - -result_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-roberta_base_token_classifier_ontonotes_pipeline_en.md index 52cb3c799c48dc..f654b8b3289b95 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-roberta_base_token_classifier_ontonotes_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [roberta_base_token_classifier_o ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") - -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -```scala - -val pipeline = new PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-roberta_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-roberta_large_token_classifier_conll03_pipeline_en.md index 617edc160336e2..790fb6ffaafeb9 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-roberta_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-roberta_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [roberta_large_token_classifier_ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_bne_capitel_ner_pipeline_es.md b/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_bne_capitel_ner_pipeline_es.md index 8ac615bb296f92..b980cbc5fabf4d 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_bne_capitel_ner_pipeline_es.md +++ b/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_bne_capitel_ner_pipeline_es.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [roberta_token_classifier_bne_ca ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") - -pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") -``` -```scala - -val pipeline = new PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") -pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_icelandic_ner_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_icelandic_ner_pipeline_is.md index b128a453a67fd2..c618f3be9344c5 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_icelandic_ner_pipeline_is.md +++ b/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_icelandic_ner_pipeline_is.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [roberta_token_classifier_icelan ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") - -pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") -``` -```scala - -val pipeline = new PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") -pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_timex_semeval_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_timex_semeval_pipeline_en.md index 973b35a190fd97..1d2b29cf0a8ef6 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_timex_semeval_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-roberta_token_classifier_timex_semeval_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [roberta_token_classifier_timex_ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -timex_pipeline = PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") - -timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") -``` -```scala - - -val timex_pipeline = new PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") -timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-spellcheck_dl_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-spellcheck_dl_pipeline_en.md index fa564958af1b5c..4ebd7962c79198 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-spellcheck_dl_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-spellcheck_dl_pipeline_en.md @@ -32,27 +32,7 @@ This pretrained spellchecker pipeline is built on the top of [spellcheck_dl](htt ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") - -text = ["During the summer we have the best ueather.", "I have a black ueather jacket, so nice."] - -pipeline.annotate(text) -``` -```scala - - -val pipeline = new PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") -val example = Array("During the summer we have the best ueather.", "I have a black ueather jacket, so nice.") - -pipeline.annotate(example) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_conll03_pipeline_en.md index 83c7e0e7dc12cd..93bb842ac0a793 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md index bfc143f8889183..5d034ba613c997 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") - -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_conll03_pipeline_de.md b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_conll03_pipeline_de.md index d7b6d8ec978ee5..fad52217696689 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_conll03_pipeline_de.md +++ b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_conll03_pipeline_de.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_large_token_classif ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") - -pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") -``` -```scala - -val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") -pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md index 32a4f08022b69b..ac1207126da6e2 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_large_token_classif ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") - -pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") -``` -```scala - -val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") -pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md index effb202a3b61c5..dd99100e318fea 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-26-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on [xlm_roberta_large_token_classifier_masakha ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -masakhaner_pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") - -masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") -``` -```scala - -val masakhaner_pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") -val masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlnet_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-xlnet_base_token_classifier_conll03_pipeline_en.md index aa40cf3ff9bbcc..6df7c6058a36e6 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-xlnet_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-xlnet_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlnet_base_token_classifier_con ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-26-xlnet_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-26-xlnet_large_token_classifier_conll03_pipeline_en.md index ed3e5055d40756..fdef44b9b80f33 100644 --- a/docs/_posts/ahmedlone127/2023-05-26-xlnet_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-26-xlnet_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlnet_large_token_classifier_co ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-albert_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-albert_base_token_classifier_conll03_pipeline_en.md index d2d6e700c449be..27cbd61e327472 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-albert_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-albert_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [albert_base_token_classifier_co ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("albert_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-albert_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-albert_large_token_classifier_conll03_pipeline_en.md index 308cd3d0f45017..6588c8f77410ad 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-albert_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-albert_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [albert_large_token_classifier_c ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("albert_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-albert_xlarge_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-albert_xlarge_token_classifier_conll03_pipeline_en.md index 9704b0293fda63..1582fc0e45f004 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-albert_xlarge_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-albert_xlarge_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [albert_xlarge_token_classifier_ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("albert_xlarge_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-analyze_sentiment_en.md b/docs/_posts/ahmedlone127/2023-05-27-analyze_sentiment_en.md index 15ae7376dcae25..8c2791c7de3f2e 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-analyze_sentiment_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-analyze_sentiment_en.md @@ -34,39 +34,7 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} - -```python - -from sparknlp.pretrained import PretrainedPipeline - -pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en') - -result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") - - -``` -```scala - -import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline -val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en") - -val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""") - -``` - -{:.nlu-block} -```python - -import nlu -text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""] -result_df = nlu.load('en.classify').predict(text) -result_df - -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-analyze_sentimentdl_glove_imdb_en.md b/docs/_posts/ahmedlone127/2023-05-27-analyze_sentimentdl_glove_imdb_en.md index 13ee9647ab1e2b..c47d167794977a 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-analyze_sentimentdl_glove_imdb_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-analyze_sentimentdl_glove_imdb_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("analyze_sentimentdl_glove_imdb", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.sentiment.glove').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-bert_sequence_classifier_age_news_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-bert_sequence_classifier_age_news_pipeline_en.md index 27a4701e743654..cbf4fe98bc05d6 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-bert_sequence_classifier_age_news_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-bert_sequence_classifier_age_news_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [bert_sequence_classifier_age_ne ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -news_pipeline = PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") - -news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") -``` -```scala - - -val news_pipeline = new PretrainedPipeline("bert_sequence_classifier_age_news_pipeline", lang = "en") -news_pipeline.annotate("Microsoft has taken its first step into the metaverse.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-bert_sequence_classifier_trec_coarse_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-bert_sequence_classifier_trec_coarse_pipeline_en.md index 58700391844037..317830b7d4660d 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-bert_sequence_classifier_trec_coarse_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-bert_sequence_classifier_trec_coarse_pipeline_en.md @@ -34,23 +34,7 @@ The TREC dataset for question classification consists of open-domain, fact-based ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -trec_pipeline = PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") - -trec_pipeline.annotate("Germany is the largest country in Europe economically.") -``` -```scala - - -val trec_pipeline = new PretrainedPipeline("bert_sequence_classifier_trec_coarse_pipeline", lang = "en") -trec_pipeline.annotate("Germany is the largest country in Europe economically.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md b/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md index 8a0322af18feb8..b8ec408c8f60d0 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md +++ b/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_dutch_udlassy_ner_pipeline_nl.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_dutch_udl ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") - -pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") -``` -```scala - -val pipeline = new PretrainedPipeline("bert_token_classifier_dutch_udlassy_ner_pipeline", lang = "nl") -pipeline.annotate("Mijn naam is Peter Fergusson. Ik woon sinds oktober 2011 in New York en werk 5 jaar bij Tesla Motor.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_hi_en_ner_pipeline_hi.md b/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_hi_en_ner_pipeline_hi.md index 96a1e85db5fcfb..c82fc583dadf1e 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_hi_en_ner_pipeline_hi.md +++ b/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_hi_en_ner_pipeline_hi.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on [bert_token_classifier_hi_en_ner](https://n ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") - -pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") -``` -```scala - -val pipeline = new PretrainedPipeline("bert_token_classifier_hi_en_ner_pipeline", lang = "hi") -val pipeline.annotate("रिलायंस इंडस्ट्रीज़ लिमिटेड (Reliance Industries Limited) एक भारतीय संगुटिका नियंत्रक कंपनी है, जिसका मुख्यालय मुंबई, महाराष्ट्र (Maharashtra) में स्थित है।रतन नवल टाटा (28 दिसंबर 1937, को मुम्बई (Mumbai), में जन्मे) टाटा समुह के वर्तमान अध्यक्ष, जो भारत की सबसे बड़ी व्यापारिक समूह है, जिसकी स्थापना जमशेदजी टाटा ने की और उनके परिवार की पीढियों ने इसका विस्तार किया और इसे दृढ़ बनाया।") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_scandi_ner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_scandi_ner_pipeline_xx.md index 29eb984bfbc288..cf7eba7ac820eb 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_scandi_ner_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-27-bert_token_classifier_scandi_ner_pipeline_xx.md @@ -32,20 +32,7 @@ This pretrained pipeline is built on [bert_token_classifier_scandi_ner](https:// ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -scandiner_pipeline = PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") -scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") -``` -```scala - -val scandiner_pipeline = new PretrainedPipeline("bert_token_classifier_scandi_ner_pipeline", lang = "xx") - -val scandiner_pipeline.annotate("Hans er professor ved Statens Universitet, som ligger i København, og han er en rigtig københavner.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-clean_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-27-clean_pattern_en.md index c25863d88ce587..faa2cdd4393fbe 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-clean_pattern_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-clean_pattern_en.md @@ -34,33 +34,7 @@ The clean_pattern is a pretrained pipeline that we can use to process text with ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('clean_pattern', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() -``` -```scala - - -val pipeline = new PretrainedPipeline("clean_pattern", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.clean.pattern').predict(text) -result_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-clean_stop_en.md b/docs/_posts/ahmedlone127/2023-05-27-clean_stop_en.md index f394d0121d9c0c..be4e00ba485582 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-clean_stop_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-clean_stop_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('clean_stop', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("clean_stop", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.clean.stop').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-distilbert_base_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-27-distilbert_base_token_classifier_masakhaner_pipeline_xx.md index 1ae8f9c38f5ea2..4d45aaeb23d020 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-distilbert_base_token_classifier_masakhaner_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-27-distilbert_base_token_classifier_masakhaner_pipeline_xx.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [distilbert_base_token_classifie ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -masakhaner_pipeline = PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") - -masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") -``` -```scala - -val masakhaner_pipeline = new PretrainedPipeline("distilbert_base_token_classifier_masakhaner_pipeline", lang = "xx") -masakhaner_pipeline.annotate("Ilé-iṣẹ́ẹ Mohammed Sani Musa, Activate Technologies Limited, ni ó kó ẹ̀rọ Ìwé-pélébé Ìdìbò Alálòpẹ́ (PVCs) tí a lò fún ìbò ọdún-un 2019, ígbà tí ó jẹ́ òǹdíjedupò lábẹ́ ẹgbẹ́ olóṣèlúu tí ó ń tukọ̀ ètò ìṣèlú lọ́wọ́ All rogressives Congress (APC) fún Aṣojú Ìlà-Oòrùn Niger, ìyẹn gẹ́gẹ́ bí ilé iṣẹ́ aṣèwádìí, Premium Times ṣe tẹ̀ ẹ́ jáde.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-distilbert_token_classifier_typo_detector_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-distilbert_token_classifier_typo_detector_pipeline_en.md index 43ac09fbb7b9ac..2097fe15c0236e 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-distilbert_token_classifier_typo_detector_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-distilbert_token_classifier_typo_detector_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [distilbert_token_classifier_typ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") - -typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") -``` -```scala - - -val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "en") -typo_pipeline.annotate("He had also stgruggled with addiction during his tine in Congress.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-distilbert_token_classifier_typo_detector_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-27-distilbert_token_classifier_typo_detector_pipeline_is.md index 6569533a9d3a6c..0f1c5f0b0a103f 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-distilbert_token_classifier_typo_detector_pipeline_is.md +++ b/docs/_posts/ahmedlone127/2023-05-27-distilbert_token_classifier_typo_detector_pipeline_is.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [distilbert_token_classifier_typ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -typo_pipeline = PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") - -typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") -``` -```scala - -val typo_pipeline = new PretrainedPipeline("distilbert_token_classifier_typo_detector_pipeline", lang = "is") -typo_pipeline.annotate("Það er miög auðvelt að draga marktækar álykanir af texta með Spark NLP.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-distilroberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-distilroberta_base_token_classifier_ontonotes_pipeline_en.md index ccb91b9f89fa58..8c6400f5c9f987 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-distilroberta_base_token_classifier_ontonotes_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-distilroberta_base_token_classifier_ontonotes_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [distilroberta_base_token_classi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") - -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -```scala - - -val pipeline = new PretrainedPipeline("distilroberta_base_token_classifier_ontonotes_pipeline", lang = "en") -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_da.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_da.md index 956306f8d60845..8f0cc8a8514e30 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_da.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_da.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "da") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("da.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_de.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_de.md index 57ae48b9514af0..2a25c7bdc49a6c 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_de.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_de.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'de') -annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "de") -val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hallo aus John Snow Labs! ""] -result_df = nlu.load('de.ner.recognizer.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_es.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_es.md index 3601a98f89ce0e..5692e0385f162e 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_es.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_es.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "es") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("es.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_fi.md index 74e7136b73807f..50b060e15af47d 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_fi.md @@ -33,26 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "fi") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("fi.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_fr.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_fr.md index 9151aaed57cb90..ab6affb487c85c 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_fr.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_fr.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'fr') -annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "fr") -val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Bonjour de John Snow Labs! ""] -result_df = nlu.load('fr.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_nl.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_nl.md index 621011ca394ae5..27d4fee565fe35 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_nl.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_nl.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "nl") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("nl.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_no.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_no.md index d40cd9f578794e..0b3d24883f9409 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_no.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_no.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "no") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("no.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_pl.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_pl.md index f0043de69ccb63..edcd3f6946f0c2 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_pl.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_pl.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "pl") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("pl.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_pt.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_pt.md index 9a2d737cb0e557..52aa85f4e274a4 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_pt.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_pt.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "pt") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("pt.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_ru.md index 7822530b0cbb2f..3f5763c45c8baa 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_ru.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "ru") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("ru.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_sv.md index c2f3168eccac11..199edf2e70fdc5 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_lg_sv.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("entity_recognizer_lg", "sv") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("sv.ner.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_da.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_da.md index 28dffc056a2ee6..78ac4deae94938 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_da.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_da.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_de.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_de.md index 8e0f9e44a29a12..1b93791f84bf57 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_de.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_de.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'de') -annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "de") -val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hallo aus John Snow Labs! ""] -result_df = nlu.load('de.ner.recognizer').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_es.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_es.md index bd33f278f598ac..aa2374f1e48061 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_es.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_fi.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_fi.md index 3f58127152c001..1d7e86af54590c 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_fi.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_fr.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_fr.md index d1690b75b4d2da..34984b9b6598ea 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_fr.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_fr.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fr') -annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fr") -val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Bonjour de John Snow Labs! ""] -result_df = nlu.load('fr.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_no.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_no.md index ff202edb8a36ad..c691a9d49de457 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_no.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_ru.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_ru.md index d3430b136bf947..ee5c1900bdc359 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_sv.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_sv.md index 9d062a431575dd..ad9918b5d0bdb4 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_md_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.ner.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_da.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_da.md index 139ac16d41ff8a..c2b37b2ba2ef66 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_da.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_da.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_es.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_es.md index 93b1542ce8ee38..de62befcc5c4f5 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_es.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_fi.md index 9c21b117ae969f..137e13cb56dbb2 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_fi.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_no.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_no.md index 51924c114e80a3..f7385a54878a98 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_no.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_ru.md index 287938422ba350..cee0376d928155 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_sv.md index 2541ef1cbada3b..13dff453d3e08e 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-27-entity_recognizer_sm_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_dl_en.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_dl_en.md index 84215d71bd76ca..b34e8580ac773d 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_dl_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_dl_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline('explain_document_dl', lang = 'en') -annotations = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_dl", lang = "en") -val result = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = ["The Mona Lisa is an oil painting from the 16th century."] -result_df = nlu.load('en.explain.dl').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_da.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_da.md index 17117261cdc03a..47ea232d475fcb 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_da.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_da.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_es.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_es.md index d5356eb6525bed..c0fe24d9346c42 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_es.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_es.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_fi.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_fi.md index 36c29d4e22735f..42025dd9750126 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_fi.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_no.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_no.md index e1d8b9650bb7c2..67f28b68d529be 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_no.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_no.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_ru.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_ru.md index af9a8fcbf5b825..41a9534bd16dfb 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_ru.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_sv.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_sv.md index deb88f991be635..a3da39a918bdd7 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_lg_sv.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_lg', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_lg", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.explain.lg').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_da.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_da.md index ad7874fdb22dd4..cc59a8b5111673 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_da.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_da.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_de.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_de.md index 1c44491294c471..4519c12aa36e03 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_de.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_de.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'de') -annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "de") -val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hallo aus John Snow Labs! ""] -result_df = nlu.load('de.explain.document').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_es.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_es.md index 7a9a3bf643f297..59e2760ef0fb8a 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_es.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_fi.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_fi.md index 653cb4d41f77a0..693a8c1aa166a9 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_fi.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_fr.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_fr.md index 9548b616c40105..0169d22c6dc4ee 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_fr.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_fr.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'fr') -annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "fr") -val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Bonjour de John Snow Labs! ""] -result_df = nlu.load('fr.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_no.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_no.md index b76bcabec18089..17c43f65e36b02 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_no.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_ru.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_ru.md index 1282459dd46571..94da63baca08bc 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_sv.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_sv.md index 6609ffedcfe7d7..a145772faf3f4b 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_md_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_md', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_md", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.explain.md').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_ml_en.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_ml_en.md index 1f90a751788864..b5a883c462adae 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_ml_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_ml_en.md @@ -34,33 +34,7 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline('explain_document_ml', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() -``` -```scala - - -val pipeline = new PretrainedPipeline("explain_document_ml", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.explain').predict(text) -result_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_da.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_da.md index 6a2168b7edb2fe..ca08330f4f8c9a 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_da.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_da.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'da') -annotations = pipeline.fullAnnotate(""Hej fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "da") -val result = pipeline.fullAnnotate("Hej fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej fra John Snow Labs! ""] -result_df = nlu.load('da.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_es.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_es.md index 88f723a3345c1f..da8ab6fd599145 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_es.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_es.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'es') -annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "es") -val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hola de John Snow Labs! ""] -result_df = nlu.load('es.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_fi.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_fi.md index ba3d50e3fec81d..d2b5db8a9e5ba3 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_fi.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_fi.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'fi') -annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "fi") -val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei John Snow Labs! ""] -result_df = nlu.load('fi.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_no.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_no.md index 6055beeb1282d2..43fd4bb3243d3d 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_no.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_no.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'no') -annotations = pipeline.fullAnnotate(""Hei fra John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "no") -val result = pipeline.fullAnnotate("Hei fra John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hei fra John Snow Labs! ""] -result_df = nlu.load('no.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_ru.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_ru.md index 687caeeaf5254e..17873b08924355 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_ru.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_ru.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'ru') -annotations = pipeline.fullAnnotate(""Здравствуйте из Джона Снежных Лабораторий! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "ru") -val result = pipeline.fullAnnotate("Здравствуйте из Джона Снежных Лабораторий! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Здравствуйте из Джона Снежных Лабораторий! ""] -result_df = nlu.load('ru.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_sv.md b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_sv.md index a4f21b19d3ecc0..2d9e9637174903 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_sv.md +++ b/docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_sv.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('explain_document_sm', lang = 'sv') -annotations = pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("explain_document_sm", lang = "sv") -val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hej från John Snow Labs! ""] -result_df = nlu.load('sv.explain').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-longformer_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-longformer_base_token_classifier_conll03_pipeline_en.md index de9bbef58143e4..ae8e23d378bff7 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-longformer_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-longformer_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [longformer_base_token_classifie ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("longformer_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-longformer_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-longformer_large_token_classifier_conll03_pipeline_en.md index 15162e39f24930..a3b56517b099c3 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-longformer_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-longformer_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [longformer_large_token_classifi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("longformer_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I am working at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-match_chunks_en.md b/docs/_posts/ahmedlone127/2023-05-27-match_chunks_en.md index 9c488db1353a41..9898486d6d0dca 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-match_chunks_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-match_chunks_en.md @@ -32,41 +32,7 @@ The pipeline uses regex `
?/*+` ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline - -pipeline_local = PretrainedPipeline('match_chunks') - -result = pipeline_local.annotate("David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.") - -result['chunk'] -``` -```scala - -import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP - -SparkNLP.version() -val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") - -val pipeline = PretrainedPipeline("match_chunks", lang="en") - -val annotation = pipeline.transform(testData) - -annotation.show() -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.match.chunks").predict("""David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.""") -``` - -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-match_datetime_en.md b/docs/_posts/ahmedlone127/2023-05-27-match_datetime_en.md index 1cc522ddabbc6e..f849fe616613d8 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-match_datetime_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-match_datetime_en.md @@ -32,35 +32,7 @@ DateMatcher based on yyyy/MM/dd ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline_local = PretrainedPipeline('match_datetime') - -tres = pipeline_local.fullAnnotate(input_list)[0] -for dte in tres['date']: - sent = tres['sentence'][int(dte.metadata['sentence'])] - print (f'text/chunk {sent.result[dte.begin:dte.end+1]} | mapped_date: {dte.result}') -``` -```scala - -import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP -SparkNLP.version() - -val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. -He also visited and the day before, but at that time he was alone. -David again visited today with his colleagues. -He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text") - -val pipeline = PretrainedPipeline("match_datetime", lang="en") - -val annotation = pipeline.transform(testData) - -annotation.show() -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-match_pattern_en.md b/docs/_posts/ahmedlone127/2023-05-27-match_pattern_en.md index c2af72d8b6e50e..b6e6389029aab9 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-match_pattern_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-match_pattern_en.md @@ -33,16 +33,7 @@ The match_pattern is a pretrained pipeline that we can use to process text with ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models") - result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-05-27-movies_sentiment_analysis_en.md b/docs/_posts/ahmedlone127/2023-05-27-movies_sentiment_analysis_en.md index cfb98943b61a57..5a4407bc3963c1 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-movies_sentiment_analysis_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-movies_sentiment_analysis_en.md @@ -33,16 +33,7 @@ The movies_sentiment_analysis is a pretrained pipeline that we can use to proces ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("movies_sentiment_analysis", "en") -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-05-27-nerdl_fewnerd_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-nerdl_fewnerd_100d_pipeline_en.md index 56e37a064f9508..70187c7950b4ec 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-nerdl_fewnerd_100d_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-nerdl_fewnerd_100d_pipeline_en.md @@ -34,21 +34,7 @@ This pretrained pipeline is built on Few-NERD model and it detects : ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") - -fewnerd_pipeline.annotate("""The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).""") -``` -```scala - -val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") -val result = pipeline.fullAnnotate("The Double Down is a sandwich offered by Kentucky Fried Chicken restaurants. He did not see active service again until 1882, when he took part in the Anglo-Egyptian War, and was present at the battle of Tell El Kebir (September 1882), for which he was mentioned in dispatches, received the Egypt Medal with clasp and the 3rd class of the Order of Medjidie, and was appointed a Companion of the Order of the Bath (CB).")(0) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-nerdl_fewnerd_subentity_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-nerdl_fewnerd_subentity_100d_pipeline_en.md index 71dbb3a9287c50..01bfc499f61406 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-nerdl_fewnerd_subentity_100d_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-nerdl_fewnerd_subentity_100d_pipeline_en.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on Few-NERD/inter public dataset and it extrac ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -fewnerd_pipeline = PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") - -fewnerd_pipeline.annotate("""12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.""") -``` -```scala - -val pipeline = new PretrainedPipeline("nerdl_fewnerd_subentity_100d_pipeline", lang = "en") -val result = pipeline.fullAnnotate("12 Corazones ('12 Hearts') is Spanish-language dating game show produced in the United States for the television network Telemundo since January 2005, based on its namesake Argentine TV show format. The show is filmed in Los Angeles and revolves around the twelve Zodiac signs that identify each contestant. In 2008, Ho filmed a cameo in the Steven Spielberg feature film The Cloverfield Paradox, as a news pundit.")(0) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-nerdl_restaurant_100d_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-nerdl_restaurant_100d_pipeline_en.md index 5c3d1ce0babc1b..5725d27c63b1c4 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-nerdl_restaurant_100d_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-nerdl_restaurant_100d_pipeline_en.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [nerdl_restaurant_100d](https:// ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -restaurant_pipeline = PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") - -restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") -``` -```scala - -val restaurant_pipeline = new PretrainedPipeline("nerdl_restaurant_100d_pipeline", lang = "en") -restaurant_pipeline.annotate("Hong Kong’s favourite pasta bar also offers one of the most reasonably priced lunch sets in town! With locations spread out all over the territory Sha Tin – Pici’s formidable lunch menu reads like a highlight reel of the restaurant. Choose from starters like the burrata and arugula salad or freshly tossed tuna tartare, and reliable handmade pasta dishes like pappardelle. Finally, round out your effortless Italian meal with a tidy one-pot tiramisu, of course, an espresso to power you through the rest of the day.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_base_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_base_en.md index 832d938cfe2787..56156639399c3e 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_base_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_base_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_base', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_base", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.base').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_large_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_large_en.md index b8df4408ff18b1..20b20a43bffe5d 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_large_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_large_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_large', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_large", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.large').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_medium_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_medium_en.md index e5666e98d41e01..98b8bae55c3420 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_medium_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_medium_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_medium', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_medium", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.medium').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_mini_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_mini_en.md index 276f8362594d22..019576d2caf576 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_mini_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_mini_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_mini', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_mini", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.mini').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_small_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_small_en.md index 582c6fa2b9a704..5d2a23a2d340a2 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_small_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_small_en.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_small', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_small", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.small').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_tiny_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_tiny_en.md index c2fc9436970439..d37f39be47cade 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_tiny_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_bert_tiny_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_bert_tiny', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_tiny", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.bert.tiny').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_base_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_base_en.md index 165075633a300c..6cc2fd6bf8aeb7 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_base_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_base_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_electra_base', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_base", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.electra.base').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_large_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_large_en.md index e87af232ca890a..6e28cc917aed8c 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_large_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_large_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline('onto_recognize_entities_electra_large', lang = 'en') -annotations = pipeline.fullAnnotate("Hello from John Snow Labs!")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_large", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.large').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_small_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_small_en.md index 2ce881cd270aa8..d30ab3a371aefb 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_small_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_electra_small_en.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_electra_small', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_small", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.electra.small').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_lg_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_lg_en.md index bc9cd1df9dce95..4a12badcf469a0 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_lg_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_lg_en.md @@ -33,25 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("onto_recognize_entities_lg", "en") - -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -{:.nlu-block} -```python -import nlu -nlu.load("en.ner.onto.lg").predict("""I love johnsnowlabs! """) -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_sm_en.md b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_sm_en.md index 0f90874f95c6e7..df748f94a3ff59 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_sm_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-onto_recognize_entities_sm_en.md @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('onto_recognize_entities_sm', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("onto_recognize_entities_sm", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.onto.sm').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-pos_ud_bokmaal_nb.md b/docs/_posts/ahmedlone127/2023-05-27-pos_ud_bokmaal_nb.md index 9b67727f887766..50889e46707ea3 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-pos_ud_bokmaal_nb.md +++ b/docs/_posts/ahmedlone127/2023-05-27-pos_ud_bokmaal_nb.md @@ -34,39 +34,7 @@ This model was trained using the dataset available at https://universaldependenc ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("pos") - -nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, pos]) -light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text"))) -results = light_pipeline.fullAnnotate("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.") -``` -```scala - -val pos = PerceptronModel.pretrained("pos_ud_bokmaal", "nb") - .setInputCols(Array("document", "token")) - .setOutputCol("pos") - -val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, pos)) -val data = Seq("Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene.").toDF("text") -val result = pipeline.fit(data).transform(data) -``` - -{:.nlu-block} -```python - -import nlu -text = ["""Annet enn å være kongen i nord, er John Snow en engelsk lege og en leder innen utvikling av anestesi og medisinsk hygiene."""] -pos_df = nlu.load('nb.pos.ud_bokmaal').predict(text) -pos_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_bert_en.md b/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_bert_en.md index cc4cbd0d2a02e5..2a4580e2629305 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_bert_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_bert_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('recognize_entities_bert', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("recognize_entities_bert", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner.bert').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_dl_en.md b/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_dl_en.md index 895bf290c52ccb..a2feddf4ea83fa 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_dl_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_dl_en.md @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipelinein -pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'en') -annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0] -annotations.keys() - -``` -```scala - -val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "en") -val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0) -``` - -{:.nlu-block} -```python - -import nlu -text = [""Hello from John Snow Labs ! ""] -result_df = nlu.load('en.ner').predict(text) -result_df - -``` -
-
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python diff --git a/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_dl_fa.md b/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_dl_fa.md index e6fadd932f60c2..80da0508ce5b6a 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_dl_fa.md +++ b/docs/_posts/ahmedlone127/2023-05-27-recognize_entities_dl_fa.md @@ -32,37 +32,7 @@ The explain_document_dl is a pretrained pipeline that we can use to process text ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -from sparknlp.pretrained import PretrainedPipeline - -pipeline = PretrainedPipeline('recognize_entities_dl', lang = 'fa') - -annotations = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")[0] - -annotations.keys() -``` -```scala - -val pipeline = new PretrainedPipeline("recognize_entities_dl", lang = "fa") -val result = pipeline.fullAnnotate("""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند""")(0) -``` - -{:.nlu-block} -```python - -import nlu - -text = ["""به گزارش خبرنگار ایرنا ، بر اساس تصمیم این مجمع ، محمد قمی نماینده مردم پاکدشت به عنوان رئیس و علی‌اکبر موسوی خوئینی و شمس‌الدین وهابی نمایندگان مردم تهران به عنوان نواب رئیس انتخاب شدند"""] - -result_df = nlu.load('fa.recognize_entities_dl').predict(text) - -result_df -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-roberta_base_token_classifier_ontonotes_pipeline_en.md index c6ca506e52958e..df6f0f9eff52ce 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-roberta_base_token_classifier_ontonotes_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [roberta_base_token_classifier_o ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") - -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -```scala - -val pipeline = new PretrainedPipeline("roberta_base_token_classifier_ontonotes_pipeline", lang = "en") -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-roberta_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-roberta_large_token_classifier_conll03_pipeline_en.md index 72dcd995dc0142..d75d117fa4d472 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-roberta_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-roberta_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [roberta_large_token_classifier_ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("roberta_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.")) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_bne_capitel_ner_pipeline_es.md b/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_bne_capitel_ner_pipeline_es.md index 81a467a8d3d26a..de6d6a2276109a 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_bne_capitel_ner_pipeline_es.md +++ b/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_bne_capitel_ner_pipeline_es.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [roberta_token_classifier_bne_ca ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") - -pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") -``` -```scala - -val pipeline = new PretrainedPipeline("roberta_token_classifier_bne_capitel_ner_pipeline", lang = "es") -pipeline.annotate("Me llamo Antonio y trabajo en la fábrica de Mercedes-Benz en Madrid.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_icelandic_ner_pipeline_is.md b/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_icelandic_ner_pipeline_is.md index 44c64073d378c7..c5dced1a6399d4 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_icelandic_ner_pipeline_is.md +++ b/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_icelandic_ner_pipeline_is.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [roberta_token_classifier_icelan ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") - -pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") -``` -```scala - -val pipeline = new PretrainedPipeline("roberta_token_classifier_icelandic_ner_pipeline", lang = "is") -pipeline.annotate("Ég heiti Peter Fergusson. Ég hef búið í New York síðan í október 2011 og unnið hjá Tesla Motor og þénað 100K $ á ári.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_timex_semeval_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_timex_semeval_pipeline_en.md index 28e0ea1c3e114f..97a09915f4829c 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_timex_semeval_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-roberta_token_classifier_timex_semeval_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [roberta_token_classifier_timex_ ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -timex_pipeline = PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") - -timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") -``` -```scala - - -val timex_pipeline = new PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en") -timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-spellcheck_dl_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-spellcheck_dl_pipeline_en.md index af1fc87cbdba9b..e7617f4c23534e 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-spellcheck_dl_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-spellcheck_dl_pipeline_en.md @@ -32,27 +32,7 @@ This pretrained spellchecker pipeline is built on the top of [spellcheck_dl](htt ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") - -text = ["During the summer we have the best ueather.", "I have a black ueather jacket, so nice."] - -pipeline.annotate(text) -``` -```scala - - -val pipeline = new PretrainedPipeline("spellcheck_dl_pipeline", lang = "en") -val example = Array("During the summer we have the best ueather.", "I have a black ueather jacket, so nice.") - -pipeline.annotate(example) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-text_cleaning_en.md b/docs/_posts/ahmedlone127/2023-05-27-text_cleaning_en.md index 07657f6ad771cc..42e23d2663dce6 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-text_cleaning_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-text_cleaning_en.md @@ -33,16 +33,7 @@ The text_cleaning is a pretrained pipeline that we can use to process text with ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -from sparknlp.pretrained import PretrainedPipeline -pipeline = PretrainedPipeline("text_cleaning", "en") -result = pipeline.annotate("""I love johnsnowlabs! """) -``` - -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_base_token_classifier_conll03_pipeline_en.md index 6ce93076e23c62..d923913d37f70f 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md index 8a480372275471..229713b9a2894f 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_base_token_classifier_ontonotes_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_base_token_classifi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") - -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlm_roberta_base_token_classifier_ontonotes_pipeline", lang = "en") -pipeline.annotate("My name is John and I have been working at John Snow Labs since November 2020.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_conll03_pipeline_de.md b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_conll03_pipeline_de.md index bf1d04ed8366d6..595292996a288b 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_conll03_pipeline_de.md +++ b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_conll03_pipeline_de.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_large_token_classif ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") - -pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") -``` -```scala - -val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_conll03_pipeline", lang = "de") -pipeline.annotate("Ibser begann seine Karriere beim ASK Ebreichsdorf. 2004 wechselte er zu Admira Wacker Mödling, wo er auch in der Akademie spielte.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md index 78657d8c33e3ed..3189a75e5c3ce6 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_hrl_pipeline_xx.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on the top of [xlm_roberta_large_token_classif ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") - -pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") -``` -```scala - -val pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_hrl_pipeline", lang = "xx") -pipeline.annotate("يمكنكم مشاهدة أمير منطقة الرياض الأمير فيصل بن بندر بن عبد العزيز في كل مناسبة وافتتاح تتعلق بمشاريع التعليم والصحة وخدمة الطرق والمشاريع الثقافية في منطقة الرياض.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md index b585db60590ce2..ed173c9ffc360a 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md +++ b/docs/_posts/ahmedlone127/2023-05-27-xlm_roberta_large_token_classifier_masakhaner_pipeline_xx.md @@ -32,21 +32,7 @@ This pretrained pipeline is built on [xlm_roberta_large_token_classifier_masakha ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -masakhaner_pipeline = PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") - -masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") -``` -```scala - -val masakhaner_pipeline = new PretrainedPipeline("xlm_roberta_large_token_classifier_masakhaner_pipeline", lang = "xx") -val masakhaner_pipeline.annotate("አህመድ ቫንዳ ከ3-10-2000 ጀምሮ በአዲስ አበባ ኖሯል።") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-xlnet_base_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-xlnet_base_token_classifier_conll03_pipeline_en.md index 09b33d78c6bd29..1c3238ebde731b 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-xlnet_base_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-xlnet_base_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlnet_base_token_classifier_con ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlnet_base_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-05-27-xlnet_large_token_classifier_conll03_pipeline_en.md b/docs/_posts/ahmedlone127/2023-05-27-xlnet_large_token_classifier_conll03_pipeline_en.md index 6db5dbe6f40b79..b20dd92e9e412b 100644 --- a/docs/_posts/ahmedlone127/2023-05-27-xlnet_large_token_classifier_conll03_pipeline_en.md +++ b/docs/_posts/ahmedlone127/2023-05-27-xlnet_large_token_classifier_conll03_pipeline_en.md @@ -32,23 +32,7 @@ This pretrained pipeline is built on the top of [xlnet_large_token_classifier_co ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - - -pipeline = PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") - -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -```scala - - -val pipeline = new PretrainedPipeline("xlnet_large_token_classifier_conll03_pipeline", lang = "en") -pipeline.annotate("My name is John and I work at John Snow Labs.") -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_base_uncased_contracts_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_base_uncased_contracts_en.md index 2b1a76c0a31802..6dddfbb3141397 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_base_uncased_contracts_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_base_uncased_contracts_en.md @@ -33,55 +33,7 @@ Pretrained Word Embeddings model, trained on legal contracts, adapted from Huggi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_base_uncased_contracts","en") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP."]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_base_uncased_contracts","en") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP.").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.bert.contracts.uncased_base").predict("""I love Spark NLP.""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_ARBERT_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_ARBERT_ar.md index a8bde5a9373131..f372d9feb1aa38 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_ARBERT_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_ARBERT_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_ARBERT","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_ARBERT","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.arbert").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_AraBertMo_base_V1_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_AraBertMo_base_V1_ar.md index 507d80ddf48d81..b049b73e401406 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_AraBertMo_base_V1_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_AraBertMo_base_V1_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_AraBertMo_base_V1","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_AraBertMo_base_V1","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.AraBertMo_base_V1").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_Ara_DialectBERT_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_Ara_DialectBERT_ar.md index aa25c8bbbd15f0..ece2d7def2015a 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_Ara_DialectBERT_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_Ara_DialectBERT_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_Ara_DialectBERT","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_Ara_DialectBERT","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.Ara_DialectBERT").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_COVID_SciBERT_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_COVID_SciBERT_en.md index 1156a182032942..b181f33ad505a1 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_COVID_SciBERT_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_COVID_SciBERT_en.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_COVID_SciBERT","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_COVID_SciBERT","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.COVID_SciBERT").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_DarijaBERT_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_DarijaBERT_ar.md index 019db2765f56c4..a610ecf9d98548 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_DarijaBERT_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_DarijaBERT_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_DarijaBERT","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_DarijaBERT","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.DarijaBERT").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_FinancialBERT_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_FinancialBERT_en.md index f209c346ba5ecc..5aac6f8deafa13 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_FinancialBERT_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_FinancialBERT_en.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_FinancialBERT","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_FinancialBERT","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.FinancialBERT").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_German_MedBERT_de.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_German_MedBERT_de.md index 6f9f54bac44943..f824ce2b421fc4 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_German_MedBERT_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_German_MedBERT_de.md @@ -33,55 +33,7 @@ Pretrained German Medical Bert Embeddings model, uploaded to Hugging Face, adapt ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_German_MedBERT","de") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_German_MedBERT","de") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Funken NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.medbert").predict("""Ich liebe Funken NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_MARBERT_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_MARBERT_ar.md index df8e6020ce97b2..1a8337eb2f6277 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_MARBERT_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_MARBERT_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_MARBERT","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_MARBERT","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.MARBERT").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_MARBERTv2_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_MARBERTv2_ar.md index 26d222b5c236ee..1f7c518cfda6a2 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_MARBERTv2_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_MARBERTv2_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_MARBERTv2","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_MARBERTv2","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.MARBERTv2").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_agriculture_bert_uncased_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_agriculture_bert_uncased_en.md index 0f8dd6b3d732db..80f22e0b87bd3f 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_agriculture_bert_uncased_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_agriculture_bert_uncased_en.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_agriculture_bert_uncased","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_agriculture_bert_uncased","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.agriculture_bert_uncased").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_alberti_bert_base_multilingual_cased_es.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_alberti_bert_base_multilingual_cased_es.md index 334f6947d0078e..652b29ca0f02d0 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_alberti_bert_base_multilingual_cased_es.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_alberti_bert_base_multilingual_cased_es.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_alberti_bert_base_multilingual_cased","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_alberti_bert_base_multilingual_cased","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.alberti_bert_base_multilingual_cased").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_arabert_c19_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_arabert_c19_ar.md index d94b26d12cfe68..b3ec63b05fd172 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_arabert_c19_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_arabert_c19_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_arabert_c19","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_arabert_c19","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.arabert_c19").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bangla_bert_base_bn.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bangla_bert_base_bn.md index 05609a11fcf313..ffad26793c5b2a 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bangla_bert_base_bn.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bangla_bert_base_bn.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bangla_bert_base","bn") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["আমি স্পার্ক এনএলপি ভালোবাসি"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bangla_bert_base","bn") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("আমি স্পার্ক এনএলপি ভালোবাসি").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("bn.embed.bangala_bert").predict("""আমি স্পার্ক এনএলপি ভালোবাসি""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bangla_bert_bn.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bangla_bert_bn.md index 27aac09a7bc754..3895c761091b82 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bangla_bert_bn.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bangla_bert_bn.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bangla_bert","bn") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["আমি স্পার্ক এনএলপি ভালোবাসি"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bangla_bert","bn") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("আমি স্পার্ক এনএলপি ভালোবাসি").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("bn.embed.bangla_bert").predict("""আমি স্পার্ক এনএলপি ভালোবাসি""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_base_uncased_issues_128_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_base_uncased_issues_128_en.md index 5679c83687a692..355a23fe4c437a 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_base_uncased_issues_128_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_base_uncased_issues_128_en.md @@ -34,50 +34,7 @@ Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provid ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} - -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") -embeddings = BertEmbeddings.pretrained("bert_embeddings_base_uncased_issues_128","ko") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") \ - .setCaseSensitive(True) - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_base_uncased_issues_128","ko") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - .setCaseSensitive(True) - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_5lang_cased_es.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_5lang_cased_es.md index 99f607f5c1530b..8ec6c83c0a2ee7 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_5lang_cased_es.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_5lang_cased_es.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_5lang_cased","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_5lang_cased","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.bert_base_5lang_cased").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabert_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabert_ar.md index e8151fef004624..1f937e2eb43d37 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabert_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabert_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabert","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabert","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.bert_base_arabert").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv01_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv01_ar.md index b0ffc23c7e5de1..654bc887a67cc8 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv01_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv01_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv01","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv01","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.bert_base_arabertv01").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv02_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv02_ar.md index 00a46a50c9de69..493095aa7f482e 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv02_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv02_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv02","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv02","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.bert_base_arabertv02").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv02_twitter_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv02_twitter_ar.md index 527a150609fdb3..6e97176e642894 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv02_twitter_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv02_twitter_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv02_twitter","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv02_twitter","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.bert_base_arabertv02_twitter").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv2_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv2_ar.md index 28aa7881be3bad..bc52734b92b843 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv2_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabertv2_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv2","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabertv2","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.bert_base_arabertv2").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_ar.md index ec1f8a7ed29f13..4308e70edfc031 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.bert_base_arabic").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_mix_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_mix_ar.md index 2db1ef256735f2..af99c4af0887d3 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_mix_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_mix_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_mix","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_mix","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.bert_base_arabic_camelbert_mix").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_ar.md index 97bcdf7edd7fe2..67201d1492542b 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.bert_base_arabic_camelbert_msa").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_eighth_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_eighth_ar.md index 6d2176403c972d..38f77b087540ed 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_eighth_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_eighth_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_eighth","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_eighth","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.bert_base_arabic_camelbert_msa_eighth").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_half_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_half_ar.md index 1ef43767d97397..004a7380dca028 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_half_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_half_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_half","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_half","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.bert_base_arabic_camelbert_msa_half").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_quarter_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_quarter_ar.md index 622c54ebe635f9..537baf4955d84a 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_quarter_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_quarter_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_quarter","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_quarter","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.bert_base_arabic_camelbert_msa_quarter").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_sixteenth_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_sixteenth_ar.md index efc6980941ceab..4af25c9faf9be9 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_sixteenth_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_sixteenth_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_sixteenth","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_arabic_camelbert_msa_sixteenth","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.bert_base_arabic_camelbert_msa_sixteenth").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_cased_pt_lenerbr_pt.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_cased_pt_lenerbr_pt.md index ae5920ac1cb795..24d80be3bc6aa5 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_cased_pt_lenerbr_pt.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_cased_pt_lenerbr_pt.md @@ -33,55 +33,7 @@ Pretrained Legal Bert Embeddings model, uploaded to Hugging Face, adapted and im ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_cased_pt_lenerbr","pt") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Eu amo Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_cased_pt_lenerbr","pt") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Eu amo Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("pt.embed.bert_base_cased_pt_lenerbr").predict("""Eu amo Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_german_cased_oldvocab_de.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_german_cased_oldvocab_de.md index 1f985340872ac9..b69acae6255291 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_german_cased_oldvocab_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_german_cased_oldvocab_de.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_german_cased_oldvocab","de") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_german_cased_oldvocab","de") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Funken NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.bert_base_german_cased_oldvocab").predict("""Ich liebe Funken NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_german_uncased_de.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_german_uncased_de.md index 91fa66ad63e5d8..9fb045ca315d82 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_german_uncased_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_german_uncased_de.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_german_uncased","de") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_german_uncased","de") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Funken NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.bert_base_german_uncased").predict("""Ich liebe Funken NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_gl_cased_pt.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_gl_cased_pt.md index 5b67a75c4e936e..90ce440ba94cdf 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_gl_cased_pt.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_gl_cased_pt.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_gl_cased","pt") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Eu amo Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_gl_cased","pt") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Eu amo Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("pt.embed.bert_base_gl_cased").predict("""Eu amo Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_historical_german_rw_cased_de.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_historical_german_rw_cased_de.md index 4626d471479c49..9d93ab59c6632f 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_historical_german_rw_cased_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_historical_german_rw_cased_de.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_historical_german_rw_cased","de") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_historical_german_rw_cased","de") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Funken NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.bert_base_historical_german_rw_cased").predict("""Ich liebe Funken NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_italian_xxl_cased_it.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_italian_xxl_cased_it.md index ab7513a407cb6c..d0dde731c9eb43 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_italian_xxl_cased_it.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_italian_xxl_cased_it.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_italian_xxl_cased","it") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_italian_xxl_cased","it") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Adoro Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("it.embed.bert_base_italian_xxl_cased").predict("""Adoro Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_italian_xxl_uncased_it.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_italian_xxl_uncased_it.md index 8f1b62389ff59c..cd2ad21dae1dd6 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_italian_xxl_uncased_it.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_italian_xxl_uncased_it.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_italian_xxl_uncased","it") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_italian_xxl_uncased","it") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Adoro Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("it.embed.bert_base_italian_xxl_uncased").predict("""Adoro Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_ko.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_ko.md index 586c43ffa392b7..91703bbd259bd3 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_ko.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_ko.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base","ko") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base","ko") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ko.embed.bert").predict("""나는 Spark NLP를 좋아합니다""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_finetuned_peticoes_pt.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_finetuned_peticoes_pt.md index 1977eade89c812..1ab745538baf8b 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_finetuned_peticoes_pt.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_finetuned_peticoes_pt.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_portuguese_cased_finetuned_peticoes","pt") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Eu amo Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_portuguese_cased_finetuned_peticoes","pt") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Eu amo Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("pt.embed.bert_base_portuguese_cased_finetuned_peticoes").predict("""Eu amo Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_finetuned_tcu_acordaos_pt.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_finetuned_tcu_acordaos_pt.md index 51c244361ef7c6..de3e15df5e39b0 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_finetuned_tcu_acordaos_pt.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_finetuned_tcu_acordaos_pt.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_portuguese_cased_finetuned_tcu_acordaos","pt") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Eu amo Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_portuguese_cased_finetuned_tcu_acordaos","pt") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Eu amo Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("pt.embed.bert_base_portuguese_cased_finetuned_tcu_acordaos").predict("""Eu amo Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_pt.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_pt.md index 7e8a494e05a9ea..c4c1a343a3ff98 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_pt.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_portuguese_cased_pt.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_portuguese_cased","pt") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Eu amo Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_portuguese_cased","pt") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Eu amo Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("pt.embed.bert_base_portuguese_cased").predict("""Eu amo Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib60_1790k_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib60_1790k_ar.md index 82419381d32b36..3868d245812acc 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib60_1790k_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib60_1790k_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_qarib60_1790k","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_qarib60_1790k","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.bert_base_qarib60_1790k").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib60_860k_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib60_860k_ar.md index b4cba476c77437..3321ae06713eda 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib60_860k_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib60_860k_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_qarib60_860k","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_qarib60_860k","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.bert_base_qarib60_860k").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib_ar.md index 1af0625cf15067..2323faa478498d 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_qarib_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_qarib","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_qarib","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.bert_base_qarib").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_dstc9_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_dstc9_en.md index 082e97e5cb0c38..80c796bbcc74b2 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_dstc9_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_dstc9_en.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_uncased_dstc9","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_uncased_dstc9","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.bert_base_uncased_dstc9").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_mnli_sparse_70_unstructured_no_classifier_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_mnli_sparse_70_unstructured_no_classifier_en.md index ec8779ce8600e9..8243b6e8636b7c 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_mnli_sparse_70_unstructured_no_classifier_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_mnli_sparse_70_unstructured_no_classifier_en.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_uncased_mnli_sparse_70_unstructured_no_classifier","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_uncased_mnli_sparse_70_unstructured_no_classifier","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.bert_base_uncased_mnli_sparse_70_unstructured_no_classifier").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_sparse_70_unstructured_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_sparse_70_unstructured_en.md index 0d1e90b869920a..e6ba3cab7a6701 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_sparse_70_unstructured_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_base_uncased_sparse_70_unstructured_en.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_uncased_sparse_70_unstructured","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_base_uncased_sparse_70_unstructured","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.bert_base_uncased_sparse_70_unstructured").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_kor_base_ko.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_kor_base_ko.md index cdf41dcad66847..6a5e4f91234bf6 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_kor_base_ko.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_kor_base_ko.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_kor_base","ko") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_kor_base","ko") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ko.embed.bert_kor_base").predict("""나는 Spark NLP를 좋아합니다""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_medium_arabic_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_medium_arabic_ar.md index e4ab0e46ec7075..1fe8347a4dec19 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_medium_arabic_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_medium_arabic_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_medium_arabic","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_medium_arabic","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.bert_medium_arabic").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_mini_arabic_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_mini_arabic_ar.md index d4f0e3cce03c7e..d93f86e89cb899 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_mini_arabic_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_mini_arabic_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_mini_arabic","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_mini_arabic","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.bert_mini_arabic").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_political_election2020_twitter_mlm_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_political_election2020_twitter_mlm_en.md index ce6a900f6c10fe..8c8930fb556e20 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_political_election2020_twitter_mlm_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bert_political_election2020_twitter_mlm_en.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_political_election2020_twitter_mlm","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_political_election2020_twitter_mlm","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.bert_political_election2020_twitter_mlm").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_beto_gn_base_cased_es.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_beto_gn_base_cased_es.md index a0e6b0632c7e43..c387471f2549a3 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_beto_gn_base_cased_es.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_beto_gn_base_cased_es.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_beto_gn_base_cased","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_beto_gn_base_cased","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.beto_gn_base_cased").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bioclinicalbert_finetuned_covid_papers_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bioclinicalbert_finetuned_covid_papers_en.md index 259fb891ba3560..2f129951af8204 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bioclinicalbert_finetuned_covid_papers_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_bioclinicalbert_finetuned_covid_papers_en.md @@ -33,55 +33,7 @@ Pretrained BERT Embeddings model, adapted from Hugging Face and curated to provi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_bioclinicalbert_finetuned_covid_papers","en") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCols(Array("text")) - .setOutputCols(Array("document")) - -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_bioclinicalbert_finetuned_covid_papers","en") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.bert.covid_bio_clinical.finetuned").predict("""PUT YOUR STRING HERE""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_carlbert_webex_mlm_spatial_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_carlbert_webex_mlm_spatial_en.md index 2f13e1e7ba8bd4..e96e1f5b0e291c 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_carlbert_webex_mlm_spatial_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_carlbert_webex_mlm_spatial_en.md @@ -33,50 +33,7 @@ Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provid ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} - -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") -embeddings = BertEmbeddings.pretrained("bert_embeddings_carlbert_webex_mlm_spatial","de") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") \ - .setCaseSensitive(True) - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Spark-NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_carlbert_webex_mlm_spatial","de") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - .setCaseSensitive(True) - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Spark-NLP").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chefberto_italian_cased_it.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chefberto_italian_cased_it.md index 1e2661ff475de6..3e2795c9dc5348 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chefberto_italian_cased_it.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chefberto_italian_cased_it.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_chefberto_italian_cased","it") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_chefberto_italian_cased","it") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Adoro Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("it.embed.chefberto_italian_cased").predict("""Adoro Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_bert_uncased_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_bert_uncased_en.md index 8b4dcbb11e39e4..973e2514e38b99 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_bert_uncased_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_bert_uncased_en.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_chemical_bert_uncased","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_chemical_bert_uncased","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.chemical_bert_uncased").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_uncased_finetuned_cust_c1_cust_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_uncased_finetuned_cust_c1_cust_en.md index 1d3affe542dc5c..422a8d343b0a40 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_uncased_finetuned_cust_c1_cust_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_uncased_finetuned_cust_c1_cust_en.md @@ -33,50 +33,7 @@ Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provid ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} - -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") -embeddings = BertEmbeddings.pretrained("bert_embeddings_chemical_uncased_finetuned_cust_c1_cust","en") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") \ - .setCaseSensitive(True) - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_chemical_uncased_finetuned_cust_c1_cust","en") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - .setCaseSensitive(True) - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_uncased_finetuned_cust_c2_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_uncased_finetuned_cust_c2_en.md index 02fdd7d0894952..6d1c7a0d327ba9 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_uncased_finetuned_cust_c2_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_chemical_uncased_finetuned_cust_c2_en.md @@ -33,50 +33,7 @@ Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provid ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} - -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") -embeddings = BertEmbeddings.pretrained("bert_embeddings_chemical_uncased_finetuned_cust_c2","en") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") \ - .setCaseSensitive(True) - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_chemical_uncased_finetuned_cust_c2","en") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - .setCaseSensitive(True) - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_childes_bert_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_childes_bert_en.md index 60503fbe2496d8..8014017794b12e 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_childes_bert_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_childes_bert_en.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_childes_bert","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_childes_bert","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.childes_bert").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_clinical_pubmed_bert_base_128_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_clinical_pubmed_bert_base_128_en.md index ecc100c7def6f7..3ad31a7d16eec6 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_clinical_pubmed_bert_base_128_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_clinical_pubmed_bert_base_128_en.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_clinical_pubmed_bert_base_128","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_clinical_pubmed_bert_base_128","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.clinical_pubmed_bert_base_128").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_clinical_pubmed_bert_base_512_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_clinical_pubmed_bert_base_512_en.md index 2c83ed64e84a41..beaf0e829739c1 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_clinical_pubmed_bert_base_512_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_clinical_pubmed_bert_base_512_en.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_clinical_pubmed_bert_base_512","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_clinical_pubmed_bert_base_512","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.clinical_pubmed_bert_base_512").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_crosloengual_bert_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_crosloengual_bert_en.md index 89a6839a95ee56..c69ade88cba059 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_crosloengual_bert_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_crosloengual_bert_en.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_crosloengual_bert","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_crosloengual_bert","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.crosloengual_bert").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dbert_ko.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dbert_ko.md index 0c6f71e2a0ddb4..070d641873d722 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dbert_ko.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dbert_ko.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_dbert","ko") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_dbert","ko") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ko.embed.dbert").predict("""나는 Spark NLP를 좋아합니다""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_deberta_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_deberta_base_uncased_en.md index 22fae6bd9c819c..10f5c4d056bc44 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_deberta_base_uncased_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_deberta_base_uncased_en.md @@ -33,57 +33,7 @@ Pretrained BertForMaskedLM model, adapted from Hugging Face and curated to provi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -bert_loaded = BertEmbeddings.pretrained("bert_embeddings_deberta_base_uncased","en") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") \ - .setCaseSensitive(True) - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, bert_loaded]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val bert_loaded = BertEmbeddings.pretrained("bert_embeddings_deberta_base_uncased","en") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - .setCaseSensitive(True) - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, bert_loaded)) - -val data = Seq("I love Spark NLP").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.deberta_base_uncased").predict("""I love Spark NLP""") -``` - -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_distil_clinical_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_distil_clinical_en.md index e29412f032a148..7300998e4ef53d 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_distil_clinical_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_distil_clinical_en.md @@ -33,50 +33,7 @@ Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provid ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} - -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") -embeddings = BertEmbeddings.pretrained("bert_embeddings_distil_clinical","en") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") \ - .setCaseSensitive(True) - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark-NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_distil_clinical","en") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - .setCaseSensitive(True) - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark-NLP").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_passage_encoder_allqa_base_es.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_passage_encoder_allqa_base_es.md index 622e96cea211e0..084a35b1638067 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_passage_encoder_allqa_base_es.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_passage_encoder_allqa_base_es.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_passage_encoder_allqa_base","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_passage_encoder_allqa_base","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.dpr_spanish_passage_encoder_allqa_base").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_passage_encoder_squades_base_es.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_passage_encoder_squades_base_es.md index 5b4101b177a992..66df617f4a1508 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_passage_encoder_squades_base_es.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_passage_encoder_squades_base_es.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_passage_encoder_squades_base","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_passage_encoder_squades_base","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.dpr_spanish_passage_encoder_squades_base").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_question_encoder_allqa_base_es.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_question_encoder_allqa_base_es.md index fbf26507302d99..bd7fdcc173b6d0 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_question_encoder_allqa_base_es.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_question_encoder_allqa_base_es.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_question_encoder_allqa_base","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_question_encoder_allqa_base","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.dpr_spanish_question_encoder_allqa_base").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_question_encoder_squades_base_es.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_question_encoder_squades_base_es.md index 537f94ae494528..c6b5b68cbcf937 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_question_encoder_squades_base_es.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dpr_spanish_question_encoder_squades_base_es.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_question_encoder_squades_base","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_dpr_spanish_question_encoder_squades_base","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.dpr_spanish_question_encoder_squades_base").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dziribert_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dziribert_ar.md index 4a1f66ee841cec..74d244071c2976 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dziribert_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_dziribert_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_dziribert","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_dziribert","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.dziribert").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_false_positives_scancode_bert_base_uncased_L8_1_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_false_positives_scancode_bert_base_uncased_L8_1_en.md index 8bee08b62d0c53..f17b0bb012a47f 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_false_positives_scancode_bert_base_uncased_L8_1_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_false_positives_scancode_bert_base_uncased_L8_1_en.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_false_positives_scancode_bert_base_uncased_L8_1","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_false_positives_scancode_bert_base_uncased_L8_1","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.false_positives_scancode_bert_base_uncased_L8_1").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_finbert_pretrain_yiyanghkust_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_finbert_pretrain_yiyanghkust_en.md index a8f6efb7ae0030..aa34411bb64ddb 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_finbert_pretrain_yiyanghkust_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_finbert_pretrain_yiyanghkust_en.md @@ -37,55 +37,7 @@ Financial English Bert Embeddings model, uploaded to Hugging Face, adapted and i ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_finbert_pretrain_yiyanghkust","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_finbert_pretrain_yiyanghkust","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.finbert_pretrain_yiyanghkust").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_finest_bert_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_finest_bert_en.md index e01933d053546d..dbba74c8aed5f5 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_finest_bert_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_finest_bert_en.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_finest_bert","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_finest_bert","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.finest_bert").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_gbert_base_de.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_gbert_base_de.md index f7c53a48bd8905..0afd6bfa4ea8b8 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_gbert_base_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_gbert_base_de.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_gbert_base","de") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_gbert_base","de") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Funken NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.gbert_base").predict("""Ich liebe Funken NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_german_financial_statements_bert_de.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_german_financial_statements_bert_de.md index bd29b725b81ae5..9771a2aeb59ebd 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_german_financial_statements_bert_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_german_financial_statements_bert_de.md @@ -33,55 +33,7 @@ Pretrained Financial Bert Word Embeddings model, trained on German Financial Sta ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_german_financial_statements_bert","de") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_german_financial_statements_bert","de") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Funken NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.german_financial_statements_bert").predict("""Ich liebe Funken NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_hateBERT_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_hateBERT_en.md index 82f3c85d2247d3..9b4c9b4143c639 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_hateBERT_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_hateBERT_en.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_hateBERT","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_hateBERT","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.hateBERT").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_hseBert_it_cased_it.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_hseBert_it_cased_it.md index 0ac43ca8f34236..82e3bf45a0a6e1 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_hseBert_it_cased_it.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_hseBert_it_cased_it.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_hseBert_it_cased","it") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_hseBert_it_cased","it") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Adoro Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("it.embed.hseBert_it_cased").predict("""Adoro Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_indic_transformers_hi_bert_hi.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_indic_transformers_hi_bert_hi.md index cb1d065f6341ec..c7d5395588b545 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_indic_transformers_hi_bert_hi.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_indic_transformers_hi_bert_hi.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_indic_transformers_hi_bert","hi") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["मुझे स्पार्क एनएलपी पसंद है"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_indic_transformers_hi_bert","hi") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("मुझे स्पार्क एनएलपी पसंद है").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("hi.embed.indic_transformers_hi_bert").predict("""मुझे स्पार्क एनएलपी पसंद है""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_indic_transformers_te_bert_te.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_indic_transformers_te_bert_te.md index 286777564e9003..c926c64fe0c410 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_indic_transformers_te_bert_te.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_indic_transformers_te_bert_te.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_indic_transformers_te_bert","te") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_indic_transformers_te_bert","te") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("te.embed.indic_transformers_te_bert").predict("""నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_javanese_bert_small_imdb_jv.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_javanese_bert_small_imdb_jv.md index 9f834ccdc83fa0..5d4cb7779b5179 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_javanese_bert_small_imdb_jv.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_javanese_bert_small_imdb_jv.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_javanese_bert_small_imdb","jv") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_javanese_bert_small_imdb","jv") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("jv.embed.javanese_bert_small_imdb").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_javanese_bert_small_jv.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_javanese_bert_small_jv.md index 92986039f002be..71dfbd37965bd5 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_javanese_bert_small_jv.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_javanese_bert_small_jv.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_javanese_bert_small","jv") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_javanese_bert_small","jv") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("jv.embed.javanese_bert_small").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_legal_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_legal_bert_base_uncased_en.md index a490d46ea569a7..0e7dceeca64069 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_legal_bert_base_uncased_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_legal_bert_base_uncased_en.md @@ -33,55 +33,7 @@ Legal Pretrained Bert Embeddings model, trained with uncased text, uploaded to H ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_legal_bert_base_uncased","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_legal_bert_base_uncased","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.legal_bert_base_uncased").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_lic_class_scancode_bert_base_cased_L32_1_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_lic_class_scancode_bert_base_cased_L32_1_en.md index 6e0309525b771a..f8cf4715db4ae2 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_lic_class_scancode_bert_base_cased_L32_1_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_lic_class_scancode_bert_base_cased_L32_1_en.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_lic_class_scancode_bert_base_cased_L32_1","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_lic_class_scancode_bert_base_cased_L32_1","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.lic_class_scancode_bert_base_cased_L32_1").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_marathi_bert_mr.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_marathi_bert_mr.md index 6b39af7927411f..6e5de8362bd022 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_marathi_bert_mr.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_marathi_bert_mr.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_marathi_bert","mr") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["मला स्पार्क एनएलपी आवडते"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_marathi_bert","mr") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("मला स्पार्क एनएलपी आवडते").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("mr.embed.marathi_bert").predict("""मला स्पार्क एनएलपी आवडते""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_mbert_ar_c19_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_mbert_ar_c19_ar.md index 3d6a26c549f735..9f2ed02c7a7a16 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_mbert_ar_c19_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_mbert_ar_c19_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_mbert_ar_c19","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_mbert_ar_c19","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.mbert_ar_c19").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_multi_dialect_bert_base_arabic_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_multi_dialect_bert_base_arabic_ar.md index bc40d7959ba191..dfd890b165bf97 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_multi_dialect_bert_base_arabic_ar.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_multi_dialect_bert_base_arabic_ar.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_multi_dialect_bert_base_arabic","ar") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_multi_dialect_bert_base_arabic","ar") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("أنا أحب شرارة NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ar.embed.multi_dialect_bert_base_arabic").predict("""أنا أحب شرارة NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_netbert_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_netbert_en.md index 2edd51e298a1d8..222fa62d923e86 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_netbert_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_netbert_en.md @@ -33,57 +33,7 @@ Pretrained BertForMaskedLM model, adapted from Hugging Face and curated to provi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -bert_loaded = BertEmbeddings.pretrained("bert_embeddings_netbert","en") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") \ - .setCaseSensitive(True) - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, bert_loaded]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val bert_loaded = BertEmbeddings.pretrained("bert_embeddings_netbert","en") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - .setCaseSensitive(True) - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, bert_loaded)) - -val data = Seq("I love Spark NLP").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.netbert").predict("""I love Spark NLP""") -``` - -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_olm_base_uncased_oct_2022_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_olm_base_uncased_oct_2022_en.md index 3566124863b301..7e639e117689d7 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_olm_base_uncased_oct_2022_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_olm_base_uncased_oct_2022_en.md @@ -33,50 +33,7 @@ Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provid ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} - -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") -embeddings = BertEmbeddings.pretrained("bert_embeddings_olm_base_uncased_oct_2022","en") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") \ - .setCaseSensitive(True) - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_olm_base_uncased_oct_2022","en") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - .setCaseSensitive(True) - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_pretrain_ko.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_pretrain_ko.md index 0308fcbcd07582..fda7650d419b4e 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_pretrain_ko.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_pretrain_ko.md @@ -33,50 +33,7 @@ Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provid ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} - -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") -embeddings = BertEmbeddings.pretrained("bert_embeddings_pretrain","ko") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") \ - .setCaseSensitive(True) - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_pretrain","ko") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - .setCaseSensitive(true) - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_psych_search_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_psych_search_en.md index f12d896dcbc372..fa3c4d651bce57 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_psych_search_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_psych_search_en.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_psych_search","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_psych_search","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.psych_search").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_scibert_scivocab_finetuned_cord19_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_scibert_scivocab_finetuned_cord19_en.md index 1e641bcf5d79b3..6e23b6176de621 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_scibert_scivocab_finetuned_cord19_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_scibert_scivocab_finetuned_cord19_en.md @@ -33,55 +33,7 @@ Pretrained BERT Embeddings model, adapted from Hugging Face and curated to provi ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_scibert_scivocab_finetuned_cord19","en") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCols(Array("text")) - .setOutputCols(Array("document")) - -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_scibert_scivocab_finetuned_cord19","en") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.scibert.cord19_scibert.finetuned").predict("""PUT YOUR STRING HERE""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sec_bert_base_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sec_bert_base_en.md index 8e1158bbb801c8..9db5143da3d138 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sec_bert_base_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sec_bert_base_en.md @@ -38,55 +38,7 @@ If you are interested in Financial Embeddings, take a look also at these two mod ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_sec_bert_base","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_sec_bert_base","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.sec_bert_base").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sec_bert_sh_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sec_bert_sh_en.md index 0958df101c9131..4560a22c0e8c08 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sec_bert_sh_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sec_bert_sh_en.md @@ -38,56 +38,7 @@ If you are interested in Financial Embeddings, take a look also at these two mod ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python - -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_sec_bert_sh","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_sec_bert_sh","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.sec_bert_sh").predict("""I love Spark NLP""") -``` - -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sikubert_zh.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sikubert_zh.md index ce8ab67067aa3f..887b8bfeb407f3 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sikubert_zh.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sikubert_zh.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_sikubert","zh") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_sikubert","zh") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("zh.embed.sikubert").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sikuroberta_zh.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sikuroberta_zh.md index 68d128c492b7cd..15264ea6774387 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sikuroberta_zh.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_sikuroberta_zh.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_sikuroberta","zh") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_sikuroberta","zh") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("zh.embed.sikuroberta").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_telugu_bertu_te.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_telugu_bertu_te.md index a521a954fb4236..bbd0d09326d723 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_telugu_bertu_te.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_telugu_bertu_te.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_telugu_bertu","te") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_telugu_bertu","te") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("te.embed.telugu_bertu").predict("""నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_wineberto_italian_cased_it.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_wineberto_italian_cased_it.md index 64d487ee4850f5..df8d1762745f19 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_wineberto_italian_cased_it.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_wineberto_italian_cased_it.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_wineberto_italian_cased","it") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_wineberto_italian_cased","it") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Adoro Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("it.embed.wineberto_italian_cased").predict("""Adoro Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_wobert_chinese_plus_zh.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_wobert_chinese_plus_zh.md index 698131409af3a5..139c9b65856aab 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_wobert_chinese_plus_zh.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_wobert_chinese_plus_zh.md @@ -33,55 +33,7 @@ Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_embeddings_wobert_chinese_plus","zh") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_embeddings_wobert_chinese_plus","zh") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("zh.embed.wobert_chinese_plus").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_sentence_embeddings_financial_de.md b/docs/_posts/ahmedlone127/2023-06-21-bert_sentence_embeddings_financial_de.md index 13d2898c521b50..31acb1ac4c499a 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-bert_sentence_embeddings_financial_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-bert_sentence_embeddings_financial_de.md @@ -35,55 +35,7 @@ Financial Pretrained BERT Embeddings model, uploaded to Hugging Face, adapted an ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("bert_sentence_embeddings_financial","de") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Spark-NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("bert_sentence_embeddings_financial","de") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Spark-NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.bert.finance").predict("""Ich liebe Spark-NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_all_pt.md b/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_all_pt.md index a38274560def80..dcb518b6390b80 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_all_pt.md +++ b/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_all_pt.md @@ -33,55 +33,7 @@ Pretrained BioBERT Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("biobert_embeddings_all","pt") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Odeio o cancro"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("biobert_embeddings_all","pt") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Odeio o cancro").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("pt.embed.gs_all").predict("""Odeio o cancro""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_biomedical_pt.md b/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_biomedical_pt.md index b86fe840106cfa..6ec01b4a73af22 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_biomedical_pt.md +++ b/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_biomedical_pt.md @@ -33,55 +33,7 @@ Pretrained BioBERT Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("biobert_embeddings_biomedical","pt") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Odeio o cancro"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("biobert_embeddings_biomedical","pt") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Odeio o cancro").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("pt.embed.gs_biomedical").predict("""Odeio o cancro""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_clinical_pt.md b/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_clinical_pt.md index d8a80c64c56b34..58e0d8cb891021 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_clinical_pt.md +++ b/docs/_posts/ahmedlone127/2023-06-21-biobert_embeddings_clinical_pt.md @@ -33,55 +33,7 @@ Pretrained BioBERT Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("biobert_embeddings_clinical","pt") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Odeio o cancro"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("biobert_embeddings_clinical","pt") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Odeio o cancro").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("pt.embed.gs_clinical").predict("""Odeio o cancro""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-biobert_pubmed_base_cased_v1.2_en.md b/docs/_posts/ahmedlone127/2023-06-21-biobert_pubmed_base_cased_v1.2_en.md index 794836f2f29942..2b7d9fd124435b 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-biobert_pubmed_base_cased_v1.2_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-biobert_pubmed_base_cased_v1.2_en.md @@ -33,55 +33,7 @@ This model is the v1.2 of [biobert_pubmed_base_cased](https://nlp.johnsnowlabs.c ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("biobert_pubmed_base_cased_v1.2","en") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I hate cancer"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("biobert_pubmed_base_cased_v1.2","en") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I hate cancer").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.biobert.pubmed.cased_base").predict("""I hate cancer""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_0_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_0_cased_generator_de.md index eb920c7ffb15b9..c920f59afe852d 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_0_cased_generator_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_0_cased_generator_de.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_0_cased_generator","de") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_0_cased_generator","de") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.electra.cased_base_64d").predict("""Ich liebe Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_1000000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_1000000_cased_generator_de.md index d6613cc7e8cf70..4b63da86ffc87f 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_1000000_cased_generator_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_1000000_cased_generator_de.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_1000000_cased_generator","de") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_1000000_cased_generator","de") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.electra.cased_base_gc4_64k_1000000.by_stefan_it").predict("""Ich liebe Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_100000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_100000_cased_generator_de.md index b1110b5c50cebd..598d68d39e87a6 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_100000_cased_generator_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_100000_cased_generator_de.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_100000_cased_generator","de") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_100000_cased_generator","de") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.electra.cased_base_gc4_64k_100000.by_stefan_it").predict("""Ich liebe Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_200000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_200000_cased_generator_de.md index e90872b5cf0574..66661999bd5a40 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_200000_cased_generator_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_200000_cased_generator_de.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_200000_cased_generator","de") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_200000_cased_generator","de") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.electra.cased_base_gc4_64k_200000.by_stefan_it").predict("""Ich liebe Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_300000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_300000_cased_generator_de.md index e9a244fe395167..1c49ee4e26a9ec 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_300000_cased_generator_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_300000_cased_generator_de.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_300000_cased_generator","de") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_300000_cased_generator","de") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.electra.cased_base_gc4_64k_300000.by_stefan_it").predict("""Ich liebe Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_400000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_400000_cased_generator_de.md index cf463da7d3ede6..fdff17758c3d20 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_400000_cased_generator_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_400000_cased_generator_de.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_400000_cased_generator","de") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_400000_cased_generator","de") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.electra.cased_base_gc4_64k_400000.by_stefan_it").predict("""Ich liebe Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_500000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_500000_cased_generator_de.md index 0e0368cae00d7c..f2aa64ef608413 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_500000_cased_generator_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_500000_cased_generator_de.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_500000_cased_generator","de") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_500000_cased_generator","de") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.electra.cased_base_gc4_64k_500000.by_stefan_it").predict("""Ich liebe Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_600000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_600000_cased_generator_de.md index 3f63d4ca68b519..87d806fe0189d8 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_600000_cased_generator_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_600000_cased_generator_de.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_600000_cased_generator","de") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_600000_cased_generator","de") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.electra.cased_base_gc4_64k_600000.by_stefan_it").predict("""Ich liebe Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_700000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_700000_cased_generator_de.md index 6ce95cc170b433..6c580f4f68a101 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_700000_cased_generator_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_700000_cased_generator_de.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_700000_cased_generator","de") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_700000_cased_generator","de") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.electra.cased_base_gc4_64k_700000.by_stefan_it").predict("""Ich liebe Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_800000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_800000_cased_generator_de.md index b3e7e29f1c1fe9..e759fe215faabb 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_800000_cased_generator_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_800000_cased_generator_de.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_800000_cased_generator","de") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_800000_cased_generator","de") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.electra.cased_base_gc4_64k_800000.by_stefan_it").predict("""Ich liebe Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_900000_cased_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_900000_cased_generator_de.md index 9e2a0a0531c231..87e842838ac52e 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_900000_cased_generator_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_gc4_64k_900000_cased_generator_de.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_900000_cased_generator","de") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_gc4_64k_900000_cased_generator","de") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.electra.cased_base_gc4_64k_900000.by_stefan_it").predict("""Ich liebe Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_generator_en.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_generator_en.md index e83a14d6f6ba62..df339d80d84000 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_generator_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_generator_en.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_generator","en") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_generator","en") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.electra.base").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_italian_xxl_cased_generator_it.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_italian_xxl_cased_generator_it.md index b650a25e829416..af7243f41c550c 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_italian_xxl_cased_generator_it.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_italian_xxl_cased_generator_it.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_italian_xxl_cased_generator","it") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_italian_xxl_cased_generator","it") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Adoro Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("it.embed.electra.cased_xxl_base").predict("""Adoro Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_turkish_mc4_cased_generator_tr.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_turkish_mc4_cased_generator_tr.md index e41b06ae521f40..543df7c17442aa 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_turkish_mc4_cased_generator_tr.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_turkish_mc4_cased_generator_tr.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_turkish_mc4_cased_generator","tr") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Spark NLP'yi seviyorum"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_turkish_mc4_cased_generator","tr") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Spark NLP'yi seviyorum").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("tr.embed.electra.cased_base").predict("""Spark NLP'yi seviyorum""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_turkish_mc4_uncased_generator_tr.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_turkish_mc4_uncased_generator_tr.md index 41859cc555c6a2..1e7838de7edb24 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_turkish_mc4_uncased_generator_tr.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_base_turkish_mc4_uncased_generator_tr.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_turkish_mc4_uncased_generator","tr") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Spark NLP'yi seviyorum"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_turkish_mc4_uncased_generator","tr") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Spark NLP'yi seviyorum").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("tr.embed.electra.uncased_base").predict("""Spark NLP'yi seviyorum""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_large_generator_en.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_large_generator_en.md index bbfb7f281e7a49..e0062b938f8f9f 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_large_generator_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_large_generator_en.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_large_generator","en") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_large_generator","en") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.electra.large").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_small_generator_en.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_small_generator_en.md index dabe96a7d7b5a2..bc972fbf5e3ca3 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_small_generator_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_small_generator_en.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_small_generator","en") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_small_generator","en") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.electra.small").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_small_japanese_generator_ja.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_small_japanese_generator_ja.md index 29d87ba99e8341..3f5281e221dc4d 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_small_japanese_generator_ja.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_small_japanese_generator_ja.md @@ -33,47 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_small_japanese_generator","ja") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Spark NLPが大好きです"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_small_japanese_generator","ja") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) -val data = Seq("Spark NLPが大好きです").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_base_cased_generator_tl.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_base_cased_generator_tl.md index 84aeba9befe4a7..61704be090fbec 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_base_cased_generator_tl.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_base_cased_generator_tl.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_base_cased_generator","tl") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Mahilig ako sa Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_base_cased_generator","tl") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Mahilig ako sa Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("tl.embed.electra.cased_base").predict("""Mahilig ako sa Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_base_uncased_generator_tl.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_base_uncased_generator_tl.md index e48ebc2ee91dfd..b2a0eacc593a2a 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_base_uncased_generator_tl.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_base_uncased_generator_tl.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_base_uncased_generator","tl") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Mahilig ako sa Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_base_uncased_generator","tl") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Mahilig ako sa Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("tl.embed.electra.uncased_base").predict("""Mahilig ako sa Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_small_cased_generator_tl.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_small_cased_generator_tl.md index df146d8836dce7..bf277c77873ae2 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_small_cased_generator_tl.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_small_cased_generator_tl.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_small_cased_generator","tl") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Mahilig ako sa Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_small_cased_generator","tl") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Mahilig ako sa Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("tl.embed.electra.cased_small").predict("""Mahilig ako sa Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_small_uncased_generator_tl.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_small_uncased_generator_tl.md index 58cea57ca00683..9f7d6634160407 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_small_uncased_generator_tl.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electra_tagalog_small_uncased_generator_tl.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_small_uncased_generator","tl") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Mahilig ako sa Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_tagalog_small_uncased_generator","tl") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Mahilig ako sa Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("tl.embed.electra.uncased_small").predict("""Mahilig ako sa Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electricidad_base_generator_es.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electricidad_base_generator_es.md index be35b303a82993..9be45ffd4604ee 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electricidad_base_generator_es.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_electricidad_base_generator_es.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_electricidad_base_generator","es") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Amo Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_electricidad_base_generator","es") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Amo Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.electra.base").predict("""Amo Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_finance_koelectra_base_generator_ko.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_finance_koelectra_base_generator_ko.md index af1b52acd3ac9e..80257ef852acaf 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_finance_koelectra_base_generator_ko.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_finance_koelectra_base_generator_ko.md @@ -33,47 +33,7 @@ Pretrained Financial Korean Electra Embeddings model, adapted from Hugging Face ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_finance_koelectra_base_generator","ko") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_finance_koelectra_base_generator","ko") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) -val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_finance_koelectra_small_generator_ko.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_finance_koelectra_small_generator_ko.md index 76acc791540c7c..5febe5b840d44f 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_finance_koelectra_small_generator_ko.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_finance_koelectra_small_generator_ko.md @@ -33,47 +33,7 @@ Pretrained Financial Korean Electra Embeddings model, adapted from Hugging Face ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_finance_koelectra_small_generator","ko") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_finance_koelectra_small_generator","ko") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) -val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_gelectra_base_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_gelectra_base_generator_de.md index 35d0fddb181a54..4c98baae2a093c 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_gelectra_base_generator_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_gelectra_base_generator_de.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_gelectra_base_generator","de") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_gelectra_base_generator","de") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.electra.base").predict("""Ich liebe Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_gelectra_large_generator_de.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_gelectra_large_generator_de.md index 6d2e16d4eeefb6..7d1524213cf86a 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_gelectra_large_generator_de.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_gelectra_large_generator_de.md @@ -33,55 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_gelectra_large_generator","de") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_gelectra_large_generator","de") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.electra.large").predict("""Ich liebe Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_generator_ko.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_generator_ko.md index 53e791d787729c..1728665140e95f 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_generator_ko.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_generator_ko.md @@ -33,47 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_base_generator","ko") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_base_generator","ko") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) -val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_v2_generator_ko.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_v2_generator_ko.md index 94addd53290ea6..6c5b26726a9caf 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_v2_generator_ko.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_v2_generator_ko.md @@ -33,47 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_base_v2_generator","ko") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_base_v2_generator","ko") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) -val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_v3_generator_ko.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_v3_generator_ko.md index d08cb9f7252863..4d7a8573b5d12b 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_v3_generator_ko.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_base_v3_generator_ko.md @@ -33,47 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_base_v3_generator","ko") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_base_v3_generator","ko") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) -val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_small_generator_ko.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_small_generator_ko.md index e323b62a1bbec4..7283cc29579396 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_small_generator_ko.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_koelectra_small_generator_ko.md @@ -33,47 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_small_generator","ko") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_koelectra_small_generator","ko") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) -val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_kr_electra_generator_ko.md b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_kr_electra_generator_ko.md index 0aeb0d295a40c1..e2f35366f4f696 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_kr_electra_generator_ko.md +++ b/docs/_posts/ahmedlone127/2023-06-21-electra_embeddings_kr_electra_generator_ko.md @@ -33,47 +33,7 @@ Pretrained Electra Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = BertEmbeddings.pretrained("electra_embeddings_kr_electra_generator","ko") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = BertEmbeddings.pretrained("electra_embeddings_kr_electra_generator","ko") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) -val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-legalectra_base_es.md b/docs/_posts/ahmedlone127/2023-06-21-legalectra_base_es.md index ed972bedaa7edf..4245ee3f06c1b4 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-legalectra_base_es.md +++ b/docs/_posts/ahmedlone127/2023-06-21-legalectra_base_es.md @@ -33,55 +33,7 @@ Pretrained Spanish Legal Word Embeddings model, adapted from Hugging Face and cu ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -electra = BertEmbeddings.pretrained("legalectra_base","es") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, electra]) - -data = spark.createDataFrame([["Amo a Spark NLP."]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val electra = BertEmbeddings.pretrained("legalectra_base","es") - .setInputCols(Array("document", "token")) - .setOutputCol("class") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, electra)) - -val data = Seq("Amo a Spark NLP.").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.bert.base_legal").predict("""Amo a Spark NLP.""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-legalectra_small_es.md b/docs/_posts/ahmedlone127/2023-06-21-legalectra_small_es.md index a2fb304397a6a0..153274bc0b1d2d 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-legalectra_small_es.md +++ b/docs/_posts/ahmedlone127/2023-06-21-legalectra_small_es.md @@ -33,55 +33,7 @@ Pretrained Spanish Legal Word Embeddings model, adapted from Hugging Face and cu ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -electra = BertEmbeddings.pretrained("legalectra_small","es") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, electra]) - -data = spark.createDataFrame([["Amo a Spark NLP."]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val electra = BertEmbeddings.pretrained("legalectra_small","es") - .setInputCols(Array("document", "token")) - .setOutputCol("class") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, electra)) - -val data = Seq("Amo a Spark NLP.").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.bert.small_legal").predict("""Amo a Spark NLP.""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-21-ms_bluebert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-06-21-ms_bluebert_base_uncased_en.md index bf7539ce60805d..14a2d437d7ec97 100644 --- a/docs/_posts/ahmedlone127/2023-06-21-ms_bluebert_base_uncased_en.md +++ b/docs/_posts/ahmedlone127/2023-06-21-ms_bluebert_base_uncased_en.md @@ -38,23 +38,7 @@ Next sentence prediction (NSP): the models concatenate two masked sentences as i ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -embeddings = BertEmbeddings.pretrained("ms_bluebert_base_uncased", "en") \ - .setInputCols(["sentence", "token"]) \ - .setOutputCol("embeddings") - -nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) -``` -```scala -val embeddings = BertEmbeddings.pretrained("ms_bluebert_base_uncased", "en") - .setInputCols("sentence", "token") - .setOutputCol("embeddings") -val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) -``` -
{% include programmingLanguageSelectScalaPythonNLU.html %} diff --git a/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_erlangshen_v2_chinese_sentencepiece_zh.md b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_erlangshen_v2_chinese_sentencepiece_zh.md index 169e9a31db0eb5..8c0c61af05239d 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_erlangshen_v2_chinese_sentencepiece_zh.md +++ b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_erlangshen_v2_chinese_sentencepiece_zh.md @@ -33,50 +33,7 @@ Pretrained DebertaV2ForMaskedLM model, adapted from Hugging Face and curated to ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} - -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") -embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_erlangshen_v2_chinese_sentencepiece","zh") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") \ - .setCaseSensitive(True) - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark-NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_erlangshen_v2_chinese_sentencepiece","zh") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - .setCaseSensitive(True) - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark-NLP").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_mlm_test_en.md b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_mlm_test_en.md index 2ee4b096c3fc6c..386c355f2cfbf9 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_mlm_test_en.md +++ b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_mlm_test_en.md @@ -33,50 +33,7 @@ Pretrained DebertaEmbeddings model, adapted from Hugging Face and curated to pro ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} - -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") -embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_mlm_test","en") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") \ - .setCaseSensitive(True) - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_mlm_test","en") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - .setCaseSensitive(true) - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_spm_vie_vie.md b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_spm_vie_vie.md index 8f303ff4ccea17..5551148c7cf964 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_spm_vie_vie.md +++ b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_spm_vie_vie.md @@ -33,50 +33,7 @@ Pretrained DebertaEmbeddings model, adapted from Hugging Face and curated to pro ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} - -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") -embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_spm_vie","vie") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") \ - .setCaseSensitive(True) - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_spm_vie","vie") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - .setCaseSensitive(true) - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_tapt_nbme_v3_base_en.md b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_tapt_nbme_v3_base_en.md index 30f49fc915d1a6..2a3f75dd9b26c9 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_tapt_nbme_v3_base_en.md +++ b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_tapt_nbme_v3_base_en.md @@ -33,50 +33,7 @@ Pretrained DebertaEmbeddings model, adapted from Hugging Face and curated to pro ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} - -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") -embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_tapt_nbme_v3_base","en") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") \ - .setCaseSensitive(True) - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_tapt_nbme_v3_base","en") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - .setCaseSensitive(true) - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_vie_small_vie.md b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_vie_small_vie.md index 6b33b485b4900c..1551e827369464 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_vie_small_vie.md +++ b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_vie_small_vie.md @@ -33,50 +33,7 @@ Pretrained DebertaEmbeddings model, adapted from Hugging Face and curated to pro ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} - -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") -embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_vie_small","vie") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") \ - .setCaseSensitive(True) - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_vie_small","vie") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - .setCaseSensitive(true) - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_xsmall_dapt_scientific_papers_pubmed_en.md b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_xsmall_dapt_scientific_papers_pubmed_en.md index 50107e47b2c704..06f7be3e4bfa64 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_xsmall_dapt_scientific_papers_pubmed_en.md +++ b/docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_xsmall_dapt_scientific_papers_pubmed_en.md @@ -33,50 +33,7 @@ Pretrained DebertaEmbeddings model, adapted from Hugging Face and curated to pro ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} - -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") -embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_xsmall_dapt_scientific_papers_pubmed","en") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") \ - .setCaseSensitive(True) - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_xsmall_dapt_scientific_papers_pubmed","en") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - .setCaseSensitive(true) - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-26-deberta_v3_small_en.md b/docs/_posts/ahmedlone127/2023-06-26-deberta_v3_small_en.md index 4f13393720b21f..4d5f8281662ff1 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-deberta_v3_small_en.md +++ b/docs/_posts/ahmedlone127/2023-06-26-deberta_v3_small_en.md @@ -33,27 +33,7 @@ The DeBERTa model was proposed in [[https://arxiv.org/abs/2006.03654 DeBERTa: De ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -embeddings = DeBertaEmbeddings.pretrained("deberta_v3_small", "en") \ -.setInputCols("sentence", "token") \ -.setOutputCol("embeddings") -``` -```scala -val embeddings = DeBertaEmbeddings.pretrained("deberta_v3_small", "en") -.setInputCols("sentence", "token") -.setOutputCol("embeddings") -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.deberta_v3_small").predict("""Put your text here.""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-26-deberta_v3_xsmall_en.md b/docs/_posts/ahmedlone127/2023-06-26-deberta_v3_xsmall_en.md index 9f3baaee954b9e..e0e826cfcf39ce 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-deberta_v3_xsmall_en.md +++ b/docs/_posts/ahmedlone127/2023-06-26-deberta_v3_xsmall_en.md @@ -33,30 +33,8 @@ The DeBERTa model was proposed in [[https://arxiv.org/abs/2006.03654 DeBERTa: De ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -embeddings = DeBertaEmbeddings.pretrained("deberta_v3_xsmall", "en") \ -.setInputCols("sentence", "token") \ -.setOutputCol("embeddings") - -``` -```scala -val embeddings = DeBertaEmbeddings.pretrained("deberta_v3_xsmall", "en") -.setInputCols("sentence", "token") -.setOutputCol("embeddings") - -``` -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.deberta_v3_xsmall").predict("""Put your text here.""") -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_BERTino_it.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_BERTino_it.md index 7233e7dd8045ec..c51718ac20e32e 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_BERTino_it.md +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_BERTino_it.md @@ -33,55 +33,7 @@ Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and im ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_BERTino","it") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_BERTino","it") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Adoro Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("it.embed.BERTino").predict("""Adoro Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_indonesian_id.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_indonesian_id.md index 4e1fad3954642c..324b0e4dc41bcc 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_indonesian_id.md +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_indonesian_id.md @@ -33,55 +33,7 @@ Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and im ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_indonesian","id") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Saya suka percikan NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_indonesian","id") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Saya suka percikan NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("id.embed.distilbert").predict("""Saya suka percikan NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa_en.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa_en.md index 9202845526f1ca..b74288176549cb 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa_en.md +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa_en.md @@ -33,55 +33,7 @@ Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and im ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.distilbert_base_uncased_sparse_85_unstructured_pruneofa").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa_en.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa_en.md index d172172048e1b6..8fac062d88d7e6 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa_en.md +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa_en.md @@ -33,55 +33,7 @@ Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and im ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.distilbert_base_uncased_sparse_90_unstructured_pruneofa").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_fa_zwnj_base_fa.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_fa_zwnj_base_fa.md index 6e1c4d47a4a93b..4e137e618ae65e 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_fa_zwnj_base_fa.md +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_distilbert_fa_zwnj_base_fa.md @@ -33,55 +33,7 @@ Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and im ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_fa_zwnj_base","fa") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["من عاشق جرقه NLP هستم"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_fa_zwnj_base","fa") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("من عاشق جرقه NLP هستم").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("fa.embed.distilbert_fa_zwnj_base").predict("""من عاشق جرقه NLP هستم""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_finetuned_sarcasm_classification_en.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_finetuned_sarcasm_classification_en.md index 7df586e990947a..fcf58d1a18f0f6 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_finetuned_sarcasm_classification_en.md +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_finetuned_sarcasm_classification_en.md @@ -33,55 +33,7 @@ Pretrained DistilBERT Embeddings model, adapted from Hugging Face and curated to ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_finetuned_sarcasm_classification","en") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["PUT YOUR STRING HERE."]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_finetuned_sarcasm_classification","en") - .setInputCols(Array("document", "token")) - .setOutputCol("class") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("PUT YOUR STRING HERE.").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.distil_bert.finetuned").predict("""PUT YOUR STRING HERE.""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_indic_transformers_bn_distilbert_bn.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_indic_transformers_bn_distilbert_bn.md index 750a960889bb4e..2bd594b92ac0b6 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_indic_transformers_bn_distilbert_bn.md +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_indic_transformers_bn_distilbert_bn.md @@ -33,55 +33,7 @@ Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and im ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_indic_transformers_bn_distilbert","bn") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["আমি স্পার্ক এনএলপি ভালোবাসি"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_indic_transformers_bn_distilbert","bn") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("আমি স্পার্ক এনএলপি ভালোবাসি").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("bn.embed.indic_transformers_bn_distilbert").predict("""আমি স্পার্ক এনএলপি ভালোবাসি""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_indic_transformers_hi_distilbert_hi.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_indic_transformers_hi_distilbert_hi.md index 709aae71d5d5cb..b5b435182a7266 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_indic_transformers_hi_distilbert_hi.md +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_indic_transformers_hi_distilbert_hi.md @@ -33,55 +33,7 @@ Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and im ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_indic_transformers_hi_distilbert","hi") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["मुझे स्पार्क एनएलपी पसंद है"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_indic_transformers_hi_distilbert","hi") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("मुझे स्पार्क एनएलपी पसंद है").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("hi.embed.indic_transformers_hi_distilbert").predict("""मुझे स्पार्क एनएलपी पसंद है""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_javanese_distilbert_small_imdb_jv.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_javanese_distilbert_small_imdb_jv.md index 9ddef8bf45a2fc..ec085b830ca872 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_javanese_distilbert_small_imdb_jv.md +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_javanese_distilbert_small_imdb_jv.md @@ -33,55 +33,7 @@ Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and im ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_javanese_distilbert_small_imdb","jv") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_javanese_distilbert_small_imdb","jv") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("jv.embed.javanese_distilbert_small_imdb").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_javanese_distilbert_small_jv.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_javanese_distilbert_small_jv.md index 15b0dcb0b86cda..abf161154855c8 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_javanese_distilbert_small_jv.md +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_javanese_distilbert_small_jv.md @@ -33,55 +33,7 @@ Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and im ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_javanese_distilbert_small","jv") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_javanese_distilbert_small","jv") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("jv.embed.distilbert").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_malaysian_distilbert_small_ms.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_malaysian_distilbert_small_ms.md index 9092f73999f3c2..81dc1d74b54af2 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_malaysian_distilbert_small_ms.md +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_malaysian_distilbert_small_ms.md @@ -33,55 +33,7 @@ Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and im ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_malaysian_distilbert_small","ms") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Saya suka Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_malaysian_distilbert_small","ms") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Saya suka Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ms.embed.distilbert").predict("""Saya suka Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_marathi_distilbert_mr.md b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_marathi_distilbert_mr.md index 9ca015f1047ab5..1b342179ec3499 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_marathi_distilbert_mr.md +++ b/docs/_posts/ahmedlone127/2023-06-26-distilbert_embeddings_marathi_distilbert_mr.md @@ -33,55 +33,7 @@ Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and im ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_marathi_distilbert","mr") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["मला स्पार्क एनएलपी आवडते"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_marathi_distilbert","mr") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("मला स्पार्क एनएलपी आवडते").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("mr.embed.distilbert").predict("""मला स्पार्क एनएलपी आवडते""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-26-roberta_base_swiss_legal_gsw.md b/docs/_posts/ahmedlone127/2023-06-26-roberta_base_swiss_legal_gsw.md index 8cc4701ee197fc..755bba585f5518 100644 --- a/docs/_posts/ahmedlone127/2023-06-26-roberta_base_swiss_legal_gsw.md +++ b/docs/_posts/ahmedlone127/2023-06-26-roberta_base_swiss_legal_gsw.md @@ -33,20 +33,7 @@ Pretrained Legal Roberta Embeddings model, adapted from Hugging Face and curated ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -sentence_embeddings = RoBertaEmbeddings.pretrained("roberta_base_swiss_legal", "gsw")\ - .setInputCols(["sentence"])\ - .setOutputCol("embeddings") -``` -```scala -val sentence_embeddings = RoBertaEmbeddings.pretrained("roberta_base_swiss_legal", "gsw") - .setInputCols("sentence") - .setOutputCol("embeddings")) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_BR_BERTo_pt.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_BR_BERTo_pt.md index aa0ec4ff6f25d1..5e12c752d13a8a 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_BR_BERTo_pt.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_BR_BERTo_pt.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_BR_BERTo","pt") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Eu amo Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_BR_BERTo","pt") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Eu amo Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("pt.embed.BR_BERTo").predict("""Eu amo Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_Bible_roberta_base_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_Bible_roberta_base_en.md index ae2c8f33cc9d83..055209da574a9c 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_Bible_roberta_base_en.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_Bible_roberta_base_en.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_Bible_roberta_base","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_Bible_roberta_base","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.Bible_roberta_base").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_KNUBert_kn.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_KNUBert_kn.md index f5810aa56beba4..693859de616e84 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_KNUBert_kn.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_KNUBert_kn.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_KNUBert","kn") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["ನಾನು ಸ್ಪಾರ್ಕ್ ಎನ್ಎಲ್ಪಿ ಪ್ರೀತಿಸುತ್ತೇನೆ"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_KNUBert","kn") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("ನಾನು ಸ್ಪಾರ್ಕ್ ಎನ್ಎಲ್ಪಿ ಪ್ರೀತಿಸುತ್ತೇನೆ").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("kn.embed.KNUBert").predict("""ನಾನು ಸ್ಪಾರ್ಕ್ ಎನ್ಎಲ್ಪಿ ಪ್ರೀತಿಸುತ್ತೇನೆ""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_KanBERTo_kn.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_KanBERTo_kn.md index 3b7e500c1b61cf..d672d2d0e88788 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_KanBERTo_kn.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_KanBERTo_kn.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_KanBERTo","kn") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["ನಾನು ಸ್ಪಾರ್ಕ್ ಎನ್ಎಲ್ಪಿ ಪ್ರೀತಿಸುತ್ತೇನೆ"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_KanBERTo","kn") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("ನಾನು ಸ್ಪಾರ್ಕ್ ಎನ್ಎಲ್ಪಿ ಪ್ರೀತಿಸುತ್ತೇನೆ").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("kn.embed.KanBERTo").predict("""ನಾನು ಸ್ಪಾರ್ಕ್ ಎನ್ಎಲ್ಪಿ ಪ್ರೀತಿಸುತ್ತೇನೆ""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_MedRoBERTa.nl_nl.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_MedRoBERTa.nl_nl.md index 954d50c138bddb..d9420dc2bdeed1 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_MedRoBERTa.nl_nl.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_MedRoBERTa.nl_nl.md @@ -33,47 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_MedRoBERTa.nl","nl") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ik hou van vonk nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") - -val tokenizer = new Tokenizer() - .setInputCols(Array("document")) - .setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_MedRoBERTa.nl","nl") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) -val data = Seq("Ik hou van vonk nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_RoBERTalex_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_RoBERTalex_es.md index 6db5ee04644354..61a76ea2ee1c9f 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_RoBERTalex_es.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_RoBERTalex_es.md @@ -33,55 +33,7 @@ RoBERTa Legal Embeddings, trained by `PlanTL-GOB-ES`. ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_RoBERTalex","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_RoBERTalex","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.RoBERTalex").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_RuPERTa_base_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_RuPERTa_base_es.md index 70dbfe309f033b..57cd226d06c2fc 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_RuPERTa_base_es.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_RuPERTa_base_es.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_RuPERTa_base","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_RuPERTa_base","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.RuPERTa_base").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_SecRoBERTa_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_SecRoBERTa_en.md index d5ad2b1a0d58e3..50ce4ede430255 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_SecRoBERTa_en.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_SecRoBERTa_en.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_SecRoBERTa","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_SecRoBERTa","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.SecRoBERTa").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_gaussian_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_gaussian_es.md index 6920c29287a315..8f82ae6d7ebfe2 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_gaussian_es.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_gaussian_es.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_gaussian","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_gaussian","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.bertin_base_gaussian").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_gaussian_exp_512seqlen_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_gaussian_exp_512seqlen_es.md index 0dbfd7bbf1f936..dd9f71a25b111b 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_gaussian_exp_512seqlen_es.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_gaussian_exp_512seqlen_es.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_gaussian_exp_512seqlen","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_gaussian_exp_512seqlen","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.bertin_base_gaussian_exp_512seqlen").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_random_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_random_es.md index 9653c8545ae5ba..202438105a4e44 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_random_es.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_random_es.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_random","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_random","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.bertin_base_random").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_random_exp_512seqlen_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_random_exp_512seqlen_es.md index cba118e80030f6..5a21435e068d64 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_random_exp_512seqlen_es.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_random_exp_512seqlen_es.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_random_exp_512seqlen","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_random_exp_512seqlen","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.bertin_base_random_exp_512seqlen").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_stepwise_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_stepwise_es.md index b668d63c59d89c..3b8fc35f977432 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_stepwise_es.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_stepwise_es.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_stepwise","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_stepwise","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.bertin_base_stepwise").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_stepwise_exp_512seqlen_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_stepwise_exp_512seqlen_es.md index ef023c27d100e6..cfe7544bc01cb8 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_stepwise_exp_512seqlen_es.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_base_stepwise_exp_512seqlen_es.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_stepwise_exp_512seqlen","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_base_stepwise_exp_512seqlen","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.bertin_base_stepwise_exp_512seqlen").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_roberta_base_spanish_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_roberta_base_spanish_es.md index 462366bdad47ef..fc23492b15d492 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_roberta_base_spanish_es.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_roberta_base_spanish_es.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model for Spanish Language, trained within the Ber ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_roberta_base_spanish","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_roberta_base_spanish","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.bertin_roberta_base_spanish").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_roberta_large_spanish_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_roberta_large_spanish_es.md index 1a10a0441cc6d9..e52590f9051e96 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_roberta_large_spanish_es.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_bertin_roberta_large_spanish_es.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_roberta_large_spanish","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_bertin_roberta_large_spanish","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.bertin_roberta_large_spanish").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_d_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_d_en.md index 235066a9fad86d..40748e78ff58c5 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_d_en.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_d_en.md @@ -35,55 +35,7 @@ Sampling strategy d: As expressed in the author's paper [here](https://arxiv.o ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_climate_d","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_climate_d","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.distilroberta_base_climate_d").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_d_s_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_d_s_en.md index bd8c33572f99c2..f7c565016f71f6 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_d_s_en.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_d_s_en.md @@ -35,55 +35,7 @@ Sampling strategy ds:As expressed in the author's paper [here](https://arxiv.o ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_climate_d_s","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_climate_d_s","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.distilroberta_base_climate_d_s").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_f_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_f_en.md index 1c3db397a2a509..c74ae36804270b 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_f_en.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_climate_f_en.md @@ -35,55 +35,7 @@ Sampling strategy f: As expressed in the author's paper [here](https://arxiv.o ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_climate_f","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_climate_f","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.distilroberta_base_climate_f").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title_en.md index f9a2af7472f6ca..c5b1aa26f94d8a 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title_en.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title_en.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.distilroberta_base_finetuned_jira_qt_issue_title").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies_en.md index b9661e848333d1..e091a4599df359 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies_en.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies_en.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_fairlex_ecthr_minilm_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_fairlex_ecthr_minilm_en.md index 5a32da11d9de7d..86d1ffbd95cbaa 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_fairlex_ecthr_minilm_en.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_fairlex_ecthr_minilm_en.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_fairlex_ecthr_minilm","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_fairlex_ecthr_minilm","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.fairlex_ecthr_minilm").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_fairlex_scotus_minilm_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_fairlex_scotus_minilm_en.md index 773d7bcf2c57d3..02116808b9ed43 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_fairlex_scotus_minilm_en.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_fairlex_scotus_minilm_en.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_fairlex_scotus_minilm","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_fairlex_scotus_minilm","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.fairlex_scotus_minilm").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_hindi_hi.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_hindi_hi.md index 01ad5f075cbd15..d3d98b4cd322d6 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_hindi_hi.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_hindi_hi.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_hindi","hi") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCols(Array("text")) - .setOutputCols(Array("document")) - -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_hindi","hi") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("hi.embed.roberta").predict("""PUT YOUR STRING HERE""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indic_transformers_hi_roberta_hi.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indic_transformers_hi_roberta_hi.md index f69f15963140ca..c126e7c873af74 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indic_transformers_hi_roberta_hi.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indic_transformers_hi_roberta_hi.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indic_transformers_hi_roberta","hi") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["मुझे स्पार्क एनएलपी पसंद है"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indic_transformers_hi_roberta","hi") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("मुझे स्पार्क एनएलपी पसंद है").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("hi.embed.indic_transformers_hi_roberta").predict("""मुझे स्पार्क एनएलपी पसंद है""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indic_transformers_te_roberta_te.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indic_transformers_te_roberta_te.md index 08583c9e8b8960..5177c5bc6d1af1 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indic_transformers_te_roberta_te.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indic_transformers_te_roberta_te.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indic_transformers_te_roberta","te") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indic_transformers_te_roberta","te") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("te.embed.indic_transformers_te_roberta").predict("""నేను స్పార్క్ nlp ను ప్రేమిస్తున్నాను""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indo_roberta_small_id.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indo_roberta_small_id.md index 6331c54d43f671..64a7f478625df0 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indo_roberta_small_id.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indo_roberta_small_id.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indo_roberta_small","id") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Saya suka percikan NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indo_roberta_small","id") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Saya suka percikan NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("id.embed.indo_roberta_small").predict("""Saya suka percikan NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indonesian_roberta_base_id.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indonesian_roberta_base_id.md index 77ff6a04ce597b..aa0a3123954d4c 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indonesian_roberta_base_id.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_indonesian_roberta_base_id.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indonesian_roberta_base","id") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Saya suka percikan NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_indonesian_roberta_base","id") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Saya suka percikan NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("id.embed.indonesian_roberta_base").predict("""Saya suka percikan NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_javanese_roberta_small_imdb_jv.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_javanese_roberta_small_imdb_jv.md index 19408b12a0bed8..3f42ce73af9a6b 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_javanese_roberta_small_imdb_jv.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_javanese_roberta_small_imdb_jv.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_javanese_roberta_small_imdb","jv") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_javanese_roberta_small_imdb","jv") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("jv.embed.javanese_roberta_small_imdb").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_javanese_roberta_small_jv.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_javanese_roberta_small_jv.md index 1da0c11ee775fd..29c0824369b104 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_javanese_roberta_small_jv.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_javanese_roberta_small_jv.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_javanese_roberta_small","jv") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_javanese_roberta_small","jv") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("jv.embed.javanese_roberta_small").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_jurisbert_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_jurisbert_es.md index 685198ff9883b0..d54c0e557f087d 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_jurisbert_es.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_jurisbert_es.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_jurisbert","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_jurisbert","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.jurisbert").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_mlm_spanish_roberta_base_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_mlm_spanish_roberta_base_es.md index 0ad5d20ef3fe54..24cdd395bb019f 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_mlm_spanish_roberta_base_es.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_mlm_spanish_roberta_base_es.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_mlm_spanish_roberta_base","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_mlm_spanish_roberta_base","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.mlm_spanish_roberta_base").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_muppet_roberta_base_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_muppet_roberta_base_en.md index 238181eea3e7a3..aa2ad61218e6f7 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_muppet_roberta_base_en.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_muppet_roberta_base_en.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_muppet_roberta_base","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_muppet_roberta_base","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.muppet_roberta_base").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_robasqu_eu.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_robasqu_eu.md index 90ed07b322abfb..538cbb13655710 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_robasqu_eu.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_robasqu_eu.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_robasqu","eu") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCols(Array("text")) - .setOutputCols(Array("document")) - -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_robasqu","eu") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("eu.embed.roberta").predict("""PUT YOUR STRING HERE""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_bne_es.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_bne_es.md index 4412023121038a..2d9c469f33f322 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_bne_es.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_bne_es.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_bne","es") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Me encanta chispa nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_bne","es") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Me encanta chispa nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("es.embed.roberta_base_bne").predict("""Me encanta chispa nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_indonesian_522M_id.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_indonesian_522M_id.md index 30a5a85951db7d..afb1ba3a5f6f53 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_indonesian_522M_id.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_indonesian_522M_id.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_indonesian_522M","id") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Saya suka percikan NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_indonesian_522M","id") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Saya suka percikan NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("id.embed.roberta_base_indonesian_522M").predict("""Saya suka percikan NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_russian_v0_ru.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_russian_v0_ru.md index 5d49a37350de0f..db6efdbfb93358 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_russian_v0_ru.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_russian_v0_ru.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_russian_v0","ru") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Я люблю искра NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_russian_v0","ru") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Я люблю искра NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ru.embed.roberta_base_russian_v0").predict("""Я люблю искра NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_chinese_zh.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_chinese_zh.md index 6b0bc0e8bcf727..35d790ffc2fca4 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_chinese_zh.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_chinese_zh.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_wechsel_chinese","zh") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["我喜欢Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_wechsel_chinese","zh") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("我喜欢Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("zh.embed.roberta_base_wechsel_chinese").predict("""我喜欢Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_french_fr.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_french_fr.md index 61dc5c17eb026f..1a39fce2f4fc59 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_french_fr.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_french_fr.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_wechsel_french","fr") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["J'adore Spark Nlp"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_wechsel_french","fr") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("J'adore Spark Nlp").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("fr.embed.roberta_base_wechsel_french").predict("""J'adore Spark Nlp""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_german_de.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_german_de.md index f1c52bdc3df88e..7c75d9b9934994 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_german_de.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_base_wechsel_german_de.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_wechsel_german","de") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_base_wechsel_german","de") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Funken NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.roberta_base_wechsel_german").predict("""Ich liebe Funken NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_ko_small_ko.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_ko_small_ko.md index 12b377d5c0d070..51d6e2296ad2c3 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_ko_small_ko.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_ko_small_ko.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_ko_small","ko") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["나는 Spark NLP를 좋아합니다"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_ko_small","ko") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("나는 Spark NLP를 좋아합니다").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ko.embed.roberta_ko_small").predict("""나는 Spark NLP를 좋아합니다""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_pubmed_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_pubmed_en.md index e3a64ba4f52a29..f2c124601a0532 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_pubmed_en.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_pubmed_en.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_pubmed","en") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_pubmed","en") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("I love Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.roberta_pubmed").predict("""I love Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_urdu_small_ur.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_urdu_small_ur.md index 8f5a603c1976a8..c2726b34c76779 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_urdu_small_ur.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_roberta_urdu_small_ur.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_urdu_small","ur") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["مجھے سپارک این ایل پی سے محبت ہے"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_roberta_urdu_small","ur") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("مجھے سپارک این ایل پی سے محبت ہے").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("ur.embed.roberta_urdu_small").predict("""مجھے سپارک این ایل پی سے محبت ہے""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_robertinh_gl.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_robertinh_gl.md index d587c92f3a8366..d5d2d16ed8bac9 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_robertinh_gl.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_robertinh_gl.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_robertinh","gl") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCols(Array("text")) - .setOutputCols(Array("document")) - -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_robertinh","gl") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("gl.embed.roberta").predict("""PUT YOUR STRING HERE""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_ruperta_base_finetuned_spa_constitution_en.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_ruperta_base_finetuned_spa_constitution_en.md index 2b84c9374f5630..00f228661e217a 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_ruperta_base_finetuned_spa_constitution_en.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_ruperta_base_finetuned_spa_constitution_en.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, adapted from Hugging Face and curated to pr ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ - .setInputCol("text") \ - .setOutputCol("document") - -tokenizer = Tokenizer() \ - .setInputCols("document") \ - .setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_ruperta_base_finetuned_spa_constitution","en") \ - .setInputCols(["document", "token"]) \ - .setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() - .setInputCols(Array("text")) - .setOutputCols(Array("document")) - -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_ruperta_base_finetuned_spa_constitution","en") - .setInputCols(Array("document", "token")) - .setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.roberta.base_finetuned").predict("""PUT YOUR STRING HERE""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_sundanese_roberta_base_su.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_sundanese_roberta_base_su.md index 7585bf0774d018..9bae396f9df976 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_sundanese_roberta_base_su.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_sundanese_roberta_base_su.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_sundanese_roberta_base","su") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Abdi bogoh Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_sundanese_roberta_base","su") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Abdi bogoh Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("su.embed.sundanese_roberta_base").predict("""Abdi bogoh Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_ukr_roberta_base_uk.md b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_ukr_roberta_base_uk.md index 00a9d4227090f5..5759a6fb262144 100644 --- a/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_ukr_roberta_base_uk.md +++ b/docs/_posts/ahmedlone127/2023-06-27-roberta_embeddings_ukr_roberta_base_uk.md @@ -33,55 +33,7 @@ Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and impor ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_ukr_roberta_base","uk") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Я люблю Spark NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_ukr_roberta_base","uk") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Я люблю Spark NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("uk.embed.ukr_roberta_base").predict("""Я люблю Spark NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_cased_en.md b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_cased_en.md index 2a6be63cb79d9b..7da5164c748ef6 100644 --- a/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_cased_en.md +++ b/docs/_posts/ahmedlone127/2023-06-28-distilbert_base_cased_en.md @@ -33,30 +33,8 @@ This model is a distilled version of the [BERT base model](https://huggingface.c ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -embeddings = DistilBertEmbeddings.pretrained("distilbert_base_cased", "en") \ -.setInputCols("sentence", "token") \ -.setOutputCol("embeddings") -nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) -``` -```scala -val embeddings = DistilBertEmbeddings.pretrained("distilbert_base_cased", "en") -.setInputCols("sentence", "token") -.setOutputCol("embeddings") -val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) -``` -{:.nlu-block} -```python -import nlu -nlu.load("en.embed.distilbert").predict("""Put your text here.""") -``` - -
- {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_de.md b/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_de.md index 5ded15b165dcb2..795157372e83b9 100644 --- a/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_de.md +++ b/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_de.md @@ -33,55 +33,7 @@ Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and im ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_german_cased","de") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_german_cased","de") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Funken NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.distilbert_base_german_cased").predict("""Ich liebe Funken NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_opt_de.md b/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_opt_de.md index aa7520c32d32f5..381873114ba53c 100644 --- a/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_opt_de.md +++ b/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_opt_de.md @@ -33,55 +33,7 @@ Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and im ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_german_cased","de") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_german_cased","de") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Funken NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.distilbert_base_german_cased").predict("""Ich liebe Funken NLP""") -``` -
{:.model-param} diff --git a/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_quantized_de.md b/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_quantized_de.md index 57b94449d930ed..7798f1e06d0585 100644 --- a/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_quantized_de.md +++ b/docs/_posts/ahmedlone127/2023-06-28-distilbert_embeddings_distilbert_base_german_cased_quantized_de.md @@ -33,55 +33,7 @@ Pretrained DistilBERT Embeddings model, uploaded to Hugging Face, adapted and im ## How to use -
-{% include programmingLanguageSelectScalaPythonNLU.html %} -```python -documentAssembler = DocumentAssembler() \ -.setInputCol("text") \ -.setOutputCol("document") - -tokenizer = Tokenizer() \ -.setInputCols("document") \ -.setOutputCol("token") - -embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_german_cased","de") \ -.setInputCols(["document", "token"]) \ -.setOutputCol("embeddings") - -pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) - -data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") - -result = pipeline.fit(data).transform(data) -``` -```scala -val documentAssembler = new DocumentAssembler() -.setInputCol("text") -.setOutputCol("document") - -val tokenizer = new Tokenizer() -.setInputCols(Array("document")) -.setOutputCol("token") - -val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_distilbert_base_german_cased","de") -.setInputCols(Array("document", "token")) -.setOutputCol("embeddings") - -val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) - -val data = Seq("Ich liebe Funken NLP").toDF("text") - -val result = pipeline.fit(data).transform(data) -``` - - -{:.nlu-block} -```python -import nlu -nlu.load("de.embed.distilbert_base_german_cased").predict("""Ich liebe Funken NLP""") -``` -
{:.model-param} From 182bc05333789075478fac625d73f7c2d492eefd Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Fri, 21 Jul 2023 19:37:58 +0700 Subject: [PATCH 26/55] Add model 2023-07-20-xlm_roberta_large_zero_shot_classifier_xnli_anli_xx (#13900) Co-authored-by: ahmedlone127 --- ...large_zero_shot_classifier_xnli_anli_xx.md | 106 ++++++++++++++++++ 1 file changed, 106 insertions(+) create mode 100644 docs/_posts/ahmedlone127/2023-07-20-xlm_roberta_large_zero_shot_classifier_xnli_anli_xx.md diff --git a/docs/_posts/ahmedlone127/2023-07-20-xlm_roberta_large_zero_shot_classifier_xnli_anli_xx.md b/docs/_posts/ahmedlone127/2023-07-20-xlm_roberta_large_zero_shot_classifier_xnli_anli_xx.md new file mode 100644 index 00000000000000..467209f7ce1fce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-20-xlm_roberta_large_zero_shot_classifier_xnli_anli_xx.md @@ -0,0 +1,106 @@ +--- +layout: model +title: XlmRoBertaZero-Shot Classification Large xlm_roberta_large_zero_shot_classifier_xnli_anli +author: John Snow Labs +name: xlm_roberta_large_zero_shot_classifier_xnli_anli +date: 2023-07-20 +tags: [zero_shot, xx, open_source, tensorflow] +task: Zero-Shot Classification +language: xx +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: XlmRoBertaForZeroShotClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is intended to be used for zero-shot text classification, especially in English. It is fine-tuned on NLI by using XlmRoberta Large model. + +XlmRoBertaForZeroShotClassificationusing a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of TFXLMRoBertaForZeroShotClassification models, but these models don’t require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it’s slower but it is much more flexible. + +We used TFXLMRobertaForSequenceClassification to train this model and used XlmRoBertaForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale! + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_zero_shot_classifier_xnli_anli_xx_5.0.2_3.0_1689886974932.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_zero_shot_classifier_xnli_anli_xx_5.0.2_3.0_1689886974932.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +document_assembler = DocumentAssembler() \ +.setInputCol('text') \ +.setOutputCol('document') + +tokenizer = Tokenizer() \ +.setInputCols(['document']) \ +.setOutputCol('token') + +zeroShotClassifier = XlmRobertaForSequenceClassification \ +.pretrained('xlm_roberta_large_zero_shot_classifier_xnli_anli', 'xx') \ +.setInputCols(['token', 'document']) \ +.setOutputCol('class') \ +.setCaseSensitive(True) \ +.setMaxSentenceLength(512) \ +.setCandidateLabels(["urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"]) + +pipeline = Pipeline(stages=[ +document_assembler, +tokenizer, +zeroShotClassifier +]) + +example = spark.createDataFrame([['I have a problem with my iphone that needs to be resolved asap!!']]).toDF("text") +result = pipeline.fit(example).transform(example) + +``` +```scala +val document_assembler = DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = Tokenizer() +.setInputCols("document") +.setOutputCol("token") + +val zeroShotClassifier = XlmRobertaForSequenceClassification.pretrained("xlm_roberta_large_zero_shot_classifier_xnli_anli", "xx") +.setInputCols("document", "token") +.setOutputCol("class") +.setCaseSensitive(true) +.setMaxSentenceLength(512) +.setCandidateLabels(Array("urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology")) + +val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier)) +val example = Seq("I have a problem with my iphone that needs to be resolved asap!!").toDS.toDF("text") +val result = pipeline.fit(example).transform(example) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_roberta_large_zero_shot_classifier_xnli_anli| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, document]| +|Output Labels:|[label]| +|Language:|xx| +|Size:|2.0 GB| +|Case sensitive:|true| \ No newline at end of file From 9a1bea5f22bcfd41581016bbda7b9c3d2bb25375 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Fri, 28 Jul 2023 16:23:26 +0700 Subject: [PATCH 27/55] Add model 2023-07-28-twitter_xlm_roberta_base_sentiment_en (#13905) Co-authored-by: veerdhwaj --- ...8-twitter_xlm_roberta_base_sentiment_en.md | 88 +++++++++++++++++++ 1 file changed, 88 insertions(+) create mode 100644 docs/_posts/veerdhwaj/2023-07-28-twitter_xlm_roberta_base_sentiment_en.md diff --git a/docs/_posts/veerdhwaj/2023-07-28-twitter_xlm_roberta_base_sentiment_en.md b/docs/_posts/veerdhwaj/2023-07-28-twitter_xlm_roberta_base_sentiment_en.md new file mode 100644 index 00000000000000..03d5d0ac48e589 --- /dev/null +++ b/docs/_posts/veerdhwaj/2023-07-28-twitter_xlm_roberta_base_sentiment_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: twitter-xlm-roberta-base-sentiment +author: veerdhwaj +name: twitter_xlm_roberta_base_sentiment +date: 2023-07-28 +tags: [sentiment, roberta, en, open_source, tensorflow] +task: Text Classification +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.2 +supported: false +engine: tensorflow +annotator: XlmRoBertaForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages +Huggingface : https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment + +## Predicted Entities + +`sentiment` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/community.johnsnowlabs.com/veerdhwaj/twitter_xlm_roberta_base_sentiment_en_5.0.0_3.2_1690535217423.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://community.johnsnowlabs.com/veerdhwaj/twitter_xlm_roberta_base_sentiment_en_5.0.0_3.2_1690535217423.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +import spark.implicits._ +import com.johnsnowlabs.nlp.base._ +import com.johnsnowlabs.nlp.annotator._ +import org.apache.spark.ml.Pipeline + +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = XlmRoBertaForSequenceClassification.pretrained('twitter_xlm_roberta_base_sentiment') + .setInputCols("token", "document") + .setOutputCol("class") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array( + documentAssembler, + tokenizer, + sequenceClassifier +)) + +val data = Seq("I loved this movie when I was a child.", "It was pretty boring.").toDF("text") +val result = pipeline.fit(data).transform(data) + +result.select("class.result").show(false) +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|twitter_xlm_roberta_base_sentiment| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Community| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.0 GB| +|Case sensitive:|true| +|Max sentence length:|512| \ No newline at end of file From cc00383ad879fb9e6c972bfef84f9033cda1b3b0 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Wed, 2 Aug 2023 14:41:12 +0700 Subject: [PATCH 28/55] 2023-07-30-albert_embeddings_ALR_BERT_ro (#13910) * Add model 2023-07-30-albert_embeddings_ALR_BERT_ro * Add model 2023-07-30-albert_embeddings_albert_base_japanese_v1_ja * Add model 2023-07-30-albert_embeddings_albert_large_arabic_ar * Add model 2023-07-30-albert_embeddings_albert_fa_base_v2_fa * Add model 2023-07-30-albert_embeddings_albert_german_ner_de * Add model 2023-07-30-albert_embeddings_albert_fa_zwnj_base_v2_fa * Add model 2023-07-30-albert_embeddings_marathi_albert_mr * Add model 2023-07-30-albert_embeddings_albert_tiny_bahasa_cased_ms * Add model 2023-07-30-albert_embeddings_albert_base_bahasa_cased_ms * Add model 2023-07-30-albert_embeddings_fralbert_base_fr * Add model 2023-07-30-albert_embeddings_marathi_albert_v2_mr * Add model 2023-07-30-albert_embeddings_albert_base_arabic_ar * Add model 2023-07-30-albert_embeddings_albert_large_bahasa_cased_ms * Add model 2023-07-30-camembert_embeddings_das22_10_camembert_pretrained_fr * Add model 2023-07-30-camembert_embeddings_zhenghuabin_generic_model_fr * Add model 2023-07-30-camembert_embeddings_das22_10_camembert_pretrained_fr * Add model 2023-07-30-camembert_embeddings_camembert_mlm_fr * Add model 2023-07-30-camembert_embeddings_edge2992_generic_model_fr * Add model 2023-07-30-camembert_embeddings_elusive_magnolia_generic_model_fr * Add model 2023-07-30-camembert_embeddings_zhenghuabin_generic_model_fr * Add model 2023-07-30-camembert_embeddings_camembert_aux_amandes_mt * Add model 2023-07-30-camembert_embeddings_elliotsmith_generic_model_fr * Add model 2023-07-30-camembert_embeddings_dianeshan_generic_model_fr * fixed wrong version * Add model 2023-07-31-camembert_embeddings_ankitkupadhyay_generic_model_fr * Add model 2023-07-31-camembert_embeddings_devtrent_generic_model_fr * Add model 2023-07-31-camembert_embeddings_eduardopds_generic_model_fr * Add model 2023-07-31-camembert_embeddings_adeiMousa_generic_model_fr * Add model 2023-07-31-camembert_embeddings_ericchchiu_generic_model_fr * Add model 2023-07-31-camembert_embeddings_Sebu_generic_model_fr * Add model 2023-07-31-camembert_embeddings_Weipeng_generic_model_fr * Add model 2023-07-31-camembert_embeddings_codingJacob_generic_model_fr * Add model 2023-07-31-camembert_embeddings_SummFinFR_fr * Add model 2023-07-31-camembert_embeddings_MYX4567_generic_model_fr * Add model 2023-07-31-camembert_embeddings_Katster_generic_model_fr * Add model 2023-07-31-camembert_embeddings_MYX4567_generic_model_fr * Add model 2023-07-31-camembert_embeddings_JonathanSum_generic_model_fr * Add model 2023-07-31-camembert_embeddings_Leisa_generic_model_fr * Add model 2023-07-31-camembert_embeddings_adam1224_generic_model_fr * Add model 2023-07-31-camembert_embeddings_est_roberta_et * Add model 2023-07-31-camembert_embeddings_generic2_fr * Add model 2023-07-31-camembert_embeddings_ysharma_generic_model_2_fr * Add model 2023-07-31-camembert_embeddings_DoyyingFace_generic_model_fr * Add model 2023-07-31-camembert_embeddings_Henrywang_generic_model_fr * Add model 2023-07-31-camembert_embeddings_xkang_generic_model_fr * Add model 2023-07-31-camembert_embeddings_wangst_generic_model_fr * Add model 2023-07-31-camembert_embeddings_seyfullah_generic_model_fr * Add model 2023-07-31-camembert_embeddings_tnagata_generic_model_fr * Add model 2023-07-31-camembert_embeddings_yancong_generic_model_fr * Add model 2023-07-31-camembert_embeddings_safik_generic_model_fr * Add model 2023-07-31-camembert_embeddings_tpanza_generic_model_fr * Add model 2023-07-31-camembert_embeddings_peterhsu_generic_model_fr * Add model 2023-07-31-camembert_embeddings_pgperrone_generic_model_fr * Add model 2023-07-31-camembert_embeddings_osanseviero_generic_model_fr * Add model 2023-07-31-camembert_embeddings_lijingxin_generic_model_fr * Add model 2023-08-01-camembert_embeddings_kaushikacharya_generic_model_fr * Add model 2023-08-01-camembert_embeddings_new_generic_model_fr * Add model 2023-08-01-camembert_embeddings_mbateman_generic_model_fr * Add model 2023-08-01-camembert_embeddings_lijingxin_generic_model_2_fr * Add model 2023-08-01-camembert_embeddings_katrin_kc_generic_model_fr * Add model 2023-08-01-camembert_embeddings_linyi_generic_model_fr * Add model 2023-08-01-camembert_embeddings_lewtun_generic_model_fr * Add model 2023-08-01-camembert_embeddings_joe8zhang_generic_model_fr * Add model 2023-08-01-camembert_embeddings_sloberta_sl * Add model 2023-08-01-camembert_embeddings_generic_model_test_fr * Add model 2023-08-01-camembert_embeddings_jcai1_generic_model_fr * Add model 2023-08-01-camembert_embeddings_umberto_commoncrawl_cased_v1_it * Add model 2023-08-01-camembert_embeddings_DataikuNLP_camembert_base_fr * Add model 2023-08-01-camembert_embeddings_umberto_wikipedia_uncased_v1_it * Add model 2023-08-01-camembert_base_oscar_4gb_fr * Add model 2023-08-01-camembert_embeddings_distilcamembert_base_fr * Add model 2023-08-01-camembert_base_wikipedia_4gb_fr * Add model 2023-08-01-camembert_base_ccnet_fr * Add model 2023-08-01-camembert_base_oscar_4gb_fr * Add model 2023-08-01-camembert_embeddings_hackertec_generic_fr * Add model 2023-08-01-camembert_base_ccnet_fr * Add model 2023-08-01-camembert_embeddings_h4d35_generic_model_fr * Add model 2023-08-01-camembert_embeddings_bertweetfr_base_fr * Add model 2023-08-01-camembert_base_ccnet_4gb_fr * Add model 2023-08-01-camembert_base_ccnet_4gb_fr * Add model 2023-08-01-xlmroberta_embeddings_fairlex_fscs_minilm_xx * Add model 2023-08-01-xlmroberta_embeddings_fairlex_cail_minilm_zh * Add model 2023-08-01-camembert_base_fr * Add model 2023-08-01-camembert_base_opt_fr * Add model 2023-08-01-camembert_base_quantized_fr * Add model 2023-08-02-albert_base_uncased_en * Add model 2023-08-02-albert_base_uncased_opt_en * Add model 2023-08-02-albert_base_uncased_quantized_en * Add model 2023-08-02-albert_large_uncased_en * Add model 2023-08-02-albert_large_uncased_en * Add model 2023-08-02-albert_large_uncased_opt_en * Add model 2023-08-02-albert_large_uncased_quantized_en --------- Co-authored-by: ahmedlone127 --- ...023-07-30-albert_embeddings_ALR_BERT_ro.md | 99 +++++++++++++++++++ ...albert_embeddings_albert_base_arabic_ar.md | 99 +++++++++++++++++++ ..._embeddings_albert_base_bahasa_cased_ms.md | 99 +++++++++++++++++++ ...t_embeddings_albert_base_japanese_v1_ja.md | 99 +++++++++++++++++++ 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create mode 100644 docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_opt_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_quantized_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_opt_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_quantized_en.md diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_ALR_BERT_ro.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_ALR_BERT_ro.md new file mode 100644 index 00000000000000..86db1efa595bc0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_ALR_BERT_ro.md @@ -0,0 +1,99 @@ +--- +layout: model +title: Romanian ALBERT Embeddings (from dragosnicolae555) +author: John Snow Labs +name: albert_embeddings_ALR_BERT +date: 2023-07-30 +tags: [albert, embeddings, ro, open_source, onnx] +task: Embeddings +language: ro +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `ALR_BERT` is a Romanian model orginally trained by `dragosnicolae555`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_ALR_BERT_ro_5.0.2_3.0_1690752767725.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_ALR_BERT_ro_5.0.2_3.0_1690752767725.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = AlbertEmbeddings.pretrained("albert_embeddings_ALR_BERT","ro") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Îmi place Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_ALR_BERT","ro") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Îmi place Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ro.embed.ALR_BERT").predict("""Îmi place Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_embeddings_ALR_BERT| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ro| +|Size:|51.7 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_arabic_ar.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_arabic_ar.md new file mode 100644 index 00000000000000..22896e9a9cd377 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_arabic_ar.md @@ -0,0 +1,99 @@ +--- +layout: model +title: Arabic ALBERT Embeddings (Base) +author: John Snow Labs +name: albert_embeddings_albert_base_arabic +date: 2023-07-30 +tags: [albert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-base-arabic` is a Arabic model orginally trained by `asafaya`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_base_arabic_ar_5.0.2_3.0_1690753212237.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_base_arabic_ar_5.0.2_3.0_1690753212237.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_base_arabic","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_base_arabic","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.albert").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_embeddings_albert_base_arabic| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|42.0 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_bahasa_cased_ms.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_bahasa_cased_ms.md new file mode 100644 index 00000000000000..f3fca4de758ac0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_bahasa_cased_ms.md @@ -0,0 +1,99 @@ +--- +layout: model +title: Malay ALBERT Embeddings (Base) +author: John Snow Labs +name: albert_embeddings_albert_base_bahasa_cased +date: 2023-07-30 +tags: [albert, embeddings, ms, open_source, onnx] +task: Embeddings +language: ms +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-base-bahasa-cased` is a Malay model orginally trained by `malay-huggingface`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_base_bahasa_cased_ms_5.0.2_3.0_1690753174981.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_base_bahasa_cased_ms_5.0.2_3.0_1690753174981.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_base_bahasa_cased","ms") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Saya suka Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_base_bahasa_cased","ms") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Saya suka Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ms.embed.albert_base_bahasa_cased").predict("""Saya suka Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_embeddings_albert_base_bahasa_cased| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ms| +|Size:|42.9 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_japanese_v1_ja.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_japanese_v1_ja.md new file mode 100644 index 00000000000000..ad1185dcccdb33 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_japanese_v1_ja.md @@ -0,0 +1,99 @@ +--- +layout: model +title: Japanese ALBERT Embeddings (from ken11) +author: John Snow Labs +name: albert_embeddings_albert_base_japanese_v1 +date: 2023-07-30 +tags: [albert, embeddings, ja, open_source, onnx] +task: Embeddings +language: ja +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-base-japanese-v1` is a Japanese model orginally trained by `ken11`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_base_japanese_v1_ja_5.0.2_3.0_1690752780150.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_base_japanese_v1_ja_5.0.2_3.0_1690752780150.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_base_japanese_v1","ja") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["私はSpark NLPを愛しています"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_base_japanese_v1","ja") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("私はSpark NLPを愛しています").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ja.embed.albert_base_japanese_v1").predict("""私はSpark NLPを愛しています""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_embeddings_albert_base_japanese_v1| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ja| +|Size:|42.8 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_fa_base_v2_fa.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_fa_base_v2_fa.md new file mode 100644 index 00000000000000..db63deaaa6b3dd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_fa_base_v2_fa.md @@ -0,0 +1,99 @@ +--- +layout: model +title: Persian ALBERT Embeddings (from m3hrdadfi) +author: John Snow Labs +name: albert_embeddings_albert_fa_base_v2 +date: 2023-07-30 +tags: [albert, embeddings, fa, open_source, onnx] +task: Embeddings +language: fa +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-fa-base-v2` is a Persian model orginally trained by `m3hrdadfi`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_fa_base_v2_fa_5.0.2_3.0_1690752839758.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_fa_base_v2_fa_5.0.2_3.0_1690752839758.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_fa_base_v2","fa") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["من عاشق جرقه NLP هستم"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_fa_base_v2","fa") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("من عاشق جرقه NLP هستم").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fa.embed.albert").predict("""من عاشق جرقه NLP هستم""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_embeddings_albert_fa_base_v2| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|fa| +|Size:|66.3 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_fa_zwnj_base_v2_fa.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_fa_zwnj_base_v2_fa.md new file mode 100644 index 00000000000000..6537edaada379d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_fa_zwnj_base_v2_fa.md @@ -0,0 +1,99 @@ +--- +layout: model +title: Persian (Farsi) ALBERT Embeddings +author: John Snow Labs +name: albert_embeddings_albert_fa_zwnj_base_v2 +date: 2023-07-30 +tags: [albert, embeddings, fa, open_source, onnx] +task: Embeddings +language: fa +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-fa-zwnj-base-v2` is a Persian model orginally trained by `HooshvareLab`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_fa_zwnj_base_v2_fa_5.0.2_3.0_1690752897049.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_fa_zwnj_base_v2_fa_5.0.2_3.0_1690752897049.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_fa_zwnj_base_v2","fa") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["من عاشق جرقه NLP هستم"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_fa_zwnj_base_v2","fa") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("من عاشق جرقه NLP هستم").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fa.embed.albert_fa_zwnj_base_v2").predict("""من عاشق جرقه NLP هستم""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_embeddings_albert_fa_zwnj_base_v2| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|fa| +|Size:|41.9 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_german_ner_de.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_german_ner_de.md new file mode 100644 index 00000000000000..038a9d98ca32ff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_german_ner_de.md @@ -0,0 +1,99 @@ +--- +layout: model +title: German ALBERT Embeddings (from abhilash1910) +author: John Snow Labs +name: albert_embeddings_albert_german_ner +date: 2023-07-30 +tags: [albert, embeddings, de, open_source, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-german-ner` is a German model orginally trained by `abhilash1910`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_german_ner_de_5.0.2_3.0_1690752850054.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_german_ner_de_5.0.2_3.0_1690752850054.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_german_ner","de") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_german_ner","de") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ich liebe Funken NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.embed.albert_german_ner").predict("""Ich liebe Funken NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_embeddings_albert_german_ner| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|de| +|Size:|42.0 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_large_arabic_ar.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_large_arabic_ar.md new file mode 100644 index 00000000000000..45627e3aa3b848 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_large_arabic_ar.md @@ -0,0 +1,99 @@ +--- +layout: model +title: Arabic ALBERT Embeddings (Large) +author: John Snow Labs +name: albert_embeddings_albert_large_arabic +date: 2023-07-30 +tags: [albert, embeddings, ar, open_source, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-large-arabic` is a Arabic model orginally trained by `asafaya`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_large_arabic_ar_5.0.2_3.0_1690752835564.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_large_arabic_ar_5.0.2_3.0_1690752835564.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_large_arabic","ar") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_large_arabic","ar") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("أنا أحب شرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.embed.albert_large_arabic").predict("""أنا أحب شرارة NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_embeddings_albert_large_arabic| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ar| +|Size:|62.8 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_large_bahasa_cased_ms.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_large_bahasa_cased_ms.md new file mode 100644 index 00000000000000..b1d5a55edb5f34 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_large_bahasa_cased_ms.md @@ -0,0 +1,99 @@ +--- +layout: model +title: Malay ALBERT Embeddings (Large) +author: John Snow Labs +name: albert_embeddings_albert_large_bahasa_cased +date: 2023-07-30 +tags: [albert, embeddings, ms, open_source, onnx] +task: Embeddings +language: ms +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-large-bahasa-cased` is a Malay model orginally trained by `malay-huggingface`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_large_bahasa_cased_ms_5.0.2_3.0_1690753388948.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_large_bahasa_cased_ms_5.0.2_3.0_1690753388948.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_large_bahasa_cased","ms") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Saya suka Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_large_bahasa_cased","ms") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Saya suka Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ms.embed.albert").predict("""Saya suka Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_embeddings_albert_large_bahasa_cased| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ms| +|Size:|63.6 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_tiny_bahasa_cased_ms.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_tiny_bahasa_cased_ms.md new file mode 100644 index 00000000000000..a50ff855bec557 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_tiny_bahasa_cased_ms.md @@ -0,0 +1,99 @@ +--- +layout: model +title: Malay ALBERT Embeddings (Tiny) +author: John Snow Labs +name: albert_embeddings_albert_tiny_bahasa_cased +date: 2023-07-30 +tags: [albert, embeddings, ms, open_source, onnx] +task: Embeddings +language: ms +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-tiny-bahasa-cased` is a Malay model orginally trained by `malay-huggingface`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_tiny_bahasa_cased_ms_5.0.2_3.0_1690752867859.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_tiny_bahasa_cased_ms_5.0.2_3.0_1690752867859.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_tiny_bahasa_cased","ms") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Saya suka Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_tiny_bahasa_cased","ms") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Saya suka Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ms.embed.albert_tiny_bahasa_cased").predict("""Saya suka Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_embeddings_albert_tiny_bahasa_cased| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|ms| +|Size:|21.3 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_fralbert_base_fr.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_fralbert_base_fr.md new file mode 100644 index 00000000000000..36baf514c0219f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_fralbert_base_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French ALBERT Embeddings (from qwant) +author: John Snow Labs +name: albert_embeddings_fralbert_base +date: 2023-07-30 +tags: [albert, embeddings, fr, open_source, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `fralbert-base` is a French model orginally trained by `qwant`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_fralbert_base_fr_5.0.2_3.0_1690752813444.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_fralbert_base_fr_5.0.2_3.0_1690752813444.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = AlbertEmbeddings.pretrained("albert_embeddings_fralbert_base","fr") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark Nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_fralbert_base","fr") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark Nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.albert").predict("""J'adore Spark Nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_embeddings_fralbert_base| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|fr| +|Size:|43.0 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_marathi_albert_mr.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_marathi_albert_mr.md new file mode 100644 index 00000000000000..f1e0b9468b9294 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_marathi_albert_mr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: Marathi ALBERT Embeddings (v1) +author: John Snow Labs +name: albert_embeddings_marathi_albert +date: 2023-07-30 +tags: [albert, embeddings, mr, open_source, onnx] +task: Embeddings +language: mr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `marathi-albert` is a Marathi model orginally trained by `l3cube-pune`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_marathi_albert_mr_5.0.2_3.0_1690752853424.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_marathi_albert_mr_5.0.2_3.0_1690752853424.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = AlbertEmbeddings.pretrained("albert_embeddings_marathi_albert","mr") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["मला स्पार्क एनएलपी आवडते"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_marathi_albert","mr") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("मला स्पार्क एनएलपी आवडते").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("mr.embed.albert").predict("""मला स्पार्क एनएलपी आवडते""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_embeddings_marathi_albert| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|mr| +|Size:|42.6 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_marathi_albert_v2_mr.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_marathi_albert_v2_mr.md new file mode 100644 index 00000000000000..401cb9ec3030b0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_marathi_albert_v2_mr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: Marathi ALBERT Embeddings (v2) +author: John Snow Labs +name: albert_embeddings_marathi_albert_v2 +date: 2023-07-30 +tags: [albert, embeddings, mr, open_source, onnx] +task: Embeddings +language: mr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `marathi-albert-v2` is a Marathi model orginally trained by `l3cube-pune`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_marathi_albert_v2_mr_5.0.2_3.0_1690753295251.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_marathi_albert_v2_mr_5.0.2_3.0_1690753295251.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ +.setInputCol("text") \ +.setOutputCol("document") + +tokenizer = Tokenizer() \ +.setInputCols("document") \ +.setOutputCol("token") + +embeddings = AlbertEmbeddings.pretrained("albert_embeddings_marathi_albert_v2","mr") \ +.setInputCols(["document", "token"]) \ +.setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["मला स्पार्क एनएलपी आवडते"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = new Tokenizer() +.setInputCols(Array("document")) +.setOutputCol("token") + +val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_marathi_albert_v2","mr") +.setInputCols(Array("document", "token")) +.setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("मला स्पार्क एनएलपी आवडते").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("mr.embed.albert_v2").predict("""मला स्पार्क एनएलपी आवडते""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_embeddings_marathi_albert_v2| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[bert]| +|Language:|mr| +|Size:|125.5 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_camembert_aux_amandes_mt.md b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_camembert_aux_amandes_mt.md new file mode 100644 index 00000000000000..2de5a5480b5f52 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_camembert_aux_amandes_mt.md @@ -0,0 +1,99 @@ +--- +layout: model +title: Maltese CamemBert Embeddings (from fenrhjen) +author: John Snow Labs +name: camembert_embeddings_camembert_aux_amandes +date: 2023-07-30 +tags: [mt, open_source, camembert, embeddings, onnx] +task: Embeddings +language: mt +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `camembert_aux_amandes` is a Maltese model orginally trained by `fenrhjen`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_camembert_aux_amandes_mt_5.0.2_3.0_1690755523370.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_camembert_aux_amandes_mt_5.0.2_3.0_1690755523370.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_camembert_aux_amandes","mt") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I Love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_camembert_aux_amandes","mt") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I Love Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("mt.embed.camembert").predict("""I Love Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_camembert_aux_amandes| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|mt| +|Size:|412.4 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_camembert_mlm_fr.md b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_camembert_mlm_fr.md new file mode 100644 index 00000000000000..bf7603675b6c3e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_camembert_mlm_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from Jodsa) +author: John Snow Labs +name: camembert_embeddings_camembert_mlm +date: 2023-07-30 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `camembert_mlm` is a French model orginally trained by `Jodsa`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_camembert_mlm_fr_5.0.2_3.0_1690755250840.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_camembert_mlm_fr_5.0.2_3.0_1690755250840.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_camembert_mlm","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_camembert_mlm","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.by_jodsa").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_camembert_mlm| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|417.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_das22_10_camembert_pretrained_fr.md b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_das22_10_camembert_pretrained_fr.md new file mode 100644 index 00000000000000..88001282ca4e52 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_das22_10_camembert_pretrained_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from HueyNemud) +author: John Snow Labs +name: camembert_embeddings_das22_10_camembert_pretrained +date: 2023-07-30 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `das22-10-camembert_pretrained` is a French model orginally trained by `HueyNemud`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_das22_10_camembert_pretrained_fr_5.0.2_3.0_1690755233634.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_das22_10_camembert_pretrained_fr_5.0.2_3.0_1690755233634.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_das22_10_camembert_pretrained","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_das22_10_camembert_pretrained","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.by_hueynemud").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_das22_10_camembert_pretrained| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|412.8 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_dianeshan_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_dianeshan_generic_model_fr.md new file mode 100644 index 00000000000000..2b802a94df3c0a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_dianeshan_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from dianeshan) +author: John Snow Labs +name: camembert_embeddings_dianeshan_generic_model +date: 2023-07-30 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `dianeshan`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_dianeshan_generic_model_fr_5.0.2_3.0_1690755640623.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_dianeshan_generic_model_fr_5.0.2_3.0_1690755640623.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_dianeshan_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_dianeshan_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_dianeshan").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_dianeshan_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_edge2992_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_edge2992_generic_model_fr.md new file mode 100644 index 00000000000000..1696cd080f1959 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_edge2992_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from edge2992) +author: John Snow Labs +name: camembert_embeddings_edge2992_generic_model +date: 2023-07-30 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `edge2992`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_edge2992_generic_model_fr_5.0.2_3.0_1690755314295.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_edge2992_generic_model_fr_5.0.2_3.0_1690755314295.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_edge2992_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_edge2992_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_edge2992").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_edge2992_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_elliotsmith_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_elliotsmith_generic_model_fr.md new file mode 100644 index 00000000000000..04b6b4dccd2773 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_elliotsmith_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from elliotsmith) +author: John Snow Labs +name: camembert_embeddings_elliotsmith_generic_model +date: 2023-07-30 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `elliotsmith`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_elliotsmith_generic_model_fr_5.0.2_3.0_1690755611886.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_elliotsmith_generic_model_fr_5.0.2_3.0_1690755611886.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_elliotsmith_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_elliotsmith_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_elliotsmith").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_elliotsmith_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_elusive_magnolia_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_elusive_magnolia_generic_model_fr.md new file mode 100644 index 00000000000000..9b0644bf825cea --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_elusive_magnolia_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from elusive-magnolia) +author: John Snow Labs +name: camembert_embeddings_elusive_magnolia_generic_model +date: 2023-07-30 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `elusive-magnolia`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_elusive_magnolia_generic_model_fr_5.0.2_3.0_1690755320528.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_elusive_magnolia_generic_model_fr_5.0.2_3.0_1690755320528.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_elusive_magnolia_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_elusive_magnolia_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_elusive_magnolia").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_elusive_magnolia_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_zhenghuabin_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_zhenghuabin_generic_model_fr.md new file mode 100644 index 00000000000000..5bfe25af51d77e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_zhenghuabin_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from zhenghuabin) +author: John Snow Labs +name: camembert_embeddings_zhenghuabin_generic_model +date: 2023-07-30 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy_model` is a French model orginally trained by `zhenghuabin`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_zhenghuabin_generic_model_fr_5.0.2_3.0_1690755345824.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_zhenghuabin_generic_model_fr_5.0.2_3.0_1690755345824.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_zhenghuabin_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_zhenghuabin_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_zhenghuabin").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_zhenghuabin_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_DoyyingFace_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_DoyyingFace_generic_model_fr.md new file mode 100644 index 00000000000000..eb8cc1f3446f54 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_DoyyingFace_generic_model_fr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: French CamemBert Embeddings (from DoyyingFace) +author: John Snow Labs +name: camembert_embeddings_DoyyingFace_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `DoyyingFace`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_DoyyingFace_generic_model_fr_5.0.2_3.0_1690840568040.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_DoyyingFace_generic_model_fr_5.0.2_3.0_1690840568040.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_DoyyingFace_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_DoyyingFace_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_DoyyingFace_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Henrywang_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Henrywang_generic_model_fr.md new file mode 100644 index 00000000000000..850aecb10f995c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Henrywang_generic_model_fr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: French CamemBert Embeddings (from Henrywang) +author: John Snow Labs +name: camembert_embeddings_Henrywang_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `Henrywang`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Henrywang_generic_model_fr_5.0.2_3.0_1690840573449.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Henrywang_generic_model_fr_5.0.2_3.0_1690840573449.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Henrywang_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Henrywang_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_Henrywang_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_JonathanSum_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_JonathanSum_generic_model_fr.md new file mode 100644 index 00000000000000..373bdacf419b42 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_JonathanSum_generic_model_fr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: French CamemBert Embeddings (from JonathanSum) +author: John Snow Labs +name: camembert_embeddings_JonathanSum_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `JonathanSum`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_JonathanSum_generic_model_fr_5.0.2_3.0_1690839719364.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_JonathanSum_generic_model_fr_5.0.2_3.0_1690839719364.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_JonathanSum_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_JonathanSum_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_JonathanSum_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Katster_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Katster_generic_model_fr.md new file mode 100644 index 00000000000000..2521d52f8bcab8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Katster_generic_model_fr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: French CamemBert Embeddings (from Katster) +author: John Snow Labs +name: camembert_embeddings_Katster_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `Katster`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Katster_generic_model_fr_5.0.2_3.0_1690839314673.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Katster_generic_model_fr_5.0.2_3.0_1690839314673.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Katster_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Katster_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_Katster_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Leisa_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Leisa_generic_model_fr.md new file mode 100644 index 00000000000000..11fe2eba2f4b9e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Leisa_generic_model_fr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: French CamemBert Embeddings (from Leisa) +author: John Snow Labs +name: camembert_embeddings_Leisa_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `Leisa`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Leisa_generic_model_fr_5.0.2_3.0_1690839743810.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Leisa_generic_model_fr_5.0.2_3.0_1690839743810.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Leisa_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Leisa_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_Leisa_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_MYX4567_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_MYX4567_generic_model_fr.md new file mode 100644 index 00000000000000..2fe8dc27a6876f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_MYX4567_generic_model_fr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: French CamemBert Embeddings (from MYX4567) +author: John Snow Labs +name: camembert_embeddings_MYX4567_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `MYX4567`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_MYX4567_generic_model_fr_5.0.2_3.0_1690839423024.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_MYX4567_generic_model_fr_5.0.2_3.0_1690839423024.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_MYX4567_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_MYX4567_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_MYX4567_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Sebu_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Sebu_generic_model_fr.md new file mode 100644 index 00000000000000..916051f233bf31 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Sebu_generic_model_fr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: French CamemBert Embeddings (from Sebu) +author: John Snow Labs +name: camembert_embeddings_Sebu_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `Sebu`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Sebu_generic_model_fr_5.0.2_3.0_1690838692036.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Sebu_generic_model_fr_5.0.2_3.0_1690838692036.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Sebu_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Sebu_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_Sebu_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_SummFinFR_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_SummFinFR_fr.md new file mode 100644 index 00000000000000..24347b800134c1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_SummFinFR_fr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: French CamemBert Embeddings (from Ghani-25) +author: John Snow Labs +name: camembert_embeddings_SummFinFR +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `SummFinFR` is a French model orginally trained by `Ghani-25`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_SummFinFR_fr_5.0.2_3.0_1690838983983.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_SummFinFR_fr_5.0.2_3.0_1690838983983.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_SummFinFR","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_SummFinFR","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_SummFinFR| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|412.4 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Weipeng_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Weipeng_generic_model_fr.md new file mode 100644 index 00000000000000..edafd431387b1b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Weipeng_generic_model_fr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: French CamemBert Embeddings (from Weipeng) +author: John Snow Labs +name: camembert_embeddings_Weipeng_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `Weipeng`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Weipeng_generic_model_fr_5.0.2_3.0_1690838714201.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Weipeng_generic_model_fr_5.0.2_3.0_1690838714201.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Weipeng_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Weipeng_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_Weipeng_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_adam1224_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_adam1224_generic_model_fr.md new file mode 100644 index 00000000000000..5fa59759dab81d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_adam1224_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from adam1224) +author: John Snow Labs +name: camembert_embeddings_adam1224_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `adam1224`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_adam1224_generic_model_fr_5.0.2_3.0_1690839841421.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_adam1224_generic_model_fr_5.0.2_3.0_1690839841421.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_adam1224_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_adam1224_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_adam1224").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_adam1224_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_adeiMousa_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_adeiMousa_generic_model_fr.md new file mode 100644 index 00000000000000..9d644c783ef626 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_adeiMousa_generic_model_fr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: French CamemBert Embeddings (from adeiMousa) +author: John Snow Labs +name: camembert_embeddings_adeiMousa_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `adeiMousa`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_adeiMousa_generic_model_fr_5.0.2_3.0_1690838633068.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_adeiMousa_generic_model_fr_5.0.2_3.0_1690838633068.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_adeiMousa_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_adeiMousa_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_adeiMousa_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ankitkupadhyay_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ankitkupadhyay_generic_model_fr.md new file mode 100644 index 00000000000000..7a4d6aab6d24ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ankitkupadhyay_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from ankitkupadhyay) +author: John Snow Labs +name: camembert_embeddings_ankitkupadhyay_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `ankitkupadhyay`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_ankitkupadhyay_generic_model_fr_5.0.2_3.0_1690838066435.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_ankitkupadhyay_generic_model_fr_5.0.2_3.0_1690838066435.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_ankitkupadhyay_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_ankitkupadhyay_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_ankitkupadhyay").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_ankitkupadhyay_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_codingJacob_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_codingJacob_generic_model_fr.md new file mode 100644 index 00000000000000..b325e41991bc1c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_codingJacob_generic_model_fr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: French CamemBert Embeddings (from codingJacob) +author: John Snow Labs +name: camembert_embeddings_codingJacob_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `codingJacob`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_codingJacob_generic_model_fr_5.0.2_3.0_1690838696305.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_codingJacob_generic_model_fr_5.0.2_3.0_1690838696305.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_codingJacob_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_codingJacob_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_codingJacob_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_devtrent_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_devtrent_generic_model_fr.md new file mode 100644 index 00000000000000..9cb2ddef4dc357 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_devtrent_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from devtrent) +author: John Snow Labs +name: camembert_embeddings_devtrent_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `devtrent`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_devtrent_generic_model_fr_5.0.2_3.0_1690838103819.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_devtrent_generic_model_fr_5.0.2_3.0_1690838103819.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_devtrent_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_devtrent_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_devtrent").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_devtrent_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_eduardopds_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_eduardopds_generic_model_fr.md new file mode 100644 index 00000000000000..b81bdd170972c8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_eduardopds_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from eduardopds) +author: John Snow Labs +name: camembert_embeddings_eduardopds_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `eduardopds`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_eduardopds_generic_model_fr_5.0.2_3.0_1690838312633.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_eduardopds_generic_model_fr_5.0.2_3.0_1690838312633.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_eduardopds_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_eduardopds_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_eduardopds").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_eduardopds_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ericchchiu_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ericchchiu_generic_model_fr.md new file mode 100644 index 00000000000000..32e911654222d4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ericchchiu_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from ericchchiu) +author: John Snow Labs +name: camembert_embeddings_ericchchiu_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `ericchchiu`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_ericchchiu_generic_model_fr_5.0.2_3.0_1690838666122.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_ericchchiu_generic_model_fr_5.0.2_3.0_1690838666122.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_ericchchiu_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_ericchchiu_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_ericchchiu").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_ericchchiu_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_est_roberta_et.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_est_roberta_et.md new file mode 100644 index 00000000000000..e6db30b8ea753c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_est_roberta_et.md @@ -0,0 +1,99 @@ +--- +layout: model +title: Estonian CamemBert Embeddings (from EMBEDDIA) +author: John Snow Labs +name: camembert_embeddings_est_roberta +date: 2023-07-31 +tags: [et, open_source, camembert, embeddings, onnx] +task: Embeddings +language: et +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `est-roberta` is a Estonian model orginally trained by `EMBEDDIA`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_est_roberta_et_5.0.2_3.0_1690839928128.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_est_roberta_et_5.0.2_3.0_1690839928128.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_est_roberta","et") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Ma armastan sädet nlp"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_est_roberta","et") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Ma armastan sädet nlp").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("et.embed.camembert").predict("""Ma armastan sädet nlp""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_est_roberta| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|et| +|Size:|277.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_generic2_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_generic2_fr.md new file mode 100644 index 00000000000000..cadd0e57fa1d59 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_generic2_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from hackertec) +author: John Snow Labs +name: camembert_embeddings_generic2 +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy2` is a French model orginally trained by `hackertec`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_generic2_fr_5.0.2_3.0_1690840126577.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_generic2_fr_5.0.2_3.0_1690840126577.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_generic2","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_generic2","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_hackertec").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_generic2| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_lijingxin_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_lijingxin_generic_model_fr.md new file mode 100644 index 00000000000000..b844d5bc5c7473 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_lijingxin_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from lijingxin) +author: John Snow Labs +name: camembert_embeddings_lijingxin_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `lijingxin`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_lijingxin_generic_model_fr_5.0.2_3.0_1690842315235.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_lijingxin_generic_model_fr_5.0.2_3.0_1690842315235.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_lijingxin_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_lijingxin_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_lijingxin").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_lijingxin_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_osanseviero_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_osanseviero_generic_model_fr.md new file mode 100644 index 00000000000000..019c9a8252d9b9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_osanseviero_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from osanseviero) +author: John Snow Labs +name: camembert_embeddings_osanseviero_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `osanseviero`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_osanseviero_generic_model_fr_5.0.2_3.0_1690842223769.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_osanseviero_generic_model_fr_5.0.2_3.0_1690842223769.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_osanseviero_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_osanseviero_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic_v2.by_osanseviero").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_osanseviero_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_peterhsu_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_peterhsu_generic_model_fr.md new file mode 100644 index 00000000000000..64617bef18c08b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_peterhsu_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from peterhsu) +author: John Snow Labs +name: camembert_embeddings_peterhsu_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `peterhsu`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_peterhsu_generic_model_fr_5.0.2_3.0_1690841897272.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_peterhsu_generic_model_fr_5.0.2_3.0_1690841897272.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_peterhsu_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_peterhsu_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_peterhsu").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_peterhsu_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_pgperrone_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_pgperrone_generic_model_fr.md new file mode 100644 index 00000000000000..1ffb45542ea74f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_pgperrone_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from pgperrone) +author: John Snow Labs +name: camembert_embeddings_pgperrone_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `pgperrone`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_pgperrone_generic_model_fr_5.0.2_3.0_1690842077639.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_pgperrone_generic_model_fr_5.0.2_3.0_1690842077639.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_pgperrone_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_pgperrone_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_pgperrone").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_pgperrone_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_safik_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_safik_generic_model_fr.md new file mode 100644 index 00000000000000..30ff64246a20f7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_safik_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from safik) +author: John Snow Labs +name: camembert_embeddings_safik_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `safik`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_safik_generic_model_fr_5.0.2_3.0_1690841450264.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_safik_generic_model_fr_5.0.2_3.0_1690841450264.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_safik_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_safik_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_safik").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_safik_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_seyfullah_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_seyfullah_generic_model_fr.md new file mode 100644 index 00000000000000..8282ef44896f1f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_seyfullah_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from seyfullah) +author: John Snow Labs +name: camembert_embeddings_seyfullah_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `seyfullah`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_seyfullah_generic_model_fr_5.0.2_3.0_1690841115822.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_seyfullah_generic_model_fr_5.0.2_3.0_1690841115822.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_seyfullah_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_seyfullah_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_seyfullah").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_seyfullah_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_tnagata_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_tnagata_generic_model_fr.md new file mode 100644 index 00000000000000..be15fa1d102c14 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_tnagata_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from tnagata) +author: John Snow Labs +name: camembert_embeddings_tnagata_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `tnagata`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_tnagata_generic_model_fr_5.0.2_3.0_1690841148034.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_tnagata_generic_model_fr_5.0.2_3.0_1690841148034.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_tnagata_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_tnagata_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_tnagata").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_tnagata_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_tpanza_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_tpanza_generic_model_fr.md new file mode 100644 index 00000000000000..af70dc70467023 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_tpanza_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from tpanza) +author: John Snow Labs +name: camembert_embeddings_tpanza_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `tpanza`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_tpanza_generic_model_fr_5.0.2_3.0_1690841821807.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_tpanza_generic_model_fr_5.0.2_3.0_1690841821807.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_tpanza_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_tpanza_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_tpanza").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_tpanza_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_wangst_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_wangst_generic_model_fr.md new file mode 100644 index 00000000000000..2e9322c530190b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_wangst_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from wangst) +author: John Snow Labs +name: camembert_embeddings_wangst_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `wangst`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_wangst_generic_model_fr_5.0.2_3.0_1690840911109.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_wangst_generic_model_fr_5.0.2_3.0_1690840911109.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_wangst_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_wangst_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_wangst").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_wangst_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_xkang_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_xkang_generic_model_fr.md new file mode 100644 index 00000000000000..b0465bddfdb95c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_xkang_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from xkang) +author: John Snow Labs +name: camembert_embeddings_xkang_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `xkang`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_xkang_generic_model_fr_5.0.2_3.0_1690840602500.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_xkang_generic_model_fr_5.0.2_3.0_1690840602500.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_xkang_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_xkang_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_xkang").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_xkang_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_yancong_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_yancong_generic_model_fr.md new file mode 100644 index 00000000000000..c11c87a7dc9850 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_yancong_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from yancong) +author: John Snow Labs +name: camembert_embeddings_yancong_generic_model +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `yancong`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_yancong_generic_model_fr_5.0.2_3.0_1690841226605.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_yancong_generic_model_fr_5.0.2_3.0_1690841226605.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_yancong_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_yancong_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_yancong").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_yancong_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ysharma_generic_model_2_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ysharma_generic_model_2_fr.md new file mode 100644 index 00000000000000..d2bcaa21f544ef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ysharma_generic_model_2_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from ysharma) +author: John Snow Labs +name: camembert_embeddings_ysharma_generic_model_2 +date: 2023-07-31 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model-2` is a French model orginally trained by `ysharma`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_ysharma_generic_model_2_fr_5.0.2_3.0_1690840552187.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_ysharma_generic_model_2_fr_5.0.2_3.0_1690840552187.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_ysharma_generic_model_2","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_ysharma_generic_model_2","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_ysharma").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_ysharma_generic_model_2| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_base_ccnet_4gb_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_ccnet_4gb_fr.md new file mode 100644 index 00000000000000..eb59efb3511280 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_ccnet_4gb_fr.md @@ -0,0 +1,86 @@ +--- +layout: model +title: CamemBERT Subsample of CCNet +author: John Snow Labs +name: camembert_base_ccnet_4gb +date: 2023-08-01 +tags: [fr, french, embeddings, camembert, ccnet, open_source, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model. +For further information or requests, please go to [Camembert Website](https://camembert-model.fr/) + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_base_ccnet_4gb_fr_5.0.2_3.0_1690928369186.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_base_ccnet_4gb_fr_5.0.2_3.0_1690928369186.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = CamemBertEmbeddings.pretrained("camembert_base_ccnet_4gb", "fr") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = CamemBertEmbeddings.pretrained("camembert_base_ccnet_4gb", "fr") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert_ccnet4g").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_base_ccnet_4gb| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|263.4 MB| +|Case sensitive:|true| + +## Benchmarking + +```bash + +| Model | #params | Arch. | Training data | +|--------------------------------|--------------------------------|-------|-----------------------------------| +| `camembert-base` | 110M | Base | OSCAR (138 GB of text) | +| `camembert/camembert-large` | 335M | Large | CCNet (135 GB of text) | +| `camembert/camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) | +| `camembert/camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) | +| `camembert/camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) | +| `camembert/camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) | +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_base_ccnet_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_ccnet_fr.md new file mode 100644 index 00000000000000..f6314446737cb8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_ccnet_fr.md @@ -0,0 +1,86 @@ +--- +layout: model +title: CamemBERT Base CCNet +author: John Snow Labs +name: camembert_base_ccnet +date: 2023-08-01 +tags: [fr, french, embeddings, camembert, ccnet, open_source, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model. +For further information or requests, please go to [Camembert Website](https://camembert-model.fr/) + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_base_ccnet_fr_5.0.2_3.0_1690927305918.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_base_ccnet_fr_5.0.2_3.0_1690927305918.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = CamemBertEmbeddings.pretrained("camembert_base_ccnet", "fr") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = CamemBertEmbeddings.pretrained("camembert_base_ccnet", "fr") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert_base_ccnet").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_base_ccnet| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|263.6 MB| +|Case sensitive:|true| + +## Benchmarking + +```bash + +| Model | #params | Arch. | Training data | +|--------------------------------|--------------------------------|-------|-----------------------------------| +| `camembert-base` | 110M | Base | OSCAR (138 GB of text) | +| `camembert/camembert-large` | 335M | Large | CCNet (135 GB of text) | +| `camembert/camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) | +| `camembert/camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) | +| `camembert/camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) | +| `camembert/camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) | +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_base_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_fr.md new file mode 100644 index 00000000000000..0a2792ccab32c0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_fr.md @@ -0,0 +1,86 @@ +--- +layout: model +title: CamemBERT Base Model +author: John Snow Labs +name: camembert_base +date: 2023-08-01 +tags: [fr, french, embeddings, camembert, base, open_source, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model. +For further information or requests, please go to [Camembert Website](https://camembert-model.fr/) + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_base_fr_5.0.2_3.0_1690933576243.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_base_fr_5.0.2_3.0_1690933576243.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = CamemBertEmbeddings.pretrained("camembert_base", "fr") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = CamemBertEmbeddings.pretrained("camembert_base", "fr") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert_base").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_base| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| + +## Benchmarking + +```bash + +| Model | #params | Arch. | Training data | +|--------------------------------|--------------------------------|-------|-----------------------------------| +| `camembert-base` | 110M | Base | OSCAR (138 GB of text) | +| `camembert/camembert-large` | 335M | Large | CCNet (135 GB of text) | +| `camembert/camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) | +| `camembert/camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) | +| `camembert/camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) | +| `camembert/camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) | +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_base_opt_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_opt_fr.md new file mode 100644 index 00000000000000..0f0f64d821060b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_opt_fr.md @@ -0,0 +1,86 @@ +--- +layout: model +title: CamemBERT Base Model +author: John Snow Labs +name: camembert_base_opt +date: 2023-08-01 +tags: [fr, french, embeddings, camembert, base, open_source, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model. +For further information or requests, please go to [Camembert Website](https://camembert-model.fr/) + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_base_opt_fr_5.0.2_3.0_1690933783384.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_base_opt_fr_5.0.2_3.0_1690933783384.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = CamemBertEmbeddings.pretrained("camembert_base", "fr") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = CamemBertEmbeddings.pretrained("camembert_base", "fr") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert_base").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_base_opt| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.3 MB| +|Case sensitive:|true| + +## Benchmarking + +```bash + +| Model | #params | Arch. | Training data | +|--------------------------------|--------------------------------|-------|-----------------------------------| +| `camembert-base` | 110M | Base | OSCAR (138 GB of text) | +| `camembert/camembert-large` | 335M | Large | CCNet (135 GB of text) | +| `camembert/camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) | +| `camembert/camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) | +| `camembert/camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) | +| `camembert/camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) | +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_base_oscar_4gb_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_oscar_4gb_fr.md new file mode 100644 index 00000000000000..20af11cc2052cd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_oscar_4gb_fr.md @@ -0,0 +1,86 @@ +--- +layout: model +title: CamemBERT Base OSCAR +author: John Snow Labs +name: camembert_base_oscar_4gb +date: 2023-08-01 +tags: [fr, french, camembert, embeddings, oscar, open_source, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model. +For further information or requests, please go to [Camembert Website](https://camembert-model.fr/) + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_base_oscar_4gb_fr_5.0.2_3.0_1690926989664.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_base_oscar_4gb_fr_5.0.2_3.0_1690926989664.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = CamemBertEmbeddings.pretrained("camembert_base_oscar_4gb", "fr") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = CamemBertEmbeddings.pretrained("camembert_base_oscar_4gb", "fr") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert_oscar_4g").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_base_oscar_4gb| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|263.6 MB| +|Case sensitive:|true| + +## Benchmarking + +```bash + +| Model | #params | Arch. | Training data | +|--------------------------------|--------------------------------|-------|-----------------------------------| +| `camembert-base` | 110M | Base | OSCAR (138 GB of text) | +| `camembert/camembert-large` | 335M | Large | CCNet (135 GB of text) | +| `camembert/camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) | +| `camembert/camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) | +| `camembert/camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) | +| `camembert/camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) | +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_base_quantized_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_quantized_fr.md new file mode 100644 index 00000000000000..64850ca0c2d212 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_quantized_fr.md @@ -0,0 +1,86 @@ +--- +layout: model +title: CamemBERT Base Model +author: John Snow Labs +name: camembert_base_quantized +date: 2023-08-01 +tags: [fr, french, embeddings, camembert, base, open_source, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model. +For further information or requests, please go to [Camembert Website](https://camembert-model.fr/) + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_base_quantized_fr_5.0.2_3.0_1690933869613.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_base_quantized_fr_5.0.2_3.0_1690933869613.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = CamemBertEmbeddings.pretrained("camembert_base", "fr") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = CamemBertEmbeddings.pretrained("camembert_base", "fr") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert_base").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_base_quantized| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|107.9 MB| +|Case sensitive:|true| + +## Benchmarking + +```bash + +| Model | #params | Arch. | Training data | +|--------------------------------|--------------------------------|-------|-----------------------------------| +| `camembert-base` | 110M | Base | OSCAR (138 GB of text) | +| `camembert/camembert-large` | 335M | Large | CCNet (135 GB of text) | +| `camembert/camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) | +| `camembert/camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) | +| `camembert/camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) | +| `camembert/camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) | +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_base_wikipedia_4gb_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_wikipedia_4gb_fr.md new file mode 100644 index 00000000000000..ab8bfd83cf9b6f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_wikipedia_4gb_fr.md @@ -0,0 +1,86 @@ +--- +layout: model +title: CamemBERT Base Wikipedia +author: John Snow Labs +name: camembert_base_wikipedia_4gb +date: 2023-08-01 +tags: [fr, french, embeddings, camembert, wikipedia, open_source, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model. +For further information or requests, please go to [Camembert Website](https://camembert-model.fr/) + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_base_wikipedia_4gb_fr_5.0.2_3.0_1690926794799.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_base_wikipedia_4gb_fr_5.0.2_3.0_1690926794799.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = CamemBertEmbeddings.pretrained("camembert_base_wikipedia_4gb", "fr") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = CamemBertEmbeddings.pretrained("camembert_base_wikipedia_4gb", "fr") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert_wiki_4g").predict("""Put your text here.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_base_wikipedia_4gb| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|263.1 MB| +|Case sensitive:|true| + +## Benchmarking + +```bash + +| Model | #params | Arch. | Training data | +|--------------------------------|--------------------------------|-------|-----------------------------------| +| `camembert-base` | 110M | Base | OSCAR (138 GB of text) | +| `camembert/camembert-large` | 335M | Large | CCNet (135 GB of text) | +| `camembert/camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) | +| `camembert/camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) | +| `camembert/camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) | +| `camembert/camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) | +``` \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_DataikuNLP_camembert_base_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_DataikuNLP_camembert_base_fr.md new file mode 100644 index 00000000000000..2548f85d5d03da --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_DataikuNLP_camembert_base_fr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: French CamemBert Embeddings (from DataikuNLP) +author: John Snow Labs +name: camembert_embeddings_DataikuNLP_camembert_base +date: 2023-08-01 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `camembert-base` is a French model orginally trained by `DataikuNLP`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_DataikuNLP_camembert_base_fr_5.0.2_3.0_1690926395140.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_DataikuNLP_camembert_base_fr_5.0.2_3.0_1690926395140.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_DataikuNLP_camembert_base","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_DataikuNLP_camembert_base","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_DataikuNLP_camembert_base| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_bertweetfr_base_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_bertweetfr_base_fr.md new file mode 100644 index 00000000000000..4eee761df87a0e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_bertweetfr_base_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from Yanzhu) +author: John Snow Labs +name: camembert_embeddings_bertweetfr_base +date: 2023-08-01 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bertweetfr-base` is a French model orginally trained by `Yanzhu`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_bertweetfr_base_fr_5.0.2_3.0_1690927634160.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_bertweetfr_base_fr_5.0.2_3.0_1690927634160.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_bertweetfr_base","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_bertweetfr_base","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.tweet.base").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_bertweetfr_base| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|412.8 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_distilcamembert_base_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_distilcamembert_base_fr.md new file mode 100644 index 00000000000000..a0bf1c37dde147 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_distilcamembert_base_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from cmarkea) +author: John Snow Labs +name: camembert_embeddings_distilcamembert_base +date: 2023-08-01 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `distilcamembert-base` is a French model orginally trained by `cmarkea`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_distilcamembert_base_fr_5.0.2_3.0_1690926517410.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_distilcamembert_base_fr_5.0.2_3.0_1690926517410.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_distilcamembert_base","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_distilcamembert_base","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.distilled_base").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_distilcamembert_base| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|253.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_generic_model_test_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_generic_model_test_fr.md new file mode 100644 index 00000000000000..f1276b1133a22d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_generic_model_test_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from osanseviero) +author: John Snow Labs +name: camembert_embeddings_generic_model_test +date: 2023-08-01 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model-test` is a French model orginally trained by `osanseviero`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_generic_model_test_fr_5.0.2_3.0_1690926134164.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_generic_model_test_fr_5.0.2_3.0_1690926134164.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_generic_model_test","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_generic_model_test","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_osanseviero").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_generic_model_test| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_h4d35_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_h4d35_generic_model_fr.md new file mode 100644 index 00000000000000..6041bc4bb5d976 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_h4d35_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from h4d35) +author: John Snow Labs +name: camembert_embeddings_h4d35_generic_model +date: 2023-08-01 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `h4d35`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_h4d35_generic_model_fr_5.0.2_3.0_1690927379924.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_h4d35_generic_model_fr_5.0.2_3.0_1690927379924.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_h4d35_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_h4d35_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_h4d35").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_h4d35_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_hackertec_generic_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_hackertec_generic_fr.md new file mode 100644 index 00000000000000..8bf610174a3f4d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_hackertec_generic_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from hackertec) +author: John Snow Labs +name: camembert_embeddings_hackertec_generic +date: 2023-08-01 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy` is a French model orginally trained by `hackertec`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_hackertec_generic_fr_5.0.2_3.0_1690927057947.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_hackertec_generic_fr_5.0.2_3.0_1690927057947.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_hackertec_generic","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_hackertec_generic","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic_v2.by_hackertec").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_hackertec_generic| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_jcai1_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_jcai1_generic_model_fr.md new file mode 100644 index 00000000000000..cba25095d6389f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_jcai1_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from jcai1) +author: John Snow Labs +name: camembert_embeddings_jcai1_generic_model +date: 2023-08-01 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `jcai1`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_jcai1_generic_model_fr_5.0.2_3.0_1690926370690.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_jcai1_generic_model_fr_5.0.2_3.0_1690926370690.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_jcai1_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_jcai1_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_jcai1").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_jcai1_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_joe8zhang_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_joe8zhang_generic_model_fr.md new file mode 100644 index 00000000000000..0177700d202a9c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_joe8zhang_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from joe8zhang) +author: John Snow Labs +name: camembert_embeddings_joe8zhang_generic_model +date: 2023-08-01 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `joe8zhang`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_joe8zhang_generic_model_fr_5.0.2_3.0_1690926063630.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_joe8zhang_generic_model_fr_5.0.2_3.0_1690926063630.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_joe8zhang_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_joe8zhang_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_joe8zhang").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_joe8zhang_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_katrin_kc_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_katrin_kc_generic_model_fr.md new file mode 100644 index 00000000000000..82d12b7109e107 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_katrin_kc_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from katrin-kc) +author: John Snow Labs +name: camembert_embeddings_katrin_kc_generic_model +date: 2023-08-01 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `katrin-kc`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_katrin_kc_generic_model_fr_5.0.2_3.0_1690925694402.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_katrin_kc_generic_model_fr_5.0.2_3.0_1690925694402.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_katrin_kc_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_katrin_kc_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_katrin_kc").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_katrin_kc_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_kaushikacharya_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_kaushikacharya_generic_model_fr.md new file mode 100644 index 00000000000000..2c98576373f2cb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_kaushikacharya_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from kaushikacharya) +author: John Snow Labs +name: camembert_embeddings_kaushikacharya_generic_model +date: 2023-08-01 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `kaushikacharya`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_kaushikacharya_generic_model_fr_5.0.2_3.0_1690925377512.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_kaushikacharya_generic_model_fr_5.0.2_3.0_1690925377512.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_kaushikacharya_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_kaushikacharya_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_kaushikacharya").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_kaushikacharya_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_lewtun_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_lewtun_generic_model_fr.md new file mode 100644 index 00000000000000..bc03b7b850c19a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_lewtun_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from lewtun) +author: John Snow Labs +name: camembert_embeddings_lewtun_generic_model +date: 2023-08-01 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `lewtun`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_lewtun_generic_model_fr_5.0.2_3.0_1690925963643.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_lewtun_generic_model_fr_5.0.2_3.0_1690925963643.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_lewtun_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_lewtun_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_lewtun").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_lewtun_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_lijingxin_generic_model_2_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_lijingxin_generic_model_2_fr.md new file mode 100644 index 00000000000000..fe2ca4486b6513 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_lijingxin_generic_model_2_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from lijingxin) +author: John Snow Labs +name: camembert_embeddings_lijingxin_generic_model_2 +date: 2023-08-01 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model-2` is a French model orginally trained by `lijingxin`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_lijingxin_generic_model_2_fr_5.0.2_3.0_1690925509326.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_lijingxin_generic_model_2_fr_5.0.2_3.0_1690925509326.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_lijingxin_generic_model_2","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_lijingxin_generic_model_2","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic_v2.by_lijingxin").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_lijingxin_generic_model_2| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_linyi_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_linyi_generic_model_fr.md new file mode 100644 index 00000000000000..d08e2fbe1cdacf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_linyi_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from linyi) +author: John Snow Labs +name: camembert_embeddings_linyi_generic_model +date: 2023-08-01 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `linyi`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_linyi_generic_model_fr_5.0.2_3.0_1690925802007.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_linyi_generic_model_fr_5.0.2_3.0_1690925802007.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_linyi_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_linyi_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_linyi").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_linyi_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_mbateman_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_mbateman_generic_model_fr.md new file mode 100644 index 00000000000000..6e92ecc54ac197 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_mbateman_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from mbateman) +author: John Snow Labs +name: camembert_embeddings_mbateman_generic_model +date: 2023-08-01 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `mbateman`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_mbateman_generic_model_fr_5.0.2_3.0_1690925436014.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_mbateman_generic_model_fr_5.0.2_3.0_1690925436014.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_mbateman_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_mbateman_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_mbateman").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_mbateman_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_new_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_new_generic_model_fr.md new file mode 100644 index 00000000000000..ba067640a36231 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_new_generic_model_fr.md @@ -0,0 +1,99 @@ +--- +layout: model +title: French CamemBert Embeddings (from gulabpatel) +author: John Snow Labs +name: camembert_embeddings_new_generic_model +date: 2023-08-01 +tags: [fr, open_source, camembert, embeddings, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `new-dummy-model` is a French model orginally trained by `gulabpatel`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_new_generic_model_fr_5.0.2_3.0_1690925425451.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_new_generic_model_fr_5.0.2_3.0_1690925425451.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_new_generic_model","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_new_generic_model","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("J'adore Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("fr.embed.camembert.generic.by_gulabpatel").predict("""J'adore Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_new_generic_model| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|264.0 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_sloberta_sl.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_sloberta_sl.md new file mode 100644 index 00000000000000..029dce1090a83b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_sloberta_sl.md @@ -0,0 +1,99 @@ +--- +layout: model +title: Slovenian CamemBert Embeddings (from EMBEDDIA) +author: John Snow Labs +name: camembert_embeddings_sloberta +date: 2023-08-01 +tags: [sl, open_source, camembert, embeddings, onnx] +task: Embeddings +language: sl +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `sloberta` is a Slovenian model orginally trained by `EMBEDDIA`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_sloberta_sl_5.0.2_3.0_1690926104653.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_sloberta_sl_5.0.2_3.0_1690926104653.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_sloberta","sl") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Obožujem Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_sloberta","sl") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Obožujem Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("sl.embed.camembert").predict("""Obožujem Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_sloberta| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|sl| +|Size:|263.5 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_umberto_commoncrawl_cased_v1_it.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_umberto_commoncrawl_cased_v1_it.md new file mode 100644 index 00000000000000..0c3c83cb8e961b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_umberto_commoncrawl_cased_v1_it.md @@ -0,0 +1,99 @@ +--- +layout: model +title: Italian CamemBert Embeddings (from Musixmatch) +author: John Snow Labs +name: camembert_embeddings_umberto_commoncrawl_cased_v1 +date: 2023-08-01 +tags: [it, open_source, camembert, embeddings, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `umberto-commoncrawl-cased-v1` is a Italian model orginally trained by `Musixmatch`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_umberto_commoncrawl_cased_v1_it_5.0.2_3.0_1690926373074.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_umberto_commoncrawl_cased_v1_it_5.0.2_3.0_1690926373074.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_umberto_commoncrawl_cased_v1","it") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_umberto_commoncrawl_cased_v1","it") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Adoro Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("it.embed.camembert.cased").predict("""Adoro Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_umberto_commoncrawl_cased_v1| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|it| +|Size:|263.1 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_umberto_wikipedia_uncased_v1_it.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_umberto_wikipedia_uncased_v1_it.md new file mode 100644 index 00000000000000..f810799f3b227d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_umberto_wikipedia_uncased_v1_it.md @@ -0,0 +1,99 @@ +--- +layout: model +title: Italian CamemBert Embeddings (from Musixmatch) +author: John Snow Labs +name: camembert_embeddings_umberto_wikipedia_uncased_v1 +date: 2023-08-01 +tags: [it, open_source, camembert, embeddings, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: CamemBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `umberto-wikipedia-uncased-v1` is a Italian model orginally trained by `Musixmatch`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_umberto_wikipedia_uncased_v1_it_5.0.2_3.0_1690926515076.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_umberto_wikipedia_uncased_v1_it_5.0.2_3.0_1690926515076.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_umberto_wikipedia_uncased_v1","it") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_umberto_wikipedia_uncased_v1","it") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("Adoro Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("it.embed.camembert.uncased").predict("""Adoro Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|camembert_embeddings_umberto_wikipedia_uncased_v1| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|it| +|Size:|262.7 MB| +|Case sensitive:|false| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-xlmroberta_embeddings_fairlex_cail_minilm_zh.md b/docs/_posts/ahmedlone127/2023-08-01-xlmroberta_embeddings_fairlex_cail_minilm_zh.md new file mode 100644 index 00000000000000..0f58c4e10d68a0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-xlmroberta_embeddings_fairlex_cail_minilm_zh.md @@ -0,0 +1,99 @@ +--- +layout: model +title: Chinese XLMRoBerta Embeddings (from coastalcph) +author: John Snow Labs +name: xlmroberta_embeddings_fairlex_cail_minilm +date: 2023-08-01 +tags: [zh, open_source, xlm_roberta, embeddings, fairlex, onnx] +task: Embeddings +language: zh +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: XlmRoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained XLMRoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `fairlex-cail-minilm` is a Chinese model orginally trained by `coastalcph`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlmroberta_embeddings_fairlex_cail_minilm_zh_5.0.2_3.0_1690928905681.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlmroberta_embeddings_fairlex_cail_minilm_zh_5.0.2_3.0_1690928905681.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = XlmRoBertaEmbeddings.pretrained("xlmroberta_embeddings_fairlex_cail_minilm","zh") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = XlmRoBertaEmbeddings.pretrained("xlmroberta_embeddings_fairlex_cail_minilm","zh") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("zh.embed.xlmr_roberta.mini_lm_mini").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlmroberta_embeddings_fairlex_cail_minilm| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|zh| +|Size:|402.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-01-xlmroberta_embeddings_fairlex_fscs_minilm_xx.md b/docs/_posts/ahmedlone127/2023-08-01-xlmroberta_embeddings_fairlex_fscs_minilm_xx.md new file mode 100644 index 00000000000000..fc4285cef2e324 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-01-xlmroberta_embeddings_fairlex_fscs_minilm_xx.md @@ -0,0 +1,99 @@ +--- +layout: model +title: Multilingual XLMRoBerta Embeddings (from coastalcph) +author: John Snow Labs +name: xlmroberta_embeddings_fairlex_fscs_minilm +date: 2023-08-01 +tags: [fr, de, it, open_source, xlm_roberta, embeddings, xx, fairlex, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: XlmRoBertaEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained XLMRoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `fairlex-fscs-minilm` is a Multilingual model orginally trained by `coastalcph`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlmroberta_embeddings_fairlex_fscs_minilm_xx_5.0.2_3.0_1690928849227.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlmroberta_embeddings_fairlex_fscs_minilm_xx_5.0.2_3.0_1690928849227.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = XlmRoBertaEmbeddings.pretrained("xlmroberta_embeddings_fairlex_fscs_minilm","xx") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val embeddings = XlmRoBertaEmbeddings.pretrained("xlmroberta_embeddings_fairlex_fscs_minilm","xx") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("xx.embed.xlmr_roberta.mini_lm_mini").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlmroberta_embeddings_fairlex_fscs_minilm| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|402.9 MB| +|Case sensitive:|true| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_en.md new file mode 100644 index 00000000000000..703963892f63d2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_en.md @@ -0,0 +1,78 @@ +--- +layout: model +title: ALBERT Embeddings (Base Uncase) +author: John Snow Labs +name: albert_base_uncased +date: 2023-08-02 +tags: [open_source, en, english, embeddings, albert, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +ALBERT is "A Lite" version of BERT, a popular unsupervised language representation learning algorithm. ALBERT uses parameter-reduction techniques that allow for large-scale configurations, overcome previous memory limitations, and achieve better behavior with respect to model degradation. The details are described in the paper "[ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.](https://arxiv.org/abs/1909.11942)" + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_uncased_en_5.0.2_3.0_1690935260361.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_uncased_en_5.0.2_3.0_1690935260361.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = AlbertEmbeddings.pretrained("albert_base_uncased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = AlbertEmbeddings.pretrained("albert_base_uncased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + +{:.nlu-block} +```python +import nlu + +text = ["I love NLP"] +embeddings_df = nlu.load('en.embed.albert.base_uncased').predict(text, output_level='token') +embeddings_df +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_uncased| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|42.0 MB| +|Case sensitive:|false| + +## References + +[https://huggingface.co/albert-base-v2](https://huggingface.co/albert-base-v2) \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_opt_en.md b/docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_opt_en.md new file mode 100644 index 00000000000000..21794c5b8aa379 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_opt_en.md @@ -0,0 +1,78 @@ +--- +layout: model +title: ALBERT Embeddings (Base Uncase) +author: John Snow Labs +name: albert_base_uncased_opt +date: 2023-08-02 +tags: [open_source, en, english, embeddings, albert, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +ALBERT is "A Lite" version of BERT, a popular unsupervised language representation learning algorithm. ALBERT uses parameter-reduction techniques that allow for large-scale configurations, overcome previous memory limitations, and achieve better behavior with respect to model degradation. The details are described in the paper "[ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.](https://arxiv.org/abs/1909.11942)" + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_uncased_opt_en_5.0.2_3.0_1690935304465.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_uncased_opt_en_5.0.2_3.0_1690935304465.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = AlbertEmbeddings.pretrained("albert_base_uncased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = AlbertEmbeddings.pretrained("albert_base_uncased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + +{:.nlu-block} +```python +import nlu + +text = ["I love NLP"] +embeddings_df = nlu.load('en.embed.albert.base_uncased').predict(text, output_level='token') +embeddings_df +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_uncased_opt| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|115.0 MB| +|Case sensitive:|false| + +## References + +[https://huggingface.co/albert-base-v2](https://huggingface.co/albert-base-v2) \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_quantized_en.md b/docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_quantized_en.md new file mode 100644 index 00000000000000..1f373a267014d1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_quantized_en.md @@ -0,0 +1,78 @@ +--- +layout: model +title: ALBERT Embeddings (Base Uncase) +author: John Snow Labs +name: albert_base_uncased_quantized +date: 2023-08-02 +tags: [open_source, en, english, embeddings, albert, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +ALBERT is "A Lite" version of BERT, a popular unsupervised language representation learning algorithm. ALBERT uses parameter-reduction techniques that allow for large-scale configurations, overcome previous memory limitations, and achieve better behavior with respect to model degradation. The details are described in the paper "[ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.](https://arxiv.org/abs/1909.11942)" + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_uncased_quantized_en_5.0.2_3.0_1690935326685.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_uncased_quantized_en_5.0.2_3.0_1690935326685.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = AlbertEmbeddings.pretrained("albert_base_uncased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = AlbertEmbeddings.pretrained("albert_base_uncased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + +{:.nlu-block} +```python +import nlu + +text = ["I love NLP"] +embeddings_df = nlu.load('en.embed.albert.base_uncased').predict(text, output_level='token') +embeddings_df +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_uncased_quantized| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|46.0 MB| +|Case sensitive:|false| + +## References + +[https://huggingface.co/albert-base-v2](https://huggingface.co/albert-base-v2) \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_en.md b/docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_en.md new file mode 100644 index 00000000000000..2868cd5390206c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_en.md @@ -0,0 +1,78 @@ +--- +layout: model +title: ALBERT Embeddings (Large Uncase) +author: John Snow Labs +name: albert_large_uncased +date: 2023-08-02 +tags: [open_source, en, english, embeddings, albert, large, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +ALBERT is "A Lite" version of BERT, a popular unsupervised language representation learning algorithm. ALBERT uses parameter-reduction techniques that allow for large-scale configurations, overcome previous memory limitations, and achieve better behavior with respect to model degradation. The details are described in the paper "[ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.](https://arxiv.org/abs/1909.11942)" + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_uncased_en_5.0.2_3.0_1690935781574.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_uncased_en_5.0.2_3.0_1690935781574.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = AlbertEmbeddings.pretrained("albert_large_uncased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = AlbertEmbeddings.pretrained("albert_large_uncased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + +{:.nlu-block} +```python +import nlu + +text = ["I love NLP"] +embeddings_df = nlu.load('en.embed.albert.large_uncased').predict(text, output_level='token') +embeddings_df +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_uncased| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|62.7 MB| +|Case sensitive:|false| + +## References + +[https://huggingface.co/albert-large-v2](https://huggingface.co/albert-large-v2) \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_opt_en.md b/docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_opt_en.md new file mode 100644 index 00000000000000..53d69c5d209c22 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_opt_en.md @@ -0,0 +1,78 @@ +--- +layout: model +title: ALBERT Embeddings (Large Uncase) +author: John Snow Labs +name: albert_large_uncased_opt +date: 2023-08-02 +tags: [open_source, en, english, embeddings, albert, large, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +ALBERT is "A Lite" version of BERT, a popular unsupervised language representation learning algorithm. ALBERT uses parameter-reduction techniques that allow for large-scale configurations, overcome previous memory limitations, and achieve better behavior with respect to model degradation. The details are described in the paper "[ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.](https://arxiv.org/abs/1909.11942)" + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_uncased_opt_en_5.0.2_3.0_1690935934328.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_uncased_opt_en_5.0.2_3.0_1690935934328.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = AlbertEmbeddings.pretrained("albert_large_uncased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = AlbertEmbeddings.pretrained("albert_large_uncased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + +{:.nlu-block} +```python +import nlu + +text = ["I love NLP"] +embeddings_df = nlu.load('en.embed.albert.large_uncased').predict(text, output_level='token') +embeddings_df +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_uncased_opt| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|333.8 MB| +|Case sensitive:|false| + +## References + +[https://huggingface.co/albert-large-v2](https://huggingface.co/albert-large-v2) \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_quantized_en.md b/docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_quantized_en.md new file mode 100644 index 00000000000000..0e7cdcec3cac9a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_quantized_en.md @@ -0,0 +1,78 @@ +--- +layout: model +title: ALBERT Embeddings (Large Uncase) +author: John Snow Labs +name: albert_large_uncased_quantized +date: 2023-08-02 +tags: [open_source, en, english, embeddings, albert, large, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +ALBERT is "A Lite" version of BERT, a popular unsupervised language representation learning algorithm. ALBERT uses parameter-reduction techniques that allow for large-scale configurations, overcome previous memory limitations, and achieve better behavior with respect to model degradation. The details are described in the paper "[ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.](https://arxiv.org/abs/1909.11942)" + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_uncased_quantized_en_5.0.2_3.0_1690935970820.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_uncased_quantized_en_5.0.2_3.0_1690935970820.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings = AlbertEmbeddings.pretrained("albert_large_uncased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +``` +```scala +val embeddings = AlbertEmbeddings.pretrained("albert_large_uncased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +``` + +{:.nlu-block} +```python +import nlu + +text = ["I love NLP"] +embeddings_df = nlu.load('en.embed.albert.large_uncased').predict(text, output_level='token') +embeddings_df +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_uncased_quantized| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, sentence]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|71.4 MB| +|Case sensitive:|false| + +## References + +[https://huggingface.co/albert-large-v2](https://huggingface.co/albert-large-v2) \ No newline at end of file From b6d3cf100b7f789786c42ade45184027c1570a9c Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Wed, 2 Aug 2023 14:41:38 +0700 Subject: [PATCH 29/55] 2023-07-28-twitter_xlm_roberta_base_sentiment_en (#13906) * Add model 2023-07-28-twitter_xlm_roberta_base_sentiment_en * Add model 2023-07-31-twitter_xlm_roberta_base_sentiment_pdc_en * Add model 2023-07-31-sentiment_twitter_xlm_roBerta_pdc_en --------- Co-authored-by: veerdhwaj --- ...8-twitter_xlm_roberta_base_sentiment_en.md | 58 ++++++------ ...31-sentiment_twitter_xlm_roBerta_pdc_en.md | 94 +++++++++++++++++++ ...itter_xlm_roberta_base_sentiment_pdc_en.md | 86 +++++++++++++++++ 3 files changed, 210 insertions(+), 28 deletions(-) create mode 100644 docs/_posts/veerdhwaj/2023-07-31-sentiment_twitter_xlm_roBerta_pdc_en.md create mode 100644 docs/_posts/veerdhwaj/2023-07-31-twitter_xlm_roberta_base_sentiment_pdc_en.md diff --git a/docs/_posts/veerdhwaj/2023-07-28-twitter_xlm_roberta_base_sentiment_en.md b/docs/_posts/veerdhwaj/2023-07-28-twitter_xlm_roberta_base_sentiment_en.md index 03d5d0ac48e589..c9328186d943cb 100644 --- a/docs/_posts/veerdhwaj/2023-07-28-twitter_xlm_roberta_base_sentiment_en.md +++ b/docs/_posts/veerdhwaj/2023-07-28-twitter_xlm_roberta_base_sentiment_en.md @@ -1,10 +1,10 @@ --- layout: model -title: twitter-xlm-roberta-base-sentiment +title: twitter_xlm_roberta_base_sentiment(Cardiff nlp) (Veer) author: veerdhwaj name: twitter_xlm_roberta_base_sentiment date: 2023-07-28 -tags: [sentiment, roberta, en, open_source, tensorflow] +tags: [en, open_source, tensorflow] task: Text Classification language: en edition: Spark NLP 5.0.0 @@ -19,18 +19,21 @@ use_language_switcher: "Python-Scala-Java" ## Description -This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages -Huggingface : https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment +This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages (see paper for details). + +Paper: XLM-T: A Multilingual Language Model Toolkit for Twitter. +Git Repo: XLM-T official repository. +This model has been integrated into the TweetNLP library. ## Predicted Entities -`sentiment` + {:.btn-box} -[Download](https://s3.amazonaws.com/community.johnsnowlabs.com/veerdhwaj/twitter_xlm_roberta_base_sentiment_en_5.0.0_3.2_1690535217423.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://community.johnsnowlabs.com/veerdhwaj/twitter_xlm_roberta_base_sentiment_en_5.0.0_3.2_1690535217423.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/community.johnsnowlabs.com/veerdhwaj/twitter_xlm_roberta_base_sentiment_en_5.0.0_3.2_1690542160993.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://community.johnsnowlabs.com/veerdhwaj/twitter_xlm_roberta_base_sentiment_en_5.0.0_3.2_1690542160993.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use @@ -39,34 +42,33 @@ Huggingface : https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentime
{% include programmingLanguageSelectScalaPythonNLU.html %} ```python -import spark.implicits._ -import com.johnsnowlabs.nlp.base._ -import com.johnsnowlabs.nlp.annotator._ -import org.apache.spark.ml.Pipeline +from pyspark.ml import Pipeline -val documentAssembler = new DocumentAssembler() - .setInputCol("text") - .setOutputCol("document") +document_assembler = DocumentAssembler() \ + .setInputCol('text') \ + .setOutputCol('document') -val tokenizer = new Tokenizer() - .setInputCols("document") - .setOutputCol("token") +tokenizer = Tokenizer() \ + .setInputCols(['document']) \ + .setOutputCol('token') -val sequenceClassifier = XlmRoBertaForSequenceClassification.pretrained('twitter_xlm_roberta_base_sentiment') - .setInputCols("token", "document") +sequenceClassifier = XlmRoBertaForSequenceClassification.pretrained('twitter_xlm_roberta_base_sentiment')\ + .setInputCols(["document",'token'])\ .setOutputCol("class") - .setCaseSensitive(true) -val pipeline = new Pipeline().setStages(Array( - documentAssembler, - tokenizer, - sequenceClassifier -)) +pipeline = Pipeline(stages=[ + document_assembler, + tokenizer, + sequenceClassifier +]) + +# couple of simple examples +example = spark.createDataFrame([['사랑해!'], ["T'estimo! ❤️"], ["I love you!"], ['Mahal kita!']]).toDF("text") -val data = Seq("I loved this movie when I was a child.", "It was pretty boring.").toDF("text") -val result = pipeline.fit(data).transform(data) +result = pipeline.fit(example).transform(example) -result.select("class.result").show(false) +# result is a DataFrame +result.select("text", "class.result").show() ```
diff --git a/docs/_posts/veerdhwaj/2023-07-31-sentiment_twitter_xlm_roBerta_pdc_en.md b/docs/_posts/veerdhwaj/2023-07-31-sentiment_twitter_xlm_roBerta_pdc_en.md new file mode 100644 index 00000000000000..7878aeac4fea03 --- /dev/null +++ b/docs/_posts/veerdhwaj/2023-07-31-sentiment_twitter_xlm_roBerta_pdc_en.md @@ -0,0 +1,94 @@ +--- +layout: model +title: Sentiment tags predictions trained on twitter +author: veerdhwaj +name: sentiment_twitter_xlm_roBerta_pdc +date: 2023-07-31 +tags: [sentiment, en, open_source, tensorflow] +task: Text Classification +language: en +edition: Spark NLP 3.3.2 +spark_version: 3.2 +supported: false +engine: tensorflow +annotator: XlmRoBertaForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages (see paper for details). + +Paper: XLM-T: A Multilingual Language Model Toolkit for Twitter. +Git Repo: XLM-T official repository. +This model has been integrated into the TweetNLP library. + +HF Model: https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment + +## Predicted Entities + +`class` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/community.johnsnowlabs.com/veerdhwaj/sentiment_twitter_xlm_roBerta_pdc_en_3.3.2_3.2_1690780495131.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://community.johnsnowlabs.com/veerdhwaj/sentiment_twitter_xlm_roBerta_pdc_en_3.3.2_3.2_1690780495131.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from pyspark.ml import Pipeline +from sparknlp.annotator import * + +document_assembler = DocumentAssembler() \ + .setInputCol('text') \ + .setOutputCol('document') + +tokenizer = Tokenizer() \ + .setInputCols(['document']) \ + .setOutputCol('token') + +sequenceClassifier = XlmRoBertaForSequenceClassification.pretrained('sentiment_twitter_xlm_roBerta_pdc_en')\ + .setInputCols(["document",'token'])\ + .setOutputCol("class") + +pipeline = Pipeline(stages=[ + document_assembler, + tokenizer, + sequenceClassifier +]) + +# couple of simple examples +example = spark.createDataFrame([['사랑해!'], ["T'estimo! ❤️"], ["I love you!"], ['Mahal kita!']]).toDF("text") + +result = pipeline.fit(example).transform(example) + +# result is a DataFrame +result.select("text", "class.result").show() +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sentiment_twitter_xlm_roBerta_pdc| +|Compatibility:|Spark NLP 3.3.2+| +|License:|Open Source| +|Edition:|Community| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.0 GB| +|Case sensitive:|true| +|Max sentence length:|512| +|Dependencies:|xlm_roBerta| \ No newline at end of file diff --git a/docs/_posts/veerdhwaj/2023-07-31-twitter_xlm_roberta_base_sentiment_pdc_en.md b/docs/_posts/veerdhwaj/2023-07-31-twitter_xlm_roberta_base_sentiment_pdc_en.md new file mode 100644 index 00000000000000..45dd26651c0dba --- /dev/null +++ b/docs/_posts/veerdhwaj/2023-07-31-twitter_xlm_roberta_base_sentiment_pdc_en.md @@ -0,0 +1,86 @@ +--- +layout: model +title: twitter_xlm_roberta_base_sentiment_pdc(cardiff) +author: veerdhwaj +name: twitter_xlm_roberta_base_sentiment_pdc +date: 2023-07-31 +tags: [en, open_source, tensorflow] +task: Text Classification +language: en +edition: Spark NLP 5.0.0 +spark_version: 3.2 +supported: false +engine: tensorflow +annotator: XlmRoBertaForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Huggingface model: https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/community.johnsnowlabs.com/veerdhwaj/twitter_xlm_roberta_base_sentiment_pdc_en_5.0.0_3.2_1690779049644.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://community.johnsnowlabs.com/veerdhwaj/twitter_xlm_roberta_base_sentiment_pdc_en_5.0.0_3.2_1690779049644.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from pyspark.ml import Pipeline + +document_assembler = DocumentAssembler() \ + .setInputCol('text') \ + .setOutputCol('document') + +tokenizer = Tokenizer() \ + .setInputCols(['document']) \ + .setOutputCol('token') + +sequenceClassifier = XlmRoBertaForSequenceClassification.pretrained('twitter_xlm_roberta_base_sentiment_pdc')\ + .setInputCols(["document",'token'])\ + .setOutputCol("class") + +pipeline = Pipeline(stages=[ + document_assembler, + tokenizer, + sequenceClassifier +]) + +# couple of simple examples +example = spark.createDataFrame([['사랑해!'], ["T'estimo! ❤️"], ["I love you!"], ['Mahal kita!']]).toDF("text") + +result = pipeline.fit(example).transform(example) + +# result is a DataFrame +result.select("text", "class.result").show() +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|twitter_xlm_roberta_base_sentiment_pdc| +|Compatibility:|Spark NLP 5.0.0+| +|License:|Open Source| +|Edition:|Community| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.0 GB| +|Case sensitive:|true| +|Max sentence length:|512| \ No newline at end of file From 0504fb718c6db688be5d24913824bfbae8179d89 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Mon, 7 Aug 2023 15:48:32 +0700 Subject: [PATCH 30/55] 2023-08-07-bart_large_zero_shot_classifier_mnli_en (#13917) * Add model 2023-08-07-bart_large_zero_shot_classifier_mnli_en * Update 2023-08-07-bart_large_zero_shot_classifier_mnli_en.md --------- Co-authored-by: ahmedlone127 Co-authored-by: Maziyar Panahi --- ...bart_large_zero_shot_classifier_mnli_en.md | 107 ++++++++++++++++++ 1 file changed, 107 insertions(+) create mode 100644 docs/_posts/ahmedlone127/2023-08-07-bart_large_zero_shot_classifier_mnli_en.md diff --git a/docs/_posts/ahmedlone127/2023-08-07-bart_large_zero_shot_classifier_mnli_en.md b/docs/_posts/ahmedlone127/2023-08-07-bart_large_zero_shot_classifier_mnli_en.md new file mode 100644 index 00000000000000..78118128732573 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-07-bart_large_zero_shot_classifier_mnli_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: Bart Zero Shot Classifier Large -MNLI (bart_large_zero_shot_classifier_mnli) +author: John Snow Labs +name: bart_large_zero_shot_classifier_mnli +date: 2023-08-07 +tags: [bart, zero_shot, en, open_source, tensorflow] +task: Zero-Shot Classification +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: BartForZeroShotClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is intended to be used for zero-shot text classification, especially in English. It is fine-tuned on MNLI by using large BART model. + +BartForZeroShotClassification using a ModelForSequenceClassification trained on MNLI tasks. Equivalent of BartForSequenceClassification models, but these models don’t require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it’s slower but it is much more flexible. + +We used TFBartForSequenceClassification to train this model and used BartForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale! + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bart_large_zero_shot_classifier_mnli_en_5.1.0_3.0_1691369930633.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bart_large_zero_shot_classifier_mnli_en_5.1.0_3.0_1691369930633.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +document_assembler = DocumentAssembler() \ +.setInputCol('text') \ +.setOutputCol('document') + +tokenizer = Tokenizer() \ +.setInputCols(['document']) \ +.setOutputCol('token') + +zeroShotClassifier = BartForZeroShotClassification \ +.pretrained('bart_large_zero_shot_classifier_mnli', 'en') \ +.setInputCols(['token', 'document']) \ +.setOutputCol('class') \ +.setCaseSensitive(True) \ +.setMaxSentenceLength(512) \ +.setCandidateLabels(["urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"]) + +pipeline = Pipeline(stages=[ +document_assembler, +tokenizer, +zeroShotClassifier +]) + +example = spark.createDataFrame([['I have a problem with my iphone that needs to be resolved asap!!']]).toDF("text") +result = pipeline.fit(example).transform(example) +``` +```scala +val document_assembler = DocumentAssembler() +.setInputCol("text") +.setOutputCol("document") + +val tokenizer = Tokenizer() +.setInputCols("document") +.setOutputCol("token") + +val zeroShotClassifier = BartForSequenceClassification.pretrained("bart_large_zero_shot_classifier_mnli", "en") +.setInputCols("document", "token") +.setOutputCol("class") +.setCaseSensitive(true) +.setMaxSentenceLength(512) +.setCandidateLabels(Array("urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology")) + +val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier)) + +val example = Seq("I have a problem with my iphone that needs to be resolved asap!!").toDS.toDF("text") + +val result = pipeline.fit(example).transform(example) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bart_large_zero_shot_classifier_mnli| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[token, document]| +|Output Labels:|[label]| +|Language:|en| +|Size:|467.1 MB| +|Case sensitive:|true| From 1a0f376017828f2554d46eb14dcaaf24f387e666 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Tue, 15 Aug 2023 21:32:26 +0700 Subject: [PATCH 31/55] 2023-08-15-gte_base_en (#13922) * Add model 2023-08-15-gte_base_en * Add model 2023-08-15-gte_large_en * Add model 2023-08-15-gte_small_en --------- Co-authored-by: maziyarpanahi --- .../maziyarpanahi/2023-08-15-gte_base_en.md | 103 ++++++++++++++++++ .../maziyarpanahi/2023-08-15-gte_large_en.md | 103 ++++++++++++++++++ .../maziyarpanahi/2023-08-15-gte_small_en.md | 103 ++++++++++++++++++ 3 files changed, 309 insertions(+) create mode 100644 docs/_posts/maziyarpanahi/2023-08-15-gte_base_en.md create mode 100644 docs/_posts/maziyarpanahi/2023-08-15-gte_large_en.md create mode 100644 docs/_posts/maziyarpanahi/2023-08-15-gte_small_en.md diff --git a/docs/_posts/maziyarpanahi/2023-08-15-gte_base_en.md b/docs/_posts/maziyarpanahi/2023-08-15-gte_base_en.md new file mode 100644 index 00000000000000..e6f01408886932 --- /dev/null +++ b/docs/_posts/maziyarpanahi/2023-08-15-gte_base_en.md @@ -0,0 +1,103 @@ +--- +layout: model +title: General Text Embeddings (GTE) English (gte_base) +author: John Snow Labs +name: gte_base +date: 2023-08-15 +tags: [open_source, bert, embeddings, english, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281) + +The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. + +| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) | +|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| +| [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 | +| [**gte-base**](https://huggingface.co/thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 | +| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 | +| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 | +| [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 | +| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 | +| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 | +| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 | +| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 | +| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 | +| [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 | +| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 | +| [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 | +| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 | + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gte_base_en_5.0.2_3.0_1692109612831.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gte_base_en_5.0.2_3.0_1692109612831.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols(["document"])\ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("gte_base", "en")\ + .setInputCols(["document", "token"])\ + .setOutputCol("embeddings") + +``` +```scala + +val document = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("gte_base", "en") + .setInputCols("document", "token") + .setOutputCol("embeddings") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gte_base| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|258.8 MB| +|Case sensitive:|true| + +## References + +GTE models are from [Dingkun Long](https://huggingface.co/thenlper) \ No newline at end of file diff --git a/docs/_posts/maziyarpanahi/2023-08-15-gte_large_en.md b/docs/_posts/maziyarpanahi/2023-08-15-gte_large_en.md new file mode 100644 index 00000000000000..62ba8e035b4874 --- /dev/null +++ b/docs/_posts/maziyarpanahi/2023-08-15-gte_large_en.md @@ -0,0 +1,103 @@ +--- +layout: model +title: General Text Embeddings (GTE) English (gte_large) +author: John Snow Labs +name: gte_large +date: 2023-08-15 +tags: [open_source, bert, embeddings, english, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281) + +The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. + +| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) | +|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| +| [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 | +| [**gte-base**](https://huggingface.co/thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 | +| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 | +| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 | +| [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 | +| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 | +| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 | +| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 | +| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 | +| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 | +| [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 | +| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 | +| [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 | +| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 | + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gte_large_en_5.0.2_3.0_1692109628705.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gte_large_en_5.0.2_3.0_1692109628705.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols(["document"])\ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("gte_large", "en")\ + .setInputCols(["document", "token"])\ + .setOutputCol("embeddings") + +``` +```scala + +val document = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("gte_large", "en") + .setInputCols("document", "token") + .setOutputCol("embeddings") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gte_large| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|794.2 MB| +|Case sensitive:|true| + +## References + +GTE models are from [Dingkun Long](https://huggingface.co/thenlper) \ No newline at end of file diff --git a/docs/_posts/maziyarpanahi/2023-08-15-gte_small_en.md b/docs/_posts/maziyarpanahi/2023-08-15-gte_small_en.md new file mode 100644 index 00000000000000..4cc7c1cd0fdbb5 --- /dev/null +++ b/docs/_posts/maziyarpanahi/2023-08-15-gte_small_en.md @@ -0,0 +1,103 @@ +--- +layout: model +title: General Text Embeddings (GTE) English (gte_small) +author: John Snow Labs +name: gte_small +date: 2023-08-15 +tags: [open_source, bert, embeddings, english, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281) + +The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. + +| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) | +|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| +| [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 | +| [**gte-base**](https://huggingface.co/thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 | +| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 | +| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 | +| [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 | +| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 | +| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 | +| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 | +| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 | +| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 | +| [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 | +| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 | +| [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 | +| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 | + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gte_small_en_5.0.2_3.0_1692109661017.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gte_small_en_5.0.2_3.0_1692109661017.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols(["document"])\ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("gte_small", "en")\ + .setInputCols(["document", "token"])\ + .setOutputCol("embeddings") + +``` +```scala + +val document = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("gte_small", "en") + .setInputCols("document", "token") + .setOutputCol("embeddings") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gte_small| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|79.9 MB| +|Case sensitive:|true| + +## References + +GTE models are from [Dingkun Long](https://huggingface.co/thenlper) \ No newline at end of file From 0e2bb838d98eba6aac0c7b15d4c1c56114bc33f0 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Tue, 15 Aug 2023 21:41:19 +0700 Subject: [PATCH 32/55] 2023-08-15-bge_small_en (#13923) * Add model 2023-08-15-bge_small_en * Add model 2023-08-15-bge_base_en * Add model 2023-08-15-bge_large_en --------- Co-authored-by: maziyarpanahi --- .../maziyarpanahi/2023-08-15-bge_base_en.md | 97 +++++++++++++++++++ .../maziyarpanahi/2023-08-15-bge_large_en.md | 97 +++++++++++++++++++ .../maziyarpanahi/2023-08-15-bge_small_en.md | 97 +++++++++++++++++++ 3 files changed, 291 insertions(+) create mode 100644 docs/_posts/maziyarpanahi/2023-08-15-bge_base_en.md create mode 100644 docs/_posts/maziyarpanahi/2023-08-15-bge_large_en.md create mode 100644 docs/_posts/maziyarpanahi/2023-08-15-bge_small_en.md diff --git a/docs/_posts/maziyarpanahi/2023-08-15-bge_base_en.md b/docs/_posts/maziyarpanahi/2023-08-15-bge_base_en.md new file mode 100644 index 00000000000000..9f6ad63ec6f75d --- /dev/null +++ b/docs/_posts/maziyarpanahi/2023-08-15-bge_base_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: BAAI general embedding English (bge_base) +author: John Snow Labs +name: bge_base +date: 2023-08-15 +tags: [open_source, bert, embeddings, english, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. +And it also can be used in vector database for LLMs. + +`bge` is short for `BAAI general embedding`. + +| Model | Language | Description | query instruction for retrieval\* | +|:-------------------------------|:--------:| :--------:| :--------:| +| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | rank **2nd** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | +| [BAAI/bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | Chinese | This model is trained without instruction, and rank **2nd** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | | +| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | a base-scale model but has similar ability with `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | +| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bge_base_en_5.0.2_3.0_1692109953168.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bge_base_en_5.0.2_3.0_1692109953168.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols(["document"])\ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bge_base", "en")\ + .setInputCols(["document", "token"])\ + .setOutputCol("embeddings") + +``` +```scala + +val document = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bge_base", "en") + .setInputCols("document", "token") + .setOutputCol("embeddings") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bge_base| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|258.8 MB| +|Case sensitive:|true| + +## References + +BAAI models are from [BAAI](https://huggingface.co/BAAI) \ No newline at end of file diff --git a/docs/_posts/maziyarpanahi/2023-08-15-bge_large_en.md b/docs/_posts/maziyarpanahi/2023-08-15-bge_large_en.md new file mode 100644 index 00000000000000..db4b23afad1756 --- /dev/null +++ b/docs/_posts/maziyarpanahi/2023-08-15-bge_large_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: BAAI general embedding English (bge_large) +author: John Snow Labs +name: bge_large +date: 2023-08-15 +tags: [open_source, bert, embeddings, english, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. +And it also can be used in vector database for LLMs. + +`bge` is short for `BAAI general embedding`. + +| Model | Language | Description | query instruction for retrieval\* | +|:-------------------------------|:--------:| :--------:| :--------:| +| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | rank **2nd** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | +| [BAAI/bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | Chinese | This model is trained without instruction, and rank **2nd** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | | +| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | a base-scale model but has similar ability with `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | +| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bge_large_en_5.0.2_3.0_1692109963281.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bge_large_en_5.0.2_3.0_1692109963281.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols(["document"])\ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bge_large", "en")\ + .setInputCols(["document", "token"])\ + .setOutputCol("embeddings") + +``` +```scala + +val document = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bge_large", "en") + .setInputCols("document", "token") + .setOutputCol("embeddings") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bge_large| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|794.2 MB| +|Case sensitive:|true| + +## References + +BAAI models are from [BAAI](https://huggingface.co/BAAI) \ No newline at end of file diff --git a/docs/_posts/maziyarpanahi/2023-08-15-bge_small_en.md b/docs/_posts/maziyarpanahi/2023-08-15-bge_small_en.md new file mode 100644 index 00000000000000..9681eb3e9c002b --- /dev/null +++ b/docs/_posts/maziyarpanahi/2023-08-15-bge_small_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: BAAI general embedding English (bge_small) +author: John Snow Labs +name: bge_small +date: 2023-08-15 +tags: [open_source, bert, embeddings, english, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.0.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. +And it also can be used in vector database for LLMs. + +`bge` is short for `BAAI general embedding`. + +| Model | Language | Description | query instruction for retrieval\* | +|:-------------------------------|:--------:| :--------:| :--------:| +| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | rank **2nd** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | +| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | +| [BAAI/bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | Chinese | This model is trained without instruction, and rank **2nd** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | | +| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | a base-scale model but has similar ability with `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | +| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bge_small_en_5.0.2_3.0_1692109948749.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bge_small_en_5.0.2_3.0_1692109948749.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols(["document"])\ + .setOutputCol("token") + +embeddings = BertEmbeddings.pretrained("bge_small", "en")\ + .setInputCols(["document", "token"])\ + .setOutputCol("embeddings") + +``` +```scala + +val document = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = BertEmbeddings.pretrained("bge_small", "en") + .setInputCols("document", "token") + .setOutputCol("embeddings") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bge_small| +|Compatibility:|Spark NLP 5.0.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|79.9 MB| +|Case sensitive:|true| + +## References + +BAAI models are from [BAAI](https://huggingface.co/BAAI) \ No newline at end of file From a11908a661de9e3640d7e128dfb9a644645d3e39 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Thu, 24 Aug 2023 14:56:13 +0700 Subject: [PATCH 33/55] 2023-08-18-mpnet_embedding_mpnet_snli_en (#13929) * Add model 2023-08-18-mpnet_embedding_mpnet_snli_en * Add model 2023-08-18-mpnet_embedding_Setfit_few_shot_classifier_en * Add model 2023-08-18-mpnet_embedding_multi_qa_mpnet_base_dot_v1_eclass_en * Add model 2023-08-18-mpnet_embedding_all_mpnet_base_v2_embedding_all_en * Add model 2023-08-18-mpnet_embedding_multi_qa_mpnet_base_dot_v1_eclass_en * Add model 2023-08-18-mpnet_embedding_PatentSBERTa_en * Add model 2023-08-18-mpnet_embedding_ecolo_pas_ecolo_v0.1_en * Add model 2023-08-18-mpnet_embedding_Setfit_few_shot_classifier_en * Add model 2023-08-18-mpnet_embedding_all_mpnet_base_v2_finetuned_v2_en * Add model 2023-08-18-mpnet_embedding_FewShotIssueClassifier_NLBSE23_en * Add model 2023-08-18-mpnet_embedding_nooks_amd_detection_v2_full_en * Add model 2023-08-18-mpnet_embedding_action_policy_plans_classifier_en * Add model 2023-08-18-mpnet_embedding_cross_all_mpnet_base_v2_finetuned_WebNLG2020_metric_average_en * Add model 2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_func_en * Add model 2023-08-18-mpnet_embedding_ouvrage_classif_en * Add model 2023-08-18-mpnet_embedding_tiny_random_MPNetForTokenClassification_en * Add model 2023-08-18-mpnet_embedding_review_intent_20230116_en * Add model 2023-08-18-mpnet_embedding_spiced_en * Add model 2023-08-18-mpnet_embedding_mpnet_snli_negatives_en * Add model 2023-08-18-mpnet_embedding_review_multiclass_20230116_en * Add model 2023-08-18-mpnet_embedding_contradiction_psb_lds_en * Add model 2023-08-18-mpnet_embedding_setfit_alpaca_es_unprocessable_sample_detection_es * Add model 2023-08-18-mpnet_embedding_multi_qa_mpnet_base_dot_v1_legal_finetune_en * Add model 2023-08-18-mpnet_embedding_nps_psb_lds_en * Add model 2023-08-18-mpnet_embedding_ATTACK_BERT_en * Add model 2023-08-18-mpnet_embedding_setfit_ethos_multilabel_example_en * Add model 2023-08-18-mpnet_embedding_mpnet_snli_en * Add model 2023-08-18-mpnet_embedding_multi_QA_v1_mpnet_asymmetric_A_en * Add model 2023-08-18-mpnet_embedding_contradiction_psb_en * Add model 2023-08-18-mpnet_embedding_sb_temfac_en * Add model 2023-08-18-mpnet_embedding_setfit_finetuned_financial_text_en * Add model 2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_comm_en * Add model 2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_life_en * Add model 2023-08-18-mpnet_embedding_setfit_model_en * Add model 2023-08-18-mpnet_embedding_multi_qa_mpnet_base_cos_v1_en * Add model 2023-08-18-mpnet_embedding_java_deprecation_classifier_en * Add model 2023-08-18-mpnet_embedding_my_awesome_setfit_model_98_en * Add model 2023-08-18-mpnet_embedding_DomainAdaptM2_en * Add model 2023-08-18-mpnet_embedding_mpnet_retriever_squad2_en * Add model 2023-08-18-mpnet_embedding_tiny_random_MPNetForQuestionAnswering_en * Add model 2023-08-18-mpnet_embedding_sml_ukr_word_classifier_medium_en * Add model 2023-08-18-mpnet_embedding_all_mpnet_base_v1_en * Add model 2023-08-18-mpnet_embedding_nooks_amd_detection_realtime_en * Add model 2023-08-18-mpnet_embedding_setfit_model_test_sensitve_v1_en * Add model 2023-08-18-mpnet_embedding_multi_qa_mpnet_base_dot_v1_en * Add model 2023-08-18-mpnet_embedding_due_eshop_21_en * Add model 2023-08-18-mpnet_embedding_setfit_ag_news_endpoint_en * Add model 2023-08-18-mpnet_embedding_setfit_ds_version_0_0_4_en * Add model 2023-08-18-mpnet_embedding_nli_mpnet_base_v2_en * Add model 2023-08-18-mpnet_embedding_sbert_paper_en * Add model 2023-08-18-mpnet_embedding_test_food_en * Add model 2023-08-18-mpnet_embedding_labels_per_job_title_fine_tune_en * Add model 2023-08-18-mpnet_embedding_keyphrase_mpnet_v1_en * Add model 2023-08-18-mpnet_embedding_setfit_occupation_en * Add model 2023-08-18-mpnet_embedding_python_developmentnotes_classifier_en * Add model 2023-08-18-mpnet_embedding_tiny_random_MPNetForSequenceClassification_en * Add model 2023-08-18-mpnet_embedding_due_eshop_21_multilabel_en * Add model 2023-08-18-mpnet_embedding_retriever_coding_guru_adapted_en * Add model 2023-08-18-mpnet_embedding_negation_categories_classifier_es * Add model 2023-08-18-mpnet_embedding_paraphrase_mpnet_base_v2_fuzzy_matcher_en * Add model 2023-08-18-mpnet_embedding_Sentiment140_fewshot_en * Add model 2023-08-18-mpnet_embedding_all_mpnet_base_v2_ftlegal_v3_en * Add model 2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_sev_en * Add model 2023-08-18-mpnet_embedding_java_usage_classifier_en * Add model 2023-08-18-mpnet_embedding_mpnet_base_snli_mnli_en * Add model 2023-08-18-mpnet_embedding_mpnet_base_articles_ner_en * Add model 2023-08-18-mpnet_embedding_sn_mpnet_base_snli_mnli_en * Add model 2023-08-18-mpnet_embedding_biencoder_all_mpnet_base_v2_mmarcoFR_fr * Add model 2023-08-18-mpnet_embedding_python_expand_classifier_en * Add model 2023-08-18-mpnet_embedding_all_datasets_v4_mpnet_base_en * Add model 2023-08-18-mpnet_embedding_pharo_collaborators_classifier_en * Add model 2023-08-18-mpnet_embedding_setfit_ds_version_0_0_5_en * Add model 2023-08-18-mpnet_embedding_stackoverflow_mpnet_base_en * Add model 2023-08-18-mpnet_embedding_paraphrase_mpnet_base_v2_SetFit_sst2_en * Add model 2023-08-18-mpnet_embedding_all_mpnet_base_v2_table_en * Add model 2023-08-18-mpnet_embedding_InvoiceOrNot_en * Add model 2023-08-18-mpnet_embedding_python_usage_classifier_en * Add model 2023-08-18-mpnet_embedding_all_mpnet_base_v2_feature_extraction_pipeline_en * Add model 2023-08-18-mpnet_embedding_ikitracs_mitigation_en * Add model 2023-08-18-mpnet_embedding_pharo_example_classifier_en * Add model 2023-08-18-mpnet_embedding_tiny_random_MPNetModel_en * Add model 2023-08-18-mpnet_embedding_mpnet_base_en * Add model 2023-08-18-mpnet_embedding_all_mpnet_base_v2_tasky_classification_en * Add model 2023-08-18-mpnet_embedding_pharo_responsibilities_classifier_en * Add model 2023-08-18-mpnet_embedding_all_mpnet_base_v2_for_sb_clustering_en * Add model 2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_trig_en * Add model 2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_likes_en * Add model 2023-08-18-mpnet_embedding_python_summary_classifier_en * Add model 2023-08-18-mpnet_embedding_few_shot_model_en * Add model 2023-08-18-mpnet_embedding_CPU_Mitigation_Classifier_en * Add model 2023-08-18-mpnet_embedding_all_mpnet_base_v2_feature_extraction_en * Add model 2023-08-18-mpnet_embedding_mpnet_adaptation_mitigation_classifier_en * Add model 2023-08-18-mpnet_embedding_java_summary_classifier_en * Add model 2023-08-18-mpnet_embedding_579_STmodel_product_rem_v3a_en * Add model 2023-08-18-mpnet_embedding_SetFit_all_data_en * Add model 2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_meas_en * Add model 2023-08-18-mpnet_embedding_setfit_ostrom_en * Add model 2023-08-18-mpnet_embedding_sentence_transformers_bible_reference_final_en * Add model 2023-08-18-mpnet_embedding_all_datasets_v3_mpnet_base_en * Add model 2023-08-18-mpnet_embedding_ikitracs_conditional_en * Add model 2023-08-18-mpnet_embedding_java_ownership_classifier_en * Add model 2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_rel_en * Add model 2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_q8a_azure_gpt35_en * Add model 2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_time_en * Add model 2023-08-18-mpnet_embedding_java_pointer_classifier_en * Add model 2023-08-18-mpnet_embedding_finetunned_sbert_en * Add model 2023-08-18-mpnet_embedding_setfit_model_Feb11_Misinformation_on_Law_en * Add model 2023-08-18-mpnet_embedding_python_parameters_classifier_en * Add model 2023-08-18-mpnet_embedding_pharo_keyimplementationpoints_classifier_en * Add model 2023-08-18-mpnet_embedding_setfit_ft_sentinent_eval_en * Add model 2023-08-18-mpnet_embedding_reddit_single_context_mpnet_base_en * Add model 2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_en * Add model 2023-08-18-mpnet_embedding_java_expand_classifier_en * Add model 2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_achiev_en * Add model 2023-08-18-mpnet_embedding_BioLORD_STAMB2_v1_en * Add model 2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_bhvr_en * Add model 2023-08-18-mpnet_embedding_fail_detect_en * Add model 2023-08-18-mpnet_embedding_CPU_Target_Classifier_en * Add model 2023-08-18-mpnet_embedding_kw_classification_setfit_model_en * Add model 2023-08-18-mpnet_embedding_paraphrase_mpnet_base_v2_en * Add model 2023-08-18-mpnet_embedding_abstract_sim_sentence_en * Add model 2023-08-18-mpnet_embedding_setfit_ds_version_0_0_2_en * Add model 2023-08-18-mpnet_embedding_mpnet_mnr_v2_fine_tuned_en * Add model 2023-08-18-mpnet_embedding_kw_classification_setfithead_model_en * Add model 2023-08-18-mpnet_embedding_due_retail_25_en * Add model 2023-08-18-mpnet_embedding_mpnet_multilabel_sector_classifier_en * Add model 2023-08-18-mpnet_embedding_vulnerable_groups_en * Add model 2023-08-18-mpnet_embedding_abstract_sim_query_en * Add model 2023-08-18-mpnet_embedding_biencoder_multi_qa_mpnet_base_cos_v1_mmarcoFR_fr * Add model 2023-08-18-mpnet_embedding_stsb_mpnet_base_v2_en * Add model 2023-08-18-mpnet_embedding_covid_qa_mpnet_en * Add model 2023-08-18-mpnet_embedding_CPU_Economywide_Classifier_en * Add model 2023-08-18-mpnet_embedding_github_issues_mpnet_st_e10_en * Add model 2023-08-18-mpnet_embedding_esci_jp_mpnet_crossencoder_en * Add model 2023-08-18-mpnet_embedding_CPU_Netzero_Classifier_en * Add model 2023-08-18-mpnet_embedding_eth_setfit_payment_model_en * Add model 2023-08-18-mpnet_embedding_PDFSegs_en * Add model 2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_dur_en * Add model 2023-08-18-mpnet_embedding_multi_qa_v1_mpnet_cls_dot_en * Add model 2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_specific_en * Add model 2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_cons_en * Add model 2023-08-18-mpnet_embedding_CPU_Conditional_Classifier_en * Add model 2023-08-18-mpnet_embedding_tiny_random_MPNetForMaskedLM_en * Add model 2023-08-18-mpnet_embedding_PatentSBERTa_V2_en * Add model 2023-08-18-mpnet_embedding_sml_ukr_message_classifier_en * Add model 2023-08-18-mpnet_embedding_CPU_Transport_GHG_Classifier_en * Add model 2023-08-18-mpnet_embedding_mpnet_nli_sts_en * Add model 2023-08-18-mpnet_embedding_initial_model_v3_en * Add model 2023-08-18-mpnet_embedding_multi_QA_v1_mpnet_asymmetric_Q_en * Add model 2023-08-18-mpnet_embedding_paraphrase_mpnet_base_v2_finetuned_polifact_en * Add model 2023-08-18-mpnet_embedding_test_model_001_en * Add model 2023-08-18-mpnet_embedding_java_rational_classifier_en * Add model 2023-08-18-mpnet_embedding_setfit_ds_version_0_0_1_en * Add model 2023-08-18-mpnet_embedding_initial_model_en * Add model 2023-08-18-mpnet_embedding_github_issues_preprocessed_mpnet_st_e10_en --------- Co-authored-by: ahmedlone127 --- ...mbedding_579_STmodel_product_rem_v3a_en.md | 88 +++++++++++++++++++ ...23-08-18-mpnet_embedding_ATTACK_BERT_en.md | 88 +++++++++++++++++++ ...18-mpnet_embedding_BioLORD_STAMB2_v1_en.md | 88 +++++++++++++++++++ ...embedding_CPU_Conditional_Classifier_en.md | 88 +++++++++++++++++++ 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a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_579_STmodel_product_rem_v3a_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_579_STmodel_product_rem_v3a_en.md new file mode 100644 index 00000000000000..2826dbe6ee9788 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_579_STmodel_product_rem_v3a_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_579_STmodel_product_rem_v3a TFMPNetModel from jamiehudson +author: John Snow Labs +name: mpnet_embedding_579_STmodel_product_rem_v3a +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_579_STmodel_product_rem_v3a` is a English model originally trained by jamiehudson. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_579_STmodel_product_rem_v3a_en_5.1.0_3.0_1692379340262.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_579_STmodel_product_rem_v3a_en_5.1.0_3.0_1692379340262.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_579_STmodel_product_rem_v3a", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_579_STmodel_product_rem_v3a", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_579_STmodel_product_rem_v3a| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_ATTACK_BERT_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_ATTACK_BERT_en.md new file mode 100644 index 00000000000000..8d7a64142160ff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_ATTACK_BERT_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_ATTACK_BERT TFMPNetModel from basel +author: John Snow Labs +name: mpnet_embedding_ATTACK_BERT +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_ATTACK_BERT` is a English model originally trained by basel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_ATTACK_BERT_en_5.1.0_3.0_1692376584683.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_ATTACK_BERT_en_5.1.0_3.0_1692376584683.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_ATTACK_BERT", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_ATTACK_BERT", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_ATTACK_BERT| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_BioLORD_STAMB2_v1_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_BioLORD_STAMB2_v1_en.md new file mode 100644 index 00000000000000..f19db3f3446eac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_BioLORD_STAMB2_v1_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_BioLORD_STAMB2_v1 TFMPNetModel from FremyCompany +author: John Snow Labs +name: mpnet_embedding_BioLORD_STAMB2_v1 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_BioLORD_STAMB2_v1` is a English model originally trained by FremyCompany. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_BioLORD_STAMB2_v1_en_5.1.0_3.0_1692377544444.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_BioLORD_STAMB2_v1_en_5.1.0_3.0_1692377544444.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_BioLORD_STAMB2_v1", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_BioLORD_STAMB2_v1", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_BioLORD_STAMB2_v1| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_CPU_Conditional_Classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_CPU_Conditional_Classifier_en.md new file mode 100644 index 00000000000000..918b41525c1172 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_CPU_Conditional_Classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_CPU_Conditional_Classifier TFMPNetModel from mtyrrell +author: John Snow Labs +name: mpnet_embedding_CPU_Conditional_Classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_CPU_Conditional_Classifier` is a English model originally trained by mtyrrell. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_CPU_Conditional_Classifier_en_5.1.0_3.0_1692380267965.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_CPU_Conditional_Classifier_en_5.1.0_3.0_1692380267965.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_CPU_Conditional_Classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_CPU_Conditional_Classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_CPU_Conditional_Classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_CPU_Economywide_Classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_CPU_Economywide_Classifier_en.md new file mode 100644 index 00000000000000..757624791b4dbd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_CPU_Economywide_Classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_CPU_Economywide_Classifier TFMPNetModel from mtyrrell +author: John Snow Labs +name: mpnet_embedding_CPU_Economywide_Classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_CPU_Economywide_Classifier` is a English model originally trained by mtyrrell. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_CPU_Economywide_Classifier_en_5.1.0_3.0_1692379293006.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_CPU_Economywide_Classifier_en_5.1.0_3.0_1692379293006.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_CPU_Economywide_Classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_CPU_Economywide_Classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_CPU_Economywide_Classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_CPU_Mitigation_Classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_CPU_Mitigation_Classifier_en.md new file mode 100644 index 00000000000000..a1d1ba79c4ef31 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_CPU_Mitigation_Classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_CPU_Mitigation_Classifier TFMPNetModel from mtyrrell +author: John Snow Labs +name: mpnet_embedding_CPU_Mitigation_Classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_CPU_Mitigation_Classifier` is a English model originally trained by mtyrrell. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_CPU_Mitigation_Classifier_en_5.1.0_3.0_1692380447837.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_CPU_Mitigation_Classifier_en_5.1.0_3.0_1692380447837.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_CPU_Mitigation_Classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_CPU_Mitigation_Classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_CPU_Mitigation_Classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.3 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_CPU_Netzero_Classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_CPU_Netzero_Classifier_en.md new file mode 100644 index 00000000000000..cb3712d55153b6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_CPU_Netzero_Classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_CPU_Netzero_Classifier TFMPNetModel from mtyrrell +author: John Snow Labs +name: mpnet_embedding_CPU_Netzero_Classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_CPU_Netzero_Classifier` is a English model originally trained by mtyrrell. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_CPU_Netzero_Classifier_en_5.1.0_3.0_1692380071810.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_CPU_Netzero_Classifier_en_5.1.0_3.0_1692380071810.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_CPU_Netzero_Classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_CPU_Netzero_Classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_CPU_Netzero_Classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.1 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_CPU_Target_Classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_CPU_Target_Classifier_en.md new file mode 100644 index 00000000000000..046ce8c8c4938e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_CPU_Target_Classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_CPU_Target_Classifier TFMPNetModel from mtyrrell +author: John Snow Labs +name: mpnet_embedding_CPU_Target_Classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_CPU_Target_Classifier` is a English model originally trained by mtyrrell. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_CPU_Target_Classifier_en_5.1.0_3.0_1692381136016.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_CPU_Target_Classifier_en_5.1.0_3.0_1692381136016.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_CPU_Target_Classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_CPU_Target_Classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_CPU_Target_Classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_CPU_Transport_GHG_Classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_CPU_Transport_GHG_Classifier_en.md new file mode 100644 index 00000000000000..0c1cad11d00706 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_CPU_Transport_GHG_Classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_CPU_Transport_GHG_Classifier TFMPNetModel from mtyrrell +author: John Snow Labs +name: mpnet_embedding_CPU_Transport_GHG_Classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_CPU_Transport_GHG_Classifier` is a English model originally trained by mtyrrell. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_CPU_Transport_GHG_Classifier_en_5.1.0_3.0_1692379880190.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_CPU_Transport_GHG_Classifier_en_5.1.0_3.0_1692379880190.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_CPU_Transport_GHG_Classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_CPU_Transport_GHG_Classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_CPU_Transport_GHG_Classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.3 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_DomainAdaptM2_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_DomainAdaptM2_en.md new file mode 100644 index 00000000000000..9a0c261ed16d69 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_DomainAdaptM2_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_DomainAdaptM2 TFMPNetModel from dani0f +author: John Snow Labs +name: mpnet_embedding_DomainAdaptM2 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_DomainAdaptM2` is a English model originally trained by dani0f. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_DomainAdaptM2_en_5.1.0_3.0_1692376943387.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_DomainAdaptM2_en_5.1.0_3.0_1692376943387.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_DomainAdaptM2", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_DomainAdaptM2", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_DomainAdaptM2| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_FewShotIssueClassifier_NLBSE23_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_FewShotIssueClassifier_NLBSE23_en.md new file mode 100644 index 00000000000000..7ce867459489e7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_FewShotIssueClassifier_NLBSE23_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_FewShotIssueClassifier_NLBSE23 TFMPNetModel from PeppoCola +author: John Snow Labs +name: mpnet_embedding_FewShotIssueClassifier_NLBSE23 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_FewShotIssueClassifier_NLBSE23` is a English model originally trained by PeppoCola. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_FewShotIssueClassifier_NLBSE23_en_5.1.0_3.0_1692375535519.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_FewShotIssueClassifier_NLBSE23_en_5.1.0_3.0_1692375535519.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_FewShotIssueClassifier_NLBSE23", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_FewShotIssueClassifier_NLBSE23", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_FewShotIssueClassifier_NLBSE23| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.0 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_InvoiceOrNot_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_InvoiceOrNot_en.md new file mode 100644 index 00000000000000..051f1bead450ea --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_InvoiceOrNot_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_InvoiceOrNot TFMPNetModel from HamzaFarhan +author: John Snow Labs +name: mpnet_embedding_InvoiceOrNot +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_InvoiceOrNot` is a English model originally trained by HamzaFarhan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_InvoiceOrNot_en_5.1.0_3.0_1692379683526.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_InvoiceOrNot_en_5.1.0_3.0_1692379683526.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_InvoiceOrNot", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_InvoiceOrNot", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_InvoiceOrNot| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.6 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_PDFSegs_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_PDFSegs_en.md new file mode 100644 index 00000000000000..2cf1cf933def81 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_PDFSegs_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_PDFSegs TFMPNetModel from HamzaFarhan +author: John Snow Labs +name: mpnet_embedding_PDFSegs +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_PDFSegs` is a English model originally trained by HamzaFarhan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_PDFSegs_en_5.1.0_3.0_1692378985073.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_PDFSegs_en_5.1.0_3.0_1692378985073.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_PDFSegs", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_PDFSegs", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_PDFSegs| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_PatentSBERTa_V2_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_PatentSBERTa_V2_en.md new file mode 100644 index 00000000000000..f9d4fca88cea02 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_PatentSBERTa_V2_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_PatentSBERTa_V2 TFMPNetModel from AAUBS +author: John Snow Labs +name: mpnet_embedding_PatentSBERTa_V2 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_PatentSBERTa_V2` is a English model originally trained by AAUBS. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_PatentSBERTa_V2_en_5.1.0_3.0_1692380355761.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_PatentSBERTa_V2_en_5.1.0_3.0_1692380355761.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_PatentSBERTa_V2", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_PatentSBERTa_V2", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_PatentSBERTa_V2| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.3 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_PatentSBERTa_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_PatentSBERTa_en.md new file mode 100644 index 00000000000000..070692a5b3a101 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_PatentSBERTa_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_PatentSBERTa TFMPNetModel from AI-Growth-Lab +author: John Snow Labs +name: mpnet_embedding_PatentSBERTa +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_PatentSBERTa` is a English model originally trained by AI-Growth-Lab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_PatentSBERTa_en_5.1.0_3.0_1692375218186.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_PatentSBERTa_en_5.1.0_3.0_1692375218186.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_PatentSBERTa", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_PatentSBERTa", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_PatentSBERTa| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.0 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_Sentiment140_fewshot_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_Sentiment140_fewshot_en.md new file mode 100644 index 00000000000000..6dae79cd0c75f3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_Sentiment140_fewshot_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_Sentiment140_fewshot TFMPNetModel from pig4431 +author: John Snow Labs +name: mpnet_embedding_Sentiment140_fewshot +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_Sentiment140_fewshot` is a English model originally trained by pig4431. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_Sentiment140_fewshot_en_5.1.0_3.0_1692378771805.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_Sentiment140_fewshot_en_5.1.0_3.0_1692378771805.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_Sentiment140_fewshot", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_Sentiment140_fewshot", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_Sentiment140_fewshot| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_SetFit_all_data_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_SetFit_all_data_en.md new file mode 100644 index 00000000000000..4d2f95602e7881 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_SetFit_all_data_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_SetFit_all_data TFMPNetModel from scaperex +author: John Snow Labs +name: mpnet_embedding_SetFit_all_data +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_SetFit_all_data` is a English model originally trained by scaperex. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_SetFit_all_data_en_5.1.0_3.0_1692380518236.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_SetFit_all_data_en_5.1.0_3.0_1692380518236.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_SetFit_all_data", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_SetFit_all_data", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_SetFit_all_data| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_Setfit_few_shot_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_Setfit_few_shot_classifier_en.md new file mode 100644 index 00000000000000..a5136966704ae5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_Setfit_few_shot_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_Setfit_few_shot_classifier TFMPNetModel from Kuaaangwen +author: John Snow Labs +name: mpnet_embedding_Setfit_few_shot_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_Setfit_few_shot_classifier` is a English model originally trained by Kuaaangwen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_Setfit_few_shot_classifier_en_5.1.0_3.0_1692375392849.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_Setfit_few_shot_classifier_en_5.1.0_3.0_1692375392849.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_Setfit_few_shot_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_Setfit_few_shot_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_Setfit_few_shot_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_abstract_sim_query_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_abstract_sim_query_en.md new file mode 100644 index 00000000000000..02fddd33189f45 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_abstract_sim_query_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_abstract_sim_query TFMPNetModel from biu-nlp +author: John Snow Labs +name: mpnet_embedding_abstract_sim_query +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_abstract_sim_query` is a English model originally trained by biu-nlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_abstract_sim_query_en_5.1.0_3.0_1692378373665.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_abstract_sim_query_en_5.1.0_3.0_1692378373665.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_abstract_sim_query", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_abstract_sim_query", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_abstract_sim_query| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.5 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_abstract_sim_sentence_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_abstract_sim_sentence_en.md new file mode 100644 index 00000000000000..7a6ef57f054364 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_abstract_sim_sentence_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_abstract_sim_sentence TFMPNetModel from biu-nlp +author: John Snow Labs +name: mpnet_embedding_abstract_sim_sentence +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_abstract_sim_sentence` is a English model originally trained by biu-nlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_abstract_sim_sentence_en_5.1.0_3.0_1692378160891.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_abstract_sim_sentence_en_5.1.0_3.0_1692378160891.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_abstract_sim_sentence", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_abstract_sim_sentence", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_abstract_sim_sentence| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.5 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_action_policy_plans_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_action_policy_plans_classifier_en.md new file mode 100644 index 00000000000000..0287ba11282f9e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_action_policy_plans_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_action_policy_plans_classifier TFMPNetModel from ppsingh +author: John Snow Labs +name: mpnet_embedding_action_policy_plans_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_action_policy_plans_classifier` is a English model originally trained by ppsingh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_action_policy_plans_classifier_en_5.1.0_3.0_1692375624301.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_action_policy_plans_classifier_en_5.1.0_3.0_1692375624301.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_action_policy_plans_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_action_policy_plans_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_action_policy_plans_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_datasets_v3_mpnet_base_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_datasets_v3_mpnet_base_en.md new file mode 100644 index 00000000000000..5f6b39f47d10cd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_datasets_v3_mpnet_base_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_all_datasets_v3_mpnet_base TFMPNetModel from flax-sentence-embeddings +author: John Snow Labs +name: mpnet_embedding_all_datasets_v3_mpnet_base +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_all_datasets_v3_mpnet_base` is a English model originally trained by flax-sentence-embeddings. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_datasets_v3_mpnet_base_en_5.1.0_3.0_1692375660568.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_datasets_v3_mpnet_base_en_5.1.0_3.0_1692375660568.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_all_datasets_v3_mpnet_base", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_all_datasets_v3_mpnet_base", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_all_datasets_v3_mpnet_base| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.6 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_datasets_v4_mpnet_base_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_datasets_v4_mpnet_base_en.md new file mode 100644 index 00000000000000..18f330d36d731a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_datasets_v4_mpnet_base_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_all_datasets_v4_mpnet_base TFMPNetModel from flax-sentence-embeddings +author: John Snow Labs +name: mpnet_embedding_all_datasets_v4_mpnet_base +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_all_datasets_v4_mpnet_base` is a English model originally trained by flax-sentence-embeddings. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_datasets_v4_mpnet_base_en_5.1.0_3.0_1692375835999.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_datasets_v4_mpnet_base_en_5.1.0_3.0_1692375835999.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_all_datasets_v4_mpnet_base", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_all_datasets_v4_mpnet_base", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_all_datasets_v4_mpnet_base| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.6 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v1_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v1_en.md new file mode 100644 index 00000000000000..891a88263da98e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v1_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_all_mpnet_base_v1 TFMPNetModel from sentence-transformers +author: John Snow Labs +name: mpnet_embedding_all_mpnet_base_v1 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_all_mpnet_base_v1` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v1_en_5.1.0_3.0_1692377622026.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v1_en_5.1.0_3.0_1692377622026.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_all_mpnet_base_v1", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_all_mpnet_base_v1", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_all_mpnet_base_v1| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.6 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_embedding_all_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_embedding_all_en.md new file mode 100644 index 00000000000000..a0ca15be2a9423 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_embedding_all_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_all_mpnet_base_v2_embedding_all TFMPNetModel from LLukas22 +author: John Snow Labs +name: mpnet_embedding_all_mpnet_base_v2_embedding_all +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_all_mpnet_base_v2_embedding_all` is a English model originally trained by LLukas22. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_embedding_all_en_5.1.0_3.0_1692375164501.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_embedding_all_en_5.1.0_3.0_1692375164501.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_all_mpnet_base_v2_embedding_all", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_all_mpnet_base_v2_embedding_all", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_all_mpnet_base_v2_embedding_all| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.3 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_feature_extraction_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_feature_extraction_en.md new file mode 100644 index 00000000000000..80204a205b4e8b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_feature_extraction_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_all_mpnet_base_v2_feature_extraction TFMPNetModel from guidecare +author: John Snow Labs +name: mpnet_embedding_all_mpnet_base_v2_feature_extraction +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_all_mpnet_base_v2_feature_extraction` is a English model originally trained by guidecare. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_feature_extraction_en_5.1.0_3.0_1692380529046.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_feature_extraction_en_5.1.0_3.0_1692380529046.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_all_mpnet_base_v2_feature_extraction", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_all_mpnet_base_v2_feature_extraction", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_all_mpnet_base_v2_feature_extraction| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.6 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_feature_extraction_pipeline_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_feature_extraction_pipeline_en.md new file mode 100644 index 00000000000000..bf081c166bb3db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_feature_extraction_pipeline_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_all_mpnet_base_v2_feature_extraction_pipeline TFMPNetModel from questgen +author: John Snow Labs +name: mpnet_embedding_all_mpnet_base_v2_feature_extraction_pipeline +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_all_mpnet_base_v2_feature_extraction_pipeline` is a English model originally trained by questgen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_feature_extraction_pipeline_en_5.1.0_3.0_1692379801719.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_feature_extraction_pipeline_en_5.1.0_3.0_1692379801719.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_all_mpnet_base_v2_feature_extraction_pipeline", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_all_mpnet_base_v2_feature_extraction_pipeline", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_all_mpnet_base_v2_feature_extraction_pipeline| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.6 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_finetuned_v2_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_finetuned_v2_en.md new file mode 100644 index 00000000000000..465721618eee23 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_finetuned_v2_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_all_mpnet_base_v2_finetuned_v2 TFMPNetModel from Humair +author: John Snow Labs +name: mpnet_embedding_all_mpnet_base_v2_finetuned_v2 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_all_mpnet_base_v2_finetuned_v2` is a English model originally trained by Humair. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_finetuned_v2_en_5.1.0_3.0_1692375397544.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_finetuned_v2_en_5.1.0_3.0_1692375397544.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_all_mpnet_base_v2_finetuned_v2", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_all_mpnet_base_v2_finetuned_v2", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_all_mpnet_base_v2_finetuned_v2| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.6 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_for_sb_clustering_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_for_sb_clustering_en.md new file mode 100644 index 00000000000000..2c6860f4fe7452 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_for_sb_clustering_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_all_mpnet_base_v2_for_sb_clustering TFMPNetModel from Thabet +author: John Snow Labs +name: mpnet_embedding_all_mpnet_base_v2_for_sb_clustering +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_all_mpnet_base_v2_for_sb_clustering` is a English model originally trained by Thabet. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_for_sb_clustering_en_5.1.0_3.0_1692380236972.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_for_sb_clustering_en_5.1.0_3.0_1692380236972.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_all_mpnet_base_v2_for_sb_clustering", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_all_mpnet_base_v2_for_sb_clustering", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_all_mpnet_base_v2_for_sb_clustering| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.0 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_ftlegal_v3_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_ftlegal_v3_en.md new file mode 100644 index 00000000000000..76e076e96782cc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_ftlegal_v3_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_all_mpnet_base_v2_ftlegal_v3 TFMPNetModel from sukantan +author: John Snow Labs +name: mpnet_embedding_all_mpnet_base_v2_ftlegal_v3 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_all_mpnet_base_v2_ftlegal_v3` is a English model originally trained by sukantan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_ftlegal_v3_en_5.1.0_3.0_1692377160570.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_ftlegal_v3_en_5.1.0_3.0_1692377160570.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_all_mpnet_base_v2_ftlegal_v3", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_all_mpnet_base_v2_ftlegal_v3", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_all_mpnet_base_v2_ftlegal_v3| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.0 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_table_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_table_en.md new file mode 100644 index 00000000000000..d557a3e88e9205 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_table_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_all_mpnet_base_v2_table TFMPNetModel from deepset +author: John Snow Labs +name: mpnet_embedding_all_mpnet_base_v2_table +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_all_mpnet_base_v2_table` is a English model originally trained by deepset. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_table_en_5.1.0_3.0_1692379618976.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_table_en_5.1.0_3.0_1692379618976.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_all_mpnet_base_v2_table", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_all_mpnet_base_v2_table", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_all_mpnet_base_v2_table| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_tasky_classification_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_tasky_classification_en.md new file mode 100644 index 00000000000000..6462f5deaec345 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_all_mpnet_base_v2_tasky_classification_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_all_mpnet_base_v2_tasky_classification TFMPNetModel from khalidalt +author: John Snow Labs +name: mpnet_embedding_all_mpnet_base_v2_tasky_classification +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_all_mpnet_base_v2_tasky_classification` is a English model originally trained by khalidalt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_tasky_classification_en_5.1.0_3.0_1692379990317.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_tasky_classification_en_5.1.0_3.0_1692379990317.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_all_mpnet_base_v2_tasky_classification", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_all_mpnet_base_v2_tasky_classification", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_all_mpnet_base_v2_tasky_classification| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.3 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_biencoder_all_mpnet_base_v2_mmarcoFR_fr.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_biencoder_all_mpnet_base_v2_mmarcoFR_fr.md new file mode 100644 index 00000000000000..7fc42dea86c473 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_biencoder_all_mpnet_base_v2_mmarcoFR_fr.md @@ -0,0 +1,88 @@ +--- +layout: model +title: French mpnet_embedding_biencoder_all_mpnet_base_v2_mmarcoFR TFMPNetModel from antoinelouis +author: John Snow Labs +name: mpnet_embedding_biencoder_all_mpnet_base_v2_mmarcoFR +date: 2023-08-18 +tags: [mpnet, fr, open_source, tensorflow] +task: Embeddings +language: fr +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_biencoder_all_mpnet_base_v2_mmarcoFR` is a French model originally trained by antoinelouis. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_biencoder_all_mpnet_base_v2_mmarcoFR_fr_5.1.0_3.0_1692379200090.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_biencoder_all_mpnet_base_v2_mmarcoFR_fr_5.1.0_3.0_1692379200090.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_biencoder_all_mpnet_base_v2_mmarcoFR", "fr")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_biencoder_all_mpnet_base_v2_mmarcoFR", "fr") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_biencoder_all_mpnet_base_v2_mmarcoFR| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|fr| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_biencoder_multi_qa_mpnet_base_cos_v1_mmarcoFR_fr.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_biencoder_multi_qa_mpnet_base_cos_v1_mmarcoFR_fr.md new file mode 100644 index 00000000000000..e477d499a0aa32 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_biencoder_multi_qa_mpnet_base_cos_v1_mmarcoFR_fr.md @@ -0,0 +1,88 @@ +--- +layout: model +title: French mpnet_embedding_biencoder_multi_qa_mpnet_base_cos_v1_mmarcoFR TFMPNetModel from antoinelouis +author: John Snow Labs +name: mpnet_embedding_biencoder_multi_qa_mpnet_base_cos_v1_mmarcoFR +date: 2023-08-18 +tags: [mpnet, fr, open_source, tensorflow] +task: Embeddings +language: fr +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_biencoder_multi_qa_mpnet_base_cos_v1_mmarcoFR` is a French model originally trained by antoinelouis. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_biencoder_multi_qa_mpnet_base_cos_v1_mmarcoFR_fr_5.1.0_3.0_1692379417337.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_biencoder_multi_qa_mpnet_base_cos_v1_mmarcoFR_fr_5.1.0_3.0_1692379417337.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_biencoder_multi_qa_mpnet_base_cos_v1_mmarcoFR", "fr")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_biencoder_multi_qa_mpnet_base_cos_v1_mmarcoFR", "fr") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_biencoder_multi_qa_mpnet_base_cos_v1_mmarcoFR| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|fr| +|Size:|410.0 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_contradiction_psb_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_contradiction_psb_en.md new file mode 100644 index 00000000000000..bccef97f878860 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_contradiction_psb_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_contradiction_psb TFMPNetModel from nategro +author: John Snow Labs +name: mpnet_embedding_contradiction_psb +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_contradiction_psb` is a English model originally trained by nategro. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_contradiction_psb_en_5.1.0_3.0_1692376734761.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_contradiction_psb_en_5.1.0_3.0_1692376734761.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_contradiction_psb", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_contradiction_psb", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_contradiction_psb| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.1 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_contradiction_psb_lds_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_contradiction_psb_lds_en.md new file mode 100644 index 00000000000000..e7e7b02497623b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_contradiction_psb_lds_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_contradiction_psb_lds TFMPNetModel from nategro +author: John Snow Labs +name: mpnet_embedding_contradiction_psb_lds +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_contradiction_psb_lds` is a English model originally trained by nategro. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_contradiction_psb_lds_en_5.1.0_3.0_1692376358959.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_contradiction_psb_lds_en_5.1.0_3.0_1692376358959.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_contradiction_psb_lds", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_contradiction_psb_lds", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_contradiction_psb_lds| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.1 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_covid_qa_mpnet_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_covid_qa_mpnet_en.md new file mode 100644 index 00000000000000..99a354419a1a37 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_covid_qa_mpnet_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_covid_qa_mpnet TFMPNetModel from shaina +author: John Snow Labs +name: mpnet_embedding_covid_qa_mpnet +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_covid_qa_mpnet` is a English model originally trained by shaina. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_covid_qa_mpnet_en_5.1.0_3.0_1692378777451.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_covid_qa_mpnet_en_5.1.0_3.0_1692378777451.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_covid_qa_mpnet", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_covid_qa_mpnet", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_covid_qa_mpnet| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|402.6 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_cross_all_mpnet_base_v2_finetuned_WebNLG2020_metric_average_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_cross_all_mpnet_base_v2_finetuned_WebNLG2020_metric_average_en.md new file mode 100644 index 00000000000000..cb67773aa568f7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_cross_all_mpnet_base_v2_finetuned_WebNLG2020_metric_average_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_cross_all_mpnet_base_v2_finetuned_WebNLG2020_metric_average TFMPNetModel from teven +author: John Snow Labs +name: mpnet_embedding_cross_all_mpnet_base_v2_finetuned_WebNLG2020_metric_average +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_cross_all_mpnet_base_v2_finetuned_WebNLG2020_metric_average` is a English model originally trained by teven. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_cross_all_mpnet_base_v2_finetuned_WebNLG2020_metric_average_en_5.1.0_3.0_1692375601480.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_cross_all_mpnet_base_v2_finetuned_WebNLG2020_metric_average_en_5.1.0_3.0_1692375601480.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_cross_all_mpnet_base_v2_finetuned_WebNLG2020_metric_average", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_cross_all_mpnet_base_v2_finetuned_WebNLG2020_metric_average", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_cross_all_mpnet_base_v2_finetuned_WebNLG2020_metric_average| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_due_eshop_21_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_due_eshop_21_en.md new file mode 100644 index 00000000000000..e083fc0f2013b3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_due_eshop_21_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_due_eshop_21 TFMPNetModel from konverner +author: John Snow Labs +name: mpnet_embedding_due_eshop_21 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_due_eshop_21` is a English model originally trained by konverner. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_due_eshop_21_en_5.1.0_3.0_1692376954136.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_due_eshop_21_en_5.1.0_3.0_1692376954136.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_due_eshop_21", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_due_eshop_21", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_due_eshop_21| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_due_eshop_21_multilabel_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_due_eshop_21_multilabel_en.md new file mode 100644 index 00000000000000..4958a984a85246 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_due_eshop_21_multilabel_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_due_eshop_21_multilabel TFMPNetModel from konverner +author: John Snow Labs +name: mpnet_embedding_due_eshop_21_multilabel +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_due_eshop_21_multilabel` is a English model originally trained by konverner. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_due_eshop_21_multilabel_en_5.1.0_3.0_1692378545704.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_due_eshop_21_multilabel_en_5.1.0_3.0_1692378545704.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_due_eshop_21_multilabel", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_due_eshop_21_multilabel", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_due_eshop_21_multilabel| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_due_retail_25_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_due_retail_25_en.md new file mode 100644 index 00000000000000..9a2a763086bb5f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_due_retail_25_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_due_retail_25 TFMPNetModel from konverner +author: John Snow Labs +name: mpnet_embedding_due_retail_25 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_due_retail_25` is a English model originally trained by konverner. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_due_retail_25_en_5.1.0_3.0_1692377165556.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_due_retail_25_en_5.1.0_3.0_1692377165556.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_due_retail_25", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_due_retail_25", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_due_retail_25| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_ecolo_pas_ecolo_v0.1_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_ecolo_pas_ecolo_v0.1_en.md new file mode 100644 index 00000000000000..764601fa7edb81 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_ecolo_pas_ecolo_v0.1_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_ecolo_pas_ecolo_v0.1 TFMPNetModel from eclaircies +author: John Snow Labs +name: mpnet_embedding_ecolo_pas_ecolo_v0.1 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_ecolo_pas_ecolo_v0.1` is a English model originally trained by eclaircies. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_ecolo_pas_ecolo_v0.1_en_5.1.0_3.0_1692375351351.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_ecolo_pas_ecolo_v0.1_en_5.1.0_3.0_1692375351351.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_ecolo_pas_ecolo_v0.1", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_ecolo_pas_ecolo_v0.1", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_ecolo_pas_ecolo_v0.1| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_esci_jp_mpnet_crossencoder_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_esci_jp_mpnet_crossencoder_en.md new file mode 100644 index 00000000000000..a7d130c9112a1c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_esci_jp_mpnet_crossencoder_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_esci_jp_mpnet_crossencoder TFMPNetModel from spacemanidol +author: John Snow Labs +name: mpnet_embedding_esci_jp_mpnet_crossencoder +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_esci_jp_mpnet_crossencoder` is a English model originally trained by spacemanidol. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_esci_jp_mpnet_crossencoder_en_5.1.0_3.0_1692380712380.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_esci_jp_mpnet_crossencoder_en_5.1.0_3.0_1692380712380.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_esci_jp_mpnet_crossencoder", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_esci_jp_mpnet_crossencoder", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_esci_jp_mpnet_crossencoder| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_eth_setfit_payment_model_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_eth_setfit_payment_model_en.md new file mode 100644 index 00000000000000..9b60461052880d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_eth_setfit_payment_model_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_eth_setfit_payment_model TFMPNetModel from kainxwang +author: John Snow Labs +name: mpnet_embedding_eth_setfit_payment_model +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_eth_setfit_payment_model` is a English model originally trained by kainxwang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_eth_setfit_payment_model_en_5.1.0_3.0_1692380076037.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_eth_setfit_payment_model_en_5.1.0_3.0_1692380076037.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_eth_setfit_payment_model", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_eth_setfit_payment_model", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_eth_setfit_payment_model| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_fail_detect_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_fail_detect_en.md new file mode 100644 index 00000000000000..6040e046ce6d0d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_fail_detect_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_fail_detect TFMPNetModel from Ngit +author: John Snow Labs +name: mpnet_embedding_fail_detect +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_fail_detect` is a English model originally trained by Ngit. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_fail_detect_en_5.1.0_3.0_1692379164436.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_fail_detect_en_5.1.0_3.0_1692379164436.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_fail_detect", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_fail_detect", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_fail_detect| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_few_shot_model_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_few_shot_model_en.md new file mode 100644 index 00000000000000..e7e7fd4bc3e539 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_few_shot_model_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_few_shot_model TFMPNetModel from jessietextstan +author: John Snow Labs +name: mpnet_embedding_few_shot_model +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_few_shot_model` is a English model originally trained by jessietextstan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_few_shot_model_en_5.1.0_3.0_1692380439322.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_few_shot_model_en_5.1.0_3.0_1692380439322.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_few_shot_model", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_few_shot_model", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_few_shot_model| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.1 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_finetunned_sbert_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_finetunned_sbert_en.md new file mode 100644 index 00000000000000..af3135e5d9c8a2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_finetunned_sbert_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_finetunned_sbert TFMPNetModel from Kick28 +author: John Snow Labs +name: mpnet_embedding_finetunned_sbert +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_finetunned_sbert` is a English model originally trained by Kick28. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_finetunned_sbert_en_5.1.0_3.0_1692380631621.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_finetunned_sbert_en_5.1.0_3.0_1692380631621.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_finetunned_sbert", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_finetunned_sbert", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_finetunned_sbert| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_github_issues_mpnet_st_e10_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_github_issues_mpnet_st_e10_en.md new file mode 100644 index 00000000000000..9326717aa52c89 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_github_issues_mpnet_st_e10_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_github_issues_mpnet_st_e10 TFMPNetModel from Collab-uniba +author: John Snow Labs +name: mpnet_embedding_github_issues_mpnet_st_e10 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_github_issues_mpnet_st_e10` is a English model originally trained by Collab-uniba. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_github_issues_mpnet_st_e10_en_5.1.0_3.0_1692376620750.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_github_issues_mpnet_st_e10_en_5.1.0_3.0_1692376620750.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_github_issues_mpnet_st_e10", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_github_issues_mpnet_st_e10", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_github_issues_mpnet_st_e10| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.3 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_github_issues_preprocessed_mpnet_st_e10_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_github_issues_preprocessed_mpnet_st_e10_en.md new file mode 100644 index 00000000000000..dd0307f194cdff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_github_issues_preprocessed_mpnet_st_e10_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_github_issues_preprocessed_mpnet_st_e10 TFMPNetModel from Collab-uniba +author: John Snow Labs +name: mpnet_embedding_github_issues_preprocessed_mpnet_st_e10 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_github_issues_preprocessed_mpnet_st_e10` is a English model originally trained by Collab-uniba. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_github_issues_preprocessed_mpnet_st_e10_en_5.1.0_3.0_1692378367005.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_github_issues_preprocessed_mpnet_st_e10_en_5.1.0_3.0_1692378367005.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_github_issues_preprocessed_mpnet_st_e10", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_github_issues_preprocessed_mpnet_st_e10", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_github_issues_preprocessed_mpnet_st_e10| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.0 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_ikitracs_conditional_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_ikitracs_conditional_en.md new file mode 100644 index 00000000000000..9a88a28d497222 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_ikitracs_conditional_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_ikitracs_conditional TFMPNetModel from ilaria-oneofftech +author: John Snow Labs +name: mpnet_embedding_ikitracs_conditional +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_ikitracs_conditional` is a English model originally trained by ilaria-oneofftech. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_ikitracs_conditional_en_5.1.0_3.0_1692376423487.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_ikitracs_conditional_en_5.1.0_3.0_1692376423487.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_ikitracs_conditional", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_ikitracs_conditional", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_ikitracs_conditional| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_ikitracs_mitigation_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_ikitracs_mitigation_en.md new file mode 100644 index 00000000000000..2fe54cca2c1b46 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_ikitracs_mitigation_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_ikitracs_mitigation TFMPNetModel from ilaria-oneofftech +author: John Snow Labs +name: mpnet_embedding_ikitracs_mitigation +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_ikitracs_mitigation` is a English model originally trained by ilaria-oneofftech. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_ikitracs_mitigation_en_5.1.0_3.0_1692379836311.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_ikitracs_mitigation_en_5.1.0_3.0_1692379836311.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_ikitracs_mitigation", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_ikitracs_mitigation", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_ikitracs_mitigation| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_initial_model_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_initial_model_en.md new file mode 100644 index 00000000000000..4664c712ad782e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_initial_model_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_initial_model TFMPNetModel from ishan +author: John Snow Labs +name: mpnet_embedding_initial_model +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_initial_model` is a English model originally trained by ishan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_initial_model_en_5.1.0_3.0_1692378787120.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_initial_model_en_5.1.0_3.0_1692378787120.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_initial_model", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_initial_model", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_initial_model| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.0 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_initial_model_v3_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_initial_model_v3_en.md new file mode 100644 index 00000000000000..f171a65e56c7c1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_initial_model_v3_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_initial_model_v3 TFMPNetModel from ishan +author: John Snow Labs +name: mpnet_embedding_initial_model_v3 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_initial_model_v3` is a English model originally trained by ishan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_initial_model_v3_en_5.1.0_3.0_1692378725094.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_initial_model_v3_en_5.1.0_3.0_1692378725094.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_initial_model_v3", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_initial_model_v3", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_initial_model_v3| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.0 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_deprecation_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_deprecation_classifier_en.md new file mode 100644 index 00000000000000..935bccbccd1285 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_deprecation_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_java_deprecation_classifier TFMPNetModel from AISE-TUDelft +author: John Snow Labs +name: mpnet_embedding_java_deprecation_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_java_deprecation_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_java_deprecation_classifier_en_5.1.0_3.0_1692377248542.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_java_deprecation_classifier_en_5.1.0_3.0_1692377248542.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_java_deprecation_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_java_deprecation_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_java_deprecation_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_expand_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_expand_classifier_en.md new file mode 100644 index 00000000000000..377df7c2b7e566 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_expand_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_java_expand_classifier TFMPNetModel from AISE-TUDelft +author: John Snow Labs +name: mpnet_embedding_java_expand_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_java_expand_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_java_expand_classifier_en_5.1.0_3.0_1692378008915.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_java_expand_classifier_en_5.1.0_3.0_1692378008915.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_java_expand_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_java_expand_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_java_expand_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_ownership_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_ownership_classifier_en.md new file mode 100644 index 00000000000000..30aa3bf0c2ef74 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_ownership_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_java_ownership_classifier TFMPNetModel from AISE-TUDelft +author: John Snow Labs +name: mpnet_embedding_java_ownership_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_java_ownership_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_java_ownership_classifier_en_5.1.0_3.0_1692379581982.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_java_ownership_classifier_en_5.1.0_3.0_1692379581982.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_java_ownership_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_java_ownership_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_java_ownership_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_pointer_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_pointer_classifier_en.md new file mode 100644 index 00000000000000..74e9f64acdd757 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_pointer_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_java_pointer_classifier TFMPNetModel from AISE-TUDelft +author: John Snow Labs +name: mpnet_embedding_java_pointer_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_java_pointer_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_java_pointer_classifier_en_5.1.0_3.0_1692377625982.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_java_pointer_classifier_en_5.1.0_3.0_1692377625982.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_java_pointer_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_java_pointer_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_java_pointer_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_rational_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_rational_classifier_en.md new file mode 100644 index 00000000000000..7bc4b726b21720 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_rational_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_java_rational_classifier TFMPNetModel from AISE-TUDelft +author: John Snow Labs +name: mpnet_embedding_java_rational_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_java_rational_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_java_rational_classifier_en_5.1.0_3.0_1692378803769.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_java_rational_classifier_en_5.1.0_3.0_1692378803769.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_java_rational_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_java_rational_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_java_rational_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_summary_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_summary_classifier_en.md new file mode 100644 index 00000000000000..e29e8ec4811483 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_summary_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_java_summary_classifier TFMPNetModel from AISE-TUDelft +author: John Snow Labs +name: mpnet_embedding_java_summary_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_java_summary_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_java_summary_classifier_en_5.1.0_3.0_1692377436133.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_java_summary_classifier_en_5.1.0_3.0_1692377436133.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_java_summary_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_java_summary_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_java_summary_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_usage_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_usage_classifier_en.md new file mode 100644 index 00000000000000..c9c83299840f67 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_java_usage_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_java_usage_classifier TFMPNetModel from AISE-TUDelft +author: John Snow Labs +name: mpnet_embedding_java_usage_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_java_usage_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_java_usage_classifier_en_5.1.0_3.0_1692377814202.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_java_usage_classifier_en_5.1.0_3.0_1692377814202.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_java_usage_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_java_usage_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_java_usage_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_keyphrase_mpnet_v1_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_keyphrase_mpnet_v1_en.md new file mode 100644 index 00000000000000..fbc3401b41f28d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_keyphrase_mpnet_v1_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_keyphrase_mpnet_v1 TFMPNetModel from uclanlp +author: John Snow Labs +name: mpnet_embedding_keyphrase_mpnet_v1 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_keyphrase_mpnet_v1` is a English model originally trained by uclanlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_keyphrase_mpnet_v1_en_5.1.0_3.0_1692377753915.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_keyphrase_mpnet_v1_en_5.1.0_3.0_1692377753915.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_keyphrase_mpnet_v1", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_keyphrase_mpnet_v1", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_keyphrase_mpnet_v1| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.6 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_kw_classification_setfit_model_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_kw_classification_setfit_model_en.md new file mode 100644 index 00000000000000..65cef5eb694cf5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_kw_classification_setfit_model_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_kw_classification_setfit_model TFMPNetModel from gyuri2020 +author: John Snow Labs +name: mpnet_embedding_kw_classification_setfit_model +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_kw_classification_setfit_model` is a English model originally trained by gyuri2020. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_kw_classification_setfit_model_en_5.1.0_3.0_1692379109726.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_kw_classification_setfit_model_en_5.1.0_3.0_1692379109726.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_kw_classification_setfit_model", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_kw_classification_setfit_model", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_kw_classification_setfit_model| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_kw_classification_setfithead_model_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_kw_classification_setfithead_model_en.md new file mode 100644 index 00000000000000..a28f3cc26cf82f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_kw_classification_setfithead_model_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_kw_classification_setfithead_model TFMPNetModel from gyuri2020 +author: John Snow Labs +name: mpnet_embedding_kw_classification_setfithead_model +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_kw_classification_setfithead_model` is a English model originally trained by gyuri2020. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_kw_classification_setfithead_model_en_5.1.0_3.0_1692379488047.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_kw_classification_setfithead_model_en_5.1.0_3.0_1692379488047.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_kw_classification_setfithead_model", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_kw_classification_setfithead_model", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_kw_classification_setfithead_model| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_labels_per_job_title_fine_tune_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_labels_per_job_title_fine_tune_en.md new file mode 100644 index 00000000000000..ea158a4635d45a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_labels_per_job_title_fine_tune_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_labels_per_job_title_fine_tune TFMPNetModel from marianodo +author: John Snow Labs +name: mpnet_embedding_labels_per_job_title_fine_tune +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_labels_per_job_title_fine_tune` is a English model originally trained by marianodo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_labels_per_job_title_fine_tune_en_5.1.0_3.0_1692377349908.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_labels_per_job_title_fine_tune_en_5.1.0_3.0_1692377349908.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_labels_per_job_title_fine_tune", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_labels_per_job_title_fine_tune", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_labels_per_job_title_fine_tune| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_adaptation_mitigation_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_adaptation_mitigation_classifier_en.md new file mode 100644 index 00000000000000..a45b85dd2423ef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_adaptation_mitigation_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_mpnet_adaptation_mitigation_classifier TFMPNetModel from ppsingh +author: John Snow Labs +name: mpnet_embedding_mpnet_adaptation_mitigation_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_mpnet_adaptation_mitigation_classifier` is a English model originally trained by ppsingh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_adaptation_mitigation_classifier_en_5.1.0_3.0_1692376773609.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_adaptation_mitigation_classifier_en_5.1.0_3.0_1692376773609.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_mpnet_adaptation_mitigation_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_mpnet_adaptation_mitigation_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_mpnet_adaptation_mitigation_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.3 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_base_articles_ner_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_base_articles_ner_en.md new file mode 100644 index 00000000000000..7804f757513786 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_base_articles_ner_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_mpnet_base_articles_ner TFMPNetModel from evangeliazve +author: John Snow Labs +name: mpnet_embedding_mpnet_base_articles_ner +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_mpnet_base_articles_ner` is a English model originally trained by evangeliazve. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_base_articles_ner_en_5.1.0_3.0_1692378970812.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_base_articles_ner_en_5.1.0_3.0_1692378970812.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_mpnet_base_articles_ner", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_mpnet_base_articles_ner", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_mpnet_base_articles_ner| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|385.7 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_base_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_base_en.md new file mode 100644 index 00000000000000..e36992b8d74edd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_base_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_mpnet_base TFMPNetModel from microsoft +author: John Snow Labs +name: mpnet_embedding_mpnet_base +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_mpnet_base` is a English model originally trained by microsoft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_base_en_5.1.0_3.0_1692377047526.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_base_en_5.1.0_3.0_1692377047526.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_mpnet_base", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_mpnet_base", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_mpnet_base| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|264.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_base_snli_mnli_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_base_snli_mnli_en.md new file mode 100644 index 00000000000000..a85dd22290550b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_base_snli_mnli_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_mpnet_base_snli_mnli TFMPNetModel from symanto +author: John Snow Labs +name: mpnet_embedding_mpnet_base_snli_mnli +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_mpnet_base_snli_mnli` is a English model originally trained by symanto. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_base_snli_mnli_en_5.1.0_3.0_1692378970961.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_base_snli_mnli_en_5.1.0_3.0_1692378970961.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_mpnet_base_snli_mnli", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_mpnet_base_snli_mnli", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_mpnet_base_snli_mnli| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.3 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_mnr_v2_fine_tuned_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_mnr_v2_fine_tuned_en.md new file mode 100644 index 00000000000000..0292e2bfd78e0b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_mnr_v2_fine_tuned_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_mpnet_mnr_v2_fine_tuned TFMPNetModel from BlazingFringe +author: John Snow Labs +name: mpnet_embedding_mpnet_mnr_v2_fine_tuned +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_mpnet_mnr_v2_fine_tuned` is a English model originally trained by BlazingFringe. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_mnr_v2_fine_tuned_en_5.1.0_3.0_1692379624059.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_mnr_v2_fine_tuned_en_5.1.0_3.0_1692379624059.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_mpnet_mnr_v2_fine_tuned", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_mpnet_mnr_v2_fine_tuned", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_mpnet_mnr_v2_fine_tuned| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.1 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_multilabel_sector_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_multilabel_sector_classifier_en.md new file mode 100644 index 00000000000000..cbba07db508f41 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_multilabel_sector_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_mpnet_multilabel_sector_classifier TFMPNetModel from ppsingh +author: John Snow Labs +name: mpnet_embedding_mpnet_multilabel_sector_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_mpnet_multilabel_sector_classifier` is a English model originally trained by ppsingh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_multilabel_sector_classifier_en_5.1.0_3.0_1692380031162.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_multilabel_sector_classifier_en_5.1.0_3.0_1692380031162.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_mpnet_multilabel_sector_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_mpnet_multilabel_sector_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_mpnet_multilabel_sector_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_nli_sts_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_nli_sts_en.md new file mode 100644 index 00000000000000..3bd76a40198054 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_nli_sts_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_mpnet_nli_sts TFMPNetModel from jamescalam +author: John Snow Labs +name: mpnet_embedding_mpnet_nli_sts +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_mpnet_nli_sts` is a English model originally trained by jamescalam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_nli_sts_en_5.1.0_3.0_1692375971185.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_nli_sts_en_5.1.0_3.0_1692375971185.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_mpnet_nli_sts", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_mpnet_nli_sts", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_mpnet_nli_sts| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|402.0 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_retriever_squad2_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_retriever_squad2_en.md new file mode 100644 index 00000000000000..0f10150d16b34d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_retriever_squad2_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_mpnet_retriever_squad2 TFMPNetModel from pinecone +author: John Snow Labs +name: mpnet_embedding_mpnet_retriever_squad2 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_mpnet_retriever_squad2` is a English model originally trained by pinecone. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_retriever_squad2_en_5.1.0_3.0_1692377425348.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_retriever_squad2_en_5.1.0_3.0_1692377425348.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_mpnet_retriever_squad2", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_mpnet_retriever_squad2", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_mpnet_retriever_squad2| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.6 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_snli_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_snli_en.md new file mode 100644 index 00000000000000..e3ffc25679bde7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_snli_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_mpnet_snli TFMPNetModel from jamescalam +author: John Snow Labs +name: mpnet_embedding_mpnet_snli +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_mpnet_snli` is a English model originally trained by jamescalam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_snli_en_5.1.0_3.0_1692375412994.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_snli_en_5.1.0_3.0_1692375412994.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_mpnet_snli", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_mpnet_snli", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_mpnet_snli| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|399.4 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_snli_negatives_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_snli_negatives_en.md new file mode 100644 index 00000000000000..38434753e80c99 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_mpnet_snli_negatives_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_mpnet_snli_negatives TFMPNetModel from jamescalam +author: John Snow Labs +name: mpnet_embedding_mpnet_snli_negatives +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_mpnet_snli_negatives` is a English model originally trained by jamescalam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_snli_negatives_en_5.1.0_3.0_1692375780359.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_mpnet_snli_negatives_en_5.1.0_3.0_1692375780359.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_mpnet_snli_negatives", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_mpnet_snli_negatives", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_mpnet_snli_negatives| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|401.1 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_QA_v1_mpnet_asymmetric_A_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_QA_v1_mpnet_asymmetric_A_en.md new file mode 100644 index 00000000000000..4125d55cb5e7b3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_QA_v1_mpnet_asymmetric_A_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_multi_QA_v1_mpnet_asymmetric_A TFMPNetModel from flax-sentence-embeddings +author: John Snow Labs +name: mpnet_embedding_multi_QA_v1_mpnet_asymmetric_A +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_multi_QA_v1_mpnet_asymmetric_A` is a English model originally trained by flax-sentence-embeddings. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_QA_v1_mpnet_asymmetric_A_en_5.1.0_3.0_1692376012864.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_QA_v1_mpnet_asymmetric_A_en_5.1.0_3.0_1692376012864.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_multi_QA_v1_mpnet_asymmetric_A", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_multi_QA_v1_mpnet_asymmetric_A", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_multi_QA_v1_mpnet_asymmetric_A| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.6 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_QA_v1_mpnet_asymmetric_Q_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_QA_v1_mpnet_asymmetric_Q_en.md new file mode 100644 index 00000000000000..97983765fa4b09 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_QA_v1_mpnet_asymmetric_Q_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_multi_QA_v1_mpnet_asymmetric_Q TFMPNetModel from flax-sentence-embeddings +author: John Snow Labs +name: mpnet_embedding_multi_QA_v1_mpnet_asymmetric_Q +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_multi_QA_v1_mpnet_asymmetric_Q` is a English model originally trained by flax-sentence-embeddings. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_QA_v1_mpnet_asymmetric_Q_en_5.1.0_3.0_1692376212995.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_QA_v1_mpnet_asymmetric_Q_en_5.1.0_3.0_1692376212995.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_multi_QA_v1_mpnet_asymmetric_Q", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_multi_QA_v1_mpnet_asymmetric_Q", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_multi_QA_v1_mpnet_asymmetric_Q| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.3 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_qa_mpnet_base_cos_v1_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_qa_mpnet_base_cos_v1_en.md new file mode 100644 index 00000000000000..0fef444dd2bc79 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_qa_mpnet_base_cos_v1_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_multi_qa_mpnet_base_cos_v1 TFMPNetModel from navteca +author: John Snow Labs +name: mpnet_embedding_multi_qa_mpnet_base_cos_v1 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_multi_qa_mpnet_base_cos_v1` is a English model originally trained by navteca. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_qa_mpnet_base_cos_v1_en_5.1.0_3.0_1692377234842.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_qa_mpnet_base_cos_v1_en_5.1.0_3.0_1692377234842.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_multi_qa_mpnet_base_cos_v1", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_multi_qa_mpnet_base_cos_v1", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_multi_qa_mpnet_base_cos_v1| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_qa_mpnet_base_dot_v1_eclass_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_qa_mpnet_base_dot_v1_eclass_en.md new file mode 100644 index 00000000000000..695ca8c2aefc6e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_qa_mpnet_base_dot_v1_eclass_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_multi_qa_mpnet_base_dot_v1_eclass TFMPNetModel from JoBeer +author: John Snow Labs +name: mpnet_embedding_multi_qa_mpnet_base_dot_v1_eclass +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_multi_qa_mpnet_base_dot_v1_eclass` is a English model originally trained by JoBeer. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_qa_mpnet_base_dot_v1_eclass_en_5.1.0_3.0_1692375204024.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_qa_mpnet_base_dot_v1_eclass_en_5.1.0_3.0_1692375204024.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_multi_qa_mpnet_base_dot_v1_eclass", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_multi_qa_mpnet_base_dot_v1_eclass", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_multi_qa_mpnet_base_dot_v1_eclass| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_qa_mpnet_base_dot_v1_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_qa_mpnet_base_dot_v1_en.md new file mode 100644 index 00000000000000..c4ff822ac63084 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_qa_mpnet_base_dot_v1_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_multi_qa_mpnet_base_dot_v1 TFMPNetModel from sentence-transformers +author: John Snow Labs +name: mpnet_embedding_multi_qa_mpnet_base_dot_v1 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_multi_qa_mpnet_base_dot_v1` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_qa_mpnet_base_dot_v1_en_5.1.0_3.0_1692377809432.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_qa_mpnet_base_dot_v1_en_5.1.0_3.0_1692377809432.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_multi_qa_mpnet_base_dot_v1", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_multi_qa_mpnet_base_dot_v1", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_multi_qa_mpnet_base_dot_v1| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_qa_mpnet_base_dot_v1_legal_finetune_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_qa_mpnet_base_dot_v1_legal_finetune_en.md new file mode 100644 index 00000000000000..3af998f5f8239d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_qa_mpnet_base_dot_v1_legal_finetune_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_multi_qa_mpnet_base_dot_v1_legal_finetune TFMPNetModel from oliviamga2 +author: John Snow Labs +name: mpnet_embedding_multi_qa_mpnet_base_dot_v1_legal_finetune +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_multi_qa_mpnet_base_dot_v1_legal_finetune` is a English model originally trained by oliviamga2. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_qa_mpnet_base_dot_v1_legal_finetune_en_5.1.0_3.0_1692376503346.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_qa_mpnet_base_dot_v1_legal_finetune_en_5.1.0_3.0_1692376503346.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_multi_qa_mpnet_base_dot_v1_legal_finetune", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_multi_qa_mpnet_base_dot_v1_legal_finetune", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_multi_qa_mpnet_base_dot_v1_legal_finetune| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.0 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_qa_v1_mpnet_cls_dot_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_qa_v1_mpnet_cls_dot_en.md new file mode 100644 index 00000000000000..3b9b76913b6c0c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_multi_qa_v1_mpnet_cls_dot_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_multi_qa_v1_mpnet_cls_dot TFMPNetModel from flax-sentence-embeddings +author: John Snow Labs +name: mpnet_embedding_multi_qa_v1_mpnet_cls_dot +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_multi_qa_v1_mpnet_cls_dot` is a English model originally trained by flax-sentence-embeddings. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_qa_v1_mpnet_cls_dot_en_5.1.0_3.0_1692376421210.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_qa_v1_mpnet_cls_dot_en_5.1.0_3.0_1692376421210.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_multi_qa_v1_mpnet_cls_dot", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_multi_qa_v1_mpnet_cls_dot", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_multi_qa_v1_mpnet_cls_dot| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_my_awesome_setfit_model_98_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_my_awesome_setfit_model_98_en.md new file mode 100644 index 00000000000000..7960690d037bc6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_my_awesome_setfit_model_98_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_my_awesome_setfit_model_98 TFMPNetModel from lewtun +author: John Snow Labs +name: mpnet_embedding_my_awesome_setfit_model_98 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_my_awesome_setfit_model_98` is a English model originally trained by lewtun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_my_awesome_setfit_model_98_en_5.1.0_3.0_1692377344662.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_my_awesome_setfit_model_98_en_5.1.0_3.0_1692377344662.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_my_awesome_setfit_model_98", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_my_awesome_setfit_model_98", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_my_awesome_setfit_model_98| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_negation_categories_classifier_es.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_negation_categories_classifier_es.md new file mode 100644 index 00000000000000..9f51d4f8c362fa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_negation_categories_classifier_es.md @@ -0,0 +1,88 @@ +--- +layout: model +title: Castilian, Spanish mpnet_embedding_negation_categories_classifier TFMPNetModel from mhammadkhan +author: John Snow Labs +name: mpnet_embedding_negation_categories_classifier +date: 2023-08-18 +tags: [mpnet, es, open_source, tensorflow] +task: Embeddings +language: es +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_negation_categories_classifier` is a Castilian, Spanish model originally trained by mhammadkhan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_negation_categories_classifier_es_5.1.0_3.0_1692378586323.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_negation_categories_classifier_es_5.1.0_3.0_1692378586323.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_negation_categories_classifier", "es")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_negation_categories_classifier", "es") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_negation_categories_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|es| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_nli_mpnet_base_v2_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_nli_mpnet_base_v2_en.md new file mode 100644 index 00000000000000..2fa2384a26ea39 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_nli_mpnet_base_v2_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_nli_mpnet_base_v2 TFMPNetModel from sentence-transformers +author: John Snow Labs +name: mpnet_embedding_nli_mpnet_base_v2 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_nli_mpnet_base_v2` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_nli_mpnet_base_v2_en_5.1.0_3.0_1692377999396.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_nli_mpnet_base_v2_en_5.1.0_3.0_1692377999396.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_nli_mpnet_base_v2", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_nli_mpnet_base_v2", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_nli_mpnet_base_v2| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|408.5 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_nooks_amd_detection_realtime_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_nooks_amd_detection_realtime_en.md new file mode 100644 index 00000000000000..e025c59600e3ae --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_nooks_amd_detection_realtime_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_nooks_amd_detection_realtime TFMPNetModel from nikcheerla +author: John Snow Labs +name: mpnet_embedding_nooks_amd_detection_realtime +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_nooks_amd_detection_realtime` is a English model originally trained by nikcheerla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_nooks_amd_detection_realtime_en_5.1.0_3.0_1692375770246.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_nooks_amd_detection_realtime_en_5.1.0_3.0_1692375770246.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_nooks_amd_detection_realtime", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_nooks_amd_detection_realtime", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_nooks_amd_detection_realtime| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_nooks_amd_detection_v2_full_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_nooks_amd_detection_v2_full_en.md new file mode 100644 index 00000000000000..dff60b46f15b81 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_nooks_amd_detection_v2_full_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_nooks_amd_detection_v2_full TFMPNetModel from nikcheerla +author: John Snow Labs +name: mpnet_embedding_nooks_amd_detection_v2_full +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_nooks_amd_detection_v2_full` is a English model originally trained by nikcheerla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_nooks_amd_detection_v2_full_en_5.1.0_3.0_1692375579854.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_nooks_amd_detection_v2_full_en_5.1.0_3.0_1692375579854.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_nooks_amd_detection_v2_full", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_nooks_amd_detection_v2_full", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_nooks_amd_detection_v2_full| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.0 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_nps_psb_lds_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_nps_psb_lds_en.md new file mode 100644 index 00000000000000..aa69e1f8dfcf36 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_nps_psb_lds_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_nps_psb_lds TFMPNetModel from nategro +author: John Snow Labs +name: mpnet_embedding_nps_psb_lds +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_nps_psb_lds` is a English model originally trained by nategro. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_nps_psb_lds_en_5.1.0_3.0_1692376546159.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_nps_psb_lds_en_5.1.0_3.0_1692376546159.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_nps_psb_lds", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_nps_psb_lds", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_nps_psb_lds| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.1 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_ouvrage_classif_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_ouvrage_classif_en.md new file mode 100644 index 00000000000000..f17a6564e9a9e8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_ouvrage_classif_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_ouvrage_classif TFMPNetModel from TomPWM +author: John Snow Labs +name: mpnet_embedding_ouvrage_classif +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_ouvrage_classif` is a English model originally trained by TomPWM. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_ouvrage_classif_en_5.1.0_3.0_1692376002579.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_ouvrage_classif_en_5.1.0_3.0_1692376002579.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_ouvrage_classif", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_ouvrage_classif", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_ouvrage_classif| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_paraphrase_mpnet_base_v2_SetFit_sst2_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_paraphrase_mpnet_base_v2_SetFit_sst2_en.md new file mode 100644 index 00000000000000..cf711e7af57488 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_paraphrase_mpnet_base_v2_SetFit_sst2_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_paraphrase_mpnet_base_v2_SetFit_sst2 TFMPNetModel from moshew +author: John Snow Labs +name: mpnet_embedding_paraphrase_mpnet_base_v2_SetFit_sst2 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_paraphrase_mpnet_base_v2_SetFit_sst2` is a English model originally trained by moshew. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_paraphrase_mpnet_base_v2_SetFit_sst2_en_5.1.0_3.0_1692379417527.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_paraphrase_mpnet_base_v2_SetFit_sst2_en_5.1.0_3.0_1692379417527.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_paraphrase_mpnet_base_v2_SetFit_sst2", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_paraphrase_mpnet_base_v2_SetFit_sst2", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_paraphrase_mpnet_base_v2_SetFit_sst2| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_paraphrase_mpnet_base_v2_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_paraphrase_mpnet_base_v2_en.md new file mode 100644 index 00000000000000..1dabd46f84cf5d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_paraphrase_mpnet_base_v2_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_paraphrase_mpnet_base_v2 TFMPNetModel from sentence-transformers +author: John Snow Labs +name: mpnet_embedding_paraphrase_mpnet_base_v2 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_paraphrase_mpnet_base_v2` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_paraphrase_mpnet_base_v2_en_5.1.0_3.0_1692378194037.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_paraphrase_mpnet_base_v2_en_5.1.0_3.0_1692378194037.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_paraphrase_mpnet_base_v2", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_paraphrase_mpnet_base_v2", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_paraphrase_mpnet_base_v2| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_paraphrase_mpnet_base_v2_finetuned_polifact_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_paraphrase_mpnet_base_v2_finetuned_polifact_en.md new file mode 100644 index 00000000000000..9b2bcf70c35b7e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_paraphrase_mpnet_base_v2_finetuned_polifact_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_paraphrase_mpnet_base_v2_finetuned_polifact TFMPNetModel from anuj55 +author: John Snow Labs +name: mpnet_embedding_paraphrase_mpnet_base_v2_finetuned_polifact +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_paraphrase_mpnet_base_v2_finetuned_polifact` is a English model originally trained by anuj55. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_paraphrase_mpnet_base_v2_finetuned_polifact_en_5.1.0_3.0_1692380188177.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_paraphrase_mpnet_base_v2_finetuned_polifact_en_5.1.0_3.0_1692380188177.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_paraphrase_mpnet_base_v2_finetuned_polifact", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_paraphrase_mpnet_base_v2_finetuned_polifact", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_paraphrase_mpnet_base_v2_finetuned_polifact| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_paraphrase_mpnet_base_v2_fuzzy_matcher_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_paraphrase_mpnet_base_v2_fuzzy_matcher_en.md new file mode 100644 index 00000000000000..a907840a8b3cc1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_paraphrase_mpnet_base_v2_fuzzy_matcher_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_paraphrase_mpnet_base_v2_fuzzy_matcher TFMPNetModel from shahrukhx01 +author: John Snow Labs +name: mpnet_embedding_paraphrase_mpnet_base_v2_fuzzy_matcher +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_paraphrase_mpnet_base_v2_fuzzy_matcher` is a English model originally trained by shahrukhx01. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_paraphrase_mpnet_base_v2_fuzzy_matcher_en_5.1.0_3.0_1692378572822.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_paraphrase_mpnet_base_v2_fuzzy_matcher_en_5.1.0_3.0_1692378572822.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_paraphrase_mpnet_base_v2_fuzzy_matcher", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_paraphrase_mpnet_base_v2_fuzzy_matcher", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_paraphrase_mpnet_base_v2_fuzzy_matcher| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_pharo_collaborators_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_pharo_collaborators_classifier_en.md new file mode 100644 index 00000000000000..8165d870dd14df --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_pharo_collaborators_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_pharo_collaborators_classifier TFMPNetModel from AISE-TUDelft +author: John Snow Labs +name: mpnet_embedding_pharo_collaborators_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_pharo_collaborators_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_pharo_collaborators_classifier_en_5.1.0_3.0_1692379408752.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_pharo_collaborators_classifier_en_5.1.0_3.0_1692379408752.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_pharo_collaborators_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_pharo_collaborators_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_pharo_collaborators_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_pharo_example_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_pharo_example_classifier_en.md new file mode 100644 index 00000000000000..bd766092e29d48 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_pharo_example_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_pharo_example_classifier TFMPNetModel from AISE-TUDelft +author: John Snow Labs +name: mpnet_embedding_pharo_example_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_pharo_example_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_pharo_example_classifier_en_5.1.0_3.0_1692379945273.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_pharo_example_classifier_en_5.1.0_3.0_1692379945273.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_pharo_example_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_pharo_example_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_pharo_example_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_pharo_keyimplementationpoints_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_pharo_keyimplementationpoints_classifier_en.md new file mode 100644 index 00000000000000..562a9d4725c4b4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_pharo_keyimplementationpoints_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_pharo_keyimplementationpoints_classifier TFMPNetModel from AISE-TUDelft +author: John Snow Labs +name: mpnet_embedding_pharo_keyimplementationpoints_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_pharo_keyimplementationpoints_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_pharo_keyimplementationpoints_classifier_en_5.1.0_3.0_1692378611446.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_pharo_keyimplementationpoints_classifier_en_5.1.0_3.0_1692378611446.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_pharo_keyimplementationpoints_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_pharo_keyimplementationpoints_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_pharo_keyimplementationpoints_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_pharo_responsibilities_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_pharo_responsibilities_classifier_en.md new file mode 100644 index 00000000000000..438cf2032d7ea4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_pharo_responsibilities_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_pharo_responsibilities_classifier TFMPNetModel from AISE-TUDelft +author: John Snow Labs +name: mpnet_embedding_pharo_responsibilities_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_pharo_responsibilities_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_pharo_responsibilities_classifier_en_5.1.0_3.0_1692380147031.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_pharo_responsibilities_classifier_en_5.1.0_3.0_1692380147031.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_pharo_responsibilities_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_pharo_responsibilities_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_pharo_responsibilities_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_python_developmentnotes_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_python_developmentnotes_classifier_en.md new file mode 100644 index 00000000000000..26b9499d6f8e78 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_python_developmentnotes_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_python_developmentnotes_classifier TFMPNetModel from AISE-TUDelft +author: John Snow Labs +name: mpnet_embedding_python_developmentnotes_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_python_developmentnotes_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_python_developmentnotes_classifier_en_5.1.0_3.0_1692378408726.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_python_developmentnotes_classifier_en_5.1.0_3.0_1692378408726.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_python_developmentnotes_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_python_developmentnotes_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_python_developmentnotes_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_python_expand_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_python_expand_classifier_en.md new file mode 100644 index 00000000000000..f8cc83dd2a7bec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_python_expand_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_python_expand_classifier TFMPNetModel from AISE-TUDelft +author: John Snow Labs +name: mpnet_embedding_python_expand_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_python_expand_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_python_expand_classifier_en_5.1.0_3.0_1692379201526.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_python_expand_classifier_en_5.1.0_3.0_1692379201526.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_python_expand_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_python_expand_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_python_expand_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_python_parameters_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_python_parameters_classifier_en.md new file mode 100644 index 00000000000000..be0de6ecbc3a87 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_python_parameters_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_python_parameters_classifier TFMPNetModel from AISE-TUDelft +author: John Snow Labs +name: mpnet_embedding_python_parameters_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_python_parameters_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_python_parameters_classifier_en_5.1.0_3.0_1692379002867.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_python_parameters_classifier_en_5.1.0_3.0_1692379002867.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_python_parameters_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_python_parameters_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_python_parameters_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_python_summary_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_python_summary_classifier_en.md new file mode 100644 index 00000000000000..593f49cd16d450 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_python_summary_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_python_summary_classifier TFMPNetModel from AISE-TUDelft +author: John Snow Labs +name: mpnet_embedding_python_summary_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_python_summary_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_python_summary_classifier_en_5.1.0_3.0_1692378198075.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_python_summary_classifier_en_5.1.0_3.0_1692378198075.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_python_summary_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_python_summary_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_python_summary_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_python_usage_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_python_usage_classifier_en.md new file mode 100644 index 00000000000000..204d72ddf8deb7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_python_usage_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_python_usage_classifier TFMPNetModel from AISE-TUDelft +author: John Snow Labs +name: mpnet_embedding_python_usage_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_python_usage_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_python_usage_classifier_en_5.1.0_3.0_1692379761686.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_python_usage_classifier_en_5.1.0_3.0_1692379761686.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_python_usage_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_python_usage_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_python_usage_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_reddit_single_context_mpnet_base_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_reddit_single_context_mpnet_base_en.md new file mode 100644 index 00000000000000..855c12b30641ce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_reddit_single_context_mpnet_base_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_reddit_single_context_mpnet_base TFMPNetModel from flax-sentence-embeddings +author: John Snow Labs +name: mpnet_embedding_reddit_single_context_mpnet_base +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_reddit_single_context_mpnet_base` is a English model originally trained by flax-sentence-embeddings. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_reddit_single_context_mpnet_base_en_5.1.0_3.0_1692376625049.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_reddit_single_context_mpnet_base_en_5.1.0_3.0_1692376625049.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_reddit_single_context_mpnet_base", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_reddit_single_context_mpnet_base", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_reddit_single_context_mpnet_base| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.5 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_retriever_coding_guru_adapted_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_retriever_coding_guru_adapted_en.md new file mode 100644 index 00000000000000..89c4d76b6d7c89 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_retriever_coding_guru_adapted_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_retriever_coding_guru_adapted TFMPNetModel from AlekseyKorshuk +author: John Snow Labs +name: mpnet_embedding_retriever_coding_guru_adapted +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_retriever_coding_guru_adapted` is a English model originally trained by AlekseyKorshuk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_retriever_coding_guru_adapted_en_5.1.0_3.0_1692378566998.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_retriever_coding_guru_adapted_en_5.1.0_3.0_1692378566998.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_retriever_coding_guru_adapted", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_retriever_coding_guru_adapted", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_retriever_coding_guru_adapted| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_review_intent_20230116_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_review_intent_20230116_en.md new file mode 100644 index 00000000000000..9c09ccabbc29b2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_review_intent_20230116_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_review_intent_20230116 TFMPNetModel from meichen91 +author: John Snow Labs +name: mpnet_embedding_review_intent_20230116 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_review_intent_20230116` is a English model originally trained by meichen91. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_review_intent_20230116_en_5.1.0_3.0_1692376123645.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_review_intent_20230116_en_5.1.0_3.0_1692376123645.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_review_intent_20230116", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_review_intent_20230116", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_review_intent_20230116| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_review_multiclass_20230116_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_review_multiclass_20230116_en.md new file mode 100644 index 00000000000000..e6ea0df2da10da --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_review_multiclass_20230116_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_review_multiclass_20230116 TFMPNetModel from meichen91 +author: John Snow Labs +name: mpnet_embedding_review_multiclass_20230116 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_review_multiclass_20230116` is a English model originally trained by meichen91. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_review_multiclass_20230116_en_5.1.0_3.0_1692376308718.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_review_multiclass_20230116_en_5.1.0_3.0_1692376308718.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_review_multiclass_20230116", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_review_multiclass_20230116", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_review_multiclass_20230116| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_sb_temfac_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_sb_temfac_en.md new file mode 100644 index 00000000000000..68f0ceefd347ff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_sb_temfac_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_sb_temfac TFMPNetModel from stealthpy +author: John Snow Labs +name: mpnet_embedding_sb_temfac +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_sb_temfac` is a English model originally trained by stealthpy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_sb_temfac_en_5.1.0_3.0_1692375226093.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_sb_temfac_en_5.1.0_3.0_1692375226093.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_sb_temfac", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_sb_temfac", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_sb_temfac| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.3 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_sbert_paper_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_sbert_paper_en.md new file mode 100644 index 00000000000000..18d08e2a8164b7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_sbert_paper_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_sbert_paper TFMPNetModel from salsabiilashifa11 +author: John Snow Labs +name: mpnet_embedding_sbert_paper +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_sbert_paper` is a English model originally trained by salsabiilashifa11. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_sbert_paper_en_5.1.0_3.0_1692377968134.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_sbert_paper_en_5.1.0_3.0_1692377968134.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_sbert_paper", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_sbert_paper", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_sbert_paper| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.6 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_sentence_transformers_bible_reference_final_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_sentence_transformers_bible_reference_final_en.md new file mode 100644 index 00000000000000..3e8320dc7970f0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_sentence_transformers_bible_reference_final_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_sentence_transformers_bible_reference_final TFMPNetModel from odunola +author: John Snow Labs +name: mpnet_embedding_sentence_transformers_bible_reference_final +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_sentence_transformers_bible_reference_final` is a English model originally trained by odunola. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_sentence_transformers_bible_reference_final_en_5.1.0_3.0_1692376207996.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_sentence_transformers_bible_reference_final_en_5.1.0_3.0_1692376207996.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_sentence_transformers_bible_reference_final", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_sentence_transformers_bible_reference_final", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_sentence_transformers_bible_reference_final| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ag_news_endpoint_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ag_news_endpoint_en.md new file mode 100644 index 00000000000000..ca5b5626b0dff0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ag_news_endpoint_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_ag_news_endpoint TFMPNetModel from philschmid +author: John Snow Labs +name: mpnet_embedding_setfit_ag_news_endpoint +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_ag_news_endpoint` is a English model originally trained by philschmid. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ag_news_endpoint_en_5.1.0_3.0_1692377945921.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ag_news_endpoint_en_5.1.0_3.0_1692377945921.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_ag_news_endpoint", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_ag_news_endpoint", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_ag_news_endpoint| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_alpaca_es_unprocessable_sample_detection_es.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_alpaca_es_unprocessable_sample_detection_es.md new file mode 100644 index 00000000000000..fba091d7f83686 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_alpaca_es_unprocessable_sample_detection_es.md @@ -0,0 +1,88 @@ +--- +layout: model +title: Castilian, Spanish mpnet_embedding_setfit_alpaca_es_unprocessable_sample_detection TFMPNetModel from hackathon-somos-nlp-2023 +author: John Snow Labs +name: mpnet_embedding_setfit_alpaca_es_unprocessable_sample_detection +date: 2023-08-18 +tags: [mpnet, es, open_source, tensorflow] +task: Embeddings +language: es +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_alpaca_es_unprocessable_sample_detection` is a Castilian, Spanish model originally trained by hackathon-somos-nlp-2023. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_alpaca_es_unprocessable_sample_detection_es_5.1.0_3.0_1692376401533.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_alpaca_es_unprocessable_sample_detection_es_5.1.0_3.0_1692376401533.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_alpaca_es_unprocessable_sample_detection", "es")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_alpaca_es_unprocessable_sample_detection", "es") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_alpaca_es_unprocessable_sample_detection| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|es| +|Size:|410.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ds_version_0_0_1_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ds_version_0_0_1_en.md new file mode 100644 index 00000000000000..cbbc3cf09bd754 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ds_version_0_0_1_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_ds_version_0_0_1 TFMPNetModel from amittian +author: John Snow Labs +name: mpnet_embedding_setfit_ds_version_0_0_1 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_ds_version_0_0_1` is a English model originally trained by amittian. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ds_version_0_0_1_en_5.1.0_3.0_1692377361480.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ds_version_0_0_1_en_5.1.0_3.0_1692377361480.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_ds_version_0_0_1", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_ds_version_0_0_1", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_ds_version_0_0_1| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ds_version_0_0_2_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ds_version_0_0_2_en.md new file mode 100644 index 00000000000000..5472a869b11882 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ds_version_0_0_2_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_ds_version_0_0_2 TFMPNetModel from amittian +author: John Snow Labs +name: mpnet_embedding_setfit_ds_version_0_0_2 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_ds_version_0_0_2` is a English model originally trained by amittian. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ds_version_0_0_2_en_5.1.0_3.0_1692377560015.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ds_version_0_0_2_en_5.1.0_3.0_1692377560015.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_ds_version_0_0_2", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_ds_version_0_0_2", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_ds_version_0_0_2| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ds_version_0_0_4_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ds_version_0_0_4_en.md new file mode 100644 index 00000000000000..77367c287800e2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ds_version_0_0_4_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_ds_version_0_0_4 TFMPNetModel from amittian +author: John Snow Labs +name: mpnet_embedding_setfit_ds_version_0_0_4 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_ds_version_0_0_4` is a English model originally trained by amittian. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ds_version_0_0_4_en_5.1.0_3.0_1692377962339.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ds_version_0_0_4_en_5.1.0_3.0_1692377962339.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_ds_version_0_0_4", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_ds_version_0_0_4", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_ds_version_0_0_4| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ds_version_0_0_5_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ds_version_0_0_5_en.md new file mode 100644 index 00000000000000..f16927af381d81 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ds_version_0_0_5_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_ds_version_0_0_5 TFMPNetModel from amittian +author: John Snow Labs +name: mpnet_embedding_setfit_ds_version_0_0_5 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_ds_version_0_0_5` is a English model originally trained by amittian. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ds_version_0_0_5_en_5.1.0_3.0_1692378155593.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ds_version_0_0_5_en_5.1.0_3.0_1692378155593.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_ds_version_0_0_5", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_ds_version_0_0_5", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_ds_version_0_0_5| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ethos_multilabel_example_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ethos_multilabel_example_en.md new file mode 100644 index 00000000000000..b7cfc9b51ed4f0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ethos_multilabel_example_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_ethos_multilabel_example TFMPNetModel from neilthematic +author: John Snow Labs +name: mpnet_embedding_setfit_ethos_multilabel_example +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_ethos_multilabel_example` is a English model originally trained by neilthematic. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ethos_multilabel_example_en_5.1.0_3.0_1692376693736.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ethos_multilabel_example_en_5.1.0_3.0_1692376693736.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_ethos_multilabel_example", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_ethos_multilabel_example", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_ethos_multilabel_example| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_finetuned_financial_text_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_finetuned_financial_text_en.md new file mode 100644 index 00000000000000..c7be99ee2518be --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_finetuned_financial_text_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_finetuned_financial_text TFMPNetModel from nickmuchi +author: John Snow Labs +name: mpnet_embedding_setfit_finetuned_financial_text +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_finetuned_financial_text` is a English model originally trained by nickmuchi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_finetuned_financial_text_en_5.1.0_3.0_1692376937069.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_finetuned_financial_text_en_5.1.0_3.0_1692376937069.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_finetuned_financial_text", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_finetuned_financial_text", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_finetuned_financial_text| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ft_sentinent_eval_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ft_sentinent_eval_en.md new file mode 100644 index 00000000000000..307710ac9c639c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ft_sentinent_eval_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_ft_sentinent_eval TFMPNetModel from StatsGary +author: John Snow Labs +name: mpnet_embedding_setfit_ft_sentinent_eval +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_ft_sentinent_eval` is a English model originally trained by StatsGary. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ft_sentinent_eval_en_5.1.0_3.0_1692381718097.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ft_sentinent_eval_en_5.1.0_3.0_1692381718097.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_ft_sentinent_eval", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_ft_sentinent_eval", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_ft_sentinent_eval| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_model_Feb11_Misinformation_on_Law_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_model_Feb11_Misinformation_on_Law_en.md new file mode 100644 index 00000000000000..974c24d3f389a1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_model_Feb11_Misinformation_on_Law_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_model_Feb11_Misinformation_on_Law TFMPNetModel from mitra-mir +author: John Snow Labs +name: mpnet_embedding_setfit_model_Feb11_Misinformation_on_Law +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_model_Feb11_Misinformation_on_Law` is a English model originally trained by mitra-mir. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_model_Feb11_Misinformation_on_Law_en_5.1.0_3.0_1692381563585.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_model_Feb11_Misinformation_on_Law_en_5.1.0_3.0_1692381563585.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_model_Feb11_Misinformation_on_Law", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_model_Feb11_Misinformation_on_Law", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_model_Feb11_Misinformation_on_Law| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_model_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_model_en.md new file mode 100644 index 00000000000000..c3727f9548b043 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_model_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_model TFMPNetModel from Pradipta11 +author: John Snow Labs +name: mpnet_embedding_setfit_model +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_model` is a English model originally trained by Pradipta11. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_model_en_5.1.0_3.0_1692377155690.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_model_en_5.1.0_3.0_1692377155690.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_model", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_model", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_model| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_model_test_sensitve_v1_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_model_test_sensitve_v1_en.md new file mode 100644 index 00000000000000..3b85c67565ca9c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_model_test_sensitve_v1_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_model_test_sensitve_v1 TFMPNetModel from Adipta +author: John Snow Labs +name: mpnet_embedding_setfit_model_test_sensitve_v1 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_model_test_sensitve_v1` is a English model originally trained by Adipta. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_model_test_sensitve_v1_en_5.1.0_3.0_1692377739573.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_model_test_sensitve_v1_en_5.1.0_3.0_1692377739573.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_model_test_sensitve_v1", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_model_test_sensitve_v1", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_model_test_sensitve_v1| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_occupation_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_occupation_en.md new file mode 100644 index 00000000000000..2e3bd0d8051043 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_occupation_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_occupation TFMPNetModel from ivanzidov +author: John Snow Labs +name: mpnet_embedding_setfit_occupation +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_occupation` is a English model originally trained by ivanzidov. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_occupation_en_5.1.0_3.0_1692378365223.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_occupation_en_5.1.0_3.0_1692378365223.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_occupation", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_occupation", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_occupation| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ostrom_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ostrom_en.md new file mode 100644 index 00000000000000..1a08532fca1bbf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_ostrom_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_ostrom TFMPNetModel from mahaswec +author: John Snow Labs +name: mpnet_embedding_setfit_ostrom +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_ostrom` is a English model originally trained by mahaswec. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ostrom_en_5.1.0_3.0_1692380969725.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ostrom_en_5.1.0_3.0_1692380969725.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_ostrom", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_ostrom", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_ostrom| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_comm_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_comm_en.md new file mode 100644 index 00000000000000..5db33b416ff729 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_comm_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_comm TFMPNetModel from aammari +author: John Snow Labs +name: mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_comm +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_comm` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_comm_en_5.1.0_3.0_1692376886552.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_comm_en_5.1.0_3.0_1692376886552.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_comm", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_comm", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_comm| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_en.md new file mode 100644 index 00000000000000..19754dca56e6e9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_zero_shot_classification_pbsp_p1 TFMPNetModel from aammari +author: John Snow Labs +name: mpnet_embedding_setfit_zero_shot_classification_pbsp_p1 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_zero_shot_classification_pbsp_p1` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_en_5.1.0_3.0_1692379514816.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_en_5.1.0_3.0_1692379514816.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p1", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p1", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_zero_shot_classification_pbsp_p1| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_life_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_life_en.md new file mode 100644 index 00000000000000..0f0182c48d53ec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_life_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_life TFMPNetModel from aammari +author: John Snow Labs +name: mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_life +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_life` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_life_en_5.1.0_3.0_1692377068378.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_life_en_5.1.0_3.0_1692377068378.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_life", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_life", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_life| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_likes_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_likes_en.md new file mode 100644 index 00000000000000..0947769eab496e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_likes_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_likes TFMPNetModel from aammari +author: John Snow Labs +name: mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_likes +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_likes` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_likes_en_5.1.0_3.0_1692380337188.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_likes_en_5.1.0_3.0_1692380337188.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_likes", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_likes", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_zero_shot_classification_pbsp_p1_likes| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_bhvr_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_bhvr_en.md new file mode 100644 index 00000000000000..93bf47a1c3ee14 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_bhvr_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_bhvr TFMPNetModel from aammari +author: John Snow Labs +name: mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_bhvr +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_bhvr` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_bhvr_en_5.1.0_3.0_1692379700885.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_bhvr_en_5.1.0_3.0_1692379700885.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_bhvr", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_bhvr", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_bhvr| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_cons_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_cons_en.md new file mode 100644 index 00000000000000..5df40d6706c611 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_cons_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_cons TFMPNetModel from aammari +author: John Snow Labs +name: mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_cons +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_cons` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_cons_en_5.1.0_3.0_1692380608777.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_cons_en_5.1.0_3.0_1692380608777.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_cons", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_cons", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_cons| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_dur_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_dur_en.md new file mode 100644 index 00000000000000..3a1faacf91fbd8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_dur_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_dur TFMPNetModel from aammari +author: John Snow Labs +name: mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_dur +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_dur` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_dur_en_5.1.0_3.0_1692379876045.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_dur_en_5.1.0_3.0_1692379876045.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_dur", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_dur", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_dur| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_func_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_func_en.md new file mode 100644 index 00000000000000..5f32be3634d7a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_func_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_func TFMPNetModel from aammari +author: John Snow Labs +name: mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_func +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_func` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_func_en_5.1.0_3.0_1692375940356.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_func_en_5.1.0_3.0_1692375940356.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_func", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_func", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_func| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_sev_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_sev_en.md new file mode 100644 index 00000000000000..a1f5ae7929e72f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_sev_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_sev TFMPNetModel from aammari +author: John Snow Labs +name: mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_sev +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_sev` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_sev_en_5.1.0_3.0_1692378917103.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_sev_en_5.1.0_3.0_1692378917103.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_sev", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_sev", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_sev| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_trig_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_trig_en.md new file mode 100644 index 00000000000000..3313b45dd2c604 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_trig_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_trig TFMPNetModel from aammari +author: John Snow Labs +name: mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_trig +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_trig` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_trig_en_5.1.0_3.0_1692380258211.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_trig_en_5.1.0_3.0_1692380258211.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_trig", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_trig", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_zero_shot_classification_pbsp_p3_trig| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_achiev_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_achiev_en.md new file mode 100644 index 00000000000000..53892db8c8776f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_achiev_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_achiev TFMPNetModel from aammari +author: John Snow Labs +name: mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_achiev +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_achiev` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_achiev_en_5.1.0_3.0_1692381069742.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_achiev_en_5.1.0_3.0_1692381069742.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_achiev", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_achiev", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_achiev| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_meas_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_meas_en.md new file mode 100644 index 00000000000000..7110724a94f6a0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_meas_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_meas TFMPNetModel from aammari +author: John Snow Labs +name: mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_meas +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_meas` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_meas_en_5.1.0_3.0_1692380894097.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_meas_en_5.1.0_3.0_1692380894097.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_meas", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_meas", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_meas| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_rel_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_rel_en.md new file mode 100644 index 00000000000000..a8557c91ea2a95 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_rel_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_rel TFMPNetModel from aammari +author: John Snow Labs +name: mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_rel +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_rel` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_rel_en_5.1.0_3.0_1692381228140.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_rel_en_5.1.0_3.0_1692381228140.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_rel", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_rel", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_rel| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_specific_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_specific_en.md new file mode 100644 index 00000000000000..3d90d68f5ef02c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_specific_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_specific TFMPNetModel from aammari +author: John Snow Labs +name: mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_specific +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_specific` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_specific_en_5.1.0_3.0_1692380711935.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_specific_en_5.1.0_3.0_1692380711935.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_specific", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_specific", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_specific| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_time_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_time_en.md new file mode 100644 index 00000000000000..e9217efceb9876 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_time_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_time TFMPNetModel from aammari +author: John Snow Labs +name: mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_time +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_time` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_time_en_5.1.0_3.0_1692381391509.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_time_en_5.1.0_3.0_1692381391509.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_time", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_time", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_zero_shot_classification_pbsp_p4_time| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_q8a_azure_gpt35_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_q8a_azure_gpt35_en.md new file mode 100644 index 00000000000000..a7c5b427ca0860 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_setfit_zero_shot_classification_pbsp_q8a_azure_gpt35_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_zero_shot_classification_pbsp_q8a_azure_gpt35 TFMPNetModel from aammari +author: John Snow Labs +name: mpnet_embedding_setfit_zero_shot_classification_pbsp_q8a_azure_gpt35 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_zero_shot_classification_pbsp_q8a_azure_gpt35` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_q8a_azure_gpt35_en_5.1.0_3.0_1692381310055.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_zero_shot_classification_pbsp_q8a_azure_gpt35_en_5.1.0_3.0_1692381310055.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_q8a_azure_gpt35", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_zero_shot_classification_pbsp_q8a_azure_gpt35", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_zero_shot_classification_pbsp_q8a_azure_gpt35| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_sml_ukr_message_classifier_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_sml_ukr_message_classifier_en.md new file mode 100644 index 00000000000000..3b7865654bb278 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_sml_ukr_message_classifier_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_sml_ukr_message_classifier TFMPNetModel from rodekruis +author: John Snow Labs +name: mpnet_embedding_sml_ukr_message_classifier +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_sml_ukr_message_classifier` is a English model originally trained by rodekruis. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_sml_ukr_message_classifier_en_5.1.0_3.0_1692375430011.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_sml_ukr_message_classifier_en_5.1.0_3.0_1692375430011.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_sml_ukr_message_classifier", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_sml_ukr_message_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_sml_ukr_message_classifier| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_sml_ukr_word_classifier_medium_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_sml_ukr_word_classifier_medium_en.md new file mode 100644 index 00000000000000..3e9a6c880b2e29 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_sml_ukr_word_classifier_medium_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_sml_ukr_word_classifier_medium TFMPNetModel from rodekruis +author: John Snow Labs +name: mpnet_embedding_sml_ukr_word_classifier_medium +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_sml_ukr_word_classifier_medium` is a English model originally trained by rodekruis. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_sml_ukr_word_classifier_medium_en_5.1.0_3.0_1692377549620.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_sml_ukr_word_classifier_medium_en_5.1.0_3.0_1692377549620.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_sml_ukr_word_classifier_medium", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_sml_ukr_word_classifier_medium", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_sml_ukr_word_classifier_medium| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_sn_mpnet_base_snli_mnli_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_sn_mpnet_base_snli_mnli_en.md new file mode 100644 index 00000000000000..cf40cbaaf3d291 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_sn_mpnet_base_snli_mnli_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_sn_mpnet_base_snli_mnli TFMPNetModel from symanto +author: John Snow Labs +name: mpnet_embedding_sn_mpnet_base_snli_mnli +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_sn_mpnet_base_snli_mnli` is a English model originally trained by symanto. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_sn_mpnet_base_snli_mnli_en_5.1.0_3.0_1692379153696.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_sn_mpnet_base_snli_mnli_en_5.1.0_3.0_1692379153696.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_sn_mpnet_base_snli_mnli", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_sn_mpnet_base_snli_mnli", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_sn_mpnet_base_snli_mnli| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_spiced_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_spiced_en.md new file mode 100644 index 00000000000000..d7339e83a36d92 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_spiced_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_spiced TFMPNetModel from copenlu +author: John Snow Labs +name: mpnet_embedding_spiced +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_spiced` is a English model originally trained by copenlu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_spiced_en_5.1.0_3.0_1692376162204.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_spiced_en_5.1.0_3.0_1692376162204.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_spiced", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_spiced", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_spiced| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_stackoverflow_mpnet_base_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_stackoverflow_mpnet_base_en.md new file mode 100644 index 00000000000000..5d7166fa502413 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_stackoverflow_mpnet_base_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_stackoverflow_mpnet_base TFMPNetModel from flax-sentence-embeddings +author: John Snow Labs +name: mpnet_embedding_stackoverflow_mpnet_base +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_stackoverflow_mpnet_base` is a English model originally trained by flax-sentence-embeddings. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_stackoverflow_mpnet_base_en_5.1.0_3.0_1692376811146.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_stackoverflow_mpnet_base_en_5.1.0_3.0_1692376811146.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_stackoverflow_mpnet_base", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_stackoverflow_mpnet_base", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_stackoverflow_mpnet_base| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.5 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_stsb_mpnet_base_v2_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_stsb_mpnet_base_v2_en.md new file mode 100644 index 00000000000000..e3776e0d3da104 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_stsb_mpnet_base_v2_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_stsb_mpnet_base_v2 TFMPNetModel from sentence-transformers +author: John Snow Labs +name: mpnet_embedding_stsb_mpnet_base_v2 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_stsb_mpnet_base_v2` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_stsb_mpnet_base_v2_en_5.1.0_3.0_1692378378354.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_stsb_mpnet_base_v2_en_5.1.0_3.0_1692378378354.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_stsb_mpnet_base_v2", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_stsb_mpnet_base_v2", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_stsb_mpnet_base_v2| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|408.6 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_test_food_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_test_food_en.md new file mode 100644 index 00000000000000..dddc714dce2a1b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_test_food_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_test_food TFMPNetModel from Linus4Lyf +author: John Snow Labs +name: mpnet_embedding_test_food +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_test_food` is a English model originally trained by Linus4Lyf. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_test_food_en_5.1.0_3.0_1692378151159.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_test_food_en_5.1.0_3.0_1692378151159.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_test_food", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_test_food", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_test_food| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_test_model_001_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_test_model_001_en.md new file mode 100644 index 00000000000000..0507ba529141ac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_test_model_001_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_test_model_001 TFMPNetModel from intellya22 +author: John Snow Labs +name: mpnet_embedding_test_model_001 +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_test_model_001` is a English model originally trained by intellya22. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_test_model_001_en_5.1.0_3.0_1692380796219.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_test_model_001_en_5.1.0_3.0_1692380796219.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_test_model_001", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_test_model_001", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_test_model_001| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|410.0 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_tiny_random_MPNetForMaskedLM_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_tiny_random_MPNetForMaskedLM_en.md new file mode 100644 index 00000000000000..cce037ebd52a24 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_tiny_random_MPNetForMaskedLM_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_tiny_random_MPNetForMaskedLM TFMPNetModel from hf-tiny-model-private +author: John Snow Labs +name: mpnet_embedding_tiny_random_MPNetForMaskedLM +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_tiny_random_MPNetForMaskedLM` is a English model originally trained by hf-tiny-model-private. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_MPNetForMaskedLM_en_5.1.0_3.0_1692375647237.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_MPNetForMaskedLM_en_5.1.0_3.0_1692375647237.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_tiny_random_MPNetForMaskedLM", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_tiny_random_MPNetForMaskedLM", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_tiny_random_MPNetForMaskedLM| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|1.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_tiny_random_MPNetForQuestionAnswering_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_tiny_random_MPNetForQuestionAnswering_en.md new file mode 100644 index 00000000000000..0eb1f3d76262ee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_tiny_random_MPNetForQuestionAnswering_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_tiny_random_MPNetForQuestionAnswering TFMPNetModel from hf-tiny-model-private +author: John Snow Labs +name: mpnet_embedding_tiny_random_MPNetForQuestionAnswering +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_tiny_random_MPNetForQuestionAnswering` is a English model originally trained by hf-tiny-model-private. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_MPNetForQuestionAnswering_en_5.1.0_3.0_1692375853395.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_MPNetForQuestionAnswering_en_5.1.0_3.0_1692375853395.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_tiny_random_MPNetForQuestionAnswering", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_tiny_random_MPNetForQuestionAnswering", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_tiny_random_MPNetForQuestionAnswering| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|1.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_tiny_random_MPNetForSequenceClassification_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_tiny_random_MPNetForSequenceClassification_en.md new file mode 100644 index 00000000000000..e04ecbb65945f0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_tiny_random_MPNetForSequenceClassification_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_tiny_random_MPNetForSequenceClassification TFMPNetModel from hf-tiny-model-private +author: John Snow Labs +name: mpnet_embedding_tiny_random_MPNetForSequenceClassification +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_tiny_random_MPNetForSequenceClassification` is a English model originally trained by hf-tiny-model-private. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_MPNetForSequenceClassification_en_5.1.0_3.0_1692375973996.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_MPNetForSequenceClassification_en_5.1.0_3.0_1692375973996.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_tiny_random_MPNetForSequenceClassification", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_tiny_random_MPNetForSequenceClassification", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_tiny_random_MPNetForSequenceClassification| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|1.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_tiny_random_MPNetForTokenClassification_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_tiny_random_MPNetForTokenClassification_en.md new file mode 100644 index 00000000000000..095e0d6663c917 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_tiny_random_MPNetForTokenClassification_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_tiny_random_MPNetForTokenClassification TFMPNetModel from hf-tiny-model-private +author: John Snow Labs +name: mpnet_embedding_tiny_random_MPNetForTokenClassification +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_tiny_random_MPNetForTokenClassification` is a English model originally trained by hf-tiny-model-private. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_MPNetForTokenClassification_en_5.1.0_3.0_1692376082863.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_MPNetForTokenClassification_en_5.1.0_3.0_1692376082863.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_tiny_random_MPNetForTokenClassification", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_tiny_random_MPNetForTokenClassification", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_tiny_random_MPNetForTokenClassification| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|1.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_tiny_random_MPNetModel_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_tiny_random_MPNetModel_en.md new file mode 100644 index 00000000000000..e8fe17dafa897c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_tiny_random_MPNetModel_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_tiny_random_MPNetModel TFMPNetModel from hf-tiny-model-private +author: John Snow Labs +name: mpnet_embedding_tiny_random_MPNetModel +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_tiny_random_MPNetModel` is a English model originally trained by hf-tiny-model-private. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_MPNetModel_en_5.1.0_3.0_1692376215032.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_MPNetModel_en_5.1.0_3.0_1692376215032.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_tiny_random_MPNetModel", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_tiny_random_MPNetModel", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_tiny_random_MPNetModel| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|1.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_vulnerable_groups_en.md b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_vulnerable_groups_en.md new file mode 100644 index 00000000000000..928e15998a7590 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_vulnerable_groups_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_vulnerable_groups TFMPNetModel from leavoigt +author: John Snow Labs +name: mpnet_embedding_vulnerable_groups +date: 2023-08-18 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_vulnerable_groups` is a English model originally trained by leavoigt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_vulnerable_groups_en_5.1.0_3.0_1692377757868.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_vulnerable_groups_en_5.1.0_3.0_1692377757868.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_vulnerable_groups", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_vulnerable_groups", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_vulnerable_groups| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file From 06f07da2156d0867c28b0493236dad6cdca38a50 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Thu, 24 Aug 2023 14:57:46 +0700 Subject: [PATCH 34/55] 2023-08-22-asr_whisper_tiny_opt_xx (#13931) * Add model 2023-08-22-asr_whisper_tiny_opt_xx * Add model 2023-08-22-asr_whisper_tiny_xx * Update 2023-08-22-asr_whisper_tiny_opt_xx.md * Update 2023-08-22-asr_whisper_tiny_xx.md --------- Co-authored-by: DevinTDHa Co-authored-by: Maziyar Panahi --- .../2023-08-22-asr_whisper_tiny_opt_xx.md | 109 ++++++++++++++++++ .../2023-08-22-asr_whisper_tiny_xx.md | 109 ++++++++++++++++++ 2 files changed, 218 insertions(+) create mode 100644 docs/_posts/DevinTDHa/2023-08-22-asr_whisper_tiny_opt_xx.md create mode 100644 docs/_posts/DevinTDHa/2023-08-22-asr_whisper_tiny_xx.md diff --git a/docs/_posts/DevinTDHa/2023-08-22-asr_whisper_tiny_opt_xx.md b/docs/_posts/DevinTDHa/2023-08-22-asr_whisper_tiny_opt_xx.md new file mode 100644 index 00000000000000..ba5dbb88aa4174 --- /dev/null +++ b/docs/_posts/DevinTDHa/2023-08-22-asr_whisper_tiny_opt_xx.md @@ -0,0 +1,109 @@ +--- +layout: model +title: Official whisper-tiny Optimized +author: John Snow Labs +name: asr_whisper_tiny_opt +date: 2023-08-22 +tags: [whisper, en, audio, open_source, asr, onnx, xx] +task: Automatic Speech Recognition +language: xx +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Official pretrained Whisper model, adapted from HuggingFace transformer and curated to provide scalability and production-readiness using Spark NLP. + +This is a multilingual model and supports the following languages: + +Afrikaans, Arabic, Armenian, Azerbaijani, Belarusian, Bosnian, Bulgarian, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, Galician, German, Greek, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Kannada, Kazakh, Korean, Latvian, Lithuanian, Macedonian, Malay, Marathi, Maori, Nepali, Norwegian, Persian, Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tagalog, Tamil, Thai, Turkish, Ukrainian, Urdu, Vietnamese, and Welsh. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_opt_xx_5.1.0_3.0_1692721787993.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_opt_xx_5.1.0_3.0_1692721787993.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +import sparknlp +from sparknlp.base import * +from sparknlp.annotator import * +from pyspark.ml import Pipeline + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + +speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_opt", "xx") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) +processedAudioFloats = spark.createDataFrame([[rawFloats]]).toDF("audio_content") +result = pipeline.fit(processedAudioFloats).transform(processedAudioFloats) +result.select("text.result").show(truncate = False) +``` +```scala +import spark.implicits._ +import com.johnsnowlabs.nlp.base._ +import com.johnsnowlabs.nlp.annotators._ +import com.johnsnowlabs.nlp.annotators.audio.WhisperForCTC +import org.apache.spark.ml.Pipeline + +val audioAssembler: AudioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText: WhisperForCTC = WhisperForCTC + .pretrained("asr_whisper_tiny_opt", "xx") + .setInputCols("audio_assembler") + .setOutputCol("text") + +val pipeline: Pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val bufferedSource = + scala.io.Source.fromFile("src/test/resources/audio/txt/librispeech_asr_0.txt") + +val rawFloats = bufferedSource + .getLines() + .map(_.split(",").head.trim.toFloat) + .toArray +bufferedSource.close + +val processedAudioFloats = Seq(rawFloats).toDF("audio_content") + +val result = pipeline.fit(processedAudioFloats).transform(processedAudioFloats) +result.select("text.result").show(truncate = false) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_opt| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|xx| +|Size:|242.7 MB| diff --git a/docs/_posts/DevinTDHa/2023-08-22-asr_whisper_tiny_xx.md b/docs/_posts/DevinTDHa/2023-08-22-asr_whisper_tiny_xx.md new file mode 100644 index 00000000000000..19f629e4c4d7d6 --- /dev/null +++ b/docs/_posts/DevinTDHa/2023-08-22-asr_whisper_tiny_xx.md @@ -0,0 +1,109 @@ +--- +layout: model +title: Official whisper-tiny +author: John Snow Labs +name: asr_whisper_tiny +date: 2023-08-22 +tags: [whisper, en, audio, open_source, asr, xx, tensorflow] +task: Automatic Speech Recognition +language: xx +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Official pretrained Whisper model, adapted from HuggingFace transformer and curated to provide scalability and production-readiness using Spark NLP. + +This is a multilingual model and supports the following languages: + +Afrikaans, Arabic, Armenian, Azerbaijani, Belarusian, Bosnian, Bulgarian, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, Galician, German, Greek, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Kannada, Kazakh, Korean, Latvian, Lithuanian, Macedonian, Malay, Marathi, Maori, Nepali, Norwegian, Persian, Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tagalog, Tamil, Thai, Turkish, Ukrainian, Urdu, Vietnamese, and Welsh. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_xx_5.1.0_3.0_1692723111563.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_xx_5.1.0_3.0_1692723111563.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +import sparknlp +from sparknlp.base import * +from sparknlp.annotator import * +from pyspark.ml import Pipeline + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + +speechToText = WhisperForCTC.pretrained("asr_whisper_tiny", "xx") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) +processedAudioFloats = spark.createDataFrame([[rawFloats]]).toDF("audio_content") +result = pipeline.fit(processedAudioFloats).transform(processedAudioFloats) +result.select("text.result").show(truncate = False) +``` +```scala +import spark.implicits._ +import com.johnsnowlabs.nlp.base._ +import com.johnsnowlabs.nlp.annotators._ +import com.johnsnowlabs.nlp.annotators.audio.WhisperForCTC +import org.apache.spark.ml.Pipeline + +val audioAssembler: AudioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText: WhisperForCTC = WhisperForCTC + .pretrained("asr_whisper_tiny", "xx") + .setInputCols("audio_assembler") + .setOutputCol("text") + +val pipeline: Pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val bufferedSource = + scala.io.Source.fromFile("src/test/resources/audio/txt/librispeech_asr_0.txt") + +val rawFloats = bufferedSource + .getLines() + .map(_.split(",").head.trim.toFloat) + .toArray +bufferedSource.close + +val processedAudioFloats = Seq(rawFloats).toDF("audio_content") + +val result = pipeline.fit(processedAudioFloats).transform(processedAudioFloats) +result.select("text.result").show(truncate = false) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|xx| +|Size:|156.6 MB| From b1b99f594624f7e9397d4348633f83c6bf73d34f Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Fri, 25 Aug 2023 19:38:12 +0700 Subject: [PATCH 35/55] 2023-08-25-e5_small_en (#13939) * Add model 2023-08-25-e5_small_en * Add model 2023-08-25-e5_small_opt_en * Add model 2023-08-25-e5_small_quantized_en * Add model 2023-08-25-e5_base_en * Add model 2023-08-25-e5_small_v2_opt_en * Add model 2023-08-25-e5_base_opt_en * Add model 2023-08-25-e5_base_quantized_en * Add model 2023-08-25-e5_small_v2_en * Add model 2023-08-25-e5_small_v2_quantized_en * Add model 2023-08-25-e5_base_v2_en * Add model 2023-08-25-e5_base_v2_opt_en * Add model 2023-08-25-e5_base_v2_quantized_en * Add model 2023-08-25-e5_large_v2_en --------- Co-authored-by: ahmedlone127 --- .../ahmedlone127/2023-08-25-e5_base_en.md | 67 ++++++++++++++++++ .../ahmedlone127/2023-08-25-e5_base_opt_en.md | 67 ++++++++++++++++++ .../2023-08-25-e5_base_quantized_en.md | 67 ++++++++++++++++++ .../ahmedlone127/2023-08-25-e5_base_v2_en.md | 68 +++++++++++++++++++ .../2023-08-25-e5_base_v2_opt_en.md | 68 +++++++++++++++++++ .../2023-08-25-e5_base_v2_quantized_en.md | 68 +++++++++++++++++++ .../ahmedlone127/2023-08-25-e5_large_v2_en.md | 67 ++++++++++++++++++ .../ahmedlone127/2023-08-25-e5_small_en.md | 67 ++++++++++++++++++ .../2023-08-25-e5_small_opt_en.md | 67 ++++++++++++++++++ .../2023-08-25-e5_small_quantized_en.md | 67 ++++++++++++++++++ .../ahmedlone127/2023-08-25-e5_small_v2_en.md | 67 ++++++++++++++++++ .../2023-08-25-e5_small_v2_opt_en.md | 67 ++++++++++++++++++ .../2023-08-25-e5_small_v2_quantized_en.md | 67 ++++++++++++++++++ 13 files changed, 874 insertions(+) create mode 100644 docs/_posts/ahmedlone127/2023-08-25-e5_base_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-25-e5_base_opt_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-25-e5_base_quantized_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-25-e5_base_v2_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-25-e5_base_v2_opt_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-25-e5_base_v2_quantized_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-25-e5_large_v2_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-25-e5_small_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-25-e5_small_opt_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-25-e5_small_quantized_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-25-e5_small_v2_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-25-e5_small_v2_opt_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-25-e5_small_v2_quantized_en.md diff --git a/docs/_posts/ahmedlone127/2023-08-25-e5_base_en.md b/docs/_posts/ahmedlone127/2023-08-25-e5_base_en.md new file mode 100644 index 00000000000000..929f5a9f950935 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-25-e5_base_en.md @@ -0,0 +1,67 @@ +--- +layout: model +title: E5 Base Sentence Embeddings +author: John Snow Labs +name: e5_base +date: 2023-08-25 +tags: [en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_base_en_5.1.0_3.0_1692963566674.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_base_en_5.1.0_3.0_1692963566674.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_base","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_base","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_base| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|258.6 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-25-e5_base_opt_en.md b/docs/_posts/ahmedlone127/2023-08-25-e5_base_opt_en.md new file mode 100644 index 00000000000000..f99faf717c9112 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-25-e5_base_opt_en.md @@ -0,0 +1,67 @@ +--- +layout: model +title: E5 Base Sentence Embeddings +author: John Snow Labs +name: e5_base_opt +date: 2023-08-25 +tags: [en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_base_opt_en_5.1.0_3.0_1692963694288.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_base_opt_en_5.1.0_3.0_1692963694288.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_base","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_base","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_base_opt| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|258.7 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-25-e5_base_quantized_en.md b/docs/_posts/ahmedlone127/2023-08-25-e5_base_quantized_en.md new file mode 100644 index 00000000000000..54ef0a2318bf87 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-25-e5_base_quantized_en.md @@ -0,0 +1,67 @@ +--- +layout: model +title: E5 Base Sentence Embeddings +author: John Snow Labs +name: e5_base_quantized +date: 2023-08-25 +tags: [en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_base_quantized_en_5.1.0_3.0_1692963757236.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_base_quantized_en_5.1.0_3.0_1692963757236.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_base","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_base","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_base_quantized| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|67.1 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-25-e5_base_v2_en.md b/docs/_posts/ahmedlone127/2023-08-25-e5_base_v2_en.md new file mode 100644 index 00000000000000..15e45179946d28 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-25-e5_base_v2_en.md @@ -0,0 +1,68 @@ +--- +layout: model +title: E5 Base v2 Sentence Embeddings +author: John Snow Labs +name: e5_base_v2 +date: 2023-08-25 +tags: [e5, sentence_embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_base_v2_en_5.1.0_3.0_1692964050132.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_base_v2_en_5.1.0_3.0_1692964050132.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_base_v2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_base_v2","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") + +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_base_v2| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|258.7 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-25-e5_base_v2_opt_en.md b/docs/_posts/ahmedlone127/2023-08-25-e5_base_v2_opt_en.md new file mode 100644 index 00000000000000..0d653f873d5d8c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-25-e5_base_v2_opt_en.md @@ -0,0 +1,68 @@ +--- +layout: model +title: E5 Base v2 Sentence Embeddings +author: John Snow Labs +name: e5_base_v2_opt +date: 2023-08-25 +tags: [e5, sentence_embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_base_v2_opt_en_5.1.0_3.0_1692964193495.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_base_v2_opt_en_5.1.0_3.0_1692964193495.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_base_v2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_base_v2","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") + +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_base_v2_opt| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|258.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-25-e5_base_v2_quantized_en.md b/docs/_posts/ahmedlone127/2023-08-25-e5_base_v2_quantized_en.md new file mode 100644 index 00000000000000..75437db6e111ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-25-e5_base_v2_quantized_en.md @@ -0,0 +1,68 @@ +--- +layout: model +title: E5 Base v2 Sentence Embeddings +author: John Snow Labs +name: e5_base_v2_quantized +date: 2023-08-25 +tags: [e5, sentence_embeddings, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_base_v2_quantized_en_5.1.0_3.0_1692964219731.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_base_v2_quantized_en_5.1.0_3.0_1692964219731.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_base_v2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_base_v2","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") + +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_base_v2_quantized| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|67.0 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-25-e5_large_v2_en.md b/docs/_posts/ahmedlone127/2023-08-25-e5_large_v2_en.md new file mode 100644 index 00000000000000..4a298c0a806b54 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-25-e5_large_v2_en.md @@ -0,0 +1,67 @@ +--- +layout: model +title: E5 Large V2 Sentence Embeddings +author: John Snow Labs +name: e5_large_v2 +date: 2023-08-25 +tags: [en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_large_v2_en_5.1.0_3.0_1692966772284.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_large_v2_en_5.1.0_3.0_1692966772284.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_large_v2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_large_v2","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_large_v2| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|796.3 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-25-e5_small_en.md b/docs/_posts/ahmedlone127/2023-08-25-e5_small_en.md new file mode 100644 index 00000000000000..fbddfdeb65d03e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-25-e5_small_en.md @@ -0,0 +1,67 @@ +--- +layout: model +title: E5 Small Sentence Embeddings +author: John Snow Labs +name: e5_small +date: 2023-08-25 +tags: [en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_small_en_5.1.0_3.0_1692963401947.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_small_en_5.1.0_3.0_1692963401947.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_small","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_small","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_small| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|79.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-25-e5_small_opt_en.md b/docs/_posts/ahmedlone127/2023-08-25-e5_small_opt_en.md new file mode 100644 index 00000000000000..1e4e5a465a95c0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-25-e5_small_opt_en.md @@ -0,0 +1,67 @@ +--- +layout: model +title: E5 Small Sentence Embeddings +author: John Snow Labs +name: e5_small_opt +date: 2023-08-25 +tags: [en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_small_opt_en_5.1.0_3.0_1692963460075.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_small_opt_en_5.1.0_3.0_1692963460075.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_small","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_small","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_small_opt| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|80.0 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-25-e5_small_quantized_en.md b/docs/_posts/ahmedlone127/2023-08-25-e5_small_quantized_en.md new file mode 100644 index 00000000000000..1390bd612bc4be --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-25-e5_small_quantized_en.md @@ -0,0 +1,67 @@ +--- +layout: model +title: E5 Small Sentence Embeddings +author: John Snow Labs +name: e5_small_quantized +date: 2023-08-25 +tags: [en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_small_quantized_en_5.1.0_3.0_1692963474009.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_small_quantized_en_5.1.0_3.0_1692963474009.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_small","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_small","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_small_quantized| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|22.6 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-25-e5_small_v2_en.md b/docs/_posts/ahmedlone127/2023-08-25-e5_small_v2_en.md new file mode 100644 index 00000000000000..1b09d77aafd5a8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-25-e5_small_v2_en.md @@ -0,0 +1,67 @@ +--- +layout: model +title: E5 Small V2 Sentence Embeddings +author: John Snow Labs +name: e5_small_v2 +date: 2023-08-25 +tags: [en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_small_v2_en_5.1.0_3.0_1692963575848.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_small_v2_en_5.1.0_3.0_1692963575848.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_small_v2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_small_v2","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_small_v2| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|79.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-25-e5_small_v2_opt_en.md b/docs/_posts/ahmedlone127/2023-08-25-e5_small_v2_opt_en.md new file mode 100644 index 00000000000000..3278b5279d205c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-25-e5_small_v2_opt_en.md @@ -0,0 +1,67 @@ +--- +layout: model +title: E5 Small V2 Sentence Embeddings +author: John Snow Labs +name: e5_small_v2_opt +date: 2023-08-25 +tags: [en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_small_v2_opt_en_5.1.0_3.0_1692963627616.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_small_v2_opt_en_5.1.0_3.0_1692963627616.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_small_v2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_small_v2","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_small_v2_opt| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|79.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-25-e5_small_v2_quantized_en.md b/docs/_posts/ahmedlone127/2023-08-25-e5_small_v2_quantized_en.md new file mode 100644 index 00000000000000..62165b979f010d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-25-e5_small_v2_quantized_en.md @@ -0,0 +1,67 @@ +--- +layout: model +title: E5 Small V2 Sentence Embeddings +author: John Snow Labs +name: e5_small_v2_quantized +date: 2023-08-25 +tags: [en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_small_v2_quantized_en_5.1.0_3.0_1692963639263.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_small_v2_quantized_en_5.1.0_3.0_1692963639263.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_small_v2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_small_v2","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_small_v2_quantized| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|22.1 MB| \ No newline at end of file From b891455a2caf402f2f20d34b1de0f05374f75cd5 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Fri, 25 Aug 2023 20:13:52 +0700 Subject: [PATCH 36/55] 2023-08-25-e5_large_v2_opt_en (#13941) * Add model 2023-08-25-e5_large_v2_opt_en * Add model 2023-08-25-e5_large_v2_quantized_en --------- Co-authored-by: ahmedlone127 --- .../2023-08-25-e5_large_v2_opt_en.md | 67 +++++++++++++++++++ .../2023-08-25-e5_large_v2_quantized_en.md | 67 +++++++++++++++++++ 2 files changed, 134 insertions(+) create mode 100644 docs/_posts/ahmedlone127/2023-08-25-e5_large_v2_opt_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-25-e5_large_v2_quantized_en.md diff --git a/docs/_posts/ahmedlone127/2023-08-25-e5_large_v2_opt_en.md b/docs/_posts/ahmedlone127/2023-08-25-e5_large_v2_opt_en.md new file mode 100644 index 00000000000000..0eb10057abd870 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-25-e5_large_v2_opt_en.md @@ -0,0 +1,67 @@ +--- +layout: model +title: E5 Large V2 Sentence Embeddings +author: John Snow Labs +name: e5_large_v2_opt +date: 2023-08-25 +tags: [en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_large_v2_opt_en_5.1.0_3.0_1692967089347.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_large_v2_opt_en_5.1.0_3.0_1692967089347.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_large_v2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_large_v2","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_large_v2_opt| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|796.6 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-25-e5_large_v2_quantized_en.md b/docs/_posts/ahmedlone127/2023-08-25-e5_large_v2_quantized_en.md new file mode 100644 index 00000000000000..c96f3b45e1a703 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-25-e5_large_v2_quantized_en.md @@ -0,0 +1,67 @@ +--- +layout: model +title: E5 Large V2 Sentence Embeddings +author: John Snow Labs +name: e5_large_v2_quantized +date: 2023-08-25 +tags: [en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: E5Embeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e5_large_v2_quantized_en_5.1.0_3.0_1692967173706.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5_large_v2_quantized_en_5.1.0_3.0_1692967173706.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +embeddings =E5Embeddings.pretrained("e5_large_v2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("instructor") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) +``` +```scala +val embeddings = E5Embeddings.pretrained("e5_large_v2","en") + .setInputCols(["document"]) + .setOutputCol("e5_embeddings") +val pipeline = new Pipeline().setStages(Array(document, embeddings)) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e5_large_v2_quantized| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[e5]| +|Language:|en| +|Size:|281.6 MB| \ No newline at end of file From 2f27b9a3398c079a9481345ed3de1ae9d1cadd34 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Wed, 30 Aug 2023 00:04:53 +0700 Subject: [PATCH 37/55] 2023-08-29-mpnet_embedding_tiny_random_mpnet_by_hf_internal_testing_en (#13949) * Add model 2023-08-29-mpnet_embedding_tiny_random_mpnet_by_hf_internal_testing_en * Add model 2023-08-29-mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_tiny_model_private_en * Add model 2023-08-29-mpnet_embedding_burmese_awesome_setfit_model_en * Add model 2023-08-29-mpnet_embedding_github_issues_mpnet_southern_sotho_e10_en * Add model 2023-08-29-mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_tiny_model_private_en * Add model 2023-08-29-mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_navteca_en * Add model 2023-08-29-mpnet_embedding_github_issues_preprocessed_mpnet_southern_sotho_e10_en * Add model 2023-08-29-mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_tiny_model_private_en * Add model 2023-08-29-mpnet_embedding_all_mpnet_base_v2_by_sentence_transformers_en * Add model 2023-08-29-mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_tiny_model_private_en * Add model 2023-08-29-mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_sentence_transformers_en * Add model 2023-08-29-mpnet_embedding_setfit_alpaca_spanish_unprocessable_sample_detection_es * Add model 2023-08-29-mpnet_embedding_setfit_model_by_rajistics_en * Add model 2023-08-29-mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_sentence_transformers_en * Add model 2023-08-29-mpnet_embedding_all_mpnet_base_v2_by_diptanuc_en * Add model 2023-08-29-mpnet_embedding_setfit_ethos_multilabel_example_by_lewtun_en * Add model 2023-08-29-mpnet_embedding_nli_mpnet_base_v2_by_sentence_transformers_en * Add model 2023-08-29-mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_internal_testing_en * Add model 2023-08-29-mpnet_embedding_setfit_model_by_pradipta11_en * Add model 2023-08-29-mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_model_embeddings_en * Add model 2023-08-29-mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_internal_testing_en * Add model 2023-08-29-mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_internal_testing_en * Add model 2023-08-29-mpnet_embedding_tiny_random_mpnetmodel_by_hf_internal_testing_en * Add model 2023-08-29-mpnet_embedding_tiny_random_mpnetmodel_by_hf_tiny_model_private_en * Add model 2023-08-29-mpnet_embedding_all_mpnet_base_v2_by_obrizum_en * Add model 2023-08-29-mpnet_embedding_burmese_awesome_setfit_model_98_en * Add model 2023-08-29-mpnet_embedding_all_mpnet_base_questions_clustering_english_en * Add model 2023-08-29-mpnet_embedding_shona_mpnet_base_snli_mnli_en * Add model 2023-08-29-mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_internal_testing_en * Add model 2023-08-29-mpnet_embedding_setfit_ethos_multilabel_example_by_neilthematic_en * Add model 2023-08-29-mpnet_embedding_paraphrase_mpnet_base_v2_by_sentence_transformers_en --------- Co-authored-by: ahmedlone127 --- ...et_base_questions_clustering_english_en.md | 88 +++++++++++++++++++ ...edding_all_mpnet_base_v2_by_diptanuc_en.md | 88 +++++++++++++++++++ ...bedding_all_mpnet_base_v2_by_obrizum_en.md | 88 +++++++++++++++++++ ...net_base_v2_by_sentence_transformers_en.md | 88 +++++++++++++++++++ ...ding_burmese_awesome_setfit_model_98_en.md | 88 +++++++++++++++++++ ...bedding_burmese_awesome_setfit_model_en.md | 88 +++++++++++++++++++ ...thub_issues_mpnet_southern_sotho_e10_en.md | 88 +++++++++++++++++++ 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...etformaskedlm_by_hf_internal_testing_en.md | 88 +++++++++++++++++++ ...formaskedlm_by_hf_tiny_model_private_en.md | 88 +++++++++++++++++++ ...tionanswering_by_hf_internal_testing_en.md | 88 +++++++++++++++++++ ...onanswering_by_hf_tiny_model_private_en.md | 88 +++++++++++++++++++ ...lassification_by_hf_internal_testing_en.md | 88 +++++++++++++++++++ ...ssification_by_hf_tiny_model_private_en.md | 88 +++++++++++++++++++ ...lassification_by_hf_internal_testing_en.md | 88 +++++++++++++++++++ ...ssification_by_hf_tiny_model_private_en.md | 88 +++++++++++++++++++ ...om_mpnetmodel_by_hf_internal_testing_en.md | 88 +++++++++++++++++++ ..._mpnetmodel_by_hf_tiny_model_private_en.md | 88 +++++++++++++++++++ 31 files changed, 2728 insertions(+) create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_all_mpnet_base_questions_clustering_english_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_all_mpnet_base_v2_by_diptanuc_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_all_mpnet_base_v2_by_obrizum_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_all_mpnet_base_v2_by_sentence_transformers_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_burmese_awesome_setfit_model_98_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_burmese_awesome_setfit_model_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_github_issues_mpnet_southern_sotho_e10_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_github_issues_preprocessed_mpnet_southern_sotho_e10_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_navteca_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_sentence_transformers_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_model_embeddings_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_sentence_transformers_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_nli_mpnet_base_v2_by_sentence_transformers_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_paraphrase_mpnet_base_v2_by_sentence_transformers_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_setfit_alpaca_spanish_unprocessable_sample_detection_es.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_setfit_ethos_multilabel_example_by_lewtun_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_setfit_ethos_multilabel_example_by_neilthematic_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_setfit_model_by_pradipta11_en.md create mode 100644 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100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_tiny_model_private_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_internal_testing_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_tiny_model_private_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetmodel_by_hf_internal_testing_en.md create mode 100644 docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetmodel_by_hf_tiny_model_private_en.md diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_all_mpnet_base_questions_clustering_english_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_all_mpnet_base_questions_clustering_english_en.md new file mode 100644 index 00000000000000..cbf67e09bd72d1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_all_mpnet_base_questions_clustering_english_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_all_mpnet_base_questions_clustering_english TFMPNetModel from aiknowyou +author: John Snow Labs +name: mpnet_embedding_all_mpnet_base_questions_clustering_english +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_all_mpnet_base_questions_clustering_english` is a English model originally trained by aiknowyou. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_questions_clustering_english_en_5.1.0_3.0_1693327280583.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_questions_clustering_english_en_5.1.0_3.0_1693327280583.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_all_mpnet_base_questions_clustering_english", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_all_mpnet_base_questions_clustering_english", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_all_mpnet_base_questions_clustering_english| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_all_mpnet_base_v2_by_diptanuc_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_all_mpnet_base_v2_by_diptanuc_en.md new file mode 100644 index 00000000000000..b6ee0d09e0cd02 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_all_mpnet_base_v2_by_diptanuc_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_all_mpnet_base_v2_by_diptanuc TFMPNetModel from diptanuc +author: John Snow Labs +name: mpnet_embedding_all_mpnet_base_v2_by_diptanuc +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_all_mpnet_base_v2_by_diptanuc` is a English model originally trained by diptanuc. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_by_diptanuc_en_5.1.0_3.0_1693326522411.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_by_diptanuc_en_5.1.0_3.0_1693326522411.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_all_mpnet_base_v2_by_diptanuc", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_all_mpnet_base_v2_by_diptanuc", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_all_mpnet_base_v2_by_diptanuc| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.6 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_all_mpnet_base_v2_by_obrizum_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_all_mpnet_base_v2_by_obrizum_en.md new file mode 100644 index 00000000000000..7e05edc8e7c6d5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_all_mpnet_base_v2_by_obrizum_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_all_mpnet_base_v2_by_obrizum TFMPNetModel from obrizum +author: John Snow Labs +name: mpnet_embedding_all_mpnet_base_v2_by_obrizum +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_all_mpnet_base_v2_by_obrizum` is a English model originally trained by obrizum. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_by_obrizum_en_5.1.0_3.0_1693327114140.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_by_obrizum_en_5.1.0_3.0_1693327114140.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_all_mpnet_base_v2_by_obrizum", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_all_mpnet_base_v2_by_obrizum", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_all_mpnet_base_v2_by_obrizum| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.6 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_all_mpnet_base_v2_by_sentence_transformers_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_all_mpnet_base_v2_by_sentence_transformers_en.md new file mode 100644 index 00000000000000..657eaa755f78c9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_all_mpnet_base_v2_by_sentence_transformers_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_all_mpnet_base_v2_by_sentence_transformers TFMPNetModel from sentence-transformers +author: John Snow Labs +name: mpnet_embedding_all_mpnet_base_v2_by_sentence_transformers +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_all_mpnet_base_v2_by_sentence_transformers` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_by_sentence_transformers_en_5.1.0_3.0_1693326097073.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_all_mpnet_base_v2_by_sentence_transformers_en_5.1.0_3.0_1693326097073.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_all_mpnet_base_v2_by_sentence_transformers", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_all_mpnet_base_v2_by_sentence_transformers", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_all_mpnet_base_v2_by_sentence_transformers| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.6 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_burmese_awesome_setfit_model_98_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_burmese_awesome_setfit_model_98_en.md new file mode 100644 index 00000000000000..8355d5f624c507 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_burmese_awesome_setfit_model_98_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_burmese_awesome_setfit_model_98 TFMPNetModel from lewtun +author: John Snow Labs +name: mpnet_embedding_burmese_awesome_setfit_model_98 +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_burmese_awesome_setfit_model_98` is a English model originally trained by lewtun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_burmese_awesome_setfit_model_98_en_5.1.0_3.0_1693326158302.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_burmese_awesome_setfit_model_98_en_5.1.0_3.0_1693326158302.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_burmese_awesome_setfit_model_98", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_burmese_awesome_setfit_model_98", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_burmese_awesome_setfit_model_98| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_burmese_awesome_setfit_model_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_burmese_awesome_setfit_model_en.md new file mode 100644 index 00000000000000..0297da3bcb502b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_burmese_awesome_setfit_model_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_burmese_awesome_setfit_model TFMPNetModel from lewtun +author: John Snow Labs +name: mpnet_embedding_burmese_awesome_setfit_model +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_burmese_awesome_setfit_model` is a English model originally trained by lewtun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_burmese_awesome_setfit_model_en_5.1.0_3.0_1693325785200.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_burmese_awesome_setfit_model_en_5.1.0_3.0_1693325785200.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_burmese_awesome_setfit_model", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_burmese_awesome_setfit_model", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_burmese_awesome_setfit_model| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_github_issues_mpnet_southern_sotho_e10_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_github_issues_mpnet_southern_sotho_e10_en.md new file mode 100644 index 00000000000000..ca029080d2706d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_github_issues_mpnet_southern_sotho_e10_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_github_issues_mpnet_southern_sotho_e10 TFMPNetModel from Collab-uniba +author: John Snow Labs +name: mpnet_embedding_github_issues_mpnet_southern_sotho_e10 +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_github_issues_mpnet_southern_sotho_e10` is a English model originally trained by Collab-uniba. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_github_issues_mpnet_southern_sotho_e10_en_5.1.0_3.0_1693325774752.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_github_issues_mpnet_southern_sotho_e10_en_5.1.0_3.0_1693325774752.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_github_issues_mpnet_southern_sotho_e10", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_github_issues_mpnet_southern_sotho_e10", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_github_issues_mpnet_southern_sotho_e10| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.3 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_github_issues_preprocessed_mpnet_southern_sotho_e10_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_github_issues_preprocessed_mpnet_southern_sotho_e10_en.md new file mode 100644 index 00000000000000..9d7814787f9f4e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_github_issues_preprocessed_mpnet_southern_sotho_e10_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_github_issues_preprocessed_mpnet_southern_sotho_e10 TFMPNetModel from Collab-uniba +author: John Snow Labs +name: mpnet_embedding_github_issues_preprocessed_mpnet_southern_sotho_e10 +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_github_issues_preprocessed_mpnet_southern_sotho_e10` is a English model originally trained by Collab-uniba. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_github_issues_preprocessed_mpnet_southern_sotho_e10_en_5.1.0_3.0_1693326028091.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_github_issues_preprocessed_mpnet_southern_sotho_e10_en_5.1.0_3.0_1693326028091.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_github_issues_preprocessed_mpnet_southern_sotho_e10", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_github_issues_preprocessed_mpnet_southern_sotho_e10", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_github_issues_preprocessed_mpnet_southern_sotho_e10| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.0 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_navteca_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_navteca_en.md new file mode 100644 index 00000000000000..910839c5101331 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_navteca_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_navteca TFMPNetModel from navteca +author: John Snow Labs +name: mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_navteca +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_navteca` is a English model originally trained by navteca. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_navteca_en_5.1.0_3.0_1693325915503.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_navteca_en_5.1.0_3.0_1693325915503.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_navteca", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_navteca", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_navteca| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_sentence_transformers_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_sentence_transformers_en.md new file mode 100644 index 00000000000000..04c6afbedc9bbf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_sentence_transformers_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_sentence_transformers TFMPNetModel from sentence-transformers +author: John Snow Labs +name: mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_sentence_transformers +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_sentence_transformers` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_sentence_transformers_en_5.1.0_3.0_1693326271811.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_sentence_transformers_en_5.1.0_3.0_1693326271811.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_sentence_transformers", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_sentence_transformers", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_multi_qa_mpnet_base_cos_v1_by_sentence_transformers| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_model_embeddings_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_model_embeddings_en.md new file mode 100644 index 00000000000000..84b64183e00dbb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_model_embeddings_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_model_embeddings TFMPNetModel from model-embeddings +author: John Snow Labs +name: mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_model_embeddings +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_model_embeddings` is a English model originally trained by model-embeddings. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_model_embeddings_en_5.1.0_3.0_1693326214899.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_model_embeddings_en_5.1.0_3.0_1693326214899.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_model_embeddings", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_model_embeddings", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_model_embeddings| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_sentence_transformers_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_sentence_transformers_en.md new file mode 100644 index 00000000000000..0f82fa66a27a2b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_sentence_transformers_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_sentence_transformers TFMPNetModel from sentence-transformers +author: John Snow Labs +name: mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_sentence_transformers +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_sentence_transformers` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_sentence_transformers_en_5.1.0_3.0_1693326438288.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_sentence_transformers_en_5.1.0_3.0_1693326438288.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_sentence_transformers", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_sentence_transformers", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_multi_qa_mpnet_base_dot_v1_by_sentence_transformers| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_nli_mpnet_base_v2_by_sentence_transformers_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_nli_mpnet_base_v2_by_sentence_transformers_en.md new file mode 100644 index 00000000000000..3a700b76b2270f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_nli_mpnet_base_v2_by_sentence_transformers_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_nli_mpnet_base_v2_by_sentence_transformers TFMPNetModel from sentence-transformers +author: John Snow Labs +name: mpnet_embedding_nli_mpnet_base_v2_by_sentence_transformers +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_nli_mpnet_base_v2_by_sentence_transformers` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_nli_mpnet_base_v2_by_sentence_transformers_en_5.1.0_3.0_1693326608886.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_nli_mpnet_base_v2_by_sentence_transformers_en_5.1.0_3.0_1693326608886.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_nli_mpnet_base_v2_by_sentence_transformers", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_nli_mpnet_base_v2_by_sentence_transformers", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_nli_mpnet_base_v2_by_sentence_transformers| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|408.5 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_paraphrase_mpnet_base_v2_by_sentence_transformers_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_paraphrase_mpnet_base_v2_by_sentence_transformers_en.md new file mode 100644 index 00000000000000..b8403e871299cc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_paraphrase_mpnet_base_v2_by_sentence_transformers_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_paraphrase_mpnet_base_v2_by_sentence_transformers TFMPNetModel from sentence-transformers +author: John Snow Labs +name: mpnet_embedding_paraphrase_mpnet_base_v2_by_sentence_transformers +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_paraphrase_mpnet_base_v2_by_sentence_transformers` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_paraphrase_mpnet_base_v2_by_sentence_transformers_en_5.1.0_3.0_1693326773862.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_paraphrase_mpnet_base_v2_by_sentence_transformers_en_5.1.0_3.0_1693326773862.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_paraphrase_mpnet_base_v2_by_sentence_transformers", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_paraphrase_mpnet_base_v2_by_sentence_transformers", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_paraphrase_mpnet_base_v2_by_sentence_transformers| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_setfit_alpaca_spanish_unprocessable_sample_detection_es.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_setfit_alpaca_spanish_unprocessable_sample_detection_es.md new file mode 100644 index 00000000000000..d9a48b1a074a01 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_setfit_alpaca_spanish_unprocessable_sample_detection_es.md @@ -0,0 +1,88 @@ +--- +layout: model +title: Castilian, Spanish mpnet_embedding_setfit_alpaca_spanish_unprocessable_sample_detection TFMPNetModel from hackathon-somos-nlp-2023 +author: John Snow Labs +name: mpnet_embedding_setfit_alpaca_spanish_unprocessable_sample_detection +date: 2023-08-29 +tags: [mpnet, es, open_source, tensorflow] +task: Embeddings +language: es +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_alpaca_spanish_unprocessable_sample_detection` is a Castilian, Spanish model originally trained by hackathon-somos-nlp-2023. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_alpaca_spanish_unprocessable_sample_detection_es_5.1.0_3.0_1693326336460.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_alpaca_spanish_unprocessable_sample_detection_es_5.1.0_3.0_1693326336460.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_alpaca_spanish_unprocessable_sample_detection", "es")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_alpaca_spanish_unprocessable_sample_detection", "es") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_alpaca_spanish_unprocessable_sample_detection| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|es| +|Size:|410.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_setfit_ethos_multilabel_example_by_lewtun_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_setfit_ethos_multilabel_example_by_lewtun_en.md new file mode 100644 index 00000000000000..0de8947f774a3a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_setfit_ethos_multilabel_example_by_lewtun_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_ethos_multilabel_example_by_lewtun TFMPNetModel from lewtun +author: John Snow Labs +name: mpnet_embedding_setfit_ethos_multilabel_example_by_lewtun +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_ethos_multilabel_example_by_lewtun` is a English model originally trained by lewtun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ethos_multilabel_example_by_lewtun_en_5.1.0_3.0_1693326530051.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ethos_multilabel_example_by_lewtun_en_5.1.0_3.0_1693326530051.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_ethos_multilabel_example_by_lewtun", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_ethos_multilabel_example_by_lewtun", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_ethos_multilabel_example_by_lewtun| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_setfit_ethos_multilabel_example_by_neilthematic_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_setfit_ethos_multilabel_example_by_neilthematic_en.md new file mode 100644 index 00000000000000..a7e0494c209087 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_setfit_ethos_multilabel_example_by_neilthematic_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_ethos_multilabel_example_by_neilthematic TFMPNetModel from neilthematic +author: John Snow Labs +name: mpnet_embedding_setfit_ethos_multilabel_example_by_neilthematic +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_ethos_multilabel_example_by_neilthematic` is a English model originally trained by neilthematic. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ethos_multilabel_example_by_neilthematic_en_5.1.0_3.0_1693325768994.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_ethos_multilabel_example_by_neilthematic_en_5.1.0_3.0_1693325768994.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_ethos_multilabel_example_by_neilthematic", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_ethos_multilabel_example_by_neilthematic", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_ethos_multilabel_example_by_neilthematic| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_setfit_model_by_pradipta11_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_setfit_model_by_pradipta11_en.md new file mode 100644 index 00000000000000..5570902aa4d11d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_setfit_model_by_pradipta11_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_model_by_pradipta11 TFMPNetModel from Pradipta11 +author: John Snow Labs +name: mpnet_embedding_setfit_model_by_pradipta11 +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_model_by_pradipta11` is a English model originally trained by Pradipta11. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_model_by_pradipta11_en_5.1.0_3.0_1693325976173.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_model_by_pradipta11_en_5.1.0_3.0_1693325976173.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_model_by_pradipta11", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_model_by_pradipta11", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_model_by_pradipta11| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_setfit_model_by_rajistics_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_setfit_model_by_rajistics_en.md new file mode 100644 index 00000000000000..c2cd9bf68ded9b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_setfit_model_by_rajistics_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_setfit_model_by_rajistics TFMPNetModel from rajistics +author: John Snow Labs +name: mpnet_embedding_setfit_model_by_rajistics +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_setfit_model_by_rajistics` is a English model originally trained by rajistics. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_model_by_rajistics_en_5.1.0_3.0_1693326352648.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_setfit_model_by_rajistics_en_5.1.0_3.0_1693326352648.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_setfit_model_by_rajistics", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_setfit_model_by_rajistics", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_setfit_model_by_rajistics| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.9 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_shona_mpnet_base_snli_mnli_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_shona_mpnet_base_snli_mnli_en.md new file mode 100644 index 00000000000000..bf6a55918c1ef7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_shona_mpnet_base_snli_mnli_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_shona_mpnet_base_snli_mnli TFMPNetModel from symanto +author: John Snow Labs +name: mpnet_embedding_shona_mpnet_base_snli_mnli +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_shona_mpnet_base_snli_mnli` is a English model originally trained by symanto. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_shona_mpnet_base_snli_mnli_en_5.1.0_3.0_1693326950074.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_shona_mpnet_base_snli_mnli_en_5.1.0_3.0_1693326950074.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_shona_mpnet_base_snli_mnli", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_shona_mpnet_base_snli_mnli", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_shona_mpnet_base_snli_mnli| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|409.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnet_by_hf_internal_testing_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnet_by_hf_internal_testing_en.md new file mode 100644 index 00000000000000..d17788dc899fff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnet_by_hf_internal_testing_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_tiny_random_mpnet_by_hf_internal_testing TFMPNetModel from hf-internal-testing +author: John Snow Labs +name: mpnet_embedding_tiny_random_mpnet_by_hf_internal_testing +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_tiny_random_mpnet_by_hf_internal_testing` is a English model originally trained by hf-internal-testing. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnet_by_hf_internal_testing_en_5.1.0_3.0_1693325729615.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnet_by_hf_internal_testing_en_5.1.0_3.0_1693325729615.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_tiny_random_mpnet_by_hf_internal_testing", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_tiny_random_mpnet_by_hf_internal_testing", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_tiny_random_mpnet_by_hf_internal_testing| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|1.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_internal_testing_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_internal_testing_en.md new file mode 100644 index 00000000000000..f22223091db9f3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_internal_testing_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_internal_testing TFMPNetModel from hf-internal-testing +author: John Snow Labs +name: mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_internal_testing +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_internal_testing` is a English model originally trained by hf-internal-testing. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_internal_testing_en_5.1.0_3.0_1693326640457.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_internal_testing_en_5.1.0_3.0_1693326640457.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_internal_testing", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_internal_testing", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_internal_testing| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|1.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_tiny_model_private_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_tiny_model_private_en.md new file mode 100644 index 00000000000000..c8a292e16fb7e5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_tiny_model_private_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_tiny_model_private TFMPNetModel from hf-tiny-model-private +author: John Snow Labs +name: mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_tiny_model_private +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_tiny_model_private` is a English model originally trained by hf-tiny-model-private. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_tiny_model_private_en_5.1.0_3.0_1693325729639.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_tiny_model_private_en_5.1.0_3.0_1693325729639.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_tiny_model_private", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_tiny_model_private", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_tiny_random_mpnetformaskedlm_by_hf_tiny_model_private| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|1.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_internal_testing_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_internal_testing_en.md new file mode 100644 index 00000000000000..a0ee1c305e2d1d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_internal_testing_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_internal_testing TFMPNetModel from hf-internal-testing +author: John Snow Labs +name: mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_internal_testing +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_internal_testing` is a English model originally trained by hf-internal-testing. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_internal_testing_en_5.1.0_3.0_1693326735164.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_internal_testing_en_5.1.0_3.0_1693326735164.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_internal_testing", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_internal_testing", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_internal_testing| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|1.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_tiny_model_private_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_tiny_model_private_en.md new file mode 100644 index 00000000000000..d34059bb7932b9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_tiny_model_private_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_tiny_model_private TFMPNetModel from hf-tiny-model-private +author: John Snow Labs +name: mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_tiny_model_private +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_tiny_model_private` is a English model originally trained by hf-tiny-model-private. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_tiny_model_private_en_5.1.0_3.0_1693325833863.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_tiny_model_private_en_5.1.0_3.0_1693325833863.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_tiny_model_private", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_tiny_model_private", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_tiny_random_mpnetforquestionanswering_by_hf_tiny_model_private| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|1.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_internal_testing_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_internal_testing_en.md new file mode 100644 index 00000000000000..cfd44ab7c5a1a3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_internal_testing_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_internal_testing TFMPNetModel from hf-internal-testing +author: John Snow Labs +name: mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_internal_testing +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_internal_testing` is a English model originally trained by hf-internal-testing. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_internal_testing_en_5.1.0_3.0_1693326833528.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_internal_testing_en_5.1.0_3.0_1693326833528.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_internal_testing", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_internal_testing", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_internal_testing| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|1.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_tiny_model_private_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_tiny_model_private_en.md new file mode 100644 index 00000000000000..0ff8efe085a76e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_tiny_model_private_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_tiny_model_private TFMPNetModel from hf-tiny-model-private +author: John Snow Labs +name: mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_tiny_model_private +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_tiny_model_private` is a English model originally trained by hf-tiny-model-private. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_tiny_model_private_en_5.1.0_3.0_1693325961986.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_tiny_model_private_en_5.1.0_3.0_1693325961986.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_tiny_model_private", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_tiny_model_private", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_tiny_random_mpnetforsequenceclassification_by_hf_tiny_model_private| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|1.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_internal_testing_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_internal_testing_en.md new file mode 100644 index 00000000000000..4084b57ef21b7e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_internal_testing_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_internal_testing TFMPNetModel from hf-internal-testing +author: John Snow Labs +name: mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_internal_testing +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_internal_testing` is a English model originally trained by hf-internal-testing. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_internal_testing_en_5.1.0_3.0_1693326935951.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_internal_testing_en_5.1.0_3.0_1693326935951.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_internal_testing", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_internal_testing", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_internal_testing| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|1.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_tiny_model_private_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_tiny_model_private_en.md new file mode 100644 index 00000000000000..87a635201166f6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_tiny_model_private_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_tiny_model_private TFMPNetModel from hf-tiny-model-private +author: John Snow Labs +name: mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_tiny_model_private +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_tiny_model_private` is a English model originally trained by hf-tiny-model-private. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_tiny_model_private_en_5.1.0_3.0_1693326064349.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_tiny_model_private_en_5.1.0_3.0_1693326064349.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_tiny_model_private", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_tiny_model_private", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_tiny_random_mpnetfortokenclassification_by_hf_tiny_model_private| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|1.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetmodel_by_hf_internal_testing_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetmodel_by_hf_internal_testing_en.md new file mode 100644 index 00000000000000..874522e89aaba9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetmodel_by_hf_internal_testing_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_tiny_random_mpnetmodel_by_hf_internal_testing TFMPNetModel from hf-internal-testing +author: John Snow Labs +name: mpnet_embedding_tiny_random_mpnetmodel_by_hf_internal_testing +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_tiny_random_mpnetmodel_by_hf_internal_testing` is a English model originally trained by hf-internal-testing. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetmodel_by_hf_internal_testing_en_5.1.0_3.0_1693327045715.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetmodel_by_hf_internal_testing_en_5.1.0_3.0_1693327045715.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_tiny_random_mpnetmodel_by_hf_internal_testing", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_tiny_random_mpnetmodel_by_hf_internal_testing", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_tiny_random_mpnetmodel_by_hf_internal_testing| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|1.2 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetmodel_by_hf_tiny_model_private_en.md b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetmodel_by_hf_tiny_model_private_en.md new file mode 100644 index 00000000000000..b4ff517ec41e29 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-08-29-mpnet_embedding_tiny_random_mpnetmodel_by_hf_tiny_model_private_en.md @@ -0,0 +1,88 @@ +--- +layout: model +title: English mpnet_embedding_tiny_random_mpnetmodel_by_hf_tiny_model_private TFMPNetModel from hf-tiny-model-private +author: John Snow Labs +name: mpnet_embedding_tiny_random_mpnetmodel_by_hf_tiny_model_private +date: 2023-08-29 +tags: [mpnet, en, open_source, tensorflow] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained mpnet model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_tiny_random_mpnetmodel_by_hf_tiny_model_private` is a English model originally trained by hf-tiny-model-private. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetmodel_by_hf_tiny_model_private_en_5.1.0_3.0_1693326163509.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_tiny_random_mpnetmodel_by_hf_tiny_model_private_en_5.1.0_3.0_1693326163509.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +instruction = MPNetEmbeddings \ + .pretrained("mpnet_embedding_tiny_random_mpnetmodel_by_hf_tiny_model_private", "en")\ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline(stages=[ + document_assembler, + instruction, +]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val instruction = MPNetEmbeddings + .pretrained("mpnet_embedding_tiny_random_mpnetmodel_by_hf_tiny_model_private", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, instruction)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_embedding_tiny_random_mpnetmodel_by_hf_tiny_model_private| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|1.2 MB| \ No newline at end of file From ae1e24f341520db989dde4ce8b1268f759cef12b Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Thu, 7 Sep 2023 23:12:05 +0700 Subject: [PATCH 38/55] 2023-08-28-asr_whisper_tiny_opt_xx (#13944) * Add model 2023-08-28-asr_whisper_tiny_opt_xx * Update 2023-08-28-asr_whisper_tiny_opt_xx.md Change Spark Version * Update 2023-08-28-asr_whisper_tiny_opt_xx.md Spark version 3.0 * Update 2023-08-28-asr_whisper_tiny_opt_xx.md spark version * Update 2023-08-28-asr_whisper_tiny_opt_xx.md --------- Co-authored-by: DevinTDHa Co-authored-by: Devin Ha <33089471+DevinTDHa@users.noreply.github.com> --- .../2023-08-28-asr_whisper_tiny_opt_xx.md | 119 ++++++++++++++++++ 1 file changed, 119 insertions(+) create mode 100644 docs/_posts/DevinTDHa/2023-08-28-asr_whisper_tiny_opt_xx.md diff --git a/docs/_posts/DevinTDHa/2023-08-28-asr_whisper_tiny_opt_xx.md b/docs/_posts/DevinTDHa/2023-08-28-asr_whisper_tiny_opt_xx.md new file mode 100644 index 00000000000000..fc11627072fe92 --- /dev/null +++ b/docs/_posts/DevinTDHa/2023-08-28-asr_whisper_tiny_opt_xx.md @@ -0,0 +1,119 @@ +--- +layout: model +title: Official whisper-tiny Optimized +author: John Snow Labs +name: asr_whisper_tiny_opt +date: 2023-08-28 +tags: [whisper, audio, open_source, asr, onnx, xx] +task: Automatic Speech Recognition +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Official pretrained Whisper model, adapted from HuggingFace transformer and curated to provide scalability and production-readiness using Spark NLP. + +This is a multilingual model and supports the following languages: + +Afrikaans, Arabic, Armenian, Azerbaijani, Belarusian, Bosnian, Bulgarian, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, Galician, German, Greek, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Kannada, Kazakh, Korean, Latvian, Lithuanian, Macedonian, Malay, Marathi, Maori, Nepali, Norwegian, Persian, Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tagalog, Tamil, Thai, Turkish, Ukrainian, Urdu, Vietnamese, and Welsh. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_opt_xx_5.1.1_3.0_1693213918398.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_opt_xx_5.1.1_3.0_1693213918398.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +import sparknlp +from sparknlp.base import * +from sparknlp.annotator import * +from pyspark.ml import Pipeline + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + +speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_opt", "xx") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) +processedAudioFloats = spark.createDataFrame([[rawFloats]]).toDF("audio_content") +result = pipeline.fit(processedAudioFloats).transform(processedAudioFloats) +result.select("text.result").show(truncate = False) +``` +```scala +import spark.implicits._ +import com.johnsnowlabs.nlp.base._ +import com.johnsnowlabs.nlp.annotators._ +import com.johnsnowlabs.nlp.annotators.audio.WhisperForCTC +import org.apache.spark.ml.Pipeline + +val audioAssembler: AudioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText: WhisperForCTC = WhisperForCTC + .pretrained("asr_whisper_tiny_opt", "xx") + .setInputCols("audio_assembler") + .setOutputCol("text") + +val pipeline: Pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val bufferedSource = + scala.io.Source.fromFile("src/test/resources/audio/txt/librispeech_asr_0.txt") + +val rawFloats = bufferedSource + .getLines() + .map(_.split(",").head.trim.toFloat) + .toArray +bufferedSource.close + +val processedAudioFloats = Seq(rawFloats).toDF("audio_content") + +val result = pipeline.fit(processedAudioFloats).transform(processedAudioFloats) +result.select("text.result").show(truncate = false) +``` +
+ +## Results + +```bash ++------------------------------------------------------------------------------------------------------------------------------------------------+ +|document | ++------------------------------------------------------------------------------------------------------------------------------------------------+ +|[{document, 0, 87, Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel., {length -> 93680, audio -> 0}, []}]| ++------------------------------------------------------------------------------------------------------------------------------------------------+ +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_opt| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[document]| +|Language:|xx| +|Size:|239.3 MB| From b1da33e366acec1a25bedd7c005a74889c407501 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Fri, 8 Sep 2023 16:51:31 +0700 Subject: [PATCH 39/55] 2023-09-07-java_pointer_classifier_en (#13968) * Add model 2023-09-07-invoiceornot_en * Add model 2023-09-07-biolord_stamb2_v1_en * Add model 2023-09-07-cross_all_mpnet_base_v2_finetuned_webnlg2020_metric_average_en * Add model 2023-09-07-tiny_random_mpnetmodel_hf_internal_testing_en * Add model 2023-09-07-ikitracs_mitigation_en * Add model 2023-09-07-mpnet_snli_en * Add model 2023-09-07-sml_ukr_message_classifier_en * Add model 2023-09-07-action_policy_plans_classifier_en * Add model 2023-09-07-nooks_amd_detection_v2_full_en * Add model 2023-09-07-tiny_random_mpnetformultiplechoice_en * Add model 2023-09-07-all_datasets_v4_mpnet_base_en * Add model 2023-09-07-review_intent_20230116_en * Add model 2023-09-07-tiny_random_mpnetfortokenclassification_hf_tiny_model_private_en * Add model 2023-09-07-multi_qa_v1_mpnet_asymmetric_q_en * Add model 2023-09-07-tiny_random_mpnetmodel_hf_tiny_model_private_en * Add model 2023-09-07-setfit_alpaca_spanish_unprocessable_sample_detection_es * Add model 2023-09-07-nps_psb_lds_en * Add model 2023-09-07-github_issues_mpnet_southern_sotho_e10_en * Add model 2023-09-07-mpnet_retriever_squad2_en * Add model 2023-09-07-mpnet_adaptation_mitigation_classifier_en * Add model 2023-09-07-stackoverflow_mpnet_base_en * Add model 2023-09-07-all_mpnet_base_v2_diptanuc_en * Add model 2023-09-07-setfit_model_pradipta11_en * Add model 2023-09-07-setfit_zero_shot_classification_pbsp_p1_life_en * Add model 2023-09-07-due_retail_25_en * Add model 2023-09-07-java_summary_classifier_en * Add model 2023-09-07-tiny_random_mpnetforsequenceclassification_hf_tiny_model_private_en * Add model 2023-09-07-multi_qa_v1_mpnet_asymmetric_a_en * Add model 2023-09-07-all_mpnet_base_v2_sentence_transformers_en * Add model 2023-09-07-setfit_ds_version_0_0_2_en * Add model 2023-09-07-multi_qa_mpnet_base_cos_v1_sentence_transformers_en * Add model 2023-09-07-setfit_ds_version_0_0_4_en * Add model 2023-09-07-java_expand_classifier_en * Add model 2023-09-07-python_summary_classifier_en * Add model 2023-09-07-test_food_en * Add model 2023-09-07-sbert_paper_en * Add model 2023-09-07-setfit_model_rajistics_en * Add model 2023-09-07-all_mpnet_base_v2_embedding_all_en * Add model 2023-09-07-due_eshop_21_multilabel_en * Add model 2023-09-07-initial_model_v3_en * Add model 2023-09-07-retriever_coding_guru_adapted_en * Add model 2023-09-07-paraphrase_mpnet_base_v2_fuzzy_matcher_en * Add model 2023-09-07-setfit_ethos_multilabel_example_lewtun_en * Add model 2023-09-07-python_expand_classifier_en * Add model 2023-09-07-kw_classification_setfit_model_en * Add model 2023-09-07-pharo_collaborators_classifier_en * Add model 2023-09-07-mpnet_base_articles_ner_en * Add model 2023-09-07-shona_mpnet_base_snli_mnli_en * Add model 2023-09-07-fail_detect_en * Add model 2023-09-07-python_usage_classifier_en * Add model 2023-09-07-invoiceornot_en * Add model 2023-09-07-tiny_random_mpnetforquestionanswering_hf_internal_testing_en * Add model 2023-09-07-cpu_netzero_classifier_en * Add model 2023-09-07-pharo_responsibilities_classifier_en * Add model 2023-09-07-all_mpnet_base_v2_obrizum_en * Add model 2023-09-07-tiny_random_mpnetmodel_hf_internal_testing_en * Add model 2023-09-07-tiny_random_mpnetfortokenclassification_hf_internal_testing_en * Add model 2023-09-07-tiny_random_mpnet_hf_internal_testing_en * Add model 2023-09-07-python_developmentnotes_classifier_en * Add model 2023-09-08-test_model_001_en * Add model 2023-09-07-covid_qa_mpnet_en * Add model 2023-09-08-setfit_ostrom_en * Add model 2023-09-08-patentsberta_v2_en * Add model 2023-09-07-pdfsegs_en * Add model 2023-09-07-mpnet_snli_negatives_en * Add model 2023-09-08-eth_setfit_payment_model_en * Add model 2023-09-08-all_mpnet_base_questions_clustering_english_en * Add model 2023-09-08-esci_jp_mpnet_crossencoder_en * Add model 2023-09-07-kw_classification_setfithead_model_en * Add model 2023-09-08-setfit_zero_shot_classification_pbsp_p4_time_en * Add model 2023-09-07-579_stmodel_product_rem_v3a_en * Add model 2023-09-07-tiny_random_mpnetformaskedlm_hf_internal_testing_en * Add model 2023-09-07-setfit_all_data_en * Add model 2023-09-07-review_multiclass_20230116_en * Add model 2023-09-07-nli_mpnet_base_v2_sentence_transformers_en * Add model 2023-09-07-reddit_single_context_mpnet_base_en * Add model 2023-09-07-tiny_random_mpnetforsequenceclassification_hf_internal_testing_en * Add model 2023-09-07-java_rational_classifier_en * Add model 2023-09-08-setfit_zero_shot_classification_pbsp_p3_bhvr_en * Add model 2023-09-07-all_mpnet_base_v2_tasky_classification_en * Add model 2023-09-08-setfit_zero_shot_classification_pbsp_p4_meas_en * Add model 2023-09-07-sb_temfac_en * Add model 2023-09-07-sb_temfac_en * Add model 2023-09-07-all_mpnet_base_v2_ftlegal_v3_en * Add model 2023-09-07-pharo_collaborators_classifier_en * Add model 2023-09-08-all_mpnet_base_v2_feature_extraction_en * Add model 2023-09-08-setfit_zero_shot_classification_pbsp_p4_achiev_en * Add model 2023-09-07-cpu_economywide_classifier_en * Add model 2023-09-08-setfit_zero_shot_classification_pbsp_p4_rel_en * Add model 2023-09-07-all_mpnet_base_v2_sentence_transformers_en * Add model 2023-09-08-setfit_zero_shot_classification_pbsp_p3_cons_en * Add model 2023-09-07-setfit_ds_version_0_0_5_en * Add model 2023-09-07-cpu_conditional_classifier_en * Add model 2023-09-07-all_mpnet_base_v2_table_en * Add model 2023-09-07-ikitracs_mitigation_en * Add model 2023-09-07-vulnerable_groups_en * Add model 2023-09-08-setfit_zero_shot_classification_pbsp_p4_specific_en * Add model 2023-09-07-tiny_random_mpnetforsequenceclassification_hf_tiny_model_private_en * Add model 2023-09-07-tiny_random_mpnetforquestionanswering_hf_tiny_model_private_en * Add model 2023-09-07-multi_qa_v1_mpnet_asymmetric_a_en * Add model 2023-09-07-biencoder_all_mpnet_base_v2_mmarcofr_fr * Add model 2023-09-07-initial_model_en * Add model 2023-09-07-sentiment140_fewshot_en * Add model 2023-09-07-python_summary_classifier_en * Add model 2023-09-08-cpu_target_classifier_en * Add model 2023-09-07-ikitracs_conditional_en * Add model 2023-09-07-setfit_ag_news_endpoint_en * Add model 2023-09-08-setfit_model_feb11_misinformation_on_law_en * Add model 2023-09-07-multi_qa_mpnet_base_dot_v1_sentence_transformers_en * Add model 2023-09-08-setfit_zero_shot_classification_pbsp_q8a_azure_gpt35_en * Add model 2023-09-07-mpnet_multilabel_sector_classifier_en * Add model 2023-09-08-paraphrase_mpnet_base_v2_finetuned_polifact_en * Add model 2023-09-07-all_datasets_v3_mpnet_base_en * Add model 2023-09-08-all_mpnet_base_v2_for_sb_clustering_en * Add model 2023-09-07-negation_categories_classifier_es * Add model 2023-09-07-python_parameters_classifier_en * Add model 2023-09-07-due_eshop_21_en * Add model 2023-09-07-contradiction_psb_en * Add model 2023-09-07-mpnet_mnr_v2_fine_tuned_en * Add model 2023-09-07-labels_per_job_title_fine_tune_en * Add model 2023-09-07-paraphrase_mpnet_base_v2_sentence_transformers_en * Add model 2023-09-07-all_datasets_v4_mpnet_base_en * Add model 2023-09-07-pharo_example_classifier_en * Add model 2023-09-07-paraphrase_mpnet_base_v2_setfit_sst2_en * Add model 2023-09-07-all_mpnet_base_v2_ftlegal_v3_en * Add model 2023-09-07-mpnet_adaptation_mitigation_classifier_en * Add model 2023-09-07-abstract_sim_query_en * Add model 2023-09-07-python_developmentnotes_classifier_en * Add model 2023-09-08-few_shot_model_en * Add model 2023-09-07-tiny_random_mpnetformaskedlm_hf_tiny_model_private_en * Add model 2023-09-07-biencoder_multi_qa_mpnet_base_cos_v1_mmarcofr_fr * Add model 2023-09-07-cpu_economywide_classifier_en * Add model 2023-09-07-contradiction_psb_lds_en * Add model 2023-09-07-setfit_ds_version_0_0_1_en * Add model 2023-09-07-kw_classification_setfit_model_en * Add model 2023-09-07-all_mpnet_base_v2_finetuned_v2_en * Add model 2023-09-07-mpnet_base_snli_mnli_en * Add model 2023-09-07-tiny_random_mpnetformaskedlm_hf_internal_testing_en * Add model 2023-09-07-mpnet_base_snli_mnli_en * Add model 2023-09-07-multi_qa_v1_mpnet_cls_dot_en * Add model 2023-09-07-tiny_random_mpnetforquestionanswering_hf_tiny_model_private_en * Add model 2023-09-07-setfit_zero_shot_classification_pbsp_p1_likes_en * Add model 2023-09-07-nooks_amd_detection_realtime_en * Add model 2023-09-07-setfit_zero_shot_classification_pbsp_p1_en * Add model 2023-09-07-github_issues_mpnet_southern_sotho_e10_en * Add model 2023-09-07-burmese_awesome_setfit_model_98_en * Add model 2023-09-07-setfit_few_shot_classifier_en * Add model 2023-09-07-pdfsegs_en * Add model 2023-09-07-mpnet_snli_en * Add model 2023-09-07-abstract_sim_query_en * Add model 2023-09-07-pharo_keyimplementationpoints_classifier_en * Add model 2023-09-07-abstract_sim_sentence_en * Add model 2023-09-07-all_mpnet_base_v2_feature_extraction_pipeline_en * Add model 2023-09-07-shona_mpnet_base_snli_mnli_en * Add model 2023-09-07-nooks_amd_detection_realtime_en * Add model 2023-09-07-pharo_keyimplementationpoints_classifier_en * Add model 2023-09-07-domainadaptm2_en * Add model 2023-09-07-setfit_finetuned_financial_text_en * Add model 2023-09-07-cpu_mitigation_classifier_en * Add model 2023-09-07-sml_ukr_word_classifier_medium_en * Add model 2023-09-07-java_expand_classifier_en * Add model 2023-09-07-tiny_random_mpnetforsequenceclassification_hf_internal_testing_en * Add model 2023-09-07-setfit_zero_shot_classification_pbsp_p1_comm_en * Add model 2023-09-07-setfit_ds_version_0_0_2_en * Add model 2023-09-07-setfit_zero_shot_classification_pbsp_p3_func_en * Add model 2023-09-07-mpnet_multilabel_sector_classifier_en * Add model 2023-09-07-all_mpnet_base_v2_finetuned_v2_en * Add model 2023-09-07-ouvrage_classif_en * Add model 2023-09-07-setfit_zero_shot_classification_pbsp_p3_sev_en * Add model 2023-09-07-kw_classification_setfithead_model_en * Add model 2023-09-07-setfit_zero_shot_classification_pbsp_p1_en * Add model 2023-09-07-tiny_random_mpnetformultiplechoice_en * Add model 2023-09-07-setfit_model_test_sensitve_v1_en * Add model 2023-09-07-spiced_en * Add model 2023-09-07-mpnet_nli_sts_en * Add model 2023-09-07-java_deprecation_classifier_en * Add model 2023-09-07-cross_all_mpnet_base_v2_finetuned_webnlg2020_metric_average_en * Add model 2023-09-07-test_food_en * Add model 2023-09-07-testing_setfit_en * Add model 2023-09-07-multi_qa_mpnet_base_dot_v1_eclass_en * Add model 2023-09-07-ecolo_pas_ecolo_v0.1_en * Add model 2023-09-07-mpnet_retriever_squad2_en * Add model 2023-09-07-github_issues_preprocessed_mpnet_southern_sotho_e10_en * Add model 2023-09-07-stsb_mpnet_base_v2_en * Add model 2023-09-07-sentence_transformers_bible_reference_final_en * Add model 2023-09-07-setfit_all_data_en * Add model 2023-09-07-ouvrage_classif_en * Add model 2023-09-07-all_mpnet_base_v1_en * Add model 2023-09-07-all_mpnet_base_v1_en * Add model 2023-09-07-ouvrage_classif_en * Add model 2023-09-07-mpnet_nli_sts_en * Add model 2023-09-07-tiny_random_mpnetforquestionanswering_hf_tiny_model_private_en * Add model 2023-09-07-sml_ukr_word_classifier_medium_en * Add model 2023-09-07-vulnerable_groups_en * Add model 2023-09-07-python_expand_classifier_en * Add model 2023-09-07-all_mpnet_base_v2_tasky_classification_en * Add model 2023-09-07-biencoder_multi_qa_mpnet_base_cos_v1_mmarcofr_fr * Add model 2023-09-07-java_ownership_classifier_en * Add model 2023-09-07-multi_qa_mpnet_base_cos_v1_navteca_en * Add model 2023-09-07-setfit_zero_shot_classification_pbsp_p3_sev_en * Add model 2023-09-07-biolord_stamb2_v1_en * Add model 2023-09-07-java_pointer_classifier_en * Add model 2023-09-07-reddit_single_context_mpnet_base_en * Add model 2023-09-07-setfit_finetuned_financial_text_en * Add model 2023-09-07-cpu_transport_ghg_classifier_en * Add model 2023-09-08-setfit_zero_shot_classification_pbsp_p3_trig_en * Add model 2023-09-07-paraphrase_mpnet_base_v2_sentence_transformers_en * Add model 2023-09-07-579_stmodel_product_rem_v3a_en * Add model 2023-09-07-multi_qa_mpnet_base_dot_v1_eclass_en * Add model 2023-09-07-nps_psb_lds_en * Add model 2023-09-07-negation_categories_classifier_es * Add model 2023-09-08-setfit_zero_shot_classification_pbsp_p3_dur_en * Add model 2023-09-07-mpnet_mnr_v2_fine_tuned_en * Add model 2023-09-07-keyphrase_mpnet_v1_en * Add model 2023-09-07-cpu_mitigation_classifier_en * Add model 2023-09-07-multi_qa_mpnet_base_cos_v1_navteca_en * Add model 2023-09-07-setfit_model_test_sensitve_v1_en * Add model 2023-09-07-spiced_en * Add model 2023-09-07-ecolo_pas_ecolo_v0.1_en * Add model 2023-09-07-paraphrase_mpnet_base_v2_setfit_sst2_en * Add model 2023-09-07-setfit_ds_version_0_0_1_en * Add model 2023-09-08-multi_qa_mpnet_base_dot_v1_model_embeddings_en * Add model 2023-09-07-setfit_occupation_en * Add model 2023-09-07-multi_qa_mpnet_base_dot_v1_legal_finetune_en * Add model 2023-09-07-burmese_awesome_setfit_model_en * Add model 2023-09-07-multi_qa_v1_mpnet_cls_dot_en * Add model 2023-09-07-tiny_random_mpnetfortokenclassification_hf_internal_testing_en * Add model 2023-09-07-setfit_ethos_multilabel_example_neilthematic_en * Add model 2023-09-07-keyphrase_mpnet_v1_en * Add model 2023-09-07-fewshotissueclassifier_nlbse23_en * Add model 2023-09-07-stsb_mpnet_base_v2_en * Add model 2023-09-07-all_mpnet_base_v2_obrizum_en * Add model 2023-09-08-setfit_ft_sentinent_eval_en * Add model 2023-09-07-attack_bert_en * Add model 2023-09-07-all_datasets_v3_mpnet_base_en * Add model 2023-09-07-cpu_transport_ghg_classifier_en * Add model 2023-09-07-fewshotissueclassifier_nlbse23_en * Add model 2023-09-07-java_deprecation_classifier_en * Add model 2023-09-07-java_usage_classifier_en * Add model 2023-09-07-sbert_paper_en * Add model 2023-09-07-setfit_ethos_multilabel_example_neilthematic_en * Add model 2023-09-07-patentsberta_en * Add model 2023-09-07-setfit_occupation_en * Add model 2023-09-07-setfit_ds_version_0_0_5_en * Add model 2023-09-07-mpnet_snli_negatives_en * Add model 2023-09-08-nli_mpnet_base_v2_en * Add model 2023-09-08-multi_qa_mpnet_base_cos_v1_en * Add model 2023-09-08-multi_qa_mpnet_base_dot_v1_en * Add model 2023-09-08-all_mpnet_base_v2_en * Add model 2023-09-08-paraphrase_mpnet_base_v2_en --------- Co-authored-by: ahmedlone127 --- ...23-09-07-579_stmodel_product_rem_v3a_en.md | 93 +++++++++++++++++++ .../2023-09-07-abstract_sim_query_en.md | 93 +++++++++++++++++++ .../2023-09-07-abstract_sim_sentence_en.md | 93 +++++++++++++++++++ ...09-07-action_policy_plans_classifier_en.md | 93 +++++++++++++++++++ ...023-09-07-all_datasets_v3_mpnet_base_en.md | 93 +++++++++++++++++++ ...023-09-07-all_datasets_v4_mpnet_base_en.md | 93 +++++++++++++++++++ .../2023-09-07-all_mpnet_base_v1_en.md | 93 +++++++++++++++++++ ...023-09-07-all_mpnet_base_v2_diptanuc_en.md | 93 +++++++++++++++++++ ...9-07-all_mpnet_base_v2_embedding_all_en.md | 93 +++++++++++++++++++ ..._base_v2_feature_extraction_pipeline_en.md | 93 +++++++++++++++++++ ...09-07-all_mpnet_base_v2_finetuned_v2_en.md | 93 +++++++++++++++++++ ...3-09-07-all_mpnet_base_v2_ftlegal_v3_en.md | 93 +++++++++++++++++++ ...2023-09-07-all_mpnet_base_v2_obrizum_en.md | 93 +++++++++++++++++++ ..._mpnet_base_v2_sentence_transformers_en.md | 93 +++++++++++++++++++ .../2023-09-07-all_mpnet_base_v2_table_en.md | 93 +++++++++++++++++++ ...l_mpnet_base_v2_tasky_classification_en.md | 93 +++++++++++++++++++ .../ahmedlone127/2023-09-07-attack_bert_en.md | 93 +++++++++++++++++++ ...biencoder_all_mpnet_base_v2_mmarcofr_fr.md | 93 +++++++++++++++++++ ..._multi_qa_mpnet_base_cos_v1_mmarcofr_fr.md | 93 +++++++++++++++++++ .../2023-09-07-biolord_stamb2_v1_en.md | 93 +++++++++++++++++++ ...9-07-burmese_awesome_setfit_model_98_en.md | 93 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93 +++++++++++++++++++ .../ahmedlone127/2023-09-07-fail_detect_en.md | 93 +++++++++++++++++++ ...09-07-fewshotissueclassifier_nlbse23_en.md | 93 +++++++++++++++++++ ...thub_issues_mpnet_southern_sotho_e10_en.md | 93 +++++++++++++++++++ ...reprocessed_mpnet_southern_sotho_e10_en.md | 93 +++++++++++++++++++ .../2023-09-07-ikitracs_conditional_en.md | 93 +++++++++++++++++++ .../2023-09-07-ikitracs_mitigation_en.md | 93 +++++++++++++++++++ .../2023-09-07-initial_model_en.md | 93 +++++++++++++++++++ .../2023-09-07-initial_model_v3_en.md | 93 +++++++++++++++++++ .../2023-09-07-invoiceornot_en.md | 93 +++++++++++++++++++ ...23-09-07-java_deprecation_classifier_en.md | 93 +++++++++++++++++++ .../2023-09-07-java_expand_classifier_en.md | 93 +++++++++++++++++++ ...2023-09-07-java_ownership_classifier_en.md | 93 +++++++++++++++++++ .../2023-09-07-java_pointer_classifier_en.md | 93 +++++++++++++++++++ .../2023-09-07-java_rational_classifier_en.md | 93 +++++++++++++++++++ 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b/docs/_posts/ahmedlone127/2023-09-07-579_stmodel_product_rem_v3a_en.md new file mode 100644 index 00000000000000..325e12c4529616 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-579_stmodel_product_rem_v3a_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English 579_stmodel_product_rem_v3a MPNetEmbeddings from jamiehudson +author: John Snow Labs +name: 579_stmodel_product_rem_v3a +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`579_stmodel_product_rem_v3a` is a English model originally trained by jamiehudson. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/579_stmodel_product_rem_v3a_en_5.1.1_3.0_1694126614739.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/579_stmodel_product_rem_v3a_en_5.1.1_3.0_1694126614739.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("579_stmodel_product_rem_v3a","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("579_stmodel_product_rem_v3a", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|579_stmodel_product_rem_v3a| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/jamiehudson/579-STmodel-product-rem-v3a \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-abstract_sim_query_en.md b/docs/_posts/ahmedlone127/2023-09-07-abstract_sim_query_en.md new file mode 100644 index 00000000000000..6bbdf8e11a679c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-abstract_sim_query_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English abstract_sim_query MPNetEmbeddings from biu-nlp +author: John Snow Labs +name: abstract_sim_query +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`abstract_sim_query` is a English model originally trained by biu-nlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/abstract_sim_query_en_5.1.1_3.0_1694125761744.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/abstract_sim_query_en_5.1.1_3.0_1694125761744.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("abstract_sim_query","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("abstract_sim_query", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|abstract_sim_query| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/biu-nlp/abstract-sim-query \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-abstract_sim_sentence_en.md b/docs/_posts/ahmedlone127/2023-09-07-abstract_sim_sentence_en.md new file mode 100644 index 00000000000000..49fe8380319cca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-abstract_sim_sentence_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English abstract_sim_sentence MPNetEmbeddings from biu-nlp +author: John Snow Labs +name: abstract_sim_sentence +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`abstract_sim_sentence` is a English model originally trained by biu-nlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/abstract_sim_sentence_en_5.1.1_3.0_1694129808816.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/abstract_sim_sentence_en_5.1.1_3.0_1694129808816.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("abstract_sim_sentence","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("abstract_sim_sentence", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|abstract_sim_sentence| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/biu-nlp/abstract-sim-sentence \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-action_policy_plans_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-action_policy_plans_classifier_en.md new file mode 100644 index 00000000000000..7cc9b8271e4686 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-action_policy_plans_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English action_policy_plans_classifier MPNetEmbeddings from ppsingh +author: John Snow Labs +name: action_policy_plans_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`action_policy_plans_classifier` is a English model originally trained by ppsingh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/action_policy_plans_classifier_en_5.1.1_3.0_1694128081191.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/action_policy_plans_classifier_en_5.1.1_3.0_1694128081191.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("action_policy_plans_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("action_policy_plans_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|action_policy_plans_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/ppsingh/action-policy-plans-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-all_datasets_v3_mpnet_base_en.md b/docs/_posts/ahmedlone127/2023-09-07-all_datasets_v3_mpnet_base_en.md new file mode 100644 index 00000000000000..4699e31ffc1bdf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-all_datasets_v3_mpnet_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English all_datasets_v3_mpnet_base Mpnet from flax-sentence-embeddings +author: John Snow Labs +name: all_datasets_v3_mpnet_base +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`all_datasets_v3_mpnet_base` is a English model originally trained by flax-sentence-embeddings. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/all_datasets_v3_mpnet_base_en_5.1.1_3.0_1694122685945.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/all_datasets_v3_mpnet_base_en_5.1.1_3.0_1694122685945.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("all_datasets_v3_mpnet_base","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("all_datasets_v3_mpnet_base", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|all_datasets_v3_mpnet_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/flax-sentence-embeddings/all_datasets_v3_mpnet-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-all_datasets_v4_mpnet_base_en.md b/docs/_posts/ahmedlone127/2023-09-07-all_datasets_v4_mpnet_base_en.md new file mode 100644 index 00000000000000..e21e755b634864 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-all_datasets_v4_mpnet_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English all_datasets_v4_mpnet_base Mpnet from flax-sentence-embeddings +author: John Snow Labs +name: all_datasets_v4_mpnet_base +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`all_datasets_v4_mpnet_base` is a English model originally trained by flax-sentence-embeddings. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/all_datasets_v4_mpnet_base_en_5.1.1_3.0_1694122798617.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/all_datasets_v4_mpnet_base_en_5.1.1_3.0_1694122798617.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("all_datasets_v4_mpnet_base","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("all_datasets_v4_mpnet_base", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|all_datasets_v4_mpnet_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/flax-sentence-embeddings/all_datasets_v4_mpnet-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v1_en.md b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v1_en.md new file mode 100644 index 00000000000000..ce015e01ebd9ad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English all_mpnet_base_v1 MPNetEmbeddings from sentence-transformers +author: John Snow Labs +name: all_mpnet_base_v1 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`all_mpnet_base_v1` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v1_en_5.1.1_3.0_1694125007752.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v1_en_5.1.1_3.0_1694125007752.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("all_mpnet_base_v1","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("all_mpnet_base_v1", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|all_mpnet_base_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/sentence-transformers/all-mpnet-base-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_diptanuc_en.md b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_diptanuc_en.md new file mode 100644 index 00000000000000..f133d854b1df83 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_diptanuc_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English all_mpnet_base_v2_diptanuc MPNetEmbeddings from diptanuc +author: John Snow Labs +name: all_mpnet_base_v2_diptanuc +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`all_mpnet_base_v2_diptanuc` is a English model originally trained by diptanuc. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_diptanuc_en_5.1.1_3.0_1694128964381.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_diptanuc_en_5.1.1_3.0_1694128964381.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("all_mpnet_base_v2_diptanuc","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("all_mpnet_base_v2_diptanuc", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|all_mpnet_base_v2_diptanuc| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/diptanuc/all-mpnet-base-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_embedding_all_en.md b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_embedding_all_en.md new file mode 100644 index 00000000000000..d5c2b27c9b6958 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_embedding_all_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English all_mpnet_base_v2_embedding_all MPNetEmbeddings from LLukas22 +author: John Snow Labs +name: all_mpnet_base_v2_embedding_all +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`all_mpnet_base_v2_embedding_all` is a English model originally trained by LLukas22. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_embedding_all_en_5.1.1_3.0_1694127834094.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_embedding_all_en_5.1.1_3.0_1694127834094.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("all_mpnet_base_v2_embedding_all","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("all_mpnet_base_v2_embedding_all", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|all_mpnet_base_v2_embedding_all| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.4 MB| + +## References + +https://huggingface.co/LLukas22/all-mpnet-base-v2-embedding-all \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_feature_extraction_pipeline_en.md b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_feature_extraction_pipeline_en.md new file mode 100644 index 00000000000000..ae366dffd48011 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_feature_extraction_pipeline_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English all_mpnet_base_v2_feature_extraction_pipeline MPNetEmbeddings from questgen +author: John Snow Labs +name: all_mpnet_base_v2_feature_extraction_pipeline +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`all_mpnet_base_v2_feature_extraction_pipeline` is a English model originally trained by questgen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_feature_extraction_pipeline_en_5.1.1_3.0_1694130976472.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_feature_extraction_pipeline_en_5.1.1_3.0_1694130976472.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("all_mpnet_base_v2_feature_extraction_pipeline","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("all_mpnet_base_v2_feature_extraction_pipeline", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|all_mpnet_base_v2_feature_extraction_pipeline| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/questgen/all-mpnet-base-v2-feature-extraction-pipeline \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_finetuned_v2_en.md b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_finetuned_v2_en.md new file mode 100644 index 00000000000000..51d5bcfe605c3c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_finetuned_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English all_mpnet_base_v2_finetuned_v2 MPNetEmbeddings from Humair +author: John Snow Labs +name: all_mpnet_base_v2_finetuned_v2 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`all_mpnet_base_v2_finetuned_v2` is a English model originally trained by Humair. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_finetuned_v2_en_5.1.1_3.0_1694127987280.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_finetuned_v2_en_5.1.1_3.0_1694127987280.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("all_mpnet_base_v2_finetuned_v2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("all_mpnet_base_v2_finetuned_v2", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|all_mpnet_base_v2_finetuned_v2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/Humair/all-mpnet-base-v2-finetuned-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_ftlegal_v3_en.md b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_ftlegal_v3_en.md new file mode 100644 index 00000000000000..a9b240367cd983 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_ftlegal_v3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English all_mpnet_base_v2_ftlegal_v3 MPNetEmbeddings from sukantan +author: John Snow Labs +name: all_mpnet_base_v2_ftlegal_v3 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`all_mpnet_base_v2_ftlegal_v3` is a English model originally trained by sukantan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_ftlegal_v3_en_5.1.1_3.0_1694124734019.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_ftlegal_v3_en_5.1.1_3.0_1694124734019.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("all_mpnet_base_v2_ftlegal_v3","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("all_mpnet_base_v2_ftlegal_v3", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|all_mpnet_base_v2_ftlegal_v3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/sukantan/all-mpnet-base-v2-ftlegal-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_obrizum_en.md b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_obrizum_en.md new file mode 100644 index 00000000000000..32f233253f2c5b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_obrizum_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English all_mpnet_base_v2_obrizum MPNetEmbeddings from obrizum +author: John Snow Labs +name: all_mpnet_base_v2_obrizum +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`all_mpnet_base_v2_obrizum` is a English model originally trained by obrizum. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_obrizum_en_5.1.1_3.0_1694126818034.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_obrizum_en_5.1.1_3.0_1694126818034.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("all_mpnet_base_v2_obrizum","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("all_mpnet_base_v2_obrizum", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|all_mpnet_base_v2_obrizum| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/obrizum/all-mpnet-base-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_sentence_transformers_en.md b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_sentence_transformers_en.md new file mode 100644 index 00000000000000..f2c147c23ecf65 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_sentence_transformers_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English all_mpnet_base_v2_sentence_transformers MPNetEmbeddings from sentence-transformers +author: John Snow Labs +name: all_mpnet_base_v2_sentence_transformers +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`all_mpnet_base_v2_sentence_transformers` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_sentence_transformers_en_5.1.1_3.0_1694125144698.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_sentence_transformers_en_5.1.1_3.0_1694125144698.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("all_mpnet_base_v2_sentence_transformers","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("all_mpnet_base_v2_sentence_transformers", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|all_mpnet_base_v2_sentence_transformers| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/sentence-transformers/all-mpnet-base-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_table_en.md b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_table_en.md new file mode 100644 index 00000000000000..cedd3ff4fe0785 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_table_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English all_mpnet_base_v2_table MPNetEmbeddings from deepset +author: John Snow Labs +name: all_mpnet_base_v2_table +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`all_mpnet_base_v2_table` is a English model originally trained by deepset. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_table_en_5.1.1_3.0_1694130760725.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_table_en_5.1.1_3.0_1694130760725.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("all_mpnet_base_v2_table","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("all_mpnet_base_v2_table", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|all_mpnet_base_v2_table| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.3 MB| + +## References + +https://huggingface.co/deepset/all-mpnet-base-v2-table \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_tasky_classification_en.md b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_tasky_classification_en.md new file mode 100644 index 00000000000000..2cfb997b9a8fef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-all_mpnet_base_v2_tasky_classification_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English all_mpnet_base_v2_tasky_classification MPNetEmbeddings from khalidalt +author: John Snow Labs +name: all_mpnet_base_v2_tasky_classification +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`all_mpnet_base_v2_tasky_classification` is a English model originally trained by khalidalt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_tasky_classification_en_5.1.1_3.0_1694127102096.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_tasky_classification_en_5.1.1_3.0_1694127102096.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("all_mpnet_base_v2_tasky_classification","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("all_mpnet_base_v2_tasky_classification", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|all_mpnet_base_v2_tasky_classification| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.4 MB| + +## References + +https://huggingface.co/khalidalt/all-mpnet-base-v2-tasky-classification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-attack_bert_en.md b/docs/_posts/ahmedlone127/2023-09-07-attack_bert_en.md new file mode 100644 index 00000000000000..f13570be2518c3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-attack_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English attack_bert MPNetEmbeddings from basel +author: John Snow Labs +name: attack_bert +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`attack_bert` is a English model originally trained by basel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/attack_bert_en_5.1.1_3.0_1694128690149.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/attack_bert_en_5.1.1_3.0_1694128690149.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("attack_bert","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("attack_bert", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|attack_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/basel/ATTACK-BERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-biencoder_all_mpnet_base_v2_mmarcofr_fr.md b/docs/_posts/ahmedlone127/2023-09-07-biencoder_all_mpnet_base_v2_mmarcofr_fr.md new file mode 100644 index 00000000000000..982fcfb9070a33 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-biencoder_all_mpnet_base_v2_mmarcofr_fr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: French biencoder_all_mpnet_base_v2_mmarcofr MPNetEmbeddings from antoinelouis +author: John Snow Labs +name: biencoder_all_mpnet_base_v2_mmarcofr +date: 2023-09-07 +tags: [mpnet, fr, open_source, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biencoder_all_mpnet_base_v2_mmarcofr` is a French model originally trained by antoinelouis. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biencoder_all_mpnet_base_v2_mmarcofr_fr_5.1.1_3.0_1694130385847.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biencoder_all_mpnet_base_v2_mmarcofr_fr_5.1.1_3.0_1694130385847.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("biencoder_all_mpnet_base_v2_mmarcofr","fr") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("biencoder_all_mpnet_base_v2_mmarcofr", "fr") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biencoder_all_mpnet_base_v2_mmarcofr| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|fr| +|Size:|406.8 MB| + +## References + +https://huggingface.co/antoinelouis/biencoder-all-mpnet-base-v2-mmarcoFR \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-biencoder_multi_qa_mpnet_base_cos_v1_mmarcofr_fr.md b/docs/_posts/ahmedlone127/2023-09-07-biencoder_multi_qa_mpnet_base_cos_v1_mmarcofr_fr.md new file mode 100644 index 00000000000000..76e704412ad457 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-biencoder_multi_qa_mpnet_base_cos_v1_mmarcofr_fr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: French biencoder_multi_qa_mpnet_base_cos_v1_mmarcofr MPNetEmbeddings from antoinelouis +author: John Snow Labs +name: biencoder_multi_qa_mpnet_base_cos_v1_mmarcofr +date: 2023-09-07 +tags: [mpnet, fr, open_source, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biencoder_multi_qa_mpnet_base_cos_v1_mmarcofr` is a French model originally trained by antoinelouis. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biencoder_multi_qa_mpnet_base_cos_v1_mmarcofr_fr_5.1.1_3.0_1694130498418.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biencoder_multi_qa_mpnet_base_cos_v1_mmarcofr_fr_5.1.1_3.0_1694130498418.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("biencoder_multi_qa_mpnet_base_cos_v1_mmarcofr","fr") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("biencoder_multi_qa_mpnet_base_cos_v1_mmarcofr", "fr") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biencoder_multi_qa_mpnet_base_cos_v1_mmarcofr| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|fr| +|Size:|407.0 MB| + +## References + +https://huggingface.co/antoinelouis/biencoder-multi-qa-mpnet-base-cos-v1-mmarcoFR \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-biolord_stamb2_v1_en.md b/docs/_posts/ahmedlone127/2023-09-07-biolord_stamb2_v1_en.md new file mode 100644 index 00000000000000..123df34811969b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-biolord_stamb2_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English biolord_stamb2_v1 MPNetEmbeddings from FremyCompany +author: John Snow Labs +name: biolord_stamb2_v1 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biolord_stamb2_v1` is a English model originally trained by FremyCompany. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biolord_stamb2_v1_en_5.1.1_3.0_1694129274443.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biolord_stamb2_v1_en_5.1.1_3.0_1694129274443.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("biolord_stamb2_v1","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("biolord_stamb2_v1", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biolord_stamb2_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/FremyCompany/BioLORD-STAMB2-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-burmese_awesome_setfit_model_98_en.md b/docs/_posts/ahmedlone127/2023-09-07-burmese_awesome_setfit_model_98_en.md new file mode 100644 index 00000000000000..377f64e866450c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-burmese_awesome_setfit_model_98_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English burmese_awesome_setfit_model_98 MPNetEmbeddings from lewtun +author: John Snow Labs +name: burmese_awesome_setfit_model_98 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`burmese_awesome_setfit_model_98` is a English model originally trained by lewtun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/burmese_awesome_setfit_model_98_en_5.1.1_3.0_1694129129625.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/burmese_awesome_setfit_model_98_en_5.1.1_3.0_1694129129625.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("burmese_awesome_setfit_model_98","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("burmese_awesome_setfit_model_98", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|burmese_awesome_setfit_model_98| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/lewtun/my-awesome-setfit-model-98 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-burmese_awesome_setfit_model_en.md b/docs/_posts/ahmedlone127/2023-09-07-burmese_awesome_setfit_model_en.md new file mode 100644 index 00000000000000..42388f7b246af7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-burmese_awesome_setfit_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English burmese_awesome_setfit_model MPNetEmbeddings from lewtun +author: John Snow Labs +name: burmese_awesome_setfit_model +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`burmese_awesome_setfit_model` is a English model originally trained by lewtun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/burmese_awesome_setfit_model_en_5.1.1_3.0_1694127834027.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/burmese_awesome_setfit_model_en_5.1.1_3.0_1694127834027.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("burmese_awesome_setfit_model","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("burmese_awesome_setfit_model", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|burmese_awesome_setfit_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/lewtun/my-awesome-setfit-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-contradiction_psb_en.md b/docs/_posts/ahmedlone127/2023-09-07-contradiction_psb_en.md new file mode 100644 index 00000000000000..d1db94ad324572 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-contradiction_psb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English contradiction_psb MPNetEmbeddings from nategro +author: John Snow Labs +name: contradiction_psb +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`contradiction_psb` is a English model originally trained by nategro. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/contradiction_psb_en_5.1.1_3.0_1694128728540.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/contradiction_psb_en_5.1.1_3.0_1694128728540.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("contradiction_psb","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("contradiction_psb", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|contradiction_psb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/nategro/contradiction-psb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-contradiction_psb_lds_en.md b/docs/_posts/ahmedlone127/2023-09-07-contradiction_psb_lds_en.md new file mode 100644 index 00000000000000..8ef8e3718e712d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-contradiction_psb_lds_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English contradiction_psb_lds MPNetEmbeddings from nategro +author: John Snow Labs +name: contradiction_psb_lds +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`contradiction_psb_lds` is a English model originally trained by nategro. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/contradiction_psb_lds_en_5.1.1_3.0_1694128518758.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/contradiction_psb_lds_en_5.1.1_3.0_1694128518758.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("contradiction_psb_lds","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("contradiction_psb_lds", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|contradiction_psb_lds| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/nategro/contradiction-psb-lds \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-covid_qa_mpnet_en.md b/docs/_posts/ahmedlone127/2023-09-07-covid_qa_mpnet_en.md new file mode 100644 index 00000000000000..2e0f935954e05a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-covid_qa_mpnet_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English covid_qa_mpnet MPNetEmbeddings from shaina +author: John Snow Labs +name: covid_qa_mpnet +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`covid_qa_mpnet` is a English model originally trained by shaina. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/covid_qa_mpnet_en_5.1.1_3.0_1694130190650.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/covid_qa_mpnet_en_5.1.1_3.0_1694130190650.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("covid_qa_mpnet","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("covid_qa_mpnet", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|covid_qa_mpnet| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|399.6 MB| + +## References + +https://huggingface.co/shaina/covid_qa_mpnet \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-cpu_conditional_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-cpu_conditional_classifier_en.md new file mode 100644 index 00000000000000..bbb2c1fa2a7a50 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-cpu_conditional_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cpu_conditional_classifier MPNetEmbeddings from mtyrrell +author: John Snow Labs +name: cpu_conditional_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cpu_conditional_classifier` is a English model originally trained by mtyrrell. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cpu_conditional_classifier_en_5.1.1_3.0_1694130906627.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cpu_conditional_classifier_en_5.1.1_3.0_1694130906627.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("cpu_conditional_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("cpu_conditional_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cpu_conditional_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/mtyrrell/CPU_Conditional_Classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-cpu_economywide_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-cpu_economywide_classifier_en.md new file mode 100644 index 00000000000000..ab6ce4e6371e5a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-cpu_economywide_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cpu_economywide_classifier MPNetEmbeddings from mtyrrell +author: John Snow Labs +name: cpu_economywide_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cpu_economywide_classifier` is a English model originally trained by mtyrrell. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cpu_economywide_classifier_en_5.1.1_3.0_1694126286182.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cpu_economywide_classifier_en_5.1.1_3.0_1694126286182.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("cpu_economywide_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("cpu_economywide_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cpu_economywide_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.3 MB| + +## References + +https://huggingface.co/mtyrrell/CPU_Economywide_Classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-cpu_mitigation_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-cpu_mitigation_classifier_en.md new file mode 100644 index 00000000000000..2b633a79c6b1a3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-cpu_mitigation_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cpu_mitigation_classifier MPNetEmbeddings from mtyrrell +author: John Snow Labs +name: cpu_mitigation_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cpu_mitigation_classifier` is a English model originally trained by mtyrrell. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cpu_mitigation_classifier_en_5.1.1_3.0_1694131013907.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cpu_mitigation_classifier_en_5.1.1_3.0_1694131013907.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("cpu_mitigation_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("cpu_mitigation_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cpu_mitigation_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.3 MB| + +## References + +https://huggingface.co/mtyrrell/CPU_Mitigation_Classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-cpu_netzero_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-cpu_netzero_classifier_en.md new file mode 100644 index 00000000000000..7e781c7f21a043 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-cpu_netzero_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cpu_netzero_classifier MPNetEmbeddings from mtyrrell +author: John Snow Labs +name: cpu_netzero_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cpu_netzero_classifier` is a English model originally trained by mtyrrell. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cpu_netzero_classifier_en_5.1.1_3.0_1694130812400.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cpu_netzero_classifier_en_5.1.1_3.0_1694130812400.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("cpu_netzero_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("cpu_netzero_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cpu_netzero_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/mtyrrell/CPU_Netzero_Classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-cpu_transport_ghg_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-cpu_transport_ghg_classifier_en.md new file mode 100644 index 00000000000000..aa734b59a7da3b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-cpu_transport_ghg_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cpu_transport_ghg_classifier MPNetEmbeddings from mtyrrell +author: John Snow Labs +name: cpu_transport_ghg_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cpu_transport_ghg_classifier` is a English model originally trained by mtyrrell. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cpu_transport_ghg_classifier_en_5.1.1_3.0_1694130718368.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cpu_transport_ghg_classifier_en_5.1.1_3.0_1694130718368.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("cpu_transport_ghg_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("cpu_transport_ghg_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cpu_transport_ghg_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.3 MB| + +## References + +https://huggingface.co/mtyrrell/CPU_Transport_GHG_Classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-cross_all_mpnet_base_v2_finetuned_webnlg2020_metric_average_en.md b/docs/_posts/ahmedlone127/2023-09-07-cross_all_mpnet_base_v2_finetuned_webnlg2020_metric_average_en.md new file mode 100644 index 00000000000000..a401539251dd4e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-cross_all_mpnet_base_v2_finetuned_webnlg2020_metric_average_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cross_all_mpnet_base_v2_finetuned_webnlg2020_metric_average MPNetEmbeddings from teven +author: John Snow Labs +name: cross_all_mpnet_base_v2_finetuned_webnlg2020_metric_average +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cross_all_mpnet_base_v2_finetuned_webnlg2020_metric_average` is a English model originally trained by teven. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cross_all_mpnet_base_v2_finetuned_webnlg2020_metric_average_en_5.1.1_3.0_1694128061507.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cross_all_mpnet_base_v2_finetuned_webnlg2020_metric_average_en_5.1.1_3.0_1694128061507.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("cross_all_mpnet_base_v2_finetuned_webnlg2020_metric_average","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("cross_all_mpnet_base_v2_finetuned_webnlg2020_metric_average", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cross_all_mpnet_base_v2_finetuned_webnlg2020_metric_average| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/teven/cross_all-mpnet-base-v2_finetuned_WebNLG2020_metric_average \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-domainadaptm2_en.md b/docs/_posts/ahmedlone127/2023-09-07-domainadaptm2_en.md new file mode 100644 index 00000000000000..3cc6a1ea64e449 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-domainadaptm2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English domainadaptm2 MPNetEmbeddings from dani0f +author: John Snow Labs +name: domainadaptm2 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`domainadaptm2` is a English model originally trained by dani0f. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/domainadaptm2_en_5.1.1_3.0_1694128846142.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/domainadaptm2_en_5.1.1_3.0_1694128846142.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("domainadaptm2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("domainadaptm2", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|domainadaptm2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/dani0f/DomainAdaptM2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-due_eshop_21_en.md b/docs/_posts/ahmedlone127/2023-09-07-due_eshop_21_en.md new file mode 100644 index 00000000000000..d304bf7e478a68 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-due_eshop_21_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English due_eshop_21 MPNetEmbeddings from konverner +author: John Snow Labs +name: due_eshop_21 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`due_eshop_21` is a English model originally trained by konverner. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/due_eshop_21_en_5.1.1_3.0_1694129070639.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/due_eshop_21_en_5.1.1_3.0_1694129070639.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("due_eshop_21","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("due_eshop_21", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|due_eshop_21| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/konverner/due_eshop_21 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-due_eshop_21_multilabel_en.md b/docs/_posts/ahmedlone127/2023-09-07-due_eshop_21_multilabel_en.md new file mode 100644 index 00000000000000..053dc4674455fe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-due_eshop_21_multilabel_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English due_eshop_21_multilabel MPNetEmbeddings from konverner +author: John Snow Labs +name: due_eshop_21_multilabel +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`due_eshop_21_multilabel` is a English model originally trained by konverner. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/due_eshop_21_multilabel_en_5.1.1_3.0_1694129883699.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/due_eshop_21_multilabel_en_5.1.1_3.0_1694129883699.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("due_eshop_21_multilabel","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("due_eshop_21_multilabel", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|due_eshop_21_multilabel| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/konverner/due_eshop_21_multilabel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-due_retail_25_en.md b/docs/_posts/ahmedlone127/2023-09-07-due_retail_25_en.md new file mode 100644 index 00000000000000..8b96c7c057aea2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-due_retail_25_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English due_retail_25 MPNetEmbeddings from konverner +author: John Snow Labs +name: due_retail_25 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`due_retail_25` is a English model originally trained by konverner. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/due_retail_25_en_5.1.1_3.0_1694129177106.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/due_retail_25_en_5.1.1_3.0_1694129177106.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("due_retail_25","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("due_retail_25", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|due_retail_25| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/konverner/due_retail_25 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-ecolo_pas_ecolo_v0.1_en.md b/docs/_posts/ahmedlone127/2023-09-07-ecolo_pas_ecolo_v0.1_en.md new file mode 100644 index 00000000000000..18274be1cdf58e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-ecolo_pas_ecolo_v0.1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ecolo_pas_ecolo_v0.1 MPNetEmbeddings from eclaircies +author: John Snow Labs +name: ecolo_pas_ecolo_v0.1 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ecolo_pas_ecolo_v0.1` is a English model originally trained by eclaircies. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ecolo_pas_ecolo_v0.1_en_5.1.1_3.0_1694127982409.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ecolo_pas_ecolo_v0.1_en_5.1.1_3.0_1694127982409.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("ecolo_pas_ecolo_v0.1","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("ecolo_pas_ecolo_v0.1", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ecolo_pas_ecolo_v0.1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/eclaircies/ecolo-pas-ecolo-v0.1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-fail_detect_en.md b/docs/_posts/ahmedlone127/2023-09-07-fail_detect_en.md new file mode 100644 index 00000000000000..5aacab8cac61e4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-fail_detect_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English fail_detect MPNetEmbeddings from Ngit +author: John Snow Labs +name: fail_detect +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fail_detect` is a English model originally trained by Ngit. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fail_detect_en_5.1.1_3.0_1694130543509.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fail_detect_en_5.1.1_3.0_1694130543509.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("fail_detect","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("fail_detect", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fail_detect| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Ngit/fail-detect \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-fewshotissueclassifier_nlbse23_en.md b/docs/_posts/ahmedlone127/2023-09-07-fewshotissueclassifier_nlbse23_en.md new file mode 100644 index 00000000000000..0e3ddeef0052d9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-fewshotissueclassifier_nlbse23_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English fewshotissueclassifier_nlbse23 MPNetEmbeddings from PeppoCola +author: John Snow Labs +name: fewshotissueclassifier_nlbse23 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fewshotissueclassifier_nlbse23` is a English model originally trained by PeppoCola. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fewshotissueclassifier_nlbse23_en_5.1.1_3.0_1694123644544.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fewshotissueclassifier_nlbse23_en_5.1.1_3.0_1694123644544.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("fewshotissueclassifier_nlbse23","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("fewshotissueclassifier_nlbse23", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fewshotissueclassifier_nlbse23| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/PeppoCola/FewShotIssueClassifier-NLBSE23 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-github_issues_mpnet_southern_sotho_e10_en.md b/docs/_posts/ahmedlone127/2023-09-07-github_issues_mpnet_southern_sotho_e10_en.md new file mode 100644 index 00000000000000..44ed625f04aa40 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-github_issues_mpnet_southern_sotho_e10_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English github_issues_mpnet_southern_sotho_e10 MPNetEmbeddings from Collab-uniba +author: John Snow Labs +name: github_issues_mpnet_southern_sotho_e10 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`github_issues_mpnet_southern_sotho_e10` is a English model originally trained by Collab-uniba. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/github_issues_mpnet_southern_sotho_e10_en_5.1.1_3.0_1694124363662.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/github_issues_mpnet_southern_sotho_e10_en_5.1.1_3.0_1694124363662.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("github_issues_mpnet_southern_sotho_e10","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("github_issues_mpnet_southern_sotho_e10", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|github_issues_mpnet_southern_sotho_e10| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.4 MB| + +## References + +https://huggingface.co/Collab-uniba/github-issues-mpnet-st-e10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-github_issues_preprocessed_mpnet_southern_sotho_e10_en.md b/docs/_posts/ahmedlone127/2023-09-07-github_issues_preprocessed_mpnet_southern_sotho_e10_en.md new file mode 100644 index 00000000000000..a8dbdfc2bc8a7f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-github_issues_preprocessed_mpnet_southern_sotho_e10_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English github_issues_preprocessed_mpnet_southern_sotho_e10 MPNetEmbeddings from Collab-uniba +author: John Snow Labs +name: github_issues_preprocessed_mpnet_southern_sotho_e10 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`github_issues_preprocessed_mpnet_southern_sotho_e10` is a English model originally trained by Collab-uniba. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/github_issues_preprocessed_mpnet_southern_sotho_e10_en_5.1.1_3.0_1694129779112.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/github_issues_preprocessed_mpnet_southern_sotho_e10_en_5.1.1_3.0_1694129779112.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("github_issues_preprocessed_mpnet_southern_sotho_e10","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("github_issues_preprocessed_mpnet_southern_sotho_e10", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|github_issues_preprocessed_mpnet_southern_sotho_e10| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.0 MB| + +## References + +https://huggingface.co/Collab-uniba/github-issues-preprocessed-mpnet-st-e10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-ikitracs_conditional_en.md b/docs/_posts/ahmedlone127/2023-09-07-ikitracs_conditional_en.md new file mode 100644 index 00000000000000..5cad8e5a032055 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-ikitracs_conditional_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ikitracs_conditional MPNetEmbeddings from ilaria-oneofftech +author: John Snow Labs +name: ikitracs_conditional +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ikitracs_conditional` is a English model originally trained by ilaria-oneofftech. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ikitracs_conditional_en_5.1.1_3.0_1694128507701.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ikitracs_conditional_en_5.1.1_3.0_1694128507701.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("ikitracs_conditional","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("ikitracs_conditional", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ikitracs_conditional| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/ilaria-oneofftech/ikitracs_conditional \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-ikitracs_mitigation_en.md b/docs/_posts/ahmedlone127/2023-09-07-ikitracs_mitigation_en.md new file mode 100644 index 00000000000000..0afa0e64224f18 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-ikitracs_mitigation_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ikitracs_mitigation MPNetEmbeddings from ilaria-oneofftech +author: John Snow Labs +name: ikitracs_mitigation +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ikitracs_mitigation` is a English model originally trained by ilaria-oneofftech. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ikitracs_mitigation_en_5.1.1_3.0_1694131004753.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ikitracs_mitigation_en_5.1.1_3.0_1694131004753.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("ikitracs_mitigation","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("ikitracs_mitigation", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ikitracs_mitigation| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.3 MB| + +## References + +https://huggingface.co/ilaria-oneofftech/ikitracs_mitigation \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-initial_model_en.md b/docs/_posts/ahmedlone127/2023-09-07-initial_model_en.md new file mode 100644 index 00000000000000..5ed4ec5f6a2209 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-initial_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English initial_model MPNetEmbeddings from ishan +author: John Snow Labs +name: initial_model +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`initial_model` is a English model originally trained by ishan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/initial_model_en_5.1.1_3.0_1694130168927.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/initial_model_en_5.1.1_3.0_1694130168927.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("initial_model","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("initial_model", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|initial_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/ishan/initial-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-initial_model_v3_en.md b/docs/_posts/ahmedlone127/2023-09-07-initial_model_v3_en.md new file mode 100644 index 00000000000000..129c0440d2676e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-initial_model_v3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English initial_model_v3 MPNetEmbeddings from ishan +author: John Snow Labs +name: initial_model_v3 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`initial_model_v3` is a English model originally trained by ishan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/initial_model_v3_en_5.1.1_3.0_1694129994652.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/initial_model_v3_en_5.1.1_3.0_1694129994652.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("initial_model_v3","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("initial_model_v3", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|initial_model_v3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/ishan/initial-model-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-invoiceornot_en.md b/docs/_posts/ahmedlone127/2023-09-07-invoiceornot_en.md new file mode 100644 index 00000000000000..c0bc8dd3f777c4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-invoiceornot_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English invoiceornot MPNetEmbeddings from HamzaFarhan +author: John Snow Labs +name: invoiceornot +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`invoiceornot` is a English model originally trained by HamzaFarhan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/invoiceornot_en_5.1.1_3.0_1694130618615.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/invoiceornot_en_5.1.1_3.0_1694130618615.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("invoiceornot","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("invoiceornot", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|invoiceornot| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/HamzaFarhan/InvoiceOrNot \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-java_deprecation_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-java_deprecation_classifier_en.md new file mode 100644 index 00000000000000..880837685563ee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-java_deprecation_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English java_deprecation_classifier MPNetEmbeddings from AISE-TUDelft +author: John Snow Labs +name: java_deprecation_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`java_deprecation_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/java_deprecation_classifier_en_5.1.1_3.0_1694129093936.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/java_deprecation_classifier_en_5.1.1_3.0_1694129093936.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("java_deprecation_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("java_deprecation_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|java_deprecation_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/AISE-TUDelft/java-deprecation-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-java_expand_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-java_expand_classifier_en.md new file mode 100644 index 00000000000000..337820017573dc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-java_expand_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English java_expand_classifier MPNetEmbeddings from AISE-TUDelft +author: John Snow Labs +name: java_expand_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`java_expand_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/java_expand_classifier_en_5.1.1_3.0_1694125429009.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/java_expand_classifier_en_5.1.1_3.0_1694125429009.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("java_expand_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("java_expand_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|java_expand_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/AISE-TUDelft/java-expand-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-java_ownership_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-java_ownership_classifier_en.md new file mode 100644 index 00000000000000..7c796181e1faf9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-java_ownership_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English java_ownership_classifier MPNetEmbeddings from AISE-TUDelft +author: John Snow Labs +name: java_ownership_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`java_ownership_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/java_ownership_classifier_en_5.1.1_3.0_1694130489843.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/java_ownership_classifier_en_5.1.1_3.0_1694130489843.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("java_ownership_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("java_ownership_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|java_ownership_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/AISE-TUDelft/java-ownership-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-java_pointer_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-java_pointer_classifier_en.md new file mode 100644 index 00000000000000..482aebd6760291 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-java_pointer_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English java_pointer_classifier MPNetEmbeddings from AISE-TUDelft +author: John Snow Labs +name: java_pointer_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`java_pointer_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/java_pointer_classifier_en_5.1.1_3.0_1694129302252.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/java_pointer_classifier_en_5.1.1_3.0_1694129302252.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("java_pointer_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("java_pointer_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|java_pointer_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/AISE-TUDelft/java-pointer-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-java_rational_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-java_rational_classifier_en.md new file mode 100644 index 00000000000000..075adffa3773b2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-java_rational_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English java_rational_classifier MPNetEmbeddings from AISE-TUDelft +author: John Snow Labs +name: java_rational_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`java_rational_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/java_rational_classifier_en_5.1.1_3.0_1694129985001.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/java_rational_classifier_en_5.1.1_3.0_1694129985001.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("java_rational_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("java_rational_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|java_rational_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/AISE-TUDelft/java-rational-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-java_summary_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-java_summary_classifier_en.md new file mode 100644 index 00000000000000..df5e665a2400e1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-java_summary_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English java_summary_classifier MPNetEmbeddings from AISE-TUDelft +author: John Snow Labs +name: java_summary_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`java_summary_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/java_summary_classifier_en_5.1.1_3.0_1694129191538.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/java_summary_classifier_en_5.1.1_3.0_1694129191538.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("java_summary_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("java_summary_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|java_summary_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/AISE-TUDelft/java-summary-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-java_usage_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-java_usage_classifier_en.md new file mode 100644 index 00000000000000..fdbc91bb5ca310 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-java_usage_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English java_usage_classifier MPNetEmbeddings from AISE-TUDelft +author: John Snow Labs +name: java_usage_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`java_usage_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/java_usage_classifier_en_5.1.1_3.0_1694129416711.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/java_usage_classifier_en_5.1.1_3.0_1694129416711.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("java_usage_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("java_usage_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|java_usage_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/AISE-TUDelft/java-usage-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-keyphrase_mpnet_v1_en.md b/docs/_posts/ahmedlone127/2023-09-07-keyphrase_mpnet_v1_en.md new file mode 100644 index 00000000000000..65fa2878f9c90a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-keyphrase_mpnet_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English keyphrase_mpnet_v1 MPNetEmbeddings from uclanlp +author: John Snow Labs +name: keyphrase_mpnet_v1 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`keyphrase_mpnet_v1` is a English model originally trained by uclanlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/keyphrase_mpnet_v1_en_5.1.1_3.0_1694129569189.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/keyphrase_mpnet_v1_en_5.1.1_3.0_1694129569189.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("keyphrase_mpnet_v1","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("keyphrase_mpnet_v1", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|keyphrase_mpnet_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/uclanlp/keyphrase-mpnet-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-kw_classification_setfit_model_en.md b/docs/_posts/ahmedlone127/2023-09-07-kw_classification_setfit_model_en.md new file mode 100644 index 00000000000000..b812624251d9b2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-kw_classification_setfit_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kw_classification_setfit_model MPNetEmbeddings from gyuri2020 +author: John Snow Labs +name: kw_classification_setfit_model +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kw_classification_setfit_model` is a English model originally trained by gyuri2020. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kw_classification_setfit_model_en_5.1.1_3.0_1694126154625.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kw_classification_setfit_model_en_5.1.1_3.0_1694126154625.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("kw_classification_setfit_model","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("kw_classification_setfit_model", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kw_classification_setfit_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/gyuri2020/kw-classification-setfit-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-kw_classification_setfithead_model_en.md b/docs/_posts/ahmedlone127/2023-09-07-kw_classification_setfithead_model_en.md new file mode 100644 index 00000000000000..1543ab64238696 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-kw_classification_setfithead_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kw_classification_setfithead_model MPNetEmbeddings from gyuri2020 +author: John Snow Labs +name: kw_classification_setfithead_model +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kw_classification_setfithead_model` is a English model originally trained by gyuri2020. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kw_classification_setfithead_model_en_5.1.1_3.0_1694126415270.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kw_classification_setfithead_model_en_5.1.1_3.0_1694126415270.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("kw_classification_setfithead_model","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("kw_classification_setfithead_model", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kw_classification_setfithead_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/gyuri2020/kw-classification-setfithead-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-labels_per_job_title_fine_tune_en.md b/docs/_posts/ahmedlone127/2023-09-07-labels_per_job_title_fine_tune_en.md new file mode 100644 index 00000000000000..483cb814171b35 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-labels_per_job_title_fine_tune_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English labels_per_job_title_fine_tune MPNetEmbeddings from marianodo +author: John Snow Labs +name: labels_per_job_title_fine_tune +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`labels_per_job_title_fine_tune` is a English model originally trained by marianodo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/labels_per_job_title_fine_tune_en_5.1.1_3.0_1694129285422.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/labels_per_job_title_fine_tune_en_5.1.1_3.0_1694129285422.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("labels_per_job_title_fine_tune","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("labels_per_job_title_fine_tune", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|labels_per_job_title_fine_tune| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/marianodo/labels-per-job-title-fine-tune \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-mpnet_adaptation_mitigation_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-mpnet_adaptation_mitigation_classifier_en.md new file mode 100644 index 00000000000000..adb7e3c51ab6be --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-mpnet_adaptation_mitigation_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mpnet_adaptation_mitigation_classifier MPNetEmbeddings from ppsingh +author: John Snow Labs +name: mpnet_adaptation_mitigation_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_adaptation_mitigation_classifier` is a English model originally trained by ppsingh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_adaptation_mitigation_classifier_en_5.1.1_3.0_1694124441818.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_adaptation_mitigation_classifier_en_5.1.1_3.0_1694124441818.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("mpnet_adaptation_mitigation_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("mpnet_adaptation_mitigation_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_adaptation_mitigation_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.3 MB| + +## References + +https://huggingface.co/ppsingh/mpnet-adaptation_mitigation-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-mpnet_base_articles_ner_en.md b/docs/_posts/ahmedlone127/2023-09-07-mpnet_base_articles_ner_en.md new file mode 100644 index 00000000000000..d0cb6906753584 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-mpnet_base_articles_ner_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mpnet_base_articles_ner MPNetEmbeddings from evangeliazve +author: John Snow Labs +name: mpnet_base_articles_ner +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_base_articles_ner` is a English model originally trained by evangeliazve. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_base_articles_ner_en_5.1.1_3.0_1694130444573.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_base_articles_ner_en_5.1.1_3.0_1694130444573.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("mpnet_base_articles_ner","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("mpnet_base_articles_ner", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_base_articles_ner| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|382.8 MB| + +## References + +https://huggingface.co/evangeliazve/mpnet-base-articles-ner \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-mpnet_base_snli_mnli_en.md b/docs/_posts/ahmedlone127/2023-09-07-mpnet_base_snli_mnli_en.md new file mode 100644 index 00000000000000..f19501355faf0f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-mpnet_base_snli_mnli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mpnet_base_snli_mnli MPNetEmbeddings from symanto +author: John Snow Labs +name: mpnet_base_snli_mnli +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_base_snli_mnli` is a English model originally trained by symanto. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_base_snli_mnli_en_5.1.1_3.0_1694130311075.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_base_snli_mnli_en_5.1.1_3.0_1694130311075.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("mpnet_base_snli_mnli","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("mpnet_base_snli_mnli", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_base_snli_mnli| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|404.3 MB| + +## References + +https://huggingface.co/symanto/mpnet-base-snli-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-mpnet_mnr_v2_fine_tuned_en.md b/docs/_posts/ahmedlone127/2023-09-07-mpnet_mnr_v2_fine_tuned_en.md new file mode 100644 index 00000000000000..ef6b64c7633596 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-mpnet_mnr_v2_fine_tuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mpnet_mnr_v2_fine_tuned MPNetEmbeddings from BlazingFringe +author: John Snow Labs +name: mpnet_mnr_v2_fine_tuned +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_mnr_v2_fine_tuned` is a English model originally trained by BlazingFringe. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_mnr_v2_fine_tuned_en_5.1.1_3.0_1694130647094.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_mnr_v2_fine_tuned_en_5.1.1_3.0_1694130647094.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("mpnet_mnr_v2_fine_tuned","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("mpnet_mnr_v2_fine_tuned", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_mnr_v2_fine_tuned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/BlazingFringe/mpnet-mnr-v2-fine-tuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-mpnet_multilabel_sector_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-mpnet_multilabel_sector_classifier_en.md new file mode 100644 index 00000000000000..24a4a489f1fb15 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-mpnet_multilabel_sector_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mpnet_multilabel_sector_classifier MPNetEmbeddings from ppsingh +author: John Snow Labs +name: mpnet_multilabel_sector_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_multilabel_sector_classifier` is a English model originally trained by ppsingh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_multilabel_sector_classifier_en_5.1.1_3.0_1694131112892.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_multilabel_sector_classifier_en_5.1.1_3.0_1694131112892.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("mpnet_multilabel_sector_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("mpnet_multilabel_sector_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_multilabel_sector_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.3 MB| + +## References + +https://huggingface.co/ppsingh/mpnet-multilabel-sector-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-mpnet_nli_sts_en.md b/docs/_posts/ahmedlone127/2023-09-07-mpnet_nli_sts_en.md new file mode 100644 index 00000000000000..9b20dfa2f6b007 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-mpnet_nli_sts_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mpnet_nli_sts MPNetEmbeddings from jamescalam +author: John Snow Labs +name: mpnet_nli_sts +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_nli_sts` is a English model originally trained by jamescalam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_nli_sts_en_5.1.1_3.0_1694123885821.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_nli_sts_en_5.1.1_3.0_1694123885821.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("mpnet_nli_sts","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("mpnet_nli_sts", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_nli_sts| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|399.1 MB| + +## References + +https://huggingface.co/jamescalam/mpnet-nli-sts \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-mpnet_retriever_squad2_en.md b/docs/_posts/ahmedlone127/2023-09-07-mpnet_retriever_squad2_en.md new file mode 100644 index 00000000000000..1387032712dc14 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-mpnet_retriever_squad2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mpnet_retriever_squad2 MPNetEmbeddings from pinecone +author: John Snow Labs +name: mpnet_retriever_squad2 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_retriever_squad2` is a English model originally trained by pinecone. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_retriever_squad2_en_5.1.1_3.0_1694129132128.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_retriever_squad2_en_5.1.1_3.0_1694129132128.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("mpnet_retriever_squad2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("mpnet_retriever_squad2", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_retriever_squad2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/pinecone/mpnet-retriever-squad2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-mpnet_snli_en.md b/docs/_posts/ahmedlone127/2023-09-07-mpnet_snli_en.md new file mode 100644 index 00000000000000..4eb8e51979a46a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-mpnet_snli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mpnet_snli Mpnet from jamescalam +author: John Snow Labs +name: mpnet_snli +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_snli` is a English model originally trained by jamescalam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_snli_en_5.1.1_3.0_1694122601394.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_snli_en_5.1.1_3.0_1694122601394.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("mpnet_snli","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("mpnet_snli", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_snli| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|396.4 MB| + +## References + +https://huggingface.co/jamescalam/mpnet-snli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-mpnet_snli_negatives_en.md b/docs/_posts/ahmedlone127/2023-09-07-mpnet_snli_negatives_en.md new file mode 100644 index 00000000000000..19cf04d8846547 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-mpnet_snli_negatives_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mpnet_snli_negatives MPNetEmbeddings from jamescalam +author: John Snow Labs +name: mpnet_snli_negatives +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_snli_negatives` is a English model originally trained by jamescalam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpnet_snli_negatives_en_5.1.1_3.0_1694123768227.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_snli_negatives_en_5.1.1_3.0_1694123768227.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("mpnet_snli_negatives","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("mpnet_snli_negatives", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpnet_snli_negatives| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|398.1 MB| + +## References + +https://huggingface.co/jamescalam/mpnet-snli-negatives \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-multi_qa_mpnet_base_cos_v1_navteca_en.md b/docs/_posts/ahmedlone127/2023-09-07-multi_qa_mpnet_base_cos_v1_navteca_en.md new file mode 100644 index 00000000000000..1da37123a54d78 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-multi_qa_mpnet_base_cos_v1_navteca_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English multi_qa_mpnet_base_cos_v1_navteca MPNetEmbeddings from navteca +author: John Snow Labs +name: multi_qa_mpnet_base_cos_v1_navteca +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multi_qa_mpnet_base_cos_v1_navteca` is a English model originally trained by navteca. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/multi_qa_mpnet_base_cos_v1_navteca_en_5.1.1_3.0_1694124714786.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multi_qa_mpnet_base_cos_v1_navteca_en_5.1.1_3.0_1694124714786.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("multi_qa_mpnet_base_cos_v1_navteca","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("multi_qa_mpnet_base_cos_v1_navteca", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|multi_qa_mpnet_base_cos_v1_navteca| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/navteca/multi-qa-mpnet-base-cos-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-multi_qa_mpnet_base_cos_v1_sentence_transformers_en.md b/docs/_posts/ahmedlone127/2023-09-07-multi_qa_mpnet_base_cos_v1_sentence_transformers_en.md new file mode 100644 index 00000000000000..c3387731a2ea8d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-multi_qa_mpnet_base_cos_v1_sentence_transformers_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English multi_qa_mpnet_base_cos_v1_sentence_transformers MPNetEmbeddings from sentence-transformers +author: John Snow Labs +name: multi_qa_mpnet_base_cos_v1_sentence_transformers +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multi_qa_mpnet_base_cos_v1_sentence_transformers` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/multi_qa_mpnet_base_cos_v1_sentence_transformers_en_5.1.1_3.0_1694129454272.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multi_qa_mpnet_base_cos_v1_sentence_transformers_en_5.1.1_3.0_1694129454272.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("multi_qa_mpnet_base_cos_v1_sentence_transformers","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("multi_qa_mpnet_base_cos_v1_sentence_transformers", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|multi_qa_mpnet_base_cos_v1_sentence_transformers| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-cos-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-multi_qa_mpnet_base_dot_v1_eclass_en.md b/docs/_posts/ahmedlone127/2023-09-07-multi_qa_mpnet_base_dot_v1_eclass_en.md new file mode 100644 index 00000000000000..6dbd652a4e48fb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-multi_qa_mpnet_base_dot_v1_eclass_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English multi_qa_mpnet_base_dot_v1_eclass MPNetEmbeddings from JoBeer +author: John Snow Labs +name: multi_qa_mpnet_base_dot_v1_eclass +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multi_qa_mpnet_base_dot_v1_eclass` is a English model originally trained by JoBeer. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/multi_qa_mpnet_base_dot_v1_eclass_en_5.1.1_3.0_1694127821437.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multi_qa_mpnet_base_dot_v1_eclass_en_5.1.1_3.0_1694127821437.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("multi_qa_mpnet_base_dot_v1_eclass","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("multi_qa_mpnet_base_dot_v1_eclass", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|multi_qa_mpnet_base_dot_v1_eclass| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/JoBeer/multi-qa-mpnet-base-dot-v1-eclass \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-multi_qa_mpnet_base_dot_v1_legal_finetune_en.md b/docs/_posts/ahmedlone127/2023-09-07-multi_qa_mpnet_base_dot_v1_legal_finetune_en.md new file mode 100644 index 00000000000000..f24c83762935d7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-multi_qa_mpnet_base_dot_v1_legal_finetune_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English multi_qa_mpnet_base_dot_v1_legal_finetune MPNetEmbeddings from oliviamga2 +author: John Snow Labs +name: multi_qa_mpnet_base_dot_v1_legal_finetune +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multi_qa_mpnet_base_dot_v1_legal_finetune` is a English model originally trained by oliviamga2. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/multi_qa_mpnet_base_dot_v1_legal_finetune_en_5.1.1_3.0_1694128585719.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multi_qa_mpnet_base_dot_v1_legal_finetune_en_5.1.1_3.0_1694128585719.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("multi_qa_mpnet_base_dot_v1_legal_finetune","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("multi_qa_mpnet_base_dot_v1_legal_finetune", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|multi_qa_mpnet_base_dot_v1_legal_finetune| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/oliviamga2/multi-qa-mpnet-base-dot-v1_legal_finetune \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-multi_qa_mpnet_base_dot_v1_sentence_transformers_en.md b/docs/_posts/ahmedlone127/2023-09-07-multi_qa_mpnet_base_dot_v1_sentence_transformers_en.md new file mode 100644 index 00000000000000..fd099032ab3896 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-multi_qa_mpnet_base_dot_v1_sentence_transformers_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English multi_qa_mpnet_base_dot_v1_sentence_transformers MPNetEmbeddings from sentence-transformers +author: John Snow Labs +name: multi_qa_mpnet_base_dot_v1_sentence_transformers +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multi_qa_mpnet_base_dot_v1_sentence_transformers` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/multi_qa_mpnet_base_dot_v1_sentence_transformers_en_5.1.1_3.0_1694129579330.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multi_qa_mpnet_base_dot_v1_sentence_transformers_en_5.1.1_3.0_1694129579330.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("multi_qa_mpnet_base_dot_v1_sentence_transformers","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("multi_qa_mpnet_base_dot_v1_sentence_transformers", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|multi_qa_mpnet_base_dot_v1_sentence_transformers| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-multi_qa_v1_mpnet_asymmetric_a_en.md b/docs/_posts/ahmedlone127/2023-09-07-multi_qa_v1_mpnet_asymmetric_a_en.md new file mode 100644 index 00000000000000..1b700e5c8cca65 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-multi_qa_v1_mpnet_asymmetric_a_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English multi_qa_v1_mpnet_asymmetric_a MPNetEmbeddings from flax-sentence-embeddings +author: John Snow Labs +name: multi_qa_v1_mpnet_asymmetric_a +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multi_qa_v1_mpnet_asymmetric_a` is a English model originally trained by flax-sentence-embeddings. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/multi_qa_v1_mpnet_asymmetric_a_en_5.1.1_3.0_1694123866355.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multi_qa_v1_mpnet_asymmetric_a_en_5.1.1_3.0_1694123866355.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("multi_qa_v1_mpnet_asymmetric_a","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("multi_qa_v1_mpnet_asymmetric_a", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|multi_qa_v1_mpnet_asymmetric_a| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.7 MB| + +## References + +https://huggingface.co/flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-A \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-multi_qa_v1_mpnet_asymmetric_q_en.md b/docs/_posts/ahmedlone127/2023-09-07-multi_qa_v1_mpnet_asymmetric_q_en.md new file mode 100644 index 00000000000000..c688cc63c1b0b1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-multi_qa_v1_mpnet_asymmetric_q_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English multi_qa_v1_mpnet_asymmetric_q MPNetEmbeddings from flax-sentence-embeddings +author: John Snow Labs +name: multi_qa_v1_mpnet_asymmetric_q +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multi_qa_v1_mpnet_asymmetric_q` is a English model originally trained by flax-sentence-embeddings. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/multi_qa_v1_mpnet_asymmetric_q_en_5.1.1_3.0_1694128463061.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multi_qa_v1_mpnet_asymmetric_q_en_5.1.1_3.0_1694128463061.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("multi_qa_v1_mpnet_asymmetric_q","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("multi_qa_v1_mpnet_asymmetric_q", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|multi_qa_v1_mpnet_asymmetric_q| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.4 MB| + +## References + +https://huggingface.co/flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-Q \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-multi_qa_v1_mpnet_cls_dot_en.md b/docs/_posts/ahmedlone127/2023-09-07-multi_qa_v1_mpnet_cls_dot_en.md new file mode 100644 index 00000000000000..649ad5625b79c2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-multi_qa_v1_mpnet_cls_dot_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English multi_qa_v1_mpnet_cls_dot MPNetEmbeddings from flax-sentence-embeddings +author: John Snow Labs +name: multi_qa_v1_mpnet_cls_dot +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multi_qa_v1_mpnet_cls_dot` is a English model originally trained by flax-sentence-embeddings. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/multi_qa_v1_mpnet_cls_dot_en_5.1.1_3.0_1694128566724.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multi_qa_v1_mpnet_cls_dot_en_5.1.1_3.0_1694128566724.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("multi_qa_v1_mpnet_cls_dot","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("multi_qa_v1_mpnet_cls_dot", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|multi_qa_v1_mpnet_cls_dot| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/flax-sentence-embeddings/multi-qa_v1-mpnet-cls_dot \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-negation_categories_classifier_es.md b/docs/_posts/ahmedlone127/2023-09-07-negation_categories_classifier_es.md new file mode 100644 index 00000000000000..e0a5fc50515b85 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-negation_categories_classifier_es.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Castilian, Spanish negation_categories_classifier MPNetEmbeddings from mhammadkhan +author: John Snow Labs +name: negation_categories_classifier +date: 2023-09-07 +tags: [mpnet, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`negation_categories_classifier` is a Castilian, Spanish model originally trained by mhammadkhan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/negation_categories_classifier_es_5.1.1_3.0_1694130051743.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/negation_categories_classifier_es_5.1.1_3.0_1694130051743.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("negation_categories_classifier","es") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("negation_categories_classifier", "es") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|negation_categories_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|es| +|Size:|406.9 MB| + +## References + +https://huggingface.co/mhammadkhan/negation-categories-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-nli_mpnet_base_v2_sentence_transformers_en.md b/docs/_posts/ahmedlone127/2023-09-07-nli_mpnet_base_v2_sentence_transformers_en.md new file mode 100644 index 00000000000000..1ba2f4dcb4932e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-nli_mpnet_base_v2_sentence_transformers_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English nli_mpnet_base_v2_sentence_transformers MPNetEmbeddings from sentence-transformers +author: John Snow Labs +name: nli_mpnet_base_v2_sentence_transformers +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nli_mpnet_base_v2_sentence_transformers` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nli_mpnet_base_v2_sentence_transformers_en_5.1.1_3.0_1694129705610.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nli_mpnet_base_v2_sentence_transformers_en_5.1.1_3.0_1694129705610.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("nli_mpnet_base_v2_sentence_transformers","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("nli_mpnet_base_v2_sentence_transformers", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nli_mpnet_base_v2_sentence_transformers| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|405.5 MB| + +## References + +https://huggingface.co/sentence-transformers/nli-mpnet-base-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-nooks_amd_detection_realtime_en.md b/docs/_posts/ahmedlone127/2023-09-07-nooks_amd_detection_realtime_en.md new file mode 100644 index 00000000000000..318e8315b4dcb2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-nooks_amd_detection_realtime_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English nooks_amd_detection_realtime MPNetEmbeddings from nikcheerla +author: John Snow Labs +name: nooks_amd_detection_realtime +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nooks_amd_detection_realtime` is a English model originally trained by nikcheerla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nooks_amd_detection_realtime_en_5.1.1_3.0_1694123725819.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nooks_amd_detection_realtime_en_5.1.1_3.0_1694123725819.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("nooks_amd_detection_realtime","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("nooks_amd_detection_realtime", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nooks_amd_detection_realtime| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/nikcheerla/nooks-amd-detection-realtime \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-nooks_amd_detection_v2_full_en.md b/docs/_posts/ahmedlone127/2023-09-07-nooks_amd_detection_v2_full_en.md new file mode 100644 index 00000000000000..4a7b7157a599d2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-nooks_amd_detection_v2_full_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English nooks_amd_detection_v2_full MPNetEmbeddings from nikcheerla +author: John Snow Labs +name: nooks_amd_detection_v2_full +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nooks_amd_detection_v2_full` is a English model originally trained by nikcheerla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nooks_amd_detection_v2_full_en_5.1.1_3.0_1694128051636.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nooks_amd_detection_v2_full_en_5.1.1_3.0_1694128051636.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("nooks_amd_detection_v2_full","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("nooks_amd_detection_v2_full", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nooks_amd_detection_v2_full| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/nikcheerla/nooks-amd-detection-v2-full \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-nps_psb_lds_en.md b/docs/_posts/ahmedlone127/2023-09-07-nps_psb_lds_en.md new file mode 100644 index 00000000000000..2e2e6e2593f597 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-nps_psb_lds_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English nps_psb_lds MPNetEmbeddings from nategro +author: John Snow Labs +name: nps_psb_lds +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nps_psb_lds` is a English model originally trained by nategro. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nps_psb_lds_en_5.1.1_3.0_1694124378110.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nps_psb_lds_en_5.1.1_3.0_1694124378110.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("nps_psb_lds","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("nps_psb_lds", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nps_psb_lds| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/nategro/nps-psb-lds \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-ouvrage_classif_en.md b/docs/_posts/ahmedlone127/2023-09-07-ouvrage_classif_en.md new file mode 100644 index 00000000000000..11b92018130918 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-ouvrage_classif_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ouvrage_classif Mpnet from TomPWM +author: John Snow Labs +name: ouvrage_classif +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ouvrage_classif` is a English model originally trained by TomPWM. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ouvrage_classif_en_5.1.1_3.0_1694122940260.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ouvrage_classif_en_5.1.1_3.0_1694122940260.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("ouvrage_classif","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("ouvrage_classif", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ouvrage_classif| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/TomPWM/ouvrage-classif \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-paraphrase_mpnet_base_v2_fuzzy_matcher_en.md b/docs/_posts/ahmedlone127/2023-09-07-paraphrase_mpnet_base_v2_fuzzy_matcher_en.md new file mode 100644 index 00000000000000..af6220ebe31d6b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-paraphrase_mpnet_base_v2_fuzzy_matcher_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English paraphrase_mpnet_base_v2_fuzzy_matcher MPNetEmbeddings from shahrukhx01 +author: John Snow Labs +name: paraphrase_mpnet_base_v2_fuzzy_matcher +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`paraphrase_mpnet_base_v2_fuzzy_matcher` is a English model originally trained by shahrukhx01. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/paraphrase_mpnet_base_v2_fuzzy_matcher_en_5.1.1_3.0_1694130070040.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/paraphrase_mpnet_base_v2_fuzzy_matcher_en_5.1.1_3.0_1694130070040.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("paraphrase_mpnet_base_v2_fuzzy_matcher","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("paraphrase_mpnet_base_v2_fuzzy_matcher", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|paraphrase_mpnet_base_v2_fuzzy_matcher| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/shahrukhx01/paraphrase-mpnet-base-v2-fuzzy-matcher \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-paraphrase_mpnet_base_v2_sentence_transformers_en.md b/docs/_posts/ahmedlone127/2023-09-07-paraphrase_mpnet_base_v2_sentence_transformers_en.md new file mode 100644 index 00000000000000..6e2070d571fd59 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-paraphrase_mpnet_base_v2_sentence_transformers_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English paraphrase_mpnet_base_v2_sentence_transformers MPNetEmbeddings from sentence-transformers +author: John Snow Labs +name: paraphrase_mpnet_base_v2_sentence_transformers +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`paraphrase_mpnet_base_v2_sentence_transformers` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/paraphrase_mpnet_base_v2_sentence_transformers_en_5.1.1_3.0_1694129822133.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/paraphrase_mpnet_base_v2_sentence_transformers_en_5.1.1_3.0_1694129822133.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("paraphrase_mpnet_base_v2_sentence_transformers","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("paraphrase_mpnet_base_v2_sentence_transformers", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|paraphrase_mpnet_base_v2_sentence_transformers| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-paraphrase_mpnet_base_v2_setfit_sst2_en.md b/docs/_posts/ahmedlone127/2023-09-07-paraphrase_mpnet_base_v2_setfit_sst2_en.md new file mode 100644 index 00000000000000..176cb9767a0be6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-paraphrase_mpnet_base_v2_setfit_sst2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English paraphrase_mpnet_base_v2_setfit_sst2 MPNetEmbeddings from moshew +author: John Snow Labs +name: paraphrase_mpnet_base_v2_setfit_sst2 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`paraphrase_mpnet_base_v2_setfit_sst2` is a English model originally trained by moshew. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/paraphrase_mpnet_base_v2_setfit_sst2_en_5.1.1_3.0_1694126561425.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/paraphrase_mpnet_base_v2_setfit_sst2_en_5.1.1_3.0_1694126561425.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("paraphrase_mpnet_base_v2_setfit_sst2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("paraphrase_mpnet_base_v2_setfit_sst2", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|paraphrase_mpnet_base_v2_setfit_sst2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/moshew/paraphrase-mpnet-base-v2_SetFit_sst2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-patentsberta_en.md b/docs/_posts/ahmedlone127/2023-09-07-patentsberta_en.md new file mode 100644 index 00000000000000..455d28a99079b8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-patentsberta_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English patentsberta MPNetEmbeddings from AI-Growth-Lab +author: John Snow Labs +name: patentsberta +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`patentsberta` is a English model originally trained by AI-Growth-Lab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/patentsberta_en_5.1.1_3.0_1694127839013.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/patentsberta_en_5.1.1_3.0_1694127839013.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("patentsberta","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("patentsberta", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|patentsberta| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/AI-Growth-Lab/PatentSBERTa \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-pdfsegs_en.md b/docs/_posts/ahmedlone127/2023-09-07-pdfsegs_en.md new file mode 100644 index 00000000000000..4ebc29044ebea6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-pdfsegs_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pdfsegs MPNetEmbeddings from HamzaFarhan +author: John Snow Labs +name: pdfsegs +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pdfsegs` is a English model originally trained by HamzaFarhan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pdfsegs_en_5.1.1_3.0_1694126162854.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pdfsegs_en_5.1.1_3.0_1694126162854.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("pdfsegs","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("pdfsegs", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pdfsegs| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/HamzaFarhan/PDFSegs \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-pharo_collaborators_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-pharo_collaborators_classifier_en.md new file mode 100644 index 00000000000000..b27c7a5cab5eef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-pharo_collaborators_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pharo_collaborators_classifier MPNetEmbeddings from AISE-TUDelft +author: John Snow Labs +name: pharo_collaborators_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pharo_collaborators_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pharo_collaborators_classifier_en_5.1.1_3.0_1694126370511.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pharo_collaborators_classifier_en_5.1.1_3.0_1694126370511.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("pharo_collaborators_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("pharo_collaborators_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pharo_collaborators_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/AISE-TUDelft/pharo-collaborators-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-pharo_example_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-pharo_example_classifier_en.md new file mode 100644 index 00000000000000..3b27b5a4a948a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-pharo_example_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pharo_example_classifier MPNetEmbeddings from AISE-TUDelft +author: John Snow Labs +name: pharo_example_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pharo_example_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pharo_example_classifier_en_5.1.1_3.0_1694130749424.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pharo_example_classifier_en_5.1.1_3.0_1694130749424.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("pharo_example_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("pharo_example_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pharo_example_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/AISE-TUDelft/pharo-example-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-pharo_keyimplementationpoints_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-pharo_keyimplementationpoints_classifier_en.md new file mode 100644 index 00000000000000..6538b55c49fc74 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-pharo_keyimplementationpoints_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pharo_keyimplementationpoints_classifier MPNetEmbeddings from AISE-TUDelft +author: John Snow Labs +name: pharo_keyimplementationpoints_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pharo_keyimplementationpoints_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pharo_keyimplementationpoints_classifier_en_5.1.1_3.0_1694129873019.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pharo_keyimplementationpoints_classifier_en_5.1.1_3.0_1694129873019.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("pharo_keyimplementationpoints_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("pharo_keyimplementationpoints_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pharo_keyimplementationpoints_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/AISE-TUDelft/pharo-keyimplementationpoints-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-pharo_responsibilities_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-pharo_responsibilities_classifier_en.md new file mode 100644 index 00000000000000..6772d835c6e16c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-pharo_responsibilities_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pharo_responsibilities_classifier MPNetEmbeddings from AISE-TUDelft +author: John Snow Labs +name: pharo_responsibilities_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pharo_responsibilities_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pharo_responsibilities_classifier_en_5.1.1_3.0_1694130852738.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pharo_responsibilities_classifier_en_5.1.1_3.0_1694130852738.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("pharo_responsibilities_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("pharo_responsibilities_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pharo_responsibilities_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/AISE-TUDelft/pharo-responsibilities-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-python_developmentnotes_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-python_developmentnotes_classifier_en.md new file mode 100644 index 00000000000000..7a363cb5ca7426 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-python_developmentnotes_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English python_developmentnotes_classifier MPNetEmbeddings from AISE-TUDelft +author: John Snow Labs +name: python_developmentnotes_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`python_developmentnotes_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/python_developmentnotes_classifier_en_5.1.1_3.0_1694129762240.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/python_developmentnotes_classifier_en_5.1.1_3.0_1694129762240.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("python_developmentnotes_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("python_developmentnotes_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|python_developmentnotes_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/AISE-TUDelft/python-developmentnotes-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-python_expand_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-python_expand_classifier_en.md new file mode 100644 index 00000000000000..8de227a5ff14f9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-python_expand_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English python_expand_classifier MPNetEmbeddings from AISE-TUDelft +author: John Snow Labs +name: python_expand_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`python_expand_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/python_expand_classifier_en_5.1.1_3.0_1694126229745.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/python_expand_classifier_en_5.1.1_3.0_1694126229745.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("python_expand_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("python_expand_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|python_expand_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/AISE-TUDelft/python-expand-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-python_parameters_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-python_parameters_classifier_en.md new file mode 100644 index 00000000000000..d18faf1ec21f89 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-python_parameters_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English python_parameters_classifier MPNetEmbeddings from AISE-TUDelft +author: John Snow Labs +name: python_parameters_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`python_parameters_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/python_parameters_classifier_en_5.1.1_3.0_1694130111574.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/python_parameters_classifier_en_5.1.1_3.0_1694130111574.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("python_parameters_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("python_parameters_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|python_parameters_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/AISE-TUDelft/python-parameters-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-python_summary_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-python_summary_classifier_en.md new file mode 100644 index 00000000000000..af155ba8188ff5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-python_summary_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English python_summary_classifier MPNetEmbeddings from AISE-TUDelft +author: John Snow Labs +name: python_summary_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`python_summary_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/python_summary_classifier_en_5.1.1_3.0_1694125555047.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/python_summary_classifier_en_5.1.1_3.0_1694125555047.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("python_summary_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("python_summary_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|python_summary_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/AISE-TUDelft/python-summary-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-python_usage_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-python_usage_classifier_en.md new file mode 100644 index 00000000000000..a018e34a5e3a4e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-python_usage_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English python_usage_classifier MPNetEmbeddings from AISE-TUDelft +author: John Snow Labs +name: python_usage_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`python_usage_classifier` is a English model originally trained by AISE-TUDelft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/python_usage_classifier_en_5.1.1_3.0_1694130638732.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/python_usage_classifier_en_5.1.1_3.0_1694130638732.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("python_usage_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("python_usage_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|python_usage_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/AISE-TUDelft/python-usage-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-reddit_single_context_mpnet_base_en.md b/docs/_posts/ahmedlone127/2023-09-07-reddit_single_context_mpnet_base_en.md new file mode 100644 index 00000000000000..7e1bae83d6a63e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-reddit_single_context_mpnet_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English reddit_single_context_mpnet_base MPNetEmbeddings from flax-sentence-embeddings +author: John Snow Labs +name: reddit_single_context_mpnet_base +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`reddit_single_context_mpnet_base` is a English model originally trained by flax-sentence-embeddings. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/reddit_single_context_mpnet_base_en_5.1.1_3.0_1694124367563.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/reddit_single_context_mpnet_base_en_5.1.1_3.0_1694124367563.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("reddit_single_context_mpnet_base","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("reddit_single_context_mpnet_base", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|reddit_single_context_mpnet_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/flax-sentence-embeddings/reddit_single-context_mpnet-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-retriever_coding_guru_adapted_en.md b/docs/_posts/ahmedlone127/2023-09-07-retriever_coding_guru_adapted_en.md new file mode 100644 index 00000000000000..5058151444fdc4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-retriever_coding_guru_adapted_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English retriever_coding_guru_adapted MPNetEmbeddings from AlekseyKorshuk +author: John Snow Labs +name: retriever_coding_guru_adapted +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`retriever_coding_guru_adapted` is a English model originally trained by AlekseyKorshuk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/retriever_coding_guru_adapted_en_5.1.1_3.0_1694130068115.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/retriever_coding_guru_adapted_en_5.1.1_3.0_1694130068115.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("retriever_coding_guru_adapted","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("retriever_coding_guru_adapted", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|retriever_coding_guru_adapted| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/AlekseyKorshuk/retriever-coding-guru-adapted \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-review_intent_20230116_en.md b/docs/_posts/ahmedlone127/2023-09-07-review_intent_20230116_en.md new file mode 100644 index 00000000000000..73a3657748b441 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-review_intent_20230116_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English review_intent_20230116 MPNetEmbeddings from meichen91 +author: John Snow Labs +name: review_intent_20230116 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`review_intent_20230116` is a English model originally trained by meichen91. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/review_intent_20230116_en_5.1.1_3.0_1694128378940.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/review_intent_20230116_en_5.1.1_3.0_1694128378940.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("review_intent_20230116","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("review_intent_20230116", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|review_intent_20230116| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/meichen91/review_intent_20230116 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-review_multiclass_20230116_en.md b/docs/_posts/ahmedlone127/2023-09-07-review_multiclass_20230116_en.md new file mode 100644 index 00000000000000..c0e1f7dd34e5b4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-review_multiclass_20230116_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English review_multiclass_20230116 MPNetEmbeddings from meichen91 +author: John Snow Labs +name: review_multiclass_20230116 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`review_multiclass_20230116` is a English model originally trained by meichen91. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/review_multiclass_20230116_en_5.1.1_3.0_1694128480525.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/review_multiclass_20230116_en_5.1.1_3.0_1694128480525.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("review_multiclass_20230116","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("review_multiclass_20230116", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|review_multiclass_20230116| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/meichen91/review_multiclass_20230116 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-sb_temfac_en.md b/docs/_posts/ahmedlone127/2023-09-07-sb_temfac_en.md new file mode 100644 index 00000000000000..c7c119507b1403 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-sb_temfac_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sb_temfac MPNetEmbeddings from stealthpy +author: John Snow Labs +name: sb_temfac +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sb_temfac` is a English model originally trained by stealthpy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sb_temfac_en_5.1.1_3.0_1694123355976.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sb_temfac_en_5.1.1_3.0_1694123355976.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("sb_temfac","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("sb_temfac", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sb_temfac| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.3 MB| + +## References + +https://huggingface.co/stealthpy/sb-temfac \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-sbert_paper_en.md b/docs/_posts/ahmedlone127/2023-09-07-sbert_paper_en.md new file mode 100644 index 00000000000000..d71df9bc5fa15a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-sbert_paper_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sbert_paper MPNetEmbeddings from salsabiilashifa11 +author: John Snow Labs +name: sbert_paper +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sbert_paper` is a English model originally trained by salsabiilashifa11. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sbert_paper_en_5.1.1_3.0_1694125460196.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sbert_paper_en_5.1.1_3.0_1694125460196.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("sbert_paper","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("sbert_paper", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sbert_paper| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/salsabiilashifa11/sbert-paper \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-sentence_transformers_bible_reference_final_en.md b/docs/_posts/ahmedlone127/2023-09-07-sentence_transformers_bible_reference_final_en.md new file mode 100644 index 00000000000000..16988365732d59 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-sentence_transformers_bible_reference_final_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sentence_transformers_bible_reference_final MPNetEmbeddings from odunola +author: John Snow Labs +name: sentence_transformers_bible_reference_final +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sentence_transformers_bible_reference_final` is a English model originally trained by odunola. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sentence_transformers_bible_reference_final_en_5.1.1_3.0_1694128406180.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sentence_transformers_bible_reference_final_en_5.1.1_3.0_1694128406180.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("sentence_transformers_bible_reference_final","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("sentence_transformers_bible_reference_final", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sentence_transformers_bible_reference_final| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/odunola/sentence-transformers-bible-reference-final \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-sentiment140_fewshot_en.md b/docs/_posts/ahmedlone127/2023-09-07-sentiment140_fewshot_en.md new file mode 100644 index 00000000000000..39b4e7a7a5f9b0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-sentiment140_fewshot_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sentiment140_fewshot MPNetEmbeddings from pig4431 +author: John Snow Labs +name: sentiment140_fewshot +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sentiment140_fewshot` is a English model originally trained by pig4431. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sentiment140_fewshot_en_5.1.1_3.0_1694130309466.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sentiment140_fewshot_en_5.1.1_3.0_1694130309466.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("sentiment140_fewshot","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("sentiment140_fewshot", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sentiment140_fewshot| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/pig4431/Sentiment140_fewshot \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_ag_news_endpoint_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_ag_news_endpoint_en.md new file mode 100644 index 00000000000000..3e1f79c843e2a5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_ag_news_endpoint_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_ag_news_endpoint MPNetEmbeddings from philschmid +author: John Snow Labs +name: setfit_ag_news_endpoint +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_ag_news_endpoint` is a English model originally trained by philschmid. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_ag_news_endpoint_en_5.1.1_3.0_1694129573309.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_ag_news_endpoint_en_5.1.1_3.0_1694129573309.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_ag_news_endpoint","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_ag_news_endpoint", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_ag_news_endpoint| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/philschmid/setfit-ag-news-endpoint \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_all_data_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_all_data_en.md new file mode 100644 index 00000000000000..98340da2e7ed0f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_all_data_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_all_data MPNetEmbeddings from scaperex +author: John Snow Labs +name: setfit_all_data +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_all_data` is a English model originally trained by scaperex. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_all_data_en_5.1.1_3.0_1694131075651.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_all_data_en_5.1.1_3.0_1694131075651.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_all_data","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_all_data", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_all_data| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/scaperex/SetFit-all-data \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_alpaca_spanish_unprocessable_sample_detection_es.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_alpaca_spanish_unprocessable_sample_detection_es.md new file mode 100644 index 00000000000000..3a645313e1cdc0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_alpaca_spanish_unprocessable_sample_detection_es.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Castilian, Spanish setfit_alpaca_spanish_unprocessable_sample_detection MPNetEmbeddings from hackathon-somos-nlp-2023 +author: John Snow Labs +name: setfit_alpaca_spanish_unprocessable_sample_detection +date: 2023-09-07 +tags: [mpnet, es, open_source, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_alpaca_spanish_unprocessable_sample_detection` is a Castilian, Spanish model originally trained by hackathon-somos-nlp-2023. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_alpaca_spanish_unprocessable_sample_detection_es_5.1.1_3.0_1694128554320.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_alpaca_spanish_unprocessable_sample_detection_es_5.1.1_3.0_1694128554320.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_alpaca_spanish_unprocessable_sample_detection","es") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_alpaca_spanish_unprocessable_sample_detection", "es") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_alpaca_spanish_unprocessable_sample_detection| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|es| +|Size:|407.2 MB| + +## References + +https://huggingface.co/hackathon-somos-nlp-2023/setfit-alpaca-es-unprocessable-sample-detection \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_ds_version_0_0_1_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_ds_version_0_0_1_en.md new file mode 100644 index 00000000000000..7dc463687ca081 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_ds_version_0_0_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_ds_version_0_0_1 MPNetEmbeddings from amittian +author: John Snow Labs +name: setfit_ds_version_0_0_1 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_ds_version_0_0_1` is a English model originally trained by amittian. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_ds_version_0_0_1_en_5.1.1_3.0_1694124869162.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_ds_version_0_0_1_en_5.1.1_3.0_1694124869162.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_ds_version_0_0_1","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_ds_version_0_0_1", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_ds_version_0_0_1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/amittian/setfit_ds_version_0_0_1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_ds_version_0_0_2_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_ds_version_0_0_2_en.md new file mode 100644 index 00000000000000..867617435c338c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_ds_version_0_0_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_ds_version_0_0_2 MPNetEmbeddings from amittian +author: John Snow Labs +name: setfit_ds_version_0_0_2 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_ds_version_0_0_2` is a English model originally trained by amittian. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_ds_version_0_0_2_en_5.1.1_3.0_1694125015548.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_ds_version_0_0_2_en_5.1.1_3.0_1694125015548.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_ds_version_0_0_2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_ds_version_0_0_2", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_ds_version_0_0_2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/amittian/setfit_ds_version_0_0_2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_ds_version_0_0_4_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_ds_version_0_0_4_en.md new file mode 100644 index 00000000000000..098836f8e09639 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_ds_version_0_0_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_ds_version_0_0_4 MPNetEmbeddings from amittian +author: John Snow Labs +name: setfit_ds_version_0_0_4 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_ds_version_0_0_4` is a English model originally trained by amittian. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_ds_version_0_0_4_en_5.1.1_3.0_1694129561333.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_ds_version_0_0_4_en_5.1.1_3.0_1694129561333.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_ds_version_0_0_4","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_ds_version_0_0_4", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_ds_version_0_0_4| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/amittian/setfit_ds_version_0_0_4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_ds_version_0_0_5_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_ds_version_0_0_5_en.md new file mode 100644 index 00000000000000..a6c6aa4de8a2f4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_ds_version_0_0_5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_ds_version_0_0_5 MPNetEmbeddings from amittian +author: John Snow Labs +name: setfit_ds_version_0_0_5 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_ds_version_0_0_5` is a English model originally trained by amittian. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_ds_version_0_0_5_en_5.1.1_3.0_1694125459042.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_ds_version_0_0_5_en_5.1.1_3.0_1694125459042.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_ds_version_0_0_5","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_ds_version_0_0_5", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_ds_version_0_0_5| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/amittian/setfit_ds_version_0_0_5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_ethos_multilabel_example_lewtun_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_ethos_multilabel_example_lewtun_en.md new file mode 100644 index 00000000000000..9b0ca6cdd2a9e1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_ethos_multilabel_example_lewtun_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_ethos_multilabel_example_lewtun MPNetEmbeddings from lewtun +author: John Snow Labs +name: setfit_ethos_multilabel_example_lewtun +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_ethos_multilabel_example_lewtun` is a English model originally trained by lewtun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_ethos_multilabel_example_lewtun_en_5.1.1_3.0_1694130189052.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_ethos_multilabel_example_lewtun_en_5.1.1_3.0_1694130189052.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_ethos_multilabel_example_lewtun","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_ethos_multilabel_example_lewtun", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_ethos_multilabel_example_lewtun| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/lewtun/setfit-ethos-multilabel-example \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_ethos_multilabel_example_neilthematic_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_ethos_multilabel_example_neilthematic_en.md new file mode 100644 index 00000000000000..972f464a1395ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_ethos_multilabel_example_neilthematic_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_ethos_multilabel_example_neilthematic MPNetEmbeddings from neilthematic +author: John Snow Labs +name: setfit_ethos_multilabel_example_neilthematic +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_ethos_multilabel_example_neilthematic` is a English model originally trained by neilthematic. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_ethos_multilabel_example_neilthematic_en_5.1.1_3.0_1694128696798.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_ethos_multilabel_example_neilthematic_en_5.1.1_3.0_1694128696798.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_ethos_multilabel_example_neilthematic","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_ethos_multilabel_example_neilthematic", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_ethos_multilabel_example_neilthematic| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/neilthematic/setfit-ethos-multilabel-example \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_few_shot_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_few_shot_classifier_en.md new file mode 100644 index 00000000000000..9a5a9a80274ebe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_few_shot_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_few_shot_classifier MPNetEmbeddings from Kuaaangwen +author: John Snow Labs +name: setfit_few_shot_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_few_shot_classifier` is a English model originally trained by Kuaaangwen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_few_shot_classifier_en_5.1.1_3.0_1694127951811.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_few_shot_classifier_en_5.1.1_3.0_1694127951811.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_few_shot_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_few_shot_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_few_shot_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Kuaaangwen/Setfit-few-shot-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_finetuned_financial_text_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_finetuned_financial_text_en.md new file mode 100644 index 00000000000000..41345e1d5b25ed --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_finetuned_financial_text_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_finetuned_financial_text MPNetEmbeddings from nickmuchi +author: John Snow Labs +name: setfit_finetuned_financial_text +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_finetuned_financial_text` is a English model originally trained by nickmuchi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_finetuned_financial_text_en_5.1.1_3.0_1694124680784.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_finetuned_financial_text_en_5.1.1_3.0_1694124680784.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_finetuned_financial_text","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_finetuned_financial_text", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_finetuned_financial_text| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/nickmuchi/setfit-finetuned-financial-text \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_model_pradipta11_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_model_pradipta11_en.md new file mode 100644 index 00000000000000..7451bd425acb40 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_model_pradipta11_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_model_pradipta11 MPNetEmbeddings from Pradipta11 +author: John Snow Labs +name: setfit_model_pradipta11 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_model_pradipta11` is a English model originally trained by Pradipta11. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_model_pradipta11_en_5.1.1_3.0_1694128986439.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_model_pradipta11_en_5.1.1_3.0_1694128986439.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_model_pradipta11","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_model_pradipta11", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_model_pradipta11| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/Pradipta11/setfit-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_model_rajistics_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_model_rajistics_en.md new file mode 100644 index 00000000000000..f8976c2df06c62 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_model_rajistics_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_model_rajistics MPNetEmbeddings from rajistics +author: John Snow Labs +name: setfit_model_rajistics +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_model_rajistics` is a English model originally trained by rajistics. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_model_rajistics_en_5.1.1_3.0_1694129815092.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_model_rajistics_en_5.1.1_3.0_1694129815092.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_model_rajistics","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_model_rajistics", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_model_rajistics| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/rajistics/setfit-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_model_test_sensitve_v1_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_model_test_sensitve_v1_en.md new file mode 100644 index 00000000000000..1b6e5b948163a0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_model_test_sensitve_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_model_test_sensitve_v1 MPNetEmbeddings from Adipta +author: John Snow Labs +name: setfit_model_test_sensitve_v1 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_model_test_sensitve_v1` is a English model originally trained by Adipta. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_model_test_sensitve_v1_en_5.1.1_3.0_1694125258840.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_model_test_sensitve_v1_en_5.1.1_3.0_1694125258840.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_model_test_sensitve_v1","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_model_test_sensitve_v1", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_model_test_sensitve_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/Adipta/setfit-model-test-sensitve-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_occupation_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_occupation_en.md new file mode 100644 index 00000000000000..6a0849f0a25aa6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_occupation_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_occupation MPNetEmbeddings from ivanzidov +author: John Snow Labs +name: setfit_occupation +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_occupation` is a English model originally trained by ivanzidov. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_occupation_en_5.1.1_3.0_1694125793111.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_occupation_en_5.1.1_3.0_1694125793111.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_occupation","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_occupation", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_occupation| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/ivanzidov/setfit-occupation \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_zero_shot_classification_pbsp_p1_comm_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_zero_shot_classification_pbsp_p1_comm_en.md new file mode 100644 index 00000000000000..91c4e14679722c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_zero_shot_classification_pbsp_p1_comm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_zero_shot_classification_pbsp_p1_comm MPNetEmbeddings from aammari +author: John Snow Labs +name: setfit_zero_shot_classification_pbsp_p1_comm +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_zero_shot_classification_pbsp_p1_comm` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p1_comm_en_5.1.1_3.0_1694128840558.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p1_comm_en_5.1.1_3.0_1694128840558.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_zero_shot_classification_pbsp_p1_comm","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_zero_shot_classification_pbsp_p1_comm", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_zero_shot_classification_pbsp_p1_comm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/aammari/setfit-zero-shot-classification-pbsp-p1-comm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_zero_shot_classification_pbsp_p1_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_zero_shot_classification_pbsp_p1_en.md new file mode 100644 index 00000000000000..1ac2188323fcb2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_zero_shot_classification_pbsp_p1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_zero_shot_classification_pbsp_p1 MPNetEmbeddings from aammari +author: John Snow Labs +name: setfit_zero_shot_classification_pbsp_p1 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_zero_shot_classification_pbsp_p1` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p1_en_5.1.1_3.0_1694131074420.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p1_en_5.1.1_3.0_1694131074420.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_zero_shot_classification_pbsp_p1","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_zero_shot_classification_pbsp_p1", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_zero_shot_classification_pbsp_p1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/aammari/setfit-zero-shot-classification-pbsp-p1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_zero_shot_classification_pbsp_p1_life_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_zero_shot_classification_pbsp_p1_life_en.md new file mode 100644 index 00000000000000..ac7b31d35b2925 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_zero_shot_classification_pbsp_p1_life_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_zero_shot_classification_pbsp_p1_life MPNetEmbeddings from aammari +author: John Snow Labs +name: setfit_zero_shot_classification_pbsp_p1_life +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_zero_shot_classification_pbsp_p1_life` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p1_life_en_5.1.1_3.0_1694128968448.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p1_life_en_5.1.1_3.0_1694128968448.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_zero_shot_classification_pbsp_p1_life","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_zero_shot_classification_pbsp_p1_life", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_zero_shot_classification_pbsp_p1_life| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/aammari/setfit-zero-shot-classification-pbsp-p1-life \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_zero_shot_classification_pbsp_p1_likes_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_zero_shot_classification_pbsp_p1_likes_en.md new file mode 100644 index 00000000000000..cc2308a516eaf2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_zero_shot_classification_pbsp_p1_likes_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_zero_shot_classification_pbsp_p1_likes MPNetEmbeddings from aammari +author: John Snow Labs +name: setfit_zero_shot_classification_pbsp_p1_likes +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_zero_shot_classification_pbsp_p1_likes` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p1_likes_en_5.1.1_3.0_1694130957746.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p1_likes_en_5.1.1_3.0_1694130957746.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_zero_shot_classification_pbsp_p1_likes","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_zero_shot_classification_pbsp_p1_likes", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_zero_shot_classification_pbsp_p1_likes| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/aammari/setfit-zero-shot-classification-pbsp-p1-likes \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_zero_shot_classification_pbsp_p3_func_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_zero_shot_classification_pbsp_p3_func_en.md new file mode 100644 index 00000000000000..c25173bb2c9229 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_zero_shot_classification_pbsp_p3_func_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_zero_shot_classification_pbsp_p3_func MPNetEmbeddings from aammari +author: John Snow Labs +name: setfit_zero_shot_classification_pbsp_p3_func +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_zero_shot_classification_pbsp_p3_func` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p3_func_en_5.1.1_3.0_1694128269303.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p3_func_en_5.1.1_3.0_1694128269303.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_zero_shot_classification_pbsp_p3_func","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_zero_shot_classification_pbsp_p3_func", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_zero_shot_classification_pbsp_p3_func| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/aammari/setfit-zero-shot-classification-pbsp-p3-func \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-setfit_zero_shot_classification_pbsp_p3_sev_en.md b/docs/_posts/ahmedlone127/2023-09-07-setfit_zero_shot_classification_pbsp_p3_sev_en.md new file mode 100644 index 00000000000000..2470877b9dadce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-setfit_zero_shot_classification_pbsp_p3_sev_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_zero_shot_classification_pbsp_p3_sev MPNetEmbeddings from aammari +author: John Snow Labs +name: setfit_zero_shot_classification_pbsp_p3_sev +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_zero_shot_classification_pbsp_p3_sev` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p3_sev_en_5.1.1_3.0_1694126027402.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p3_sev_en_5.1.1_3.0_1694126027402.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_zero_shot_classification_pbsp_p3_sev","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_zero_shot_classification_pbsp_p3_sev", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_zero_shot_classification_pbsp_p3_sev| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/aammari/setfit-zero-shot-classification-pbsp-p3-sev \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-shona_mpnet_base_snli_mnli_en.md b/docs/_posts/ahmedlone127/2023-09-07-shona_mpnet_base_snli_mnli_en.md new file mode 100644 index 00000000000000..da00824661f0c6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-shona_mpnet_base_snli_mnli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English shona_mpnet_base_snli_mnli MPNetEmbeddings from symanto +author: John Snow Labs +name: shona_mpnet_base_snli_mnli +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`shona_mpnet_base_snli_mnli` is a English model originally trained by symanto. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/shona_mpnet_base_snli_mnli_en_5.1.1_3.0_1694126372344.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/shona_mpnet_base_snli_mnli_en_5.1.1_3.0_1694126372344.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("shona_mpnet_base_snli_mnli","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("shona_mpnet_base_snli_mnli", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|shona_mpnet_base_snli_mnli| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/symanto/sn-mpnet-base-snli-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-sml_ukr_message_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-07-sml_ukr_message_classifier_en.md new file mode 100644 index 00000000000000..a046db097809b1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-sml_ukr_message_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sml_ukr_message_classifier MPNetEmbeddings from rodekruis +author: John Snow Labs +name: sml_ukr_message_classifier +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sml_ukr_message_classifier` is a English model originally trained by rodekruis. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sml_ukr_message_classifier_en_5.1.1_3.0_1694127978542.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sml_ukr_message_classifier_en_5.1.1_3.0_1694127978542.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("sml_ukr_message_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("sml_ukr_message_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sml_ukr_message_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/rodekruis/sml-ukr-message-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-sml_ukr_word_classifier_medium_en.md b/docs/_posts/ahmedlone127/2023-09-07-sml_ukr_word_classifier_medium_en.md new file mode 100644 index 00000000000000..a4cbc69a46720d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-sml_ukr_word_classifier_medium_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sml_ukr_word_classifier_medium MPNetEmbeddings from rodekruis +author: John Snow Labs +name: sml_ukr_word_classifier_medium +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sml_ukr_word_classifier_medium` is a English model originally trained by rodekruis. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sml_ukr_word_classifier_medium_en_5.1.1_3.0_1694129410581.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sml_ukr_word_classifier_medium_en_5.1.1_3.0_1694129410581.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("sml_ukr_word_classifier_medium","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("sml_ukr_word_classifier_medium", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sml_ukr_word_classifier_medium| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/rodekruis/sml-ukr-word-classifier-medium \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-spiced_en.md b/docs/_posts/ahmedlone127/2023-09-07-spiced_en.md new file mode 100644 index 00000000000000..bd1ef7411b483b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-spiced_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English spiced MPNetEmbeddings from copenlu +author: John Snow Labs +name: spiced +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`spiced` is a English model originally trained by copenlu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/spiced_en_5.1.1_3.0_1694128411873.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/spiced_en_5.1.1_3.0_1694128411873.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("spiced","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("spiced", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|spiced| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/copenlu/spiced \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-stackoverflow_mpnet_base_en.md b/docs/_posts/ahmedlone127/2023-09-07-stackoverflow_mpnet_base_en.md new file mode 100644 index 00000000000000..3f40800faed42e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-stackoverflow_mpnet_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English stackoverflow_mpnet_base MPNetEmbeddings from flax-sentence-embeddings +author: John Snow Labs +name: stackoverflow_mpnet_base +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`stackoverflow_mpnet_base` is a English model originally trained by flax-sentence-embeddings. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/stackoverflow_mpnet_base_en_5.1.1_3.0_1694128832593.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/stackoverflow_mpnet_base_en_5.1.1_3.0_1694128832593.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("stackoverflow_mpnet_base","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("stackoverflow_mpnet_base", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|stackoverflow_mpnet_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/flax-sentence-embeddings/stackoverflow_mpnet-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-stsb_mpnet_base_v2_en.md b/docs/_posts/ahmedlone127/2023-09-07-stsb_mpnet_base_v2_en.md new file mode 100644 index 00000000000000..90b776d91f0e98 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-stsb_mpnet_base_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English stsb_mpnet_base_v2 MPNetEmbeddings from sentence-transformers +author: John Snow Labs +name: stsb_mpnet_base_v2 +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`stsb_mpnet_base_v2` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/stsb_mpnet_base_v2_en_5.1.1_3.0_1694129940879.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/stsb_mpnet_base_v2_en_5.1.1_3.0_1694129940879.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("stsb_mpnet_base_v2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("stsb_mpnet_base_v2", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|stsb_mpnet_base_v2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|405.6 MB| + +## References + +https://huggingface.co/sentence-transformers/stsb-mpnet-base-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-test_food_en.md b/docs/_posts/ahmedlone127/2023-09-07-test_food_en.md new file mode 100644 index 00000000000000..9cca95eb6ebdb8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-test_food_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English test_food MPNetEmbeddings from Linus4Lyf +author: John Snow Labs +name: test_food +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`test_food` is a English model originally trained by Linus4Lyf. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/test_food_en_5.1.1_3.0_1694125513298.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/test_food_en_5.1.1_3.0_1694125513298.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("test_food","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("test_food", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|test_food| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/Linus4Lyf/test-food \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-testing_setfit_en.md b/docs/_posts/ahmedlone127/2023-09-07-testing_setfit_en.md new file mode 100644 index 00000000000000..0ea4d6c55ed45d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-testing_setfit_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English testing_setfit MPNetEmbeddings from BernierS +author: John Snow Labs +name: testing_setfit +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`testing_setfit` is a English model originally trained by BernierS. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/testing_setfit_en_5.1.1_3.0_1694128182708.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/testing_setfit_en_5.1.1_3.0_1694128182708.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("testing_setfit","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("testing_setfit", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|testing_setfit| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/BernierS/Testing_Setfit \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnet_hf_internal_testing_en.md b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnet_hf_internal_testing_en.md new file mode 100644 index 00000000000000..b9d6f3fccd1e5f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnet_hf_internal_testing_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_random_mpnet_hf_internal_testing MPNetEmbeddings from hf-internal-testing +author: John Snow Labs +name: tiny_random_mpnet_hf_internal_testing +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_random_mpnet_hf_internal_testing` is a English model originally trained by hf-internal-testing. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_random_mpnet_hf_internal_testing_en_5.1.1_3.0_1694128912406.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_random_mpnet_hf_internal_testing_en_5.1.1_3.0_1694128912406.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("tiny_random_mpnet_hf_internal_testing","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("tiny_random_mpnet_hf_internal_testing", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_random_mpnet_hf_internal_testing| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|877.8 KB| + +## References + +https://huggingface.co/hf-internal-testing/tiny-random-mpnet \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetformaskedlm_hf_internal_testing_en.md b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetformaskedlm_hf_internal_testing_en.md new file mode 100644 index 00000000000000..95a87e95432a50 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetformaskedlm_hf_internal_testing_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_random_mpnetformaskedlm_hf_internal_testing MPNetEmbeddings from hf-internal-testing +author: John Snow Labs +name: tiny_random_mpnetformaskedlm_hf_internal_testing +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_random_mpnetformaskedlm_hf_internal_testing` is a English model originally trained by hf-internal-testing. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetformaskedlm_hf_internal_testing_en_5.1.1_3.0_1694126709962.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetformaskedlm_hf_internal_testing_en_5.1.1_3.0_1694126709962.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("tiny_random_mpnetformaskedlm_hf_internal_testing","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("tiny_random_mpnetformaskedlm_hf_internal_testing", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_random_mpnetformaskedlm_hf_internal_testing| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|877.9 KB| + +## References + +https://huggingface.co/hf-internal-testing/tiny-random-MPNetForMaskedLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetformaskedlm_hf_tiny_model_private_en.md b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetformaskedlm_hf_tiny_model_private_en.md new file mode 100644 index 00000000000000..7e370458b977a8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetformaskedlm_hf_tiny_model_private_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_random_mpnetformaskedlm_hf_tiny_model_private MPNetEmbeddings from hf-tiny-model-private +author: John Snow Labs +name: tiny_random_mpnetformaskedlm_hf_tiny_model_private +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_random_mpnetformaskedlm_hf_tiny_model_private` is a English model originally trained by hf-tiny-model-private. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetformaskedlm_hf_tiny_model_private_en_5.1.1_3.0_1694128151940.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetformaskedlm_hf_tiny_model_private_en_5.1.1_3.0_1694128151940.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("tiny_random_mpnetformaskedlm_hf_tiny_model_private","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("tiny_random_mpnetformaskedlm_hf_tiny_model_private", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_random_mpnetformaskedlm_hf_tiny_model_private| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|877.8 KB| + +## References + +https://huggingface.co/hf-tiny-model-private/tiny-random-MPNetForMaskedLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetformultiplechoice_en.md b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetformultiplechoice_en.md new file mode 100644 index 00000000000000..057672f0ad2754 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetformultiplechoice_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_random_mpnetformultiplechoice MPNetEmbeddings from hf-tiny-model-private +author: John Snow Labs +name: tiny_random_mpnetformultiplechoice +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_random_mpnetformultiplechoice` is a English model originally trained by hf-tiny-model-private. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetformultiplechoice_en_5.1.1_3.0_1694123775173.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetformultiplechoice_en_5.1.1_3.0_1694123775173.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("tiny_random_mpnetformultiplechoice","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("tiny_random_mpnetformultiplechoice", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_random_mpnetformultiplechoice| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|877.7 KB| + +## References + +https://huggingface.co/hf-tiny-model-private/tiny-random-MPNetForMultipleChoice \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetforquestionanswering_hf_internal_testing_en.md b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetforquestionanswering_hf_internal_testing_en.md new file mode 100644 index 00000000000000..1df34bf13fcbc4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetforquestionanswering_hf_internal_testing_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_random_mpnetforquestionanswering_hf_internal_testing MPNetEmbeddings from hf-internal-testing +author: John Snow Labs +name: tiny_random_mpnetforquestionanswering_hf_internal_testing +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_random_mpnetforquestionanswering_hf_internal_testing` is a English model originally trained by hf-internal-testing. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetforquestionanswering_hf_internal_testing_en_5.1.1_3.0_1694130790158.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetforquestionanswering_hf_internal_testing_en_5.1.1_3.0_1694130790158.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("tiny_random_mpnetforquestionanswering_hf_internal_testing","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("tiny_random_mpnetforquestionanswering_hf_internal_testing", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_random_mpnetforquestionanswering_hf_internal_testing| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|878.0 KB| + +## References + +https://huggingface.co/hf-internal-testing/tiny-random-MPNetForQuestionAnswering \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetforquestionanswering_hf_tiny_model_private_en.md b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetforquestionanswering_hf_tiny_model_private_en.md new file mode 100644 index 00000000000000..f9370bd7db0516 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetforquestionanswering_hf_tiny_model_private_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_random_mpnetforquestionanswering_hf_tiny_model_private MPNetEmbeddings from hf-tiny-model-private +author: John Snow Labs +name: tiny_random_mpnetforquestionanswering_hf_tiny_model_private +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_random_mpnetforquestionanswering_hf_tiny_model_private` is a English model originally trained by hf-tiny-model-private. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetforquestionanswering_hf_tiny_model_private_en_5.1.1_3.0_1694123843375.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetforquestionanswering_hf_tiny_model_private_en_5.1.1_3.0_1694123843375.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("tiny_random_mpnetforquestionanswering_hf_tiny_model_private","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("tiny_random_mpnetforquestionanswering_hf_tiny_model_private", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_random_mpnetforquestionanswering_hf_tiny_model_private| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|877.9 KB| + +## References + +https://huggingface.co/hf-tiny-model-private/tiny-random-MPNetForQuestionAnswering \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetforsequenceclassification_hf_internal_testing_en.md b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetforsequenceclassification_hf_internal_testing_en.md new file mode 100644 index 00000000000000..417282d3428415 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetforsequenceclassification_hf_internal_testing_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_random_mpnetforsequenceclassification_hf_internal_testing MPNetEmbeddings from hf-internal-testing +author: John Snow Labs +name: tiny_random_mpnetforsequenceclassification_hf_internal_testing +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_random_mpnetforsequenceclassification_hf_internal_testing` is a English model originally trained by hf-internal-testing. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetforsequenceclassification_hf_internal_testing_en_5.1.1_3.0_1694126839890.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetforsequenceclassification_hf_internal_testing_en_5.1.1_3.0_1694126839890.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("tiny_random_mpnetforsequenceclassification_hf_internal_testing","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("tiny_random_mpnetforsequenceclassification_hf_internal_testing", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_random_mpnetforsequenceclassification_hf_internal_testing| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|877.9 KB| + +## References + +https://huggingface.co/hf-internal-testing/tiny-random-MPNetForSequenceClassification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetforsequenceclassification_hf_tiny_model_private_en.md b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetforsequenceclassification_hf_tiny_model_private_en.md new file mode 100644 index 00000000000000..eb260ac309709f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetforsequenceclassification_hf_tiny_model_private_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_random_mpnetforsequenceclassification_hf_tiny_model_private MPNetEmbeddings from hf-tiny-model-private +author: John Snow Labs +name: tiny_random_mpnetforsequenceclassification_hf_tiny_model_private +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_random_mpnetforsequenceclassification_hf_tiny_model_private` is a English model originally trained by hf-tiny-model-private. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetforsequenceclassification_hf_tiny_model_private_en_5.1.1_3.0_1694123901079.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetforsequenceclassification_hf_tiny_model_private_en_5.1.1_3.0_1694123901079.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("tiny_random_mpnetforsequenceclassification_hf_tiny_model_private","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("tiny_random_mpnetforsequenceclassification_hf_tiny_model_private", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_random_mpnetforsequenceclassification_hf_tiny_model_private| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|877.8 KB| + +## References + +https://huggingface.co/hf-tiny-model-private/tiny-random-MPNetForSequenceClassification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetfortokenclassification_hf_internal_testing_en.md b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetfortokenclassification_hf_internal_testing_en.md new file mode 100644 index 00000000000000..d5e59377b06df4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetfortokenclassification_hf_internal_testing_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_random_mpnetfortokenclassification_hf_internal_testing MPNetEmbeddings from hf-internal-testing +author: John Snow Labs +name: tiny_random_mpnetfortokenclassification_hf_internal_testing +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_random_mpnetfortokenclassification_hf_internal_testing` is a English model originally trained by hf-internal-testing. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetfortokenclassification_hf_internal_testing_en_5.1.1_3.0_1694126904935.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetfortokenclassification_hf_internal_testing_en_5.1.1_3.0_1694126904935.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("tiny_random_mpnetfortokenclassification_hf_internal_testing","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("tiny_random_mpnetfortokenclassification_hf_internal_testing", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_random_mpnetfortokenclassification_hf_internal_testing| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|877.8 KB| + +## References + +https://huggingface.co/hf-internal-testing/tiny-random-MPNetForTokenClassification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetfortokenclassification_hf_tiny_model_private_en.md b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetfortokenclassification_hf_tiny_model_private_en.md new file mode 100644 index 00000000000000..c1e208bf8aad21 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetfortokenclassification_hf_tiny_model_private_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_random_mpnetfortokenclassification_hf_tiny_model_private MPNetEmbeddings from hf-tiny-model-private +author: John Snow Labs +name: tiny_random_mpnetfortokenclassification_hf_tiny_model_private +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_random_mpnetfortokenclassification_hf_tiny_model_private` is a English model originally trained by hf-tiny-model-private. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetfortokenclassification_hf_tiny_model_private_en_5.1.1_3.0_1694128395671.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetfortokenclassification_hf_tiny_model_private_en_5.1.1_3.0_1694128395671.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("tiny_random_mpnetfortokenclassification_hf_tiny_model_private","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("tiny_random_mpnetfortokenclassification_hf_tiny_model_private", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_random_mpnetfortokenclassification_hf_tiny_model_private| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|877.6 KB| + +## References + +https://huggingface.co/hf-tiny-model-private/tiny-random-MPNetForTokenClassification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetmodel_hf_internal_testing_en.md b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetmodel_hf_internal_testing_en.md new file mode 100644 index 00000000000000..864f8d80ab634d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetmodel_hf_internal_testing_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_random_mpnetmodel_hf_internal_testing MPNetEmbeddings from hf-internal-testing +author: John Snow Labs +name: tiny_random_mpnetmodel_hf_internal_testing +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_random_mpnetmodel_hf_internal_testing` is a English model originally trained by hf-internal-testing. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetmodel_hf_internal_testing_en_5.1.1_3.0_1694130963864.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetmodel_hf_internal_testing_en_5.1.1_3.0_1694130963864.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("tiny_random_mpnetmodel_hf_internal_testing","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("tiny_random_mpnetmodel_hf_internal_testing", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_random_mpnetmodel_hf_internal_testing| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|877.9 KB| + +## References + +https://huggingface.co/hf-internal-testing/tiny-random-MPNetModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetmodel_hf_tiny_model_private_en.md b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetmodel_hf_tiny_model_private_en.md new file mode 100644 index 00000000000000..3b380efd311580 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-tiny_random_mpnetmodel_hf_tiny_model_private_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_random_mpnetmodel_hf_tiny_model_private MPNetEmbeddings from hf-tiny-model-private +author: John Snow Labs +name: tiny_random_mpnetmodel_hf_tiny_model_private +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_random_mpnetmodel_hf_tiny_model_private` is a English model originally trained by hf-tiny-model-private. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetmodel_hf_tiny_model_private_en_5.1.1_3.0_1694128454499.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_random_mpnetmodel_hf_tiny_model_private_en_5.1.1_3.0_1694128454499.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("tiny_random_mpnetmodel_hf_tiny_model_private","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("tiny_random_mpnetmodel_hf_tiny_model_private", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_random_mpnetmodel_hf_tiny_model_private| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|877.9 KB| + +## References + +https://huggingface.co/hf-tiny-model-private/tiny-random-MPNetModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-07-vulnerable_groups_en.md b/docs/_posts/ahmedlone127/2023-09-07-vulnerable_groups_en.md new file mode 100644 index 00000000000000..744d9aecfdb19d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-07-vulnerable_groups_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English vulnerable_groups MPNetEmbeddings from leavoigt +author: John Snow Labs +name: vulnerable_groups +date: 2023-09-07 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`vulnerable_groups` is a English model originally trained by leavoigt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/vulnerable_groups_en_5.1.1_3.0_1694129396114.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/vulnerable_groups_en_5.1.1_3.0_1694129396114.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("vulnerable_groups","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("vulnerable_groups", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|vulnerable_groups| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/leavoigt/vulnerable_groups \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-all_mpnet_base_questions_clustering_english_en.md b/docs/_posts/ahmedlone127/2023-09-08-all_mpnet_base_questions_clustering_english_en.md new file mode 100644 index 00000000000000..eb2099e78d45c9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-all_mpnet_base_questions_clustering_english_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English all_mpnet_base_questions_clustering_english MPNetEmbeddings from aiknowyou +author: John Snow Labs +name: all_mpnet_base_questions_clustering_english +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`all_mpnet_base_questions_clustering_english` is a English model originally trained by aiknowyou. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/all_mpnet_base_questions_clustering_english_en_5.1.1_3.0_1694131732803.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/all_mpnet_base_questions_clustering_english_en_5.1.1_3.0_1694131732803.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("all_mpnet_base_questions_clustering_english","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("all_mpnet_base_questions_clustering_english", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|all_mpnet_base_questions_clustering_english| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/aiknowyou/all-mpnet-base-questions-clustering-en \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-all_mpnet_base_v2_en.md b/docs/_posts/ahmedlone127/2023-09-08-all_mpnet_base_v2_en.md new file mode 100644 index 00000000000000..a2bd2a65b27eee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-all_mpnet_base_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English all_mpnet_base_v2 MPNetEmbeddings from sentence-transformers +author: John Snow Labs +name: all_mpnet_base_v2 +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`all_mpnet_base_v2` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_en_5.1.1_3.0_1694164524893.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_en_5.1.1_3.0_1694164524893.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("all_mpnet_base_v2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("all_mpnet_base_v2", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|all_mpnet_base_v2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/sentence-transformers/all-mpnet-base-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-all_mpnet_base_v2_feature_extraction_en.md b/docs/_posts/ahmedlone127/2023-09-08-all_mpnet_base_v2_feature_extraction_en.md new file mode 100644 index 00000000000000..d8ad11d0837f8a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-all_mpnet_base_v2_feature_extraction_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English all_mpnet_base_v2_feature_extraction MPNetEmbeddings from guidecare +author: John Snow Labs +name: all_mpnet_base_v2_feature_extraction +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`all_mpnet_base_v2_feature_extraction` is a English model originally trained by guidecare. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_feature_extraction_en_5.1.1_3.0_1694131495572.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_feature_extraction_en_5.1.1_3.0_1694131495572.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("all_mpnet_base_v2_feature_extraction","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("all_mpnet_base_v2_feature_extraction", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|all_mpnet_base_v2_feature_extraction| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/guidecare/all-mpnet-base-v2-feature-extraction \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-all_mpnet_base_v2_for_sb_clustering_en.md b/docs/_posts/ahmedlone127/2023-09-08-all_mpnet_base_v2_for_sb_clustering_en.md new file mode 100644 index 00000000000000..9679a527d2d9ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-all_mpnet_base_v2_for_sb_clustering_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English all_mpnet_base_v2_for_sb_clustering MPNetEmbeddings from Thabet +author: John Snow Labs +name: all_mpnet_base_v2_for_sb_clustering +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`all_mpnet_base_v2_for_sb_clustering` is a English model originally trained by Thabet. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_for_sb_clustering_en_5.1.1_3.0_1694131254349.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/all_mpnet_base_v2_for_sb_clustering_en_5.1.1_3.0_1694131254349.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("all_mpnet_base_v2_for_sb_clustering","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("all_mpnet_base_v2_for_sb_clustering", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|all_mpnet_base_v2_for_sb_clustering| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Thabet/all-mpnet-base-v2_for_sb_clustering \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-cpu_target_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-08-cpu_target_classifier_en.md new file mode 100644 index 00000000000000..2abbe2611066a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-cpu_target_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cpu_target_classifier MPNetEmbeddings from mtyrrell +author: John Snow Labs +name: cpu_target_classifier +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cpu_target_classifier` is a English model originally trained by mtyrrell. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cpu_target_classifier_en_5.1.1_3.0_1694131643680.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cpu_target_classifier_en_5.1.1_3.0_1694131643680.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("cpu_target_classifier","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("cpu_target_classifier", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cpu_target_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/mtyrrell/CPU_Target_Classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-esci_jp_mpnet_crossencoder_en.md b/docs/_posts/ahmedlone127/2023-09-08-esci_jp_mpnet_crossencoder_en.md new file mode 100644 index 00000000000000..63f1a9f6c8fd53 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-esci_jp_mpnet_crossencoder_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English esci_jp_mpnet_crossencoder MPNetEmbeddings from spacemanidol +author: John Snow Labs +name: esci_jp_mpnet_crossencoder +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`esci_jp_mpnet_crossencoder` is a English model originally trained by spacemanidol. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/esci_jp_mpnet_crossencoder_en_5.1.1_3.0_1694131619285.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/esci_jp_mpnet_crossencoder_en_5.1.1_3.0_1694131619285.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("esci_jp_mpnet_crossencoder","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("esci_jp_mpnet_crossencoder", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|esci_jp_mpnet_crossencoder| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/spacemanidol/esci-jp-mpnet-crossencoder \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-eth_setfit_payment_model_en.md b/docs/_posts/ahmedlone127/2023-09-08-eth_setfit_payment_model_en.md new file mode 100644 index 00000000000000..c58d76b782fdea --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-eth_setfit_payment_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English eth_setfit_payment_model MPNetEmbeddings from kainxwang +author: John Snow Labs +name: eth_setfit_payment_model +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`eth_setfit_payment_model` is a English model originally trained by kainxwang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/eth_setfit_payment_model_en_5.1.1_3.0_1694131517678.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/eth_setfit_payment_model_en_5.1.1_3.0_1694131517678.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("eth_setfit_payment_model","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("eth_setfit_payment_model", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|eth_setfit_payment_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/kainxwang/eth-setfit-payment-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-few_shot_model_en.md b/docs/_posts/ahmedlone127/2023-09-08-few_shot_model_en.md new file mode 100644 index 00000000000000..5226f69d8f13a4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-few_shot_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English few_shot_model MPNetEmbeddings from jessietextstan +author: John Snow Labs +name: few_shot_model +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`few_shot_model` is a English model originally trained by jessietextstan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/few_shot_model_en_5.1.1_3.0_1694131785073.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/few_shot_model_en_5.1.1_3.0_1694131785073.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("few_shot_model","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("few_shot_model", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|few_shot_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/jessietextstan/few-shot-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-multi_qa_mpnet_base_cos_v1_en.md b/docs/_posts/ahmedlone127/2023-09-08-multi_qa_mpnet_base_cos_v1_en.md new file mode 100644 index 00000000000000..78ca405367341c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-multi_qa_mpnet_base_cos_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English multi_qa_mpnet_base_cos_v1 MPNetEmbeddings from sentence-transformers +author: John Snow Labs +name: multi_qa_mpnet_base_cos_v1 +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multi_qa_mpnet_base_cos_v1` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/multi_qa_mpnet_base_cos_v1_en_5.1.1_3.0_1694164524065.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multi_qa_mpnet_base_cos_v1_en_5.1.1_3.0_1694164524065.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("multi_qa_mpnet_base_cos_v1","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("multi_qa_mpnet_base_cos_v1", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|multi_qa_mpnet_base_cos_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-cos-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-multi_qa_mpnet_base_dot_v1_en.md b/docs/_posts/ahmedlone127/2023-09-08-multi_qa_mpnet_base_dot_v1_en.md new file mode 100644 index 00000000000000..131d76f36c24a2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-multi_qa_mpnet_base_dot_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English multi_qa_mpnet_base_dot_v1 MPNetEmbeddings from sentence-transformers +author: John Snow Labs +name: multi_qa_mpnet_base_dot_v1 +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multi_qa_mpnet_base_dot_v1` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/multi_qa_mpnet_base_dot_v1_en_5.1.1_3.0_1694164614713.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multi_qa_mpnet_base_dot_v1_en_5.1.1_3.0_1694164614713.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("multi_qa_mpnet_base_dot_v1","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("multi_qa_mpnet_base_dot_v1", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|multi_qa_mpnet_base_dot_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-multi_qa_mpnet_base_dot_v1_model_embeddings_en.md b/docs/_posts/ahmedlone127/2023-09-08-multi_qa_mpnet_base_dot_v1_model_embeddings_en.md new file mode 100644 index 00000000000000..b608ef602d7bb4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-multi_qa_mpnet_base_dot_v1_model_embeddings_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English multi_qa_mpnet_base_dot_v1_model_embeddings MPNetEmbeddings from model-embeddings +author: John Snow Labs +name: multi_qa_mpnet_base_dot_v1_model_embeddings +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multi_qa_mpnet_base_dot_v1_model_embeddings` is a English model originally trained by model-embeddings. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/multi_qa_mpnet_base_dot_v1_model_embeddings_en_5.1.1_3.0_1694131503800.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multi_qa_mpnet_base_dot_v1_model_embeddings_en_5.1.1_3.0_1694131503800.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("multi_qa_mpnet_base_dot_v1_model_embeddings","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("multi_qa_mpnet_base_dot_v1_model_embeddings", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|multi_qa_mpnet_base_dot_v1_model_embeddings| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/model-embeddings/multi-qa-mpnet-base-dot-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-nli_mpnet_base_v2_en.md b/docs/_posts/ahmedlone127/2023-09-08-nli_mpnet_base_v2_en.md new file mode 100644 index 00000000000000..d74b0a6c585501 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-nli_mpnet_base_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English nli_mpnet_base_v2 MPNetEmbeddings from sentence-transformers +author: John Snow Labs +name: nli_mpnet_base_v2 +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nli_mpnet_base_v2` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nli_mpnet_base_v2_en_5.1.1_3.0_1694164513118.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nli_mpnet_base_v2_en_5.1.1_3.0_1694164513118.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("nli_mpnet_base_v2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("nli_mpnet_base_v2", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nli_mpnet_base_v2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|405.5 MB| + +## References + +https://huggingface.co/sentence-transformers/nli-mpnet-base-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-paraphrase_mpnet_base_v2_en.md b/docs/_posts/ahmedlone127/2023-09-08-paraphrase_mpnet_base_v2_en.md new file mode 100644 index 00000000000000..781d343be18885 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-paraphrase_mpnet_base_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English paraphrase_mpnet_base_v2 MPNetEmbeddings from sentence-transformers +author: John Snow Labs +name: paraphrase_mpnet_base_v2 +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`paraphrase_mpnet_base_v2` is a English model originally trained by sentence-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/paraphrase_mpnet_base_v2_en_5.1.1_3.0_1694164524556.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/paraphrase_mpnet_base_v2_en_5.1.1_3.0_1694164524556.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("paraphrase_mpnet_base_v2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("paraphrase_mpnet_base_v2", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|paraphrase_mpnet_base_v2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-paraphrase_mpnet_base_v2_finetuned_polifact_en.md b/docs/_posts/ahmedlone127/2023-09-08-paraphrase_mpnet_base_v2_finetuned_polifact_en.md new file mode 100644 index 00000000000000..e1f1a5b83d23e7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-paraphrase_mpnet_base_v2_finetuned_polifact_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English paraphrase_mpnet_base_v2_finetuned_polifact MPNetEmbeddings from anuj55 +author: John Snow Labs +name: paraphrase_mpnet_base_v2_finetuned_polifact +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`paraphrase_mpnet_base_v2_finetuned_polifact` is a English model originally trained by anuj55. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/paraphrase_mpnet_base_v2_finetuned_polifact_en_5.1.1_3.0_1694131210870.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/paraphrase_mpnet_base_v2_finetuned_polifact_en_5.1.1_3.0_1694131210870.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("paraphrase_mpnet_base_v2_finetuned_polifact","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("paraphrase_mpnet_base_v2_finetuned_polifact", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|paraphrase_mpnet_base_v2_finetuned_polifact| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/anuj55/paraphrase-mpnet-base-v2-finetuned-polifact \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-patentsberta_v2_en.md b/docs/_posts/ahmedlone127/2023-09-08-patentsberta_v2_en.md new file mode 100644 index 00000000000000..d8b4738e4bc070 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-patentsberta_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English patentsberta_v2 MPNetEmbeddings from AAUBS +author: John Snow Labs +name: patentsberta_v2 +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`patentsberta_v2` is a English model originally trained by AAUBS. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/patentsberta_v2_en_5.1.1_3.0_1694131353971.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/patentsberta_v2_en_5.1.1_3.0_1694131353971.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("patentsberta_v2","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("patentsberta_v2", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|patentsberta_v2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.4 MB| + +## References + +https://huggingface.co/AAUBS/PatentSBERTa_V2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-setfit_ft_sentinent_eval_en.md b/docs/_posts/ahmedlone127/2023-09-08-setfit_ft_sentinent_eval_en.md new file mode 100644 index 00000000000000..45f673fb2a38d2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-setfit_ft_sentinent_eval_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_ft_sentinent_eval MPNetEmbeddings from StatsGary +author: John Snow Labs +name: setfit_ft_sentinent_eval +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_ft_sentinent_eval` is a English model originally trained by StatsGary. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_ft_sentinent_eval_en_5.1.1_3.0_1694132009719.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_ft_sentinent_eval_en_5.1.1_3.0_1694132009719.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_ft_sentinent_eval","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_ft_sentinent_eval", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_ft_sentinent_eval| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/StatsGary/setfit-ft-sentinent-eval \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-setfit_model_feb11_misinformation_on_law_en.md b/docs/_posts/ahmedlone127/2023-09-08-setfit_model_feb11_misinformation_on_law_en.md new file mode 100644 index 00000000000000..3c585db18a2196 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-setfit_model_feb11_misinformation_on_law_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_model_feb11_misinformation_on_law MPNetEmbeddings from mitra-mir +author: John Snow Labs +name: setfit_model_feb11_misinformation_on_law +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_model_feb11_misinformation_on_law` is a English model originally trained by mitra-mir. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_model_feb11_misinformation_on_law_en_5.1.1_3.0_1694131910541.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_model_feb11_misinformation_on_law_en_5.1.1_3.0_1694131910541.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_model_feb11_misinformation_on_law","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_model_feb11_misinformation_on_law", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_model_feb11_misinformation_on_law| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/mitra-mir/setfit-model-Feb11-Misinformation-on-Law \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-setfit_ostrom_en.md b/docs/_posts/ahmedlone127/2023-09-08-setfit_ostrom_en.md new file mode 100644 index 00000000000000..92bee868df0b21 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-setfit_ostrom_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_ostrom MPNetEmbeddings from mahaswec +author: John Snow Labs +name: setfit_ostrom +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_ostrom` is a English model originally trained by mahaswec. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_ostrom_en_5.1.1_3.0_1694131365430.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_ostrom_en_5.1.1_3.0_1694131365430.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_ostrom","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_ostrom", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_ostrom| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/mahaswec/setfit_ostrom \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p3_bhvr_en.md b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p3_bhvr_en.md new file mode 100644 index 00000000000000..eb7e529bd36574 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p3_bhvr_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_zero_shot_classification_pbsp_p3_bhvr MPNetEmbeddings from aammari +author: John Snow Labs +name: setfit_zero_shot_classification_pbsp_p3_bhvr +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_zero_shot_classification_pbsp_p3_bhvr` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p3_bhvr_en_5.1.1_3.0_1694131223110.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p3_bhvr_en_5.1.1_3.0_1694131223110.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_zero_shot_classification_pbsp_p3_bhvr","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_zero_shot_classification_pbsp_p3_bhvr", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_zero_shot_classification_pbsp_p3_bhvr| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/aammari/setfit-zero-shot-classification-pbsp-p3-bhvr \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p3_cons_en.md b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p3_cons_en.md new file mode 100644 index 00000000000000..efdd4f8cb14c2b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p3_cons_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_zero_shot_classification_pbsp_p3_cons MPNetEmbeddings from aammari +author: John Snow Labs +name: setfit_zero_shot_classification_pbsp_p3_cons +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_zero_shot_classification_pbsp_p3_cons` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p3_cons_en_5.1.1_3.0_1694131904273.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p3_cons_en_5.1.1_3.0_1694131904273.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_zero_shot_classification_pbsp_p3_cons","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_zero_shot_classification_pbsp_p3_cons", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_zero_shot_classification_pbsp_p3_cons| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/aammari/setfit-zero-shot-classification-pbsp-p3-cons \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p3_dur_en.md b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p3_dur_en.md new file mode 100644 index 00000000000000..0699084d7a40a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p3_dur_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_zero_shot_classification_pbsp_p3_dur MPNetEmbeddings from aammari +author: John Snow Labs +name: setfit_zero_shot_classification_pbsp_p3_dur +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_zero_shot_classification_pbsp_p3_dur` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p3_dur_en_5.1.1_3.0_1694131370642.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p3_dur_en_5.1.1_3.0_1694131370642.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_zero_shot_classification_pbsp_p3_dur","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_zero_shot_classification_pbsp_p3_dur", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_zero_shot_classification_pbsp_p3_dur| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/aammari/setfit-zero-shot-classification-pbsp-p3-dur \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p3_trig_en.md b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p3_trig_en.md new file mode 100644 index 00000000000000..e622b812b9e93e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p3_trig_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_zero_shot_classification_pbsp_p3_trig MPNetEmbeddings from aammari +author: John Snow Labs +name: setfit_zero_shot_classification_pbsp_p3_trig +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_zero_shot_classification_pbsp_p3_trig` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p3_trig_en_5.1.1_3.0_1694131664015.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p3_trig_en_5.1.1_3.0_1694131664015.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_zero_shot_classification_pbsp_p3_trig","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_zero_shot_classification_pbsp_p3_trig", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_zero_shot_classification_pbsp_p3_trig| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/aammari/setfit-zero-shot-classification-pbsp-p3-trig \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p4_achiev_en.md b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p4_achiev_en.md new file mode 100644 index 00000000000000..37b13d9bc75bfa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p4_achiev_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_zero_shot_classification_pbsp_p4_achiev MPNetEmbeddings from aammari +author: John Snow Labs +name: setfit_zero_shot_classification_pbsp_p4_achiev +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_zero_shot_classification_pbsp_p4_achiev` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p4_achiev_en_5.1.1_3.0_1694131523151.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p4_achiev_en_5.1.1_3.0_1694131523151.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_zero_shot_classification_pbsp_p4_achiev","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_zero_shot_classification_pbsp_p4_achiev", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_zero_shot_classification_pbsp_p4_achiev| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/aammari/setfit-zero-shot-classification-pbsp-p4-achiev \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p4_meas_en.md b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p4_meas_en.md new file mode 100644 index 00000000000000..f6588e4fbef863 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p4_meas_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_zero_shot_classification_pbsp_p4_meas MPNetEmbeddings from aammari +author: John Snow Labs +name: setfit_zero_shot_classification_pbsp_p4_meas +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_zero_shot_classification_pbsp_p4_meas` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p4_meas_en_5.1.1_3.0_1694131375079.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p4_meas_en_5.1.1_3.0_1694131375079.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_zero_shot_classification_pbsp_p4_meas","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_zero_shot_classification_pbsp_p4_meas", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_zero_shot_classification_pbsp_p4_meas| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/aammari/setfit-zero-shot-classification-pbsp-p4-meas \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p4_rel_en.md b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p4_rel_en.md new file mode 100644 index 00000000000000..3e49849a78cc9c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p4_rel_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_zero_shot_classification_pbsp_p4_rel MPNetEmbeddings from aammari +author: John Snow Labs +name: setfit_zero_shot_classification_pbsp_p4_rel +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_zero_shot_classification_pbsp_p4_rel` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p4_rel_en_5.1.1_3.0_1694131656638.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p4_rel_en_5.1.1_3.0_1694131656638.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_zero_shot_classification_pbsp_p4_rel","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_zero_shot_classification_pbsp_p4_rel", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_zero_shot_classification_pbsp_p4_rel| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/aammari/setfit-zero-shot-classification-pbsp-p4-rel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p4_specific_en.md b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p4_specific_en.md new file mode 100644 index 00000000000000..742c48339a1a50 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p4_specific_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_zero_shot_classification_pbsp_p4_specific MPNetEmbeddings from aammari +author: John Snow Labs +name: setfit_zero_shot_classification_pbsp_p4_specific +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_zero_shot_classification_pbsp_p4_specific` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p4_specific_en_5.1.1_3.0_1694131229509.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p4_specific_en_5.1.1_3.0_1694131229509.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_zero_shot_classification_pbsp_p4_specific","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_zero_shot_classification_pbsp_p4_specific", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_zero_shot_classification_pbsp_p4_specific| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/aammari/setfit-zero-shot-classification-pbsp-p4-specific \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p4_time_en.md b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p4_time_en.md new file mode 100644 index 00000000000000..9a87f506d20bb7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_p4_time_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_zero_shot_classification_pbsp_p4_time MPNetEmbeddings from aammari +author: John Snow Labs +name: setfit_zero_shot_classification_pbsp_p4_time +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_zero_shot_classification_pbsp_p4_time` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p4_time_en_5.1.1_3.0_1694131780751.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_p4_time_en_5.1.1_3.0_1694131780751.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_zero_shot_classification_pbsp_p4_time","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_zero_shot_classification_pbsp_p4_time", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_zero_shot_classification_pbsp_p4_time| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/aammari/setfit-zero-shot-classification-pbsp-p4-time \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_q8a_azure_gpt35_en.md b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_q8a_azure_gpt35_en.md new file mode 100644 index 00000000000000..c57d017d9bae16 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-setfit_zero_shot_classification_pbsp_q8a_azure_gpt35_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English setfit_zero_shot_classification_pbsp_q8a_azure_gpt35 MPNetEmbeddings from aammari +author: John Snow Labs +name: setfit_zero_shot_classification_pbsp_q8a_azure_gpt35 +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`setfit_zero_shot_classification_pbsp_q8a_azure_gpt35` is a English model originally trained by aammari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_q8a_azure_gpt35_en_5.1.1_3.0_1694131776102.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/setfit_zero_shot_classification_pbsp_q8a_azure_gpt35_en_5.1.1_3.0_1694131776102.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("setfit_zero_shot_classification_pbsp_q8a_azure_gpt35","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("setfit_zero_shot_classification_pbsp_q8a_azure_gpt35", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|setfit_zero_shot_classification_pbsp_q8a_azure_gpt35| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/aammari/setfit-zero-shot-classification-pbsp-q8a-azure-gpt35 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-08-test_model_001_en.md b/docs/_posts/ahmedlone127/2023-09-08-test_model_001_en.md new file mode 100644 index 00000000000000..0ab02d9cce413d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-08-test_model_001_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English test_model_001 MPNetEmbeddings from intellya22 +author: John Snow Labs +name: test_model_001 +date: 2023-09-08 +tags: [mpnet, en, open_source, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: MPNetEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`test_model_001` is a English model originally trained by intellya22. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/test_model_001_en_5.1.1_3.0_1694131205719.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/test_model_001_en_5.1.1_3.0_1694131205719.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =MPNetEmbeddings.pretrained("test_model_001","en") \ + .setInputCols(["documents"]) \ + .setOutputCol("mpnet_embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val embeddings = MPNetEmbeddings + .pretrained("test_model_001", "en") + .setInputCols(Array("documents")) + .setOutputCol("mpnet_embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|test_model_001| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents]| +|Output Labels:|[mpnet_embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/intellya22/test-model-001 \ No newline at end of file From 16c83c2d488ca55b60013374dea36a49c1ffc45f Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Mon, 11 Sep 2023 14:34:04 +0700 Subject: [PATCH 40/55] 2023-09-09-medium_mlm_imdb_en (#13970) * Add model 2023-09-09-medium_mlm_imdb_en * Add model 2023-09-09-bert_base_cased_finetuned_mrpc_en * Add model 2023-09-09-base_mlm_imdb_en * Add model 2023-09-09-vbert_2021_large_en * Add model 2023-09-09-bert_base_german_dbmdz_cased_de * Add model 2023-09-09-arabic_mbertmodel_mberttok_en * Add model 2023-09-09-bert_base_german_dbmdz_uncased_de * Add model 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2023-09-09-indobertweet_base_uncased_id * Add model 2023-09-09-bert_large_ct_en * Add model 2023-09-09-jobbert_org_add_words_trial_26_12_2022_en * Add model 2023-09-09-indobert_base_uncased_id * Add model 2023-09-09-bert_base_uncased_finetuned_jira_qt_issue_titles_and_bodies_en * Add model 2023-09-09-bert_base_uncased_dish_descriptions_128_en * Add model 2023-09-09-inlegalbert_cbp_lkg_triples_finetuned_en * Add model 2023-09-09-spacebert_en * Add model 2023-09-09-javabert_en * Add model 2023-09-09-bert_large_uncased_semeval2016_laptops_en * Add model 2023-09-09-bert_base_multilingual_cased_finetuned_hausa_xx * Add model 2023-09-09-bert_base_uncased_dish_descriptions_128_0.5m_en * Add model 2023-09-09-jobbert_org_add_words_trial_26_12_2022_en * Add model 2023-09-09-bert_base_multilingual_cased_finetuned_hausa_xx * Add model 2023-09-09-bert_base_uncased_fined_en * Add model 2023-09-09-bert_ucb_3_en * Add model 2023-09-09-bert_base_multilingual_cased_finetuned_igbo_xx * Add model 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Add model 2023-09-09-mtl_bert_base_uncased_en * Add model 2023-09-09-bert_base_multilingual_cased_finetuned_wolof_xx * Add model 2023-09-09-bert_base_multilingual_cased_finetuned_yoruba_xx * Add model 2023-09-09-kinyabert_large_en * Add model 2023-09-09-medbert_breastcancer_en * Add model 2023-09-09-scholarbert_10_wb_en * Add model 2023-09-09-kbert_base_esg_e10_en * Add model 2023-09-09-alglegal3_bert_base_arabertv2_en * Add model 2023-09-09-kbert_base_esg_e3_en * Add model 2023-09-09-bert_large_portuguese_cased_legal_tsdae_pt * Add model 2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_0_en * Add model 2023-09-09-kbert_base_esg_e5_en * Add model 2023-09-09-mtl_bert_base_uncased_ww_en * Add model 2023-09-09-mlm_20230427_indobertlarge_001_en * Add model 2023-09-09-kaz_legal_bert_en * Add model 2023-09-09-bert_ucb_4_en * Add model 2023-09-09-mlm_20230427_mbert_001_en * Add model 2023-09-09-scholarbert_100_wb_en * Add model 2023-09-09-bert_sparql_en * Add model 2023-09-09-kaz_legal_bert_5_en * Add model 2023-09-09-knowbias_bert_base_uncased_gender_en * Add model 2023-09-09-bert_tagalog_base_uncased_tl * Add model 2023-09-09-bert_base_arabertv2_algarlegalbert_en * Add model 2023-09-09-sae_bert_base_uncased_en * Add model 2023-09-09-bert_base_multilingual_cased_finetuned_naija_xx * Add model 2023-09-09-bantu_bert_xx * Add model 2023-09-09-adaptive_lm_molecules_en * Add model 2023-09-09-bert_base_portuguese_cased_test_server_en * Add model 2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_2_en * Add model 2023-09-09-nepali_bert_npvec1_en * Add model 2023-09-09-jzmodel01_en * Add model 2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_3_en * Add model 2023-09-09-biomednlp_pubmedbert_large_uncased_abstract_en * Add model 2023-09-09-bert_base_uncased_finetune_security_en * Add model 2023-09-09-guidebias_bert_base_uncased_gender_en * Add model 2023-09-09-arabert32_flickr8k_en * Add model 2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_4_en * Add model 2023-09-09-metaphor_finetuned_bert_5epochs_en * Add model 2023-09-09-indojave_codemixed_indobertweet_base_id * Add model 2023-09-09-tiny_mlm_snli_en * Add model 2023-09-09-bert_base_uncased_issues_128_tanviraumi_en * Add model 2023-09-09-romanian_bert_tweet_ro * Add model 2023-09-09-tendencias_en * Add model 2023-09-09-bert_base_aeslc_danish_en * Add model 2023-09-09-materialsbert_en * Add model 2023-09-09-222_en * Add model 2023-09-09-bert_base_aeslc_aktsvigun_en * Add model 2023-09-09-dfm_encoder_large_v1_da * Add model 2023-09-09-ai12_junzai_en * Add model 2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_5_en * Add model 2023-09-09-bert_large_portuguese_cased_legal_mlm_pt * Add model 2023-09-09-bert_finetuning_test1227_hug_en * Add model 2023-09-09-german_poetry_bert_en * Add model 2023-09-09-mlm_20230428_indobert_base_p2_001_en * Add model 2023-09-09-bodo_bert_mlm_base_article_en * Add model 2023-09-09-tiny_mlm_glue_cola_en * Add model 2023-09-09-bert_test_junzai_en * Add model 2023-09-09-bert_base_cnndm_en * Add model 2023-09-09-gww_en * Add model 2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_6_en * Add model 2023-09-09-tiny_mlm_glue_qnli_en * Add model 2023-09-09-bert_funting_test_ai10_junzai_en * Add model 2023-09-09-tiny_mlm_glue_mnli_en * Add model 2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_7_en * Add model 2023-09-09-tiny_mlm_glue_mrpc_en * Add model 2023-09-09-dal_bert_fa * Add model 2023-09-09-tiny_mlm_glue_qqp_en * Add model 2023-09-09-bert_patent_reference_extraction_en * Add model 2023-09-09-test_ru * Add model 2023-09-09-bert_finetuning_test_hug_en * Add model 2023-09-09-bert_base_pubmed_en * Add model 2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_8_en * Add model 2023-09-09-bert_base_aeslc_kenkaneki_en * Add model 2023-09-09-absa_maskedlm_en --------- Co-authored-by: ahmedlone127 --- docs/_posts/ahmedlone127/2023-09-09-222_en.md | 93 +++++++++++++++++++ .../2023-09-09-absa_maskedlm_en.md | 93 +++++++++++++++++++ .../2023-09-09-adaptive_lm_molecules_en.md | 93 +++++++++++++++++++ ...base_bertje_data_rotterdam_epochs_10_en.md | 93 +++++++++++++++++++ ...je_data_rotterdam_epochs_30_epoch_30_en.md | 93 +++++++++++++++++++ ...023-09-09-aethiqs_gembert_bertje_50k_en.md | 93 +++++++++++++++++++ .../ahmedlone127/2023-09-09-ai12_junzai_en.md | 93 +++++++++++++++++++ ...2023-09-09-alephbertgimmel_10_epochs_en.md | 93 +++++++++++++++++++ ...2023-09-09-alephbertgimmel_20_epochs_en.md | 93 +++++++++++++++++++ ...2023-09-09-alephbertgimmel_50_epochs_en.md | 93 +++++++++++++++++++ ...-09-09-alglegal3_bert_base_arabertv2_en.md | 93 +++++++++++++++++++ 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docs/_posts/ahmedlone127/2023-09-09-turkish_monomodel_monotok_en.md create mode 100644 docs/_posts/ahmedlone127/2023-09-09-vbert_2021_large_en.md create mode 100644 docs/_posts/ahmedlone127/2023-09-09-wordpred_arabert_en.md create mode 100644 docs/_posts/ahmedlone127/2023-09-09-xtreme_squad_bert_base_multilingual_cased_xx.md diff --git a/docs/_posts/ahmedlone127/2023-09-09-222_en.md b/docs/_posts/ahmedlone127/2023-09-09-222_en.md new file mode 100644 index 00000000000000..d4f1b357ee8cd8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-222_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English 222 BertEmbeddings from junzai +author: John Snow Labs +name: 222 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`222` is a English model originally trained by junzai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/222_en_5.1.1_3.0_1694284019307.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/222_en_5.1.1_3.0_1694284019307.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("222","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("222", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|222| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/junzai/222 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-absa_maskedlm_en.md b/docs/_posts/ahmedlone127/2023-09-09-absa_maskedlm_en.md new file mode 100644 index 00000000000000..6e0ed5b0a845b5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-absa_maskedlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English absa_maskedlm BertEmbeddings from UchihaMadara +author: John Snow Labs +name: absa_maskedlm +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`absa_maskedlm` is a English model originally trained by UchihaMadara. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/absa_maskedlm_en_5.1.1_3.0_1694284442111.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/absa_maskedlm_en_5.1.1_3.0_1694284442111.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("absa_maskedlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("absa_maskedlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|absa_maskedlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/UchihaMadara/ABSA-MaskedLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-adaptive_lm_molecules_en.md b/docs/_posts/ahmedlone127/2023-09-09-adaptive_lm_molecules_en.md new file mode 100644 index 00000000000000..c84859d17d32aa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-adaptive_lm_molecules_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English adaptive_lm_molecules BertEmbeddings from mossaic-candle +author: John Snow Labs +name: adaptive_lm_molecules +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`adaptive_lm_molecules` is a English model originally trained by mossaic-candle. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/adaptive_lm_molecules_en_5.1.1_3.0_1694282672899.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/adaptive_lm_molecules_en_5.1.1_3.0_1694282672899.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("adaptive_lm_molecules","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("adaptive_lm_molecules", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|adaptive_lm_molecules| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|263.8 MB| + +## References + +https://huggingface.co/mossaic-candle/adaptive-lm-molecules \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-aethiqs_base_bertje_data_rotterdam_epochs_10_en.md b/docs/_posts/ahmedlone127/2023-09-09-aethiqs_base_bertje_data_rotterdam_epochs_10_en.md new file mode 100644 index 00000000000000..43d43d688ae696 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-aethiqs_base_bertje_data_rotterdam_epochs_10_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English aethiqs_base_bertje_data_rotterdam_epochs_10 BertEmbeddings from AethiQs-Max +author: John Snow Labs +name: aethiqs_base_bertje_data_rotterdam_epochs_10 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`aethiqs_base_bertje_data_rotterdam_epochs_10` is a English model originally trained by AethiQs-Max. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/aethiqs_base_bertje_data_rotterdam_epochs_10_en_5.1.1_3.0_1694260296232.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/aethiqs_base_bertje_data_rotterdam_epochs_10_en_5.1.1_3.0_1694260296232.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("aethiqs_base_bertje_data_rotterdam_epochs_10","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("aethiqs_base_bertje_data_rotterdam_epochs_10", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|aethiqs_base_bertje_data_rotterdam_epochs_10| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/AethiQs-Max/aethiqs-base_bertje-data_rotterdam-epochs_10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-aethiqs_base_bertje_data_rotterdam_epochs_30_epoch_30_en.md b/docs/_posts/ahmedlone127/2023-09-09-aethiqs_base_bertje_data_rotterdam_epochs_30_epoch_30_en.md new file mode 100644 index 00000000000000..a049d5aa5f4ac4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-aethiqs_base_bertje_data_rotterdam_epochs_30_epoch_30_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English aethiqs_base_bertje_data_rotterdam_epochs_30_epoch_30 BertEmbeddings from AethiQs-Max +author: John Snow Labs +name: aethiqs_base_bertje_data_rotterdam_epochs_30_epoch_30 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`aethiqs_base_bertje_data_rotterdam_epochs_30_epoch_30` is a English model originally trained by AethiQs-Max. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/aethiqs_base_bertje_data_rotterdam_epochs_30_epoch_30_en_5.1.1_3.0_1694260436558.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/aethiqs_base_bertje_data_rotterdam_epochs_30_epoch_30_en_5.1.1_3.0_1694260436558.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("aethiqs_base_bertje_data_rotterdam_epochs_30_epoch_30","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("aethiqs_base_bertje_data_rotterdam_epochs_30_epoch_30", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|aethiqs_base_bertje_data_rotterdam_epochs_30_epoch_30| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/AethiQs-Max/aethiqs-base_bertje-data_rotterdam-epochs_30-epoch_30 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-aethiqs_gembert_bertje_50k_en.md b/docs/_posts/ahmedlone127/2023-09-09-aethiqs_gembert_bertje_50k_en.md new file mode 100644 index 00000000000000..c5922cf52a9aed --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-aethiqs_gembert_bertje_50k_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English aethiqs_gembert_bertje_50k BertEmbeddings from AethiQs-Max +author: John Snow Labs +name: aethiqs_gembert_bertje_50k +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`aethiqs_gembert_bertje_50k` is a English model originally trained by AethiQs-Max. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/aethiqs_gembert_bertje_50k_en_5.1.1_3.0_1694260128112.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/aethiqs_gembert_bertje_50k_en_5.1.1_3.0_1694260128112.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("aethiqs_gembert_bertje_50k","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("aethiqs_gembert_bertje_50k", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|aethiqs_gembert_bertje_50k| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/AethiQs-Max/AethiQs_GemBERT_bertje_50k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-ai12_junzai_en.md b/docs/_posts/ahmedlone127/2023-09-09-ai12_junzai_en.md new file mode 100644 index 00000000000000..92ffb5f30604b8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-ai12_junzai_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ai12_junzai BertEmbeddings from junzai +author: John Snow Labs +name: ai12_junzai +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ai12_junzai` is a English model originally trained by junzai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ai12_junzai_en_5.1.1_3.0_1694284179005.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ai12_junzai_en_5.1.1_3.0_1694284179005.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ai12_junzai","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ai12_junzai", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ai12_junzai| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/junzai/ai12 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-alephbertgimmel_10_epochs_en.md b/docs/_posts/ahmedlone127/2023-09-09-alephbertgimmel_10_epochs_en.md new file mode 100644 index 00000000000000..8bd444bfae6fe7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-alephbertgimmel_10_epochs_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English alephbertgimmel_10_epochs BertEmbeddings from Embible +author: John Snow Labs +name: alephbertgimmel_10_epochs +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`alephbertgimmel_10_epochs` is a English model originally trained by Embible. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/alephbertgimmel_10_epochs_en_5.1.1_3.0_1694259605594.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/alephbertgimmel_10_epochs_en_5.1.1_3.0_1694259605594.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("alephbertgimmel_10_epochs","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("alephbertgimmel_10_epochs", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|alephbertgimmel_10_epochs| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|690.3 MB| + +## References + +https://huggingface.co/Embible/AlephBertGimmel-10-epochs \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-alephbertgimmel_20_epochs_en.md b/docs/_posts/ahmedlone127/2023-09-09-alephbertgimmel_20_epochs_en.md new file mode 100644 index 00000000000000..6254d9ff0fcaa7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-alephbertgimmel_20_epochs_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English alephbertgimmel_20_epochs BertEmbeddings from Embible +author: John Snow Labs +name: alephbertgimmel_20_epochs +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`alephbertgimmel_20_epochs` is a English model originally trained by Embible. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/alephbertgimmel_20_epochs_en_5.1.1_3.0_1694259790377.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/alephbertgimmel_20_epochs_en_5.1.1_3.0_1694259790377.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("alephbertgimmel_20_epochs","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("alephbertgimmel_20_epochs", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|alephbertgimmel_20_epochs| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|690.3 MB| + +## References + +https://huggingface.co/Embible/AlephBertGimmel-20-epochs \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-alephbertgimmel_50_epochs_en.md b/docs/_posts/ahmedlone127/2023-09-09-alephbertgimmel_50_epochs_en.md new file mode 100644 index 00000000000000..3816e91148def1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-alephbertgimmel_50_epochs_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English alephbertgimmel_50_epochs BertEmbeddings from Embible +author: John Snow Labs +name: alephbertgimmel_50_epochs +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`alephbertgimmel_50_epochs` is a English model originally trained by Embible. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/alephbertgimmel_50_epochs_en_5.1.1_3.0_1694259959436.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/alephbertgimmel_50_epochs_en_5.1.1_3.0_1694259959436.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("alephbertgimmel_50_epochs","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("alephbertgimmel_50_epochs", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|alephbertgimmel_50_epochs| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|690.0 MB| + +## References + +https://huggingface.co/Embible/AlephBertGimmel-50-epochs \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-alglegal3_bert_base_arabertv2_en.md b/docs/_posts/ahmedlone127/2023-09-09-alglegal3_bert_base_arabertv2_en.md new file mode 100644 index 00000000000000..908a790f6a0698 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-alglegal3_bert_base_arabertv2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English alglegal3_bert_base_arabertv2 BertEmbeddings from hatemestinbejaia +author: John Snow Labs +name: alglegal3_bert_base_arabertv2 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`alglegal3_bert_base_arabertv2` is a English model originally trained by hatemestinbejaia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/alglegal3_bert_base_arabertv2_en_5.1.1_3.0_1694279972460.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/alglegal3_bert_base_arabertv2_en_5.1.1_3.0_1694279972460.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("alglegal3_bert_base_arabertv2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("alglegal3_bert_base_arabertv2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|alglegal3_bert_base_arabertv2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|504.8 MB| + +## References + +https://huggingface.co/hatemestinbejaia/AlgLegal3_bert-base-arabertv2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-arabert32_flickr8k_en.md b/docs/_posts/ahmedlone127/2023-09-09-arabert32_flickr8k_en.md new file mode 100644 index 00000000000000..7d86d5ef95dee0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-arabert32_flickr8k_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English arabert32_flickr8k BertEmbeddings from jontooy +author: John Snow Labs +name: arabert32_flickr8k +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabert32_flickr8k` is a English model originally trained by jontooy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabert32_flickr8k_en_5.1.1_3.0_1694283279124.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabert32_flickr8k_en_5.1.1_3.0_1694283279124.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabert32_flickr8k","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabert32_flickr8k", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabert32_flickr8k| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|505.1 MB| + +## References + +https://huggingface.co/jontooy/AraBERT32-Flickr8k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-arabic_mbertmodel_mberttok_en.md b/docs/_posts/ahmedlone127/2023-09-09-arabic_mbertmodel_mberttok_en.md new file mode 100644 index 00000000000000..e904b67348a0ad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-arabic_mbertmodel_mberttok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English arabic_mbertmodel_mberttok BertEmbeddings from hgiyt +author: John Snow Labs +name: arabic_mbertmodel_mberttok +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabic_mbertmodel_mberttok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabic_mbertmodel_mberttok_en_5.1.1_3.0_1694258818763.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabic_mbertmodel_mberttok_en_5.1.1_3.0_1694258818763.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabic_mbertmodel_mberttok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabic_mbertmodel_mberttok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabic_mbertmodel_mberttok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|545.9 MB| + +## References + +https://huggingface.co/hgiyt/ar-mbertmodel-mberttok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-arabic_mbertmodel_monotok_adapter_en.md b/docs/_posts/ahmedlone127/2023-09-09-arabic_mbertmodel_monotok_adapter_en.md new file mode 100644 index 00000000000000..e71d7559eee9b8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-arabic_mbertmodel_monotok_adapter_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English arabic_mbertmodel_monotok_adapter BertEmbeddings from hgiyt +author: John Snow Labs +name: arabic_mbertmodel_monotok_adapter +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabic_mbertmodel_monotok_adapter` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabic_mbertmodel_monotok_adapter_en_5.1.1_3.0_1694258970126.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabic_mbertmodel_monotok_adapter_en_5.1.1_3.0_1694258970126.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabic_mbertmodel_monotok_adapter","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabic_mbertmodel_monotok_adapter", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabic_mbertmodel_monotok_adapter| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|505.0 MB| + +## References + +https://huggingface.co/hgiyt/ar-mbertmodel-monotok-adapter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-arabic_mbertmodel_monotok_en.md b/docs/_posts/ahmedlone127/2023-09-09-arabic_mbertmodel_monotok_en.md new file mode 100644 index 00000000000000..0005089a4a791e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-arabic_mbertmodel_monotok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English arabic_mbertmodel_monotok BertEmbeddings from hgiyt +author: John Snow Labs +name: arabic_mbertmodel_monotok +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabic_mbertmodel_monotok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabic_mbertmodel_monotok_en_5.1.1_3.0_1694259124754.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabic_mbertmodel_monotok_en_5.1.1_3.0_1694259124754.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabic_mbertmodel_monotok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabic_mbertmodel_monotok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabic_mbertmodel_monotok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|505.0 MB| + +## References + +https://huggingface.co/hgiyt/ar-mbertmodel-monotok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-arabic_monomodel_mberttok_en.md b/docs/_posts/ahmedlone127/2023-09-09-arabic_monomodel_mberttok_en.md new file mode 100644 index 00000000000000..720a49cab11338 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-arabic_monomodel_mberttok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English arabic_monomodel_mberttok BertEmbeddings from hgiyt +author: John Snow Labs +name: arabic_monomodel_mberttok +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabic_monomodel_mberttok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabic_monomodel_mberttok_en_5.1.1_3.0_1694259279000.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabic_monomodel_mberttok_en_5.1.1_3.0_1694259279000.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabic_monomodel_mberttok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabic_monomodel_mberttok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabic_monomodel_mberttok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|545.3 MB| + +## References + +https://huggingface.co/hgiyt/ar-monomodel-mberttok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-arabic_monomodel_monotok_en.md b/docs/_posts/ahmedlone127/2023-09-09-arabic_monomodel_monotok_en.md new file mode 100644 index 00000000000000..775ea9ba366e70 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-arabic_monomodel_monotok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English arabic_monomodel_monotok BertEmbeddings from hgiyt +author: John Snow Labs +name: arabic_monomodel_monotok +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabic_monomodel_monotok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabic_monomodel_monotok_en_5.1.1_3.0_1694259428031.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabic_monomodel_monotok_en_5.1.1_3.0_1694259428031.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabic_monomodel_monotok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabic_monomodel_monotok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabic_monomodel_monotok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|504.1 MB| + +## References + +https://huggingface.co/hgiyt/ar-monomodel-monotok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bantu_bert_xx.md b/docs/_posts/ahmedlone127/2023-09-09-bantu_bert_xx.md new file mode 100644 index 00000000000000..b9f2468616edc5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bantu_bert_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bantu_bert BertEmbeddings from nairaxo +author: John Snow Labs +name: bantu_bert +date: 2023-09-09 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bantu_bert` is a Multilingual model originally trained by nairaxo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bantu_bert_xx_5.1.1_3.0_1694282661097.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bantu_bert_xx_5.1.1_3.0_1694282661097.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bantu_bert","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bantu_bert", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bantu_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|752.8 MB| + +## References + +https://huggingface.co/nairaxo/bantu-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-base_mlm_imdb_en.md b/docs/_posts/ahmedlone127/2023-09-09-base_mlm_imdb_en.md new file mode 100644 index 00000000000000..ad797eaa2a29b7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-base_mlm_imdb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English base_mlm_imdb BertEmbeddings from muhtasham +author: John Snow Labs +name: base_mlm_imdb +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`base_mlm_imdb` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/base_mlm_imdb_en_5.1.1_3.0_1694258742410.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/base_mlm_imdb_en_5.1.1_3.0_1694258742410.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("base_mlm_imdb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("base_mlm_imdb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|base_mlm_imdb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.6 MB| + +## References + +https://huggingface.co/muhtasham/base-mlm-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert__racism80000_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert__racism80000_en.md new file mode 100644 index 00000000000000..844281cb6dbbf8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert__racism80000_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert__racism80000 BertEmbeddings from MutazYoune +author: John Snow Labs +name: bert__racism80000 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert__racism80000` is a English model originally trained by MutazYoune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert__racism80000_en_5.1.1_3.0_1694278959145.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert__racism80000_en_5.1.1_3.0_1694278959145.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert__racism80000","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert__racism80000", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert__racism80000| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.4 MB| + +## References + +https://huggingface.co/MutazYoune/bert__racism80000 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_aeslc_aktsvigun_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_aeslc_aktsvigun_en.md new file mode 100644 index 00000000000000..f0010b84d0cdf3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_aeslc_aktsvigun_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_aeslc_aktsvigun BertEmbeddings from Aktsvigun +author: John Snow Labs +name: bert_base_aeslc_aktsvigun +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_aeslc_aktsvigun` is a English model originally trained by Aktsvigun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_aeslc_aktsvigun_en_5.1.1_3.0_1694284037320.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_aeslc_aktsvigun_en_5.1.1_3.0_1694284037320.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_aeslc_aktsvigun","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_aeslc_aktsvigun", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_aeslc_aktsvigun| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Aktsvigun/bert-base-aeslc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_aeslc_danish_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_aeslc_danish_en.md new file mode 100644 index 00000000000000..b96a08caf9ebc6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_aeslc_danish_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_aeslc_danish BertEmbeddings from kenkaneki +author: John Snow Labs +name: bert_base_aeslc_danish +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_aeslc_danish` is a English model originally trained by kenkaneki. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_aeslc_danish_en_5.1.1_3.0_1694283902070.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_aeslc_danish_en_5.1.1_3.0_1694283902070.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_aeslc_danish","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_aeslc_danish", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_aeslc_danish| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/kenkaneki/bert-base-aeslc-da \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_aeslc_kenkaneki_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_aeslc_kenkaneki_en.md new file mode 100644 index 00000000000000..b00570b73050e0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_aeslc_kenkaneki_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_aeslc_kenkaneki BertEmbeddings from kenkaneki +author: John Snow Labs +name: bert_base_aeslc_kenkaneki +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_aeslc_kenkaneki` is a English model originally trained by kenkaneki. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_aeslc_kenkaneki_en_5.1.1_3.0_1694284188438.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_aeslc_kenkaneki_en_5.1.1_3.0_1694284188438.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_aeslc_kenkaneki","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_aeslc_kenkaneki", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_aeslc_kenkaneki| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/kenkaneki/bert-base-aeslc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabertv2_algarlegalbert_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabertv2_algarlegalbert_en.md new file mode 100644 index 00000000000000..f942e4c9350d25 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabertv2_algarlegalbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_arabertv2_algarlegalbert BertEmbeddings from hatemestinbejaia +author: John Snow Labs +name: bert_base_arabertv2_algarlegalbert +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabertv2_algarlegalbert` is a English model originally trained by hatemestinbejaia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabertv2_algarlegalbert_en_5.1.1_3.0_1694279683923.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabertv2_algarlegalbert_en_5.1.1_3.0_1694279683923.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_arabertv2_algarlegalbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_arabertv2_algarlegalbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabertv2_algarlegalbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|504.8 MB| + +## References + +https://huggingface.co/hatemestinbejaia/bert-base-arabertv2-AlgArLegalBert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_catalan_ar.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_catalan_ar.md new file mode 100644 index 00000000000000..b536cd3a994515 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_catalan_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_base_arabic_camelbert_catalan BertEmbeddings from CAMeL-Lab +author: John Snow Labs +name: bert_base_arabic_camelbert_catalan +date: 2023-09-09 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabic_camelbert_catalan` is a Arabic model originally trained by CAMeL-Lab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_catalan_ar_5.1.1_3.0_1694265014880.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_catalan_ar_5.1.1_3.0_1694265014880.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_arabic_camelbert_catalan","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_arabic_camelbert_catalan", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabic_camelbert_catalan| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|406.6 MB| + +## References + +https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_danish_ar.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_danish_ar.md new file mode 100644 index 00000000000000..f95314aaeb3a18 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_danish_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_base_arabic_camelbert_danish BertEmbeddings from CAMeL-Lab +author: John Snow Labs +name: bert_base_arabic_camelbert_danish +date: 2023-09-09 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabic_camelbert_danish` is a Arabic model originally trained by CAMeL-Lab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_danish_ar_5.1.1_3.0_1694265139823.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_danish_ar_5.1.1_3.0_1694265139823.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_arabic_camelbert_danish","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_arabic_camelbert_danish", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabic_camelbert_danish| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|406.7 MB| + +## References + +https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_mix_ar.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_mix_ar.md new file mode 100644 index 00000000000000..00e71eb80f25bb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_mix_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_base_arabic_camelbert_mix BertEmbeddings from CAMeL-Lab +author: John Snow Labs +name: bert_base_arabic_camelbert_mix +date: 2023-09-09 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabic_camelbert_mix` is a Arabic model originally trained by CAMeL-Lab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_mix_ar_5.1.1_3.0_1694265271288.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_mix_ar_5.1.1_3.0_1694265271288.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_arabic_camelbert_mix","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_arabic_camelbert_mix", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabic_camelbert_mix| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|406.6 MB| + +## References + +https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_msa_ar.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_msa_ar.md new file mode 100644 index 00000000000000..255e8350f28897 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_msa_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_base_arabic_camelbert_msa BertEmbeddings from CAMeL-Lab +author: John Snow Labs +name: bert_base_arabic_camelbert_msa +date: 2023-09-09 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabic_camelbert_msa` is a Arabic model originally trained by CAMeL-Lab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_msa_ar_5.1.1_3.0_1694265945366.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_msa_ar_5.1.1_3.0_1694265945366.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_arabic_camelbert_msa","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_arabic_camelbert_msa", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabic_camelbert_msa| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|406.3 MB| + +## References + +https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_msa_eighth_ar.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_msa_eighth_ar.md new file mode 100644 index 00000000000000..8dab1d62b4b18d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_msa_eighth_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_base_arabic_camelbert_msa_eighth BertEmbeddings from CAMeL-Lab +author: John Snow Labs +name: bert_base_arabic_camelbert_msa_eighth +date: 2023-09-09 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabic_camelbert_msa_eighth` is a Arabic model originally trained by CAMeL-Lab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_msa_eighth_ar_5.1.1_3.0_1694265412738.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_msa_eighth_ar_5.1.1_3.0_1694265412738.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_arabic_camelbert_msa_eighth","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_arabic_camelbert_msa_eighth", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabic_camelbert_msa_eighth| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|406.4 MB| + +## References + +https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa-eighth \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_msa_half_ar.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_msa_half_ar.md new file mode 100644 index 00000000000000..43d39e82eed204 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_msa_half_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_base_arabic_camelbert_msa_half BertEmbeddings from CAMeL-Lab +author: John Snow Labs +name: bert_base_arabic_camelbert_msa_half +date: 2023-09-09 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabic_camelbert_msa_half` is a Arabic model originally trained by CAMeL-Lab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_msa_half_ar_5.1.1_3.0_1694265547981.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_msa_half_ar_5.1.1_3.0_1694265547981.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_arabic_camelbert_msa_half","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_arabic_camelbert_msa_half", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabic_camelbert_msa_half| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|406.4 MB| + +## References + +https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa-half \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_msa_quarter_ar.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_msa_quarter_ar.md new file mode 100644 index 00000000000000..c041ec5e73b569 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_msa_quarter_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_base_arabic_camelbert_msa_quarter BertEmbeddings from CAMeL-Lab +author: John Snow Labs +name: bert_base_arabic_camelbert_msa_quarter +date: 2023-09-09 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabic_camelbert_msa_quarter` is a Arabic model originally trained by CAMeL-Lab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_msa_quarter_ar_5.1.1_3.0_1694265681109.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_msa_quarter_ar_5.1.1_3.0_1694265681109.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_arabic_camelbert_msa_quarter","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_arabic_camelbert_msa_quarter", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabic_camelbert_msa_quarter| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|406.3 MB| + +## References + +https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa-quarter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_msa_sixteenth_ar.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_msa_sixteenth_ar.md new file mode 100644 index 00000000000000..8954c6af5cf342 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_arabic_camelbert_msa_sixteenth_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_base_arabic_camelbert_msa_sixteenth BertEmbeddings from CAMeL-Lab +author: John Snow Labs +name: bert_base_arabic_camelbert_msa_sixteenth +date: 2023-09-09 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabic_camelbert_msa_sixteenth` is a Arabic model originally trained by CAMeL-Lab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_msa_sixteenth_ar_5.1.1_3.0_1694265828638.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_msa_sixteenth_ar_5.1.1_3.0_1694265828638.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_arabic_camelbert_msa_sixteenth","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_arabic_camelbert_msa_sixteenth", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabic_camelbert_msa_sixteenth| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|406.4 MB| + +## References + +https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_cased_finetuned_mrpc_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_cased_finetuned_mrpc_en.md new file mode 100644 index 00000000000000..f8489fc55c6d06 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_cased_finetuned_mrpc_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_mrpc BertEmbeddings from huggingface +author: John Snow Labs +name: bert_base_cased_finetuned_mrpc +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_mrpc` is a English model originally trained by huggingface. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_mrpc_en_5.1.1_3.0_1694258661421.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_mrpc_en_5.1.1_3.0_1694258661421.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_mrpc","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_mrpc", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_mrpc| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/bert-base-cased-finetuned-mrpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_cnndm_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_cnndm_en.md new file mode 100644 index 00000000000000..cb5f347c371570 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_cnndm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cnndm BertEmbeddings from Aktsvigun +author: John Snow Labs +name: bert_base_cnndm +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cnndm` is a English model originally trained by Aktsvigun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cnndm_en_5.1.1_3.0_1694284674309.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cnndm_en_5.1.1_3.0_1694284674309.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cnndm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cnndm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cnndm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Aktsvigun/bert-base-cnndm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_ct_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_ct_en.md new file mode 100644 index 00000000000000..3cd78134be5f60 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_ct_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_ct BertEmbeddings from Contrastive-Tension +author: John Snow Labs +name: bert_base_ct +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_ct` is a English model originally trained by Contrastive-Tension. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_ct_en_5.1.1_3.0_1694266339519.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_ct_en_5.1.1_3.0_1694266339519.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_ct","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_ct", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_ct| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Contrastive-Tension/BERT-Base-CT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_german_dbmdz_cased_de.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_german_dbmdz_cased_de.md new file mode 100644 index 00000000000000..225a4eefd0ccce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_german_dbmdz_cased_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German bert_base_german_dbmdz_cased BertEmbeddings from huggingface +author: John Snow Labs +name: bert_base_german_dbmdz_cased +date: 2023-09-09 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_german_dbmdz_cased` is a German model originally trained by huggingface. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_german_dbmdz_cased_de_5.1.1_3.0_1694258795026.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_german_dbmdz_cased_de_5.1.1_3.0_1694258795026.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_german_dbmdz_cased","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_german_dbmdz_cased", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_german_dbmdz_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|409.9 MB| + +## References + +https://huggingface.co/bert-base-german-dbmdz-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_german_dbmdz_uncased_de.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_german_dbmdz_uncased_de.md new file mode 100644 index 00000000000000..dd015644b96b61 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_german_dbmdz_uncased_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German bert_base_german_dbmdz_uncased BertEmbeddings from huggingface +author: John Snow Labs +name: bert_base_german_dbmdz_uncased +date: 2023-09-09 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_german_dbmdz_uncased` is a German model originally trained by huggingface. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_german_dbmdz_uncased_de_5.1.1_3.0_1694258923096.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_german_dbmdz_uncased_de_5.1.1_3.0_1694258923096.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_german_dbmdz_uncased","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_german_dbmdz_uncased", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_german_dbmdz_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|409.9 MB| + +## References + +https://huggingface.co/bert-base-german-dbmdz-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_irish_cased_v1_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_irish_cased_v1_en.md new file mode 100644 index 00000000000000..112ecf1c8f349e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_irish_cased_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_irish_cased_v1 BertEmbeddings from DCU-NLP +author: John Snow Labs +name: bert_base_irish_cased_v1 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_irish_cased_v1` is a English model originally trained by DCU-NLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_irish_cased_v1_en_5.1.1_3.0_1694267227315.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_irish_cased_v1_en_5.1.1_3.0_1694267227315.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_irish_cased_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_irish_cased_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_irish_cased_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/DCU-NLP/bert-base-irish-cased-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_dholuo_xx.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_dholuo_xx.md new file mode 100644 index 00000000000000..655e943e52ecb9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_dholuo_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_cased_finetuned_dholuo BertEmbeddings from Davlan +author: John Snow Labs +name: bert_base_multilingual_cased_finetuned_dholuo +date: 2023-09-09 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_cased_finetuned_dholuo` is a Multilingual model originally trained by Davlan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_dholuo_xx_5.1.1_3.0_1694278951025.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_dholuo_xx_5.1.1_3.0_1694278951025.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_multilingual_cased_finetuned_dholuo","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_multilingual_cased_finetuned_dholuo", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_cased_finetuned_dholuo| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|665.0 MB| + +## References + +https://huggingface.co/Davlan/bert-base-multilingual-cased-finetuned-luo \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_hausa_xx.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_hausa_xx.md new file mode 100644 index 00000000000000..149ed07c2c64ef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_hausa_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_cased_finetuned_hausa BertEmbeddings from Davlan +author: John Snow Labs +name: bert_base_multilingual_cased_finetuned_hausa +date: 2023-09-09 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_cased_finetuned_hausa` is a Multilingual model originally trained by Davlan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_hausa_xx_5.1.1_3.0_1694278252314.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_hausa_xx_5.1.1_3.0_1694278252314.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_multilingual_cased_finetuned_hausa","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_multilingual_cased_finetuned_hausa", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_cased_finetuned_hausa| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|664.4 MB| + +## References + +https://huggingface.co/Davlan/bert-base-multilingual-cased-finetuned-hausa \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_igbo_xx.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_igbo_xx.md new file mode 100644 index 00000000000000..0e4a6b4a957421 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_igbo_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_cased_finetuned_igbo BertEmbeddings from Davlan +author: John Snow Labs +name: bert_base_multilingual_cased_finetuned_igbo +date: 2023-09-09 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_cased_finetuned_igbo` is a Multilingual model originally trained by Davlan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_igbo_xx_5.1.1_3.0_1694278402439.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_igbo_xx_5.1.1_3.0_1694278402439.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_multilingual_cased_finetuned_igbo","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_multilingual_cased_finetuned_igbo", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_cased_finetuned_igbo| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|665.0 MB| + +## References + +https://huggingface.co/Davlan/bert-base-multilingual-cased-finetuned-igbo \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_kinyarwanda_xx.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_kinyarwanda_xx.md new file mode 100644 index 00000000000000..957c00f8f8fdda --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_kinyarwanda_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_cased_finetuned_kinyarwanda BertEmbeddings from Davlan +author: John Snow Labs +name: bert_base_multilingual_cased_finetuned_kinyarwanda +date: 2023-09-09 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_cased_finetuned_kinyarwanda` is a Multilingual model originally trained by Davlan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_kinyarwanda_xx_5.1.1_3.0_1694278577489.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_kinyarwanda_xx_5.1.1_3.0_1694278577489.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_multilingual_cased_finetuned_kinyarwanda","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_multilingual_cased_finetuned_kinyarwanda", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_cased_finetuned_kinyarwanda| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|665.0 MB| + +## References + +https://huggingface.co/Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_luganda_xx.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_luganda_xx.md new file mode 100644 index 00000000000000..c46ab88595d621 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_luganda_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_cased_finetuned_luganda BertEmbeddings from Davlan +author: John Snow Labs +name: bert_base_multilingual_cased_finetuned_luganda +date: 2023-09-09 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_cased_finetuned_luganda` is a Multilingual model originally trained by Davlan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_luganda_xx_5.1.1_3.0_1694278771746.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_luganda_xx_5.1.1_3.0_1694278771746.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_multilingual_cased_finetuned_luganda","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_multilingual_cased_finetuned_luganda", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_cased_finetuned_luganda| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|665.0 MB| + +## References + +https://huggingface.co/Davlan/bert-base-multilingual-cased-finetuned-luganda \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_naija_xx.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_naija_xx.md new file mode 100644 index 00000000000000..421d7091f94175 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_naija_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_cased_finetuned_naija BertEmbeddings from Davlan +author: John Snow Labs +name: bert_base_multilingual_cased_finetuned_naija +date: 2023-09-09 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_cased_finetuned_naija` is a Multilingual model originally trained by Davlan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_naija_xx_5.1.1_3.0_1694279152866.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_naija_xx_5.1.1_3.0_1694279152866.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_multilingual_cased_finetuned_naija","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_multilingual_cased_finetuned_naija", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_cased_finetuned_naija| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|665.0 MB| + +## References + +https://huggingface.co/Davlan/bert-base-multilingual-cased-finetuned-naija \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_swahili_xx.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_swahili_xx.md new file mode 100644 index 00000000000000..269a10c63d7aef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_swahili_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_cased_finetuned_swahili BertEmbeddings from Davlan +author: John Snow Labs +name: bert_base_multilingual_cased_finetuned_swahili +date: 2023-09-09 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_cased_finetuned_swahili` is a Multilingual model originally trained by Davlan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_swahili_xx_5.1.1_3.0_1694279327334.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_swahili_xx_5.1.1_3.0_1694279327334.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_multilingual_cased_finetuned_swahili","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_multilingual_cased_finetuned_swahili", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_cased_finetuned_swahili| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|664.1 MB| + +## References + +https://huggingface.co/Davlan/bert-base-multilingual-cased-finetuned-swahili \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_wolof_xx.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_wolof_xx.md new file mode 100644 index 00000000000000..6e5d0c0b946af8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_wolof_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_cased_finetuned_wolof BertEmbeddings from Davlan +author: John Snow Labs +name: bert_base_multilingual_cased_finetuned_wolof +date: 2023-09-09 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_cased_finetuned_wolof` is a Multilingual model originally trained by Davlan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_wolof_xx_5.1.1_3.0_1694279557140.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_wolof_xx_5.1.1_3.0_1694279557140.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_multilingual_cased_finetuned_wolof","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_multilingual_cased_finetuned_wolof", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_cased_finetuned_wolof| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|665.0 MB| + +## References + +https://huggingface.co/Davlan/bert-base-multilingual-cased-finetuned-wolof \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_yoruba_xx.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_yoruba_xx.md new file mode 100644 index 00000000000000..9e9d21f5fe7c9a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_multilingual_cased_finetuned_yoruba_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_cased_finetuned_yoruba BertEmbeddings from Davlan +author: John Snow Labs +name: bert_base_multilingual_cased_finetuned_yoruba +date: 2023-09-09 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_cased_finetuned_yoruba` is a Multilingual model originally trained by Davlan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_yoruba_xx_5.1.1_3.0_1694279730284.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_yoruba_xx_5.1.1_3.0_1694279730284.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_multilingual_cased_finetuned_yoruba","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_multilingual_cased_finetuned_yoruba", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_cased_finetuned_yoruba| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|665.0 MB| + +## References + +https://huggingface.co/Davlan/bert-base-multilingual-cased-finetuned-yoruba \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_nli_ct_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_nli_ct_en.md new file mode 100644 index 00000000000000..1eafc57ae39a42 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_nli_ct_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_nli_ct BertEmbeddings from Contrastive-Tension +author: John Snow Labs +name: bert_base_nli_ct +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_nli_ct` is a English model originally trained by Contrastive-Tension. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_nli_ct_en_5.1.1_3.0_1694266469274.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_nli_ct_en_5.1.1_3.0_1694266469274.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_nli_ct","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_nli_ct", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_nli_ct| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Contrastive-Tension/BERT-Base-NLI-CT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_parsbert_uncased_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_parsbert_uncased_finetuned_en.md new file mode 100644 index 00000000000000..7a6ba5464ba044 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_parsbert_uncased_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_parsbert_uncased_finetuned BertEmbeddings from Ashkanmh +author: John Snow Labs +name: bert_base_parsbert_uncased_finetuned +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_parsbert_uncased_finetuned` is a English model originally trained by Ashkanmh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_parsbert_uncased_finetuned_en_5.1.1_3.0_1694261097567.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_parsbert_uncased_finetuned_en_5.1.1_3.0_1694261097567.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_parsbert_uncased_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_parsbert_uncased_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_parsbert_uncased_finetuned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|606.4 MB| + +## References + +https://huggingface.co/Ashkanmh/bert-base-parsbert-uncased-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_portuguese_cased_test_server_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_portuguese_cased_test_server_en.md new file mode 100644 index 00000000000000..2b291b6de7d25f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_portuguese_cased_test_server_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_portuguese_cased_test_server BertEmbeddings from tiagoseca +author: John Snow Labs +name: bert_base_portuguese_cased_test_server +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_portuguese_cased_test_server` is a English model originally trained by tiagoseca. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_portuguese_cased_test_server_en_5.1.1_3.0_1694282814248.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_portuguese_cased_test_server_en_5.1.1_3.0_1694282814248.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_portuguese_cased_test_server","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_portuguese_cased_test_server", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_portuguese_cased_test_server| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/tiagoseca/bert-base-portuguese-cased-test-server \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_pubmed_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_pubmed_en.md new file mode 100644 index 00000000000000..e0e99afb07122a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_pubmed_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_pubmed BertEmbeddings from Aktsvigun +author: John Snow Labs +name: bert_base_pubmed +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_pubmed` is a English model originally trained by Aktsvigun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_pubmed_en_5.1.1_3.0_1694284978014.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_pubmed_en_5.1.1_3.0_1694284978014.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_pubmed","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_pubmed", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_pubmed| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Aktsvigun/bert-base-pubmed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_dish_descriptions_128_0.5m_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_dish_descriptions_128_0.5m_en.md new file mode 100644 index 00000000000000..2e110d21a00bd6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_dish_descriptions_128_0.5m_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_dish_descriptions_128_0.5m BertEmbeddings from abhilashawasthi +author: John Snow Labs +name: bert_base_uncased_dish_descriptions_128_0.5m +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_dish_descriptions_128_0.5m` is a English model originally trained by abhilashawasthi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_dish_descriptions_128_0.5m_en_5.1.1_3.0_1694267375313.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_dish_descriptions_128_0.5m_en_5.1.1_3.0_1694267375313.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_dish_descriptions_128_0.5m","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_dish_descriptions_128_0.5m", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_dish_descriptions_128_0.5m| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/abhilashawasthi/bert-base-uncased_dish_descriptions_128_0.5M \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_dish_descriptions_128_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_dish_descriptions_128_en.md new file mode 100644 index 00000000000000..b4649ff02aed7c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_dish_descriptions_128_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_dish_descriptions_128 BertEmbeddings from abhilashawasthi +author: John Snow Labs +name: bert_base_uncased_dish_descriptions_128 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_dish_descriptions_128` is a English model originally trained by abhilashawasthi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_dish_descriptions_128_en_5.1.1_3.0_1694267255228.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_dish_descriptions_128_en_5.1.1_3.0_1694267255228.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_dish_descriptions_128","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_dish_descriptions_128", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_dish_descriptions_128| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/abhilashawasthi/bert-base-uncased_dish_descriptions_128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_fined_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_fined_en.md new file mode 100644 index 00000000000000..105aab3e571653 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_fined_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_fined BertEmbeddings from WaylonZHANG +author: John Snow Labs +name: bert_base_uncased_fined +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_fined` is a English model originally trained by WaylonZHANG. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_fined_en_5.1.1_3.0_1694278266100.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_fined_en_5.1.1_3.0_1694278266100.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_fined","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_fined", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_fined| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/WaylonZHANG/bert_base_uncased_fined \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_finetune_security_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_finetune_security_en.md new file mode 100644 index 00000000000000..ab915d7a7e8509 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_finetune_security_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetune_security BertEmbeddings from forcorpus +author: John Snow Labs +name: bert_base_uncased_finetune_security +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetune_security` is a English model originally trained by forcorpus. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetune_security_en_5.1.1_3.0_1694283220765.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetune_security_en_5.1.1_3.0_1694283220765.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetune_security","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetune_security", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetune_security| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/forcorpus/bert-base-uncased-finetune-security \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_finetuned_jira_hyperledger_issue_titles_and_bodies_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_finetuned_jira_hyperledger_issue_titles_and_bodies_en.md new file mode 100644 index 00000000000000..b4be4993f6f65d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_finetuned_jira_hyperledger_issue_titles_and_bodies_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_jira_hyperledger_issue_titles_and_bodies BertEmbeddings from ietz +author: John Snow Labs +name: bert_base_uncased_finetuned_jira_hyperledger_issue_titles_and_bodies +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_jira_hyperledger_issue_titles_and_bodies` is a English model originally trained by ietz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_jira_hyperledger_issue_titles_and_bodies_en_5.1.1_3.0_1694266366291.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_jira_hyperledger_issue_titles_and_bodies_en_5.1.1_3.0_1694266366291.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_jira_hyperledger_issue_titles_and_bodies","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_jira_hyperledger_issue_titles_and_bodies", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_jira_hyperledger_issue_titles_and_bodies| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/ietz/bert-base-uncased-finetuned-jira-hyperledger-issue-titles-and-bodies \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_finetuned_jira_inteldaos_issue_titles_and_bodies_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_finetuned_jira_inteldaos_issue_titles_and_bodies_en.md new file mode 100644 index 00000000000000..57f22521a24775 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_finetuned_jira_inteldaos_issue_titles_and_bodies_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_jira_inteldaos_issue_titles_and_bodies BertEmbeddings from ietz +author: John Snow Labs +name: bert_base_uncased_finetuned_jira_inteldaos_issue_titles_and_bodies +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_jira_inteldaos_issue_titles_and_bodies` is a English model originally trained by ietz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_jira_inteldaos_issue_titles_and_bodies_en_5.1.1_3.0_1694266491902.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_jira_inteldaos_issue_titles_and_bodies_en_5.1.1_3.0_1694266491902.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_jira_inteldaos_issue_titles_and_bodies","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_jira_inteldaos_issue_titles_and_bodies", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_jira_inteldaos_issue_titles_and_bodies| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/ietz/bert-base-uncased-finetuned-jira-inteldaos-issue-titles-and-bodies \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_finetuned_jira_jira_issue_titles_and_bodies_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_finetuned_jira_jira_issue_titles_and_bodies_en.md new file mode 100644 index 00000000000000..ade00fc8ff535c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_finetuned_jira_jira_issue_titles_and_bodies_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_jira_jira_issue_titles_and_bodies BertEmbeddings from ietz +author: John Snow Labs +name: bert_base_uncased_finetuned_jira_jira_issue_titles_and_bodies +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_jira_jira_issue_titles_and_bodies` is a English model originally trained by ietz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_jira_jira_issue_titles_and_bodies_en_5.1.1_3.0_1694266630282.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_jira_jira_issue_titles_and_bodies_en_5.1.1_3.0_1694266630282.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_jira_jira_issue_titles_and_bodies","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_jira_jira_issue_titles_and_bodies", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_jira_jira_issue_titles_and_bodies| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/ietz/bert-base-uncased-finetuned-jira-jira-issue-titles-and-bodies \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_finetuned_jira_qt_issue_titles_and_bodies_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_finetuned_jira_qt_issue_titles_and_bodies_en.md new file mode 100644 index 00000000000000..d0c1d0e0c61841 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_finetuned_jira_qt_issue_titles_and_bodies_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_jira_qt_issue_titles_and_bodies BertEmbeddings from ietz +author: John Snow Labs +name: bert_base_uncased_finetuned_jira_qt_issue_titles_and_bodies +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_jira_qt_issue_titles_and_bodies` is a English model originally trained by ietz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_jira_qt_issue_titles_and_bodies_en_5.1.1_3.0_1694266778727.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_jira_qt_issue_titles_and_bodies_en_5.1.1_3.0_1694266778727.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_jira_qt_issue_titles_and_bodies","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_jira_qt_issue_titles_and_bodies", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_jira_qt_issue_titles_and_bodies| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/ietz/bert-base-uncased-finetuned-jira-qt-issue-titles-and-bodies \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_issues_128_susnato_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_issues_128_susnato_en.md new file mode 100644 index 00000000000000..320f013ae433a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_issues_128_susnato_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_susnato BertEmbeddings from susnato +author: John Snow Labs +name: bert_base_uncased_issues_128_susnato +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_susnato` is a English model originally trained by susnato. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_susnato_en_5.1.1_3.0_1694259405770.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_susnato_en_5.1.1_3.0_1694259405770.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_susnato","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_susnato", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_susnato| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/susnato/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_issues_128_tanviraumi_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_issues_128_tanviraumi_en.md new file mode 100644 index 00000000000000..61d67ec04e8d27 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_issues_128_tanviraumi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_tanviraumi BertEmbeddings from tanviraumi +author: John Snow Labs +name: bert_base_uncased_issues_128_tanviraumi +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_tanviraumi` is a English model originally trained by tanviraumi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_tanviraumi_en_5.1.1_3.0_1694283686698.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_tanviraumi_en_5.1.1_3.0_1694283686698.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_tanviraumi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_tanviraumi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_tanviraumi| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/tanviraumi/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_semeval2014_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_semeval2014_en.md new file mode 100644 index 00000000000000..8ade600e6871f3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_semeval2014_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_semeval2014 BertEmbeddings from StevenLimcorn +author: John Snow Labs +name: bert_base_uncased_semeval2014 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_semeval2014` is a English model originally trained by StevenLimcorn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_semeval2014_en_5.1.1_3.0_1694260733939.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_semeval2014_en_5.1.1_3.0_1694260733939.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_semeval2014","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_semeval2014", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_semeval2014| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/StevenLimcorn/bert-base-uncased-semeval2014 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_zhibinhong_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_zhibinhong_en.md new file mode 100644 index 00000000000000..62401ec1c801ac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_base_uncased_zhibinhong_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_zhibinhong BertEmbeddings from Zhibinhong +author: John Snow Labs +name: bert_base_uncased_zhibinhong +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_zhibinhong` is a English model originally trained by Zhibinhong. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_zhibinhong_en_5.1.1_3.0_1694278831256.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_zhibinhong_en_5.1.1_3.0_1694278831256.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_zhibinhong","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_zhibinhong", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_zhibinhong| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Zhibinhong/bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_finetuning_test1227_hug_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_finetuning_test1227_hug_en.md new file mode 100644 index 00000000000000..bc672cd4755582 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_finetuning_test1227_hug_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_finetuning_test1227_hug BertEmbeddings from junzai +author: John Snow Labs +name: bert_finetuning_test1227_hug +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuning_test1227_hug` is a English model originally trained by junzai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuning_test1227_hug_en_5.1.1_3.0_1694284329023.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuning_test1227_hug_en_5.1.1_3.0_1694284329023.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_finetuning_test1227_hug","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_finetuning_test1227_hug", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuning_test1227_hug| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/junzai/bert_finetuning_test1227_hug \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_finetuning_test_hug_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_finetuning_test_hug_en.md new file mode 100644 index 00000000000000..35e6d3797193ba --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_finetuning_test_hug_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_finetuning_test_hug BertEmbeddings from junzai +author: John Snow Labs +name: bert_finetuning_test_hug +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuning_test_hug` is a English model originally trained by junzai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuning_test_hug_en_5.1.1_3.0_1694284465366.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuning_test_hug_en_5.1.1_3.0_1694284465366.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_finetuning_test_hug","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_finetuning_test_hug", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuning_test_hug| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/junzai/bert_finetuning_test_hug \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_funting_test_ai10_junzai_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_funting_test_ai10_junzai_en.md new file mode 100644 index 00000000000000..1e5dc78bc4c5f5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_funting_test_ai10_junzai_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_funting_test_ai10_junzai BertEmbeddings from junzai +author: John Snow Labs +name: bert_funting_test_ai10_junzai +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_funting_test_ai10_junzai` is a English model originally trained by junzai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_funting_test_ai10_junzai_en_5.1.1_3.0_1694284595771.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_funting_test_ai10_junzai_en_5.1.1_3.0_1694284595771.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_funting_test_ai10_junzai","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_funting_test_ai10_junzai", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_funting_test_ai10_junzai| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/junzai/bert_funting_test_ai10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_gb_2021_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_gb_2021_en.md new file mode 100644 index 00000000000000..1e05c2f55e62cd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_gb_2021_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_gb_2021 BertEmbeddings from mossaic-candle +author: John Snow Labs +name: bert_gb_2021 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_gb_2021` is a English model originally trained by mossaic-candle. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_gb_2021_en_5.1.1_3.0_1694265192765.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_gb_2021_en_5.1.1_3.0_1694265192765.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_gb_2021","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_gb_2021", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_gb_2021| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|258.6 MB| + +## References + +https://huggingface.co/mossaic-candle/bert-gb-2021 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_hateracism90000_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_hateracism90000_en.md new file mode 100644 index 00000000000000..aa59d80094ddeb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_hateracism90000_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_hateracism90000 BertEmbeddings from MutazYoune +author: John Snow Labs +name: bert_hateracism90000 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_hateracism90000` is a English model originally trained by MutazYoune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_hateracism90000_en_5.1.1_3.0_1694278823263.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_hateracism90000_en_5.1.1_3.0_1694278823263.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_hateracism90000","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_hateracism90000", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_hateracism90000| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.3 MB| + +## References + +https://huggingface.co/MutazYoune/bert_hateracism90000 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_8_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_8_en.md new file mode 100644 index 00000000000000..34ed104b7d5d82 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_8_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_8 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_8 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_8` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_8_en_5.1.1_3.0_1694258979428.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_8_en_5.1.1_3.0_1694258979428.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_8","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_8", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_8| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-8 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_9_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_9_en.md new file mode 100644 index 00000000000000..aa78adbc9c3e68 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_9_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_9 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_9 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_9` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_9_en_5.1.1_3.0_1694259240953.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_9_en_5.1.1_3.0_1694259240953.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_9","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_9", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_9| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-9 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_0_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_0_en.md new file mode 100644 index 00000000000000..c29abd12730a56 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_0 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_0 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_0` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_0_en_5.1.1_3.0_1694280291790.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_0_en_5.1.1_3.0_1694280291790.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_0| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_2_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_2_en.md new file mode 100644 index 00000000000000..736e65d5738670 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_2 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_2 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_2` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_2_en_5.1.1_3.0_1694282816860.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_2_en_5.1.1_3.0_1694282816860.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_3_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_3_en.md new file mode 100644 index 00000000000000..78ce6d3556d706 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_3 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_3 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_3` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_3_en_5.1.1_3.0_1694283082305.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_3_en_5.1.1_3.0_1694283082305.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_4_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_4_en.md new file mode 100644 index 00000000000000..d5628516f2eb61 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_4 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_4 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_4` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_4_en_5.1.1_3.0_1694283342684.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_4_en_5.1.1_3.0_1694283342684.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_4| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_5_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_5_en.md new file mode 100644 index 00000000000000..feec7fe169dbd1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_5 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_5 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_5` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_5_en_5.1.1_3.0_1694284168464.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_5_en_5.1.1_3.0_1694284168464.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_5| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_6_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_6_en.md new file mode 100644 index 00000000000000..8e90a33d05ac78 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_6_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_6 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_6 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_6` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_6_en_5.1.1_3.0_1694284456608.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_6_en_5.1.1_3.0_1694284456608.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_6","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_6", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_6| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_7_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_7_en.md new file mode 100644 index 00000000000000..afdd80eaec72b3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_7_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_7 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_7 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_7` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_7_en_5.1.1_3.0_1694284718630.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_7_en_5.1.1_3.0_1694284718630.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_7","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_7", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_7| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_8_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_8_en.md new file mode 100644 index 00000000000000..8528046ec74e1f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_8_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_8 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_8 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_8` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_8_en_5.1.1_3.0_1694284970323.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_8_en_5.1.1_3.0_1694284970323.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_8","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_8", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_8| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-8 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_whole_word_masking_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_whole_word_masking_en.md new file mode 100644 index 00000000000000..7a5098c64d7ad6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_cased_whole_word_masking_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_whole_word_masking BertEmbeddings from huggingface +author: John Snow Labs +name: bert_large_cased_whole_word_masking +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_whole_word_masking` is a English model originally trained by huggingface. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_whole_word_masking_en_5.1.1_3.0_1694259195109.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_whole_word_masking_en_5.1.1_3.0_1694259195109.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_whole_word_masking","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_whole_word_masking", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_whole_word_masking| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/bert-large-cased-whole-word-masking \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_ct_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_ct_en.md new file mode 100644 index 00000000000000..1e073ff2832ad7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_ct_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_ct BertEmbeddings from Contrastive-Tension +author: John Snow Labs +name: bert_large_ct +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_ct` is a English model originally trained by Contrastive-Tension. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_ct_en_5.1.1_3.0_1694266770807.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_ct_en_5.1.1_3.0_1694266770807.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_ct","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_ct", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_ct| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/Contrastive-Tension/BERT-Large-CT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_nli_ct_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_nli_ct_en.md new file mode 100644 index 00000000000000..269fab63ec9a09 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_nli_ct_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_nli_ct BertEmbeddings from Contrastive-Tension +author: John Snow Labs +name: bert_large_nli_ct +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_nli_ct` is a English model originally trained by Contrastive-Tension. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_nli_ct_en_5.1.1_3.0_1694267030145.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_nli_ct_en_5.1.1_3.0_1694267030145.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_nli_ct","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_nli_ct", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_nli_ct| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/Contrastive-Tension/BERT-Large-NLI-CT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_portuguese_cased_legal_mlm_pt.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_portuguese_cased_legal_mlm_pt.md new file mode 100644 index 00000000000000..b65c271db09561 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_portuguese_cased_legal_mlm_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese bert_large_portuguese_cased_legal_mlm BertEmbeddings from stjiris +author: John Snow Labs +name: bert_large_portuguese_cased_legal_mlm +date: 2023-09-09 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_portuguese_cased_legal_mlm` is a Portuguese model originally trained by stjiris. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_portuguese_cased_legal_mlm_pt_5.1.1_3.0_1694284288827.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_portuguese_cased_legal_mlm_pt_5.1.1_3.0_1694284288827.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_portuguese_cased_legal_mlm","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_portuguese_cased_legal_mlm", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_portuguese_cased_legal_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|1.2 GB| + +## References + +https://huggingface.co/stjiris/bert-large-portuguese-cased-legal-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_portuguese_cased_legal_tsdae_pt.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_portuguese_cased_legal_tsdae_pt.md new file mode 100644 index 00000000000000..481a8fb59f782a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_portuguese_cased_legal_tsdae_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese bert_large_portuguese_cased_legal_tsdae BertEmbeddings from stjiris +author: John Snow Labs +name: bert_large_portuguese_cased_legal_tsdae +date: 2023-09-09 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_portuguese_cased_legal_tsdae` is a Portuguese model originally trained by stjiris. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_portuguese_cased_legal_tsdae_pt_5.1.1_3.0_1694280251721.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_portuguese_cased_legal_tsdae_pt_5.1.1_3.0_1694280251721.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_portuguese_cased_legal_tsdae","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_portuguese_cased_legal_tsdae", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_portuguese_cased_legal_tsdae| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|1.2 GB| + +## References + +https://huggingface.co/stjiris/bert-large-portuguese-cased-legal-tsdae \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_facebook_election_ads_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_facebook_election_ads_en.md new file mode 100644 index 00000000000000..641ffc3a53bf4a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_facebook_election_ads_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_facebook_election_ads BertEmbeddings from StevenLimcorn +author: John Snow Labs +name: bert_large_uncased_facebook_election_ads +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_facebook_election_ads` is a English model originally trained by StevenLimcorn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_facebook_election_ads_en_5.1.1_3.0_1694278584320.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_facebook_election_ads_en_5.1.1_3.0_1694278584320.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_facebook_election_ads","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_facebook_election_ads", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_facebook_election_ads| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/StevenLimcorn/bert-large-uncased-facebook-election-ads \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2014_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2014_en.md new file mode 100644 index 00000000000000..85f86123fd8f8d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2014_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_semeval2014 BertEmbeddings from StevenLimcorn +author: John Snow Labs +name: bert_large_uncased_semeval2014 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_semeval2014` is a English model originally trained by StevenLimcorn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_semeval2014_en_5.1.1_3.0_1694261000799.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_semeval2014_en_5.1.1_3.0_1694261000799.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_semeval2014","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_semeval2014", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_semeval2014| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/StevenLimcorn/bert-large-uncased-semeval2014 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2014_laptops_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2014_laptops_en.md new file mode 100644 index 00000000000000..955dcedb8ddbf5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2014_laptops_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_semeval2014_laptops BertEmbeddings from StevenLimcorn +author: John Snow Labs +name: bert_large_uncased_semeval2014_laptops +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_semeval2014_laptops` is a English model originally trained by StevenLimcorn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_semeval2014_laptops_en_5.1.1_3.0_1694265613724.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_semeval2014_laptops_en_5.1.1_3.0_1694265613724.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_semeval2014_laptops","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_semeval2014_laptops", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_semeval2014_laptops| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/StevenLimcorn/bert-large-uncased-semeval2014-laptops \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2014_restaurants_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2014_restaurants_en.md new file mode 100644 index 00000000000000..7872f10823545d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2014_restaurants_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_semeval2014_restaurants BertEmbeddings from StevenLimcorn +author: John Snow Labs +name: bert_large_uncased_semeval2014_restaurants +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_semeval2014_restaurants` is a English model originally trained by StevenLimcorn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_semeval2014_restaurants_en_5.1.1_3.0_1694265347275.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_semeval2014_restaurants_en_5.1.1_3.0_1694265347275.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_semeval2014_restaurants","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_semeval2014_restaurants", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_semeval2014_restaurants| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/StevenLimcorn/bert-large-uncased-semeval2014-restaurants \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2015_laptops_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2015_laptops_en.md new file mode 100644 index 00000000000000..55537fe49f637b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2015_laptops_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_semeval2015_laptops BertEmbeddings from StevenLimcorn +author: John Snow Labs +name: bert_large_uncased_semeval2015_laptops +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_semeval2015_laptops` is a English model originally trained by StevenLimcorn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_semeval2015_laptops_en_5.1.1_3.0_1694266817614.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_semeval2015_laptops_en_5.1.1_3.0_1694266817614.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_semeval2015_laptops","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_semeval2015_laptops", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_semeval2015_laptops| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/StevenLimcorn/bert-large-uncased-semeval2015-laptops \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2015_restaurants_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2015_restaurants_en.md new file mode 100644 index 00000000000000..4ecf75d897841d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2015_restaurants_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_semeval2015_restaurants BertEmbeddings from StevenLimcorn +author: John Snow Labs +name: bert_large_uncased_semeval2015_restaurants +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_semeval2015_restaurants` is a English model originally trained by StevenLimcorn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_semeval2015_restaurants_en_5.1.1_3.0_1694266273762.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_semeval2015_restaurants_en_5.1.1_3.0_1694266273762.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_semeval2015_restaurants","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_semeval2015_restaurants", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_semeval2015_restaurants| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/StevenLimcorn/bert-large-uncased-semeval2015-restaurants \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2016_laptops_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2016_laptops_en.md new file mode 100644 index 00000000000000..fa9b772ccdcb54 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2016_laptops_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_semeval2016_laptops BertEmbeddings from StevenLimcorn +author: John Snow Labs +name: bert_large_uncased_semeval2016_laptops +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_semeval2016_laptops` is a English model originally trained by StevenLimcorn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_semeval2016_laptops_en_5.1.1_3.0_1694267362070.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_semeval2016_laptops_en_5.1.1_3.0_1694267362070.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_semeval2016_laptops","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_semeval2016_laptops", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_semeval2016_laptops| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/StevenLimcorn/bert-large-uncased-semeval2016-laptops \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2016_restaurants_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2016_restaurants_en.md new file mode 100644 index 00000000000000..3dfb2372eafc23 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_semeval2016_restaurants_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_semeval2016_restaurants BertEmbeddings from StevenLimcorn +author: John Snow Labs +name: bert_large_uncased_semeval2016_restaurants +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_semeval2016_restaurants` is a English model originally trained by StevenLimcorn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_semeval2016_restaurants_en_5.1.1_3.0_1694267083539.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_semeval2016_restaurants_en_5.1.1_3.0_1694267083539.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_semeval2016_restaurants","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_semeval2016_restaurants", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_semeval2016_restaurants| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/StevenLimcorn/bert-large-uncased-semeval2016-restaurants \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_whole_word_masking_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_whole_word_masking_en.md new file mode 100644 index 00000000000000..134d8c8bab6e0a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_large_uncased_whole_word_masking_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_whole_word_masking BertEmbeddings from huggingface +author: John Snow Labs +name: bert_large_uncased_whole_word_masking +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_whole_word_masking` is a English model originally trained by huggingface. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_whole_word_masking_en_5.1.1_3.0_1694259457325.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_whole_word_masking_en_5.1.1_3.0_1694259457325.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_whole_word_masking","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_whole_word_masking", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_whole_word_masking| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/bert-large-uncased-whole-word-masking \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_nlp_project_news_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_nlp_project_news_en.md new file mode 100644 index 00000000000000..75d00146e0681c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_nlp_project_news_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_nlp_project_news BertEmbeddings from jestemleon +author: John Snow Labs +name: bert_nlp_project_news +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_nlp_project_news` is a English model originally trained by jestemleon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_nlp_project_news_en_5.1.1_3.0_1694260118481.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_nlp_project_news_en_5.1.1_3.0_1694260118481.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_nlp_project_news","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_nlp_project_news", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_nlp_project_news| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/jestemleon/bert-nlp-project-news \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_patent_reference_extraction_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_patent_reference_extraction_en.md new file mode 100644 index 00000000000000..42c962ffcd6101 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_patent_reference_extraction_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_patent_reference_extraction BertEmbeddings from kaesve +author: John Snow Labs +name: bert_patent_reference_extraction +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_patent_reference_extraction` is a English model originally trained by kaesve. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_patent_reference_extraction_en_5.1.1_3.0_1694284932215.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_patent_reference_extraction_en_5.1.1_3.0_1694284932215.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_patent_reference_extraction","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_patent_reference_extraction", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_patent_reference_extraction| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/kaesve/BERT_patent_reference_extraction \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_racism15_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_racism15_en.md new file mode 100644 index 00000000000000..e9e3efe744910e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_racism15_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_racism15 BertEmbeddings from MutazYoune +author: John Snow Labs +name: bert_racism15 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_racism15` is a English model originally trained by MutazYoune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_racism15_en_5.1.1_3.0_1694266961024.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_racism15_en_5.1.1_3.0_1694266961024.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_racism15","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_racism15", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_racism15| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.2 MB| + +## References + +https://huggingface.co/MutazYoune/BERT_racism15 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_racism_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_racism_en.md new file mode 100644 index 00000000000000..1a10893b096360 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_racism_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_racism BertEmbeddings from MutazYoune +author: John Snow Labs +name: bert_racism +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_racism` is a English model originally trained by MutazYoune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_racism_en_5.1.1_3.0_1694266727516.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_racism_en_5.1.1_3.0_1694266727516.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_racism","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_racism", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_racism| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.1 MB| + +## References + +https://huggingface.co/MutazYoune/BERT_racism \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_random_weights_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_random_weights_en.md new file mode 100644 index 00000000000000..eee950bc418190 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_random_weights_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_random_weights BertEmbeddings from ashraq +author: John Snow Labs +name: bert_random_weights +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_random_weights` is a English model originally trained by ashraq. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_random_weights_en_5.1.1_3.0_1694265078198.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_random_weights_en_5.1.1_3.0_1694265078198.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_random_weights","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_random_weights", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_random_weights| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.8 GB| + +## References + +https://huggingface.co/ashraq/bert-random-weights \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_sparql_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_sparql_en.md new file mode 100644 index 00000000000000..0a85fafc6f1dc5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_sparql_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_sparql BertEmbeddings from JulienRPA +author: John Snow Labs +name: bert_sparql +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_sparql` is a English model originally trained by JulienRPA. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sparql_en_5.1.1_3.0_1694279133283.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sparql_en_5.1.1_3.0_1694279133283.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_sparql","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_sparql", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sparql| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|413.6 MB| + +## References + +https://huggingface.co/JulienRPA/BERT_SPARQL \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_srb_base_cased_oscar_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_srb_base_cased_oscar_en.md new file mode 100644 index 00000000000000..dd2fa004633eba --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_srb_base_cased_oscar_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_srb_base_cased_oscar BertEmbeddings from Aleksandar +author: John Snow Labs +name: bert_srb_base_cased_oscar +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_srb_base_cased_oscar` is a English model originally trained by Aleksandar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_srb_base_cased_oscar_en_5.1.1_3.0_1694260821609.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_srb_base_cased_oscar_en_5.1.1_3.0_1694260821609.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_srb_base_cased_oscar","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_srb_base_cased_oscar", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_srb_base_cased_oscar| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.9 MB| + +## References + +https://huggingface.co/Aleksandar/bert-srb-base-cased-oscar \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_tagalog_base_cased_tl.md b/docs/_posts/ahmedlone127/2023-09-09-bert_tagalog_base_cased_tl.md new file mode 100644 index 00000000000000..a6f876f661e217 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_tagalog_base_cased_tl.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Tagalog bert_tagalog_base_cased BertEmbeddings from jcblaise +author: John Snow Labs +name: bert_tagalog_base_cased +date: 2023-09-09 +tags: [bert, tl, open_source, fill_mask, onnx] +task: Embeddings +language: tl +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tagalog_base_cased` is a Tagalog model originally trained by jcblaise. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tagalog_base_cased_tl_5.1.1_3.0_1694278808394.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tagalog_base_cased_tl_5.1.1_3.0_1694278808394.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_tagalog_base_cased","tl") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_tagalog_base_cased", "tl") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tagalog_base_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|tl| +|Size:|406.9 MB| + +## References + +https://huggingface.co/jcblaise/bert-tagalog-base-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_tagalog_base_cased_wwm_tl.md b/docs/_posts/ahmedlone127/2023-09-09-bert_tagalog_base_cased_wwm_tl.md new file mode 100644 index 00000000000000..5e3c837c6d007a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_tagalog_base_cased_wwm_tl.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Tagalog bert_tagalog_base_cased_wwm BertEmbeddings from jcblaise +author: John Snow Labs +name: bert_tagalog_base_cased_wwm +date: 2023-09-09 +tags: [bert, tl, open_source, fill_mask, onnx] +task: Embeddings +language: tl +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tagalog_base_cased_wwm` is a Tagalog model originally trained by jcblaise. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tagalog_base_cased_wwm_tl_5.1.1_3.0_1694278641553.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tagalog_base_cased_wwm_tl_5.1.1_3.0_1694278641553.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_tagalog_base_cased_wwm","tl") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_tagalog_base_cased_wwm", "tl") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tagalog_base_cased_wwm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|tl| +|Size:|406.9 MB| + +## References + +https://huggingface.co/jcblaise/bert-tagalog-base-cased-WWM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_tagalog_base_uncased_tl.md b/docs/_posts/ahmedlone127/2023-09-09-bert_tagalog_base_uncased_tl.md new file mode 100644 index 00000000000000..82b278c29a6f6c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_tagalog_base_uncased_tl.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Tagalog bert_tagalog_base_uncased BertEmbeddings from jcblaise +author: John Snow Labs +name: bert_tagalog_base_uncased +date: 2023-09-09 +tags: [bert, tl, open_source, fill_mask, onnx] +task: Embeddings +language: tl +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tagalog_base_uncased` is a Tagalog model originally trained by jcblaise. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tagalog_base_uncased_tl_5.1.1_3.0_1694279118482.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tagalog_base_uncased_tl_5.1.1_3.0_1694279118482.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_tagalog_base_uncased","tl") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_tagalog_base_uncased", "tl") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tagalog_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|tl| +|Size:|406.9 MB| + +## References + +https://huggingface.co/jcblaise/bert-tagalog-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_tagalog_base_uncased_wwm_tl.md b/docs/_posts/ahmedlone127/2023-09-09-bert_tagalog_base_uncased_wwm_tl.md new file mode 100644 index 00000000000000..5dcf7652d336f8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_tagalog_base_uncased_wwm_tl.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Tagalog bert_tagalog_base_uncased_wwm BertEmbeddings from jcblaise +author: John Snow Labs +name: bert_tagalog_base_uncased_wwm +date: 2023-09-09 +tags: [bert, tl, open_source, fill_mask, onnx] +task: Embeddings +language: tl +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tagalog_base_uncased_wwm` is a Tagalog model originally trained by jcblaise. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tagalog_base_uncased_wwm_tl_5.1.1_3.0_1694278968293.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tagalog_base_uncased_wwm_tl_5.1.1_3.0_1694278968293.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_tagalog_base_uncased_wwm","tl") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_tagalog_base_uncased_wwm", "tl") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tagalog_base_uncased_wwm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|tl| +|Size:|406.9 MB| + +## References + +https://huggingface.co/jcblaise/bert-tagalog-base-uncased-WWM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_test_junzai_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_test_junzai_en.md new file mode 100644 index 00000000000000..a41496c98f9933 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_test_junzai_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_test_junzai BertEmbeddings from junzai +author: John Snow Labs +name: bert_test_junzai +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_test_junzai` is a English model originally trained by junzai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_test_junzai_en_5.1.1_3.0_1694284713154.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_test_junzai_en_5.1.1_3.0_1694284713154.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_test_junzai","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_test_junzai", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_test_junzai| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/junzai/bert_test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_ucb_3_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_ucb_3_en.md new file mode 100644 index 00000000000000..fa1fabe6db1efd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_ucb_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_ucb_3 BertEmbeddings from Diegomejia +author: John Snow Labs +name: bert_ucb_3 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ucb_3` is a English model originally trained by Diegomejia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ucb_3_en_5.1.1_3.0_1694278278062.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ucb_3_en_5.1.1_3.0_1694278278062.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_ucb_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_ucb_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ucb_3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/Diegomejia/bert-ucb-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bert_ucb_4_en.md b/docs/_posts/ahmedlone127/2023-09-09-bert_ucb_4_en.md new file mode 100644 index 00000000000000..b29a4c374e7b60 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bert_ucb_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_ucb_4 BertEmbeddings from Diegomejia +author: John Snow Labs +name: bert_ucb_4 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ucb_4` is a English model originally trained by Diegomejia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ucb_4_en_5.1.1_3.0_1694280320658.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ucb_4_en_5.1.1_3.0_1694280320658.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_ucb_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_ucb_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ucb_4| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/Diegomejia/bert-ucb-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bertimbau_large_fine_tuned_md_en.md b/docs/_posts/ahmedlone127/2023-09-09-bertimbau_large_fine_tuned_md_en.md new file mode 100644 index 00000000000000..92bba85958c954 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bertimbau_large_fine_tuned_md_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertimbau_large_fine_tuned_md BertEmbeddings from AVSilva +author: John Snow Labs +name: bertimbau_large_fine_tuned_md +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertimbau_large_fine_tuned_md` is a English model originally trained by AVSilva. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertimbau_large_fine_tuned_md_en_5.1.1_3.0_1694259726163.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertimbau_large_fine_tuned_md_en_5.1.1_3.0_1694259726163.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertimbau_large_fine_tuned_md","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertimbau_large_fine_tuned_md", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertimbau_large_fine_tuned_md| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/AVSilva/bertimbau-large-fine-tuned-md \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bertimbau_large_fine_tuned_sindhi_en.md b/docs/_posts/ahmedlone127/2023-09-09-bertimbau_large_fine_tuned_sindhi_en.md new file mode 100644 index 00000000000000..b47ebff72ea991 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bertimbau_large_fine_tuned_sindhi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertimbau_large_fine_tuned_sindhi BertEmbeddings from AVSilva +author: John Snow Labs +name: bertimbau_large_fine_tuned_sindhi +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertimbau_large_fine_tuned_sindhi` is a English model originally trained by AVSilva. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertimbau_large_fine_tuned_sindhi_en_5.1.1_3.0_1694259990408.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertimbau_large_fine_tuned_sindhi_en_5.1.1_3.0_1694259990408.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertimbau_large_fine_tuned_sindhi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertimbau_large_fine_tuned_sindhi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertimbau_large_fine_tuned_sindhi| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/AVSilva/bertimbau-large-fine-tuned-sd \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bertunam_en.md b/docs/_posts/ahmedlone127/2023-09-09-bertunam_en.md new file mode 100644 index 00000000000000..eac5fd127acb8f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bertunam_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertunam BertEmbeddings from benanxio +author: John Snow Labs +name: bertunam +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertunam` is a English model originally trained by benanxio. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertunam_en_5.1.1_3.0_1694259167797.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertunam_en_5.1.1_3.0_1694259167797.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertunam","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertunam", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertunam| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/benanxio/BERTUNAM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-biodivbert_en.md b/docs/_posts/ahmedlone127/2023-09-09-biodivbert_en.md new file mode 100644 index 00000000000000..5f3fa527413705 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-biodivbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English biodivbert BertEmbeddings from NoYo25 +author: John Snow Labs +name: biodivbert +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biodivbert` is a English model originally trained by NoYo25. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biodivbert_en_5.1.1_3.0_1694264878557.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biodivbert_en_5.1.1_3.0_1694264878557.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biodivbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biodivbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biodivbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/NoYo25/BiodivBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-biomednlp_pubmedbert_large_uncased_abstract_en.md b/docs/_posts/ahmedlone127/2023-09-09-biomednlp_pubmedbert_large_uncased_abstract_en.md new file mode 100644 index 00000000000000..210bd2205e7880 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-biomednlp_pubmedbert_large_uncased_abstract_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English biomednlp_pubmedbert_large_uncased_abstract BertEmbeddings from microsoft +author: John Snow Labs +name: biomednlp_pubmedbert_large_uncased_abstract +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biomednlp_pubmedbert_large_uncased_abstract` is a English model originally trained by microsoft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biomednlp_pubmedbert_large_uncased_abstract_en_5.1.1_3.0_1694283092570.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biomednlp_pubmedbert_large_uncased_abstract_en_5.1.1_3.0_1694283092570.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biomednlp_pubmedbert_large_uncased_abstract","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biomednlp_pubmedbert_large_uncased_abstract", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biomednlp_pubmedbert_large_uncased_abstract| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-large-uncased-abstract \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-bodo_bert_mlm_base_article_en.md b/docs/_posts/ahmedlone127/2023-09-09-bodo_bert_mlm_base_article_en.md new file mode 100644 index 00000000000000..cf619bdcc1e965 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-bodo_bert_mlm_base_article_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bodo_bert_mlm_base_article BertEmbeddings from alayaran +author: John Snow Labs +name: bodo_bert_mlm_base_article +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bodo_bert_mlm_base_article` is a English model originally trained by alayaran. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bodo_bert_mlm_base_article_en_5.1.1_3.0_1694284578897.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bodo_bert_mlm_base_article_en_5.1.1_3.0_1694284578897.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bodo_bert_mlm_base_article","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bodo_bert_mlm_base_article", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bodo_bert_mlm_base_article| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.2 MB| + +## References + +https://huggingface.co/alayaran/bodo-bert-mlm-base-article \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-covid_trained_bert_en.md b/docs/_posts/ahmedlone127/2023-09-09-covid_trained_bert_en.md new file mode 100644 index 00000000000000..b498c6e8696c74 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-covid_trained_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English covid_trained_bert BertEmbeddings from timoneda +author: John Snow Labs +name: covid_trained_bert +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`covid_trained_bert` is a English model originally trained by timoneda. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/covid_trained_bert_en_5.1.1_3.0_1694260269784.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/covid_trained_bert_en_5.1.1_3.0_1694260269784.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("covid_trained_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("covid_trained_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|covid_trained_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/timoneda/covid_trained_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-csci544_project_mabel_en.md b/docs/_posts/ahmedlone127/2023-09-09-csci544_project_mabel_en.md new file mode 100644 index 00000000000000..8ae4edc18715c5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-csci544_project_mabel_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English csci544_project_mabel BertEmbeddings from adityaanulekh98 +author: John Snow Labs +name: csci544_project_mabel +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`csci544_project_mabel` is a English model originally trained by adityaanulekh98. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/csci544_project_mabel_en_5.1.1_3.0_1694265752338.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/csci544_project_mabel_en_5.1.1_3.0_1694265752338.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("csci544_project_mabel","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("csci544_project_mabel", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|csci544_project_mabel| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/adityaanulekh98/csci544-project-mabel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-dal_bert_fa.md b/docs/_posts/ahmedlone127/2023-09-09-dal_bert_fa.md new file mode 100644 index 00000000000000..996c3881e5aebf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-dal_bert_fa.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Persian dal_bert BertEmbeddings from sharif-dal +author: John Snow Labs +name: dal_bert +date: 2023-09-09 +tags: [bert, fa, open_source, fill_mask, onnx] +task: Embeddings +language: fa +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dal_bert` is a Persian model originally trained by sharif-dal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dal_bert_fa_5.1.1_3.0_1694284485636.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dal_bert_fa_5.1.1_3.0_1694284485636.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dal_bert","fa") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dal_bert", "fa") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dal_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|fa| +|Size:|432.1 MB| + +## References + +https://huggingface.co/sharif-dal/dal-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-dfm_encoder_large_v1_da.md b/docs/_posts/ahmedlone127/2023-09-09-dfm_encoder_large_v1_da.md new file mode 100644 index 00000000000000..49dbf3d48c632c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-dfm_encoder_large_v1_da.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Danish dfm_encoder_large_v1 BertEmbeddings from chcaa +author: John Snow Labs +name: dfm_encoder_large_v1 +date: 2023-09-09 +tags: [bert, da, open_source, fill_mask, onnx] +task: Embeddings +language: da +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dfm_encoder_large_v1` is a Danish model originally trained by chcaa. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dfm_encoder_large_v1_da_5.1.1_3.0_1694283760247.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dfm_encoder_large_v1_da_5.1.1_3.0_1694283760247.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dfm_encoder_large_v1","da") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dfm_encoder_large_v1", "da") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dfm_encoder_large_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|da| +|Size:|1.3 GB| + +## References + +https://huggingface.co/chcaa/dfm-encoder-large-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-dummy_model2_en.md b/docs/_posts/ahmedlone127/2023-09-09-dummy_model2_en.md new file mode 100644 index 00000000000000..a2c46d9328f1cf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-dummy_model2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dummy_model2 BertEmbeddings from vsrinivas +author: John Snow Labs +name: dummy_model2 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dummy_model2` is a English model originally trained by vsrinivas. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dummy_model2_en_5.1.1_3.0_1694259021309.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dummy_model2_en_5.1.1_3.0_1694259021309.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dummy_model2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dummy_model2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dummy_model2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/vsrinivas/dummy_model2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-finnish_mbertmodel_mberttok_en.md b/docs/_posts/ahmedlone127/2023-09-09-finnish_mbertmodel_mberttok_en.md new file mode 100644 index 00000000000000..351c5b299c51b2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-finnish_mbertmodel_mberttok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English finnish_mbertmodel_mberttok BertEmbeddings from hgiyt +author: John Snow Labs +name: finnish_mbertmodel_mberttok +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finnish_mbertmodel_mberttok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finnish_mbertmodel_mberttok_en_5.1.1_3.0_1694259583032.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finnish_mbertmodel_mberttok_en_5.1.1_3.0_1694259583032.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("finnish_mbertmodel_mberttok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("finnish_mbertmodel_mberttok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finnish_mbertmodel_mberttok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|495.8 MB| + +## References + +https://huggingface.co/hgiyt/fi-mbertmodel-mberttok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-finnish_mbertmodel_monotok_adapter_en.md b/docs/_posts/ahmedlone127/2023-09-09-finnish_mbertmodel_monotok_adapter_en.md new file mode 100644 index 00000000000000..9d3df96780c4c7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-finnish_mbertmodel_monotok_adapter_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English finnish_mbertmodel_monotok_adapter BertEmbeddings from hgiyt +author: John Snow Labs +name: finnish_mbertmodel_monotok_adapter +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finnish_mbertmodel_monotok_adapter` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finnish_mbertmodel_monotok_adapter_en_5.1.1_3.0_1694259725340.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finnish_mbertmodel_monotok_adapter_en_5.1.1_3.0_1694259725340.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("finnish_mbertmodel_monotok_adapter","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("finnish_mbertmodel_monotok_adapter", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finnish_mbertmodel_monotok_adapter| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.9 MB| + +## References + +https://huggingface.co/hgiyt/fi-mbertmodel-monotok-adapter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-finnish_mbertmodel_monotok_en.md b/docs/_posts/ahmedlone127/2023-09-09-finnish_mbertmodel_monotok_en.md new file mode 100644 index 00000000000000..be93bdb8ed7bae --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-finnish_mbertmodel_monotok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English finnish_mbertmodel_monotok BertEmbeddings from hgiyt +author: John Snow Labs +name: finnish_mbertmodel_monotok +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finnish_mbertmodel_monotok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finnish_mbertmodel_monotok_en_5.1.1_3.0_1694259855521.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finnish_mbertmodel_monotok_en_5.1.1_3.0_1694259855521.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("finnish_mbertmodel_monotok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("finnish_mbertmodel_monotok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finnish_mbertmodel_monotok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.8 MB| + +## References + +https://huggingface.co/hgiyt/fi-mbertmodel-monotok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-finnish_monomodel_mberttok_en.md b/docs/_posts/ahmedlone127/2023-09-09-finnish_monomodel_mberttok_en.md new file mode 100644 index 00000000000000..9fe1f07f764d9e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-finnish_monomodel_mberttok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English finnish_monomodel_mberttok BertEmbeddings from hgiyt +author: John Snow Labs +name: finnish_monomodel_mberttok +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finnish_monomodel_mberttok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finnish_monomodel_mberttok_en_5.1.1_3.0_1694260007471.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finnish_monomodel_mberttok_en_5.1.1_3.0_1694260007471.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("finnish_monomodel_mberttok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("finnish_monomodel_mberttok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finnish_monomodel_mberttok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|495.0 MB| + +## References + +https://huggingface.co/hgiyt/fi-monomodel-mberttok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-finnish_monomodel_monotok_en.md b/docs/_posts/ahmedlone127/2023-09-09-finnish_monomodel_monotok_en.md new file mode 100644 index 00000000000000..b691d803a28a66 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-finnish_monomodel_monotok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English finnish_monomodel_monotok BertEmbeddings from hgiyt +author: John Snow Labs +name: finnish_monomodel_monotok +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finnish_monomodel_monotok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finnish_monomodel_monotok_en_5.1.1_3.0_1694260151850.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finnish_monomodel_monotok_en_5.1.1_3.0_1694260151850.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("finnish_monomodel_monotok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("finnish_monomodel_monotok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finnish_monomodel_monotok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.3 MB| + +## References + +https://huggingface.co/hgiyt/fi-monomodel-monotok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-german_poetry_bert_en.md b/docs/_posts/ahmedlone127/2023-09-09-german_poetry_bert_en.md new file mode 100644 index 00000000000000..86e9ae2018fe3d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-german_poetry_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English german_poetry_bert BertEmbeddings from Anjoe +author: John Snow Labs +name: german_poetry_bert +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`german_poetry_bert` is a English model originally trained by Anjoe. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/german_poetry_bert_en_5.1.1_3.0_1694284344164.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/german_poetry_bert_en_5.1.1_3.0_1694284344164.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("german_poetry_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("german_poetry_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|german_poetry_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/Anjoe/german-poetry-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-guidebias_bert_base_uncased_gender_en.md b/docs/_posts/ahmedlone127/2023-09-09-guidebias_bert_base_uncased_gender_en.md new file mode 100644 index 00000000000000..eb4ca7e3a3c3d4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-guidebias_bert_base_uncased_gender_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English guidebias_bert_base_uncased_gender BertEmbeddings from squiduu +author: John Snow Labs +name: guidebias_bert_base_uncased_gender +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`guidebias_bert_base_uncased_gender` is a English model originally trained by squiduu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/guidebias_bert_base_uncased_gender_en_5.1.1_3.0_1694283230300.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/guidebias_bert_base_uncased_gender_en_5.1.1_3.0_1694283230300.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("guidebias_bert_base_uncased_gender","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("guidebias_bert_base_uncased_gender", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|guidebias_bert_base_uncased_gender| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/squiduu/guidebias-bert-base-uncased-gender \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-gujbert_senti_a_en.md b/docs/_posts/ahmedlone127/2023-09-09-gujbert_senti_a_en.md new file mode 100644 index 00000000000000..58864a3b814cd5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-gujbert_senti_a_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English gujbert_senti_a BertEmbeddings from kvaditya +author: John Snow Labs +name: gujbert_senti_a +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`gujbert_senti_a` is a English model originally trained by kvaditya. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gujbert_senti_a_en_5.1.1_3.0_1694265938762.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gujbert_senti_a_en_5.1.1_3.0_1694265938762.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("gujbert_senti_a","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("gujbert_senti_a", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gujbert_senti_a| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|890.4 MB| + +## References + +https://huggingface.co/kvaditya/gujbert-senti-a \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-gww_en.md b/docs/_posts/ahmedlone127/2023-09-09-gww_en.md new file mode 100644 index 00000000000000..cb117e2e539933 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-gww_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English gww BertEmbeddings from dunlp +author: John Snow Labs +name: gww +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`gww` is a English model originally trained by dunlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gww_en_5.1.1_3.0_1694284784080.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gww_en_5.1.1_3.0_1694284784080.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("gww","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("gww", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gww| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/dunlp/GWW \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-hubert_base_cc_finetuned_forum_en.md b/docs/_posts/ahmedlone127/2023-09-09-hubert_base_cc_finetuned_forum_en.md new file mode 100644 index 00000000000000..42ed9d84a626f4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-hubert_base_cc_finetuned_forum_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English hubert_base_cc_finetuned_forum BertEmbeddings from papsebestyen +author: John Snow Labs +name: hubert_base_cc_finetuned_forum +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hubert_base_cc_finetuned_forum` is a English model originally trained by papsebestyen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hubert_base_cc_finetuned_forum_en_5.1.1_3.0_1694266159584.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hubert_base_cc_finetuned_forum_en_5.1.1_3.0_1694266159584.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hubert_base_cc_finetuned_forum","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hubert_base_cc_finetuned_forum", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hubert_base_cc_finetuned_forum| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.4 MB| + +## References + +https://huggingface.co/papsebestyen/hubert-base-cc-finetuned-forum \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-indobert_base_uncased_id.md b/docs/_posts/ahmedlone127/2023-09-09-indobert_base_uncased_id.md new file mode 100644 index 00000000000000..2c85ca6c717784 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-indobert_base_uncased_id.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Indonesian indobert_base_uncased BertEmbeddings from indolem +author: John Snow Labs +name: indobert_base_uncased +date: 2023-09-09 +tags: [bert, id, open_source, fill_mask, onnx] +task: Embeddings +language: id +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indobert_base_uncased` is a Indonesian model originally trained by indolem. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indobert_base_uncased_id_5.1.1_3.0_1694267045811.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indobert_base_uncased_id_5.1.1_3.0_1694267045811.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("indobert_base_uncased","id") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("indobert_base_uncased", "id") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indobert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|id| +|Size:|411.7 MB| + +## References + +https://huggingface.co/indolem/indobert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-indobertweet_base_uncased_id.md b/docs/_posts/ahmedlone127/2023-09-09-indobertweet_base_uncased_id.md new file mode 100644 index 00000000000000..0b5e8b7cbaa93b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-indobertweet_base_uncased_id.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Indonesian indobertweet_base_uncased BertEmbeddings from indolem +author: John Snow Labs +name: indobertweet_base_uncased +date: 2023-09-09 +tags: [bert, id, open_source, fill_mask, onnx] +task: Embeddings +language: id +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indobertweet_base_uncased` is a Indonesian model originally trained by indolem. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indobertweet_base_uncased_id_5.1.1_3.0_1694267225929.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indobertweet_base_uncased_id_5.1.1_3.0_1694267225929.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("indobertweet_base_uncased","id") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("indobertweet_base_uncased", "id") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indobertweet_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|id| +|Size:|411.8 MB| + +## References + +https://huggingface.co/indolem/indobertweet-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-indojave_codemixed_indobertweet_base_id.md b/docs/_posts/ahmedlone127/2023-09-09-indojave_codemixed_indobertweet_base_id.md new file mode 100644 index 00000000000000..90d3b0e0b2d697 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-indojave_codemixed_indobertweet_base_id.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Indonesian indojave_codemixed_indobertweet_base BertEmbeddings from fathan +author: John Snow Labs +name: indojave_codemixed_indobertweet_base +date: 2023-09-09 +tags: [bert, id, open_source, fill_mask, onnx] +task: Embeddings +language: id +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indojave_codemixed_indobertweet_base` is a Indonesian model originally trained by fathan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indojave_codemixed_indobertweet_base_id_5.1.1_3.0_1694283480900.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indojave_codemixed_indobertweet_base_id_5.1.1_3.0_1694283480900.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("indojave_codemixed_indobertweet_base","id") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("indojave_codemixed_indobertweet_base", "id") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indojave_codemixed_indobertweet_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|id| +|Size:|411.8 MB| + +## References + +https://huggingface.co/fathan/indojave-codemixed-indobertweet-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-indonesian_mbertmodel_mberttok_en.md b/docs/_posts/ahmedlone127/2023-09-09-indonesian_mbertmodel_mberttok_en.md new file mode 100644 index 00000000000000..a58d7856fa417f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-indonesian_mbertmodel_mberttok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English indonesian_mbertmodel_mberttok BertEmbeddings from hgiyt +author: John Snow Labs +name: indonesian_mbertmodel_mberttok +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indonesian_mbertmodel_mberttok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indonesian_mbertmodel_mberttok_en_5.1.1_3.0_1694260328909.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indonesian_mbertmodel_mberttok_en_5.1.1_3.0_1694260328909.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("indonesian_mbertmodel_mberttok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("indonesian_mbertmodel_mberttok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indonesian_mbertmodel_mberttok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|530.3 MB| + +## References + +https://huggingface.co/hgiyt/id-mbertmodel-mberttok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-indonesian_mbertmodel_monotok_adapter_en.md b/docs/_posts/ahmedlone127/2023-09-09-indonesian_mbertmodel_monotok_adapter_en.md new file mode 100644 index 00000000000000..14486aab74aafb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-indonesian_mbertmodel_monotok_adapter_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English indonesian_mbertmodel_monotok_adapter BertEmbeddings from hgiyt +author: John Snow Labs +name: indonesian_mbertmodel_monotok_adapter +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indonesian_mbertmodel_monotok_adapter` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indonesian_mbertmodel_monotok_adapter_en_5.1.1_3.0_1694260454540.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indonesian_mbertmodel_monotok_adapter_en_5.1.1_3.0_1694260454540.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("indonesian_mbertmodel_monotok_adapter","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("indonesian_mbertmodel_monotok_adapter", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indonesian_mbertmodel_monotok_adapter| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.3 MB| + +## References + +https://huggingface.co/hgiyt/id-mbertmodel-monotok-adapter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-indonesian_mbertmodel_monotok_en.md b/docs/_posts/ahmedlone127/2023-09-09-indonesian_mbertmodel_monotok_en.md new file mode 100644 index 00000000000000..dc4930da8ba45e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-indonesian_mbertmodel_monotok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English indonesian_mbertmodel_monotok BertEmbeddings from hgiyt +author: John Snow Labs +name: indonesian_mbertmodel_monotok +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indonesian_mbertmodel_monotok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indonesian_mbertmodel_monotok_en_5.1.1_3.0_1694260586722.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indonesian_mbertmodel_monotok_en_5.1.1_3.0_1694260586722.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("indonesian_mbertmodel_monotok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("indonesian_mbertmodel_monotok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indonesian_mbertmodel_monotok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.3 MB| + +## References + +https://huggingface.co/hgiyt/id-mbertmodel-monotok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-indonesian_monomodel_mberttok_en.md b/docs/_posts/ahmedlone127/2023-09-09-indonesian_monomodel_mberttok_en.md new file mode 100644 index 00000000000000..04218e1af7b8b6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-indonesian_monomodel_mberttok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English indonesian_monomodel_mberttok BertEmbeddings from hgiyt +author: John Snow Labs +name: indonesian_monomodel_mberttok +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indonesian_monomodel_mberttok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indonesian_monomodel_mberttok_en_5.1.1_3.0_1694260741116.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indonesian_monomodel_mberttok_en_5.1.1_3.0_1694260741116.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("indonesian_monomodel_mberttok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("indonesian_monomodel_mberttok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indonesian_monomodel_mberttok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|529.3 MB| + +## References + +https://huggingface.co/hgiyt/id-monomodel-mberttok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-indonesian_monomodel_monotok_en.md b/docs/_posts/ahmedlone127/2023-09-09-indonesian_monomodel_monotok_en.md new file mode 100644 index 00000000000000..63f5f1a1a70b5d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-indonesian_monomodel_monotok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English indonesian_monomodel_monotok BertEmbeddings from hgiyt +author: John Snow Labs +name: indonesian_monomodel_monotok +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indonesian_monomodel_monotok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indonesian_monomodel_monotok_en_5.1.1_3.0_1694260879482.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indonesian_monomodel_monotok_en_5.1.1_3.0_1694260879482.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("indonesian_monomodel_monotok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("indonesian_monomodel_monotok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indonesian_monomodel_monotok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.0 MB| + +## References + +https://huggingface.co/hgiyt/id-monomodel-monotok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-inlegalbert_cbp_lkg_triples_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-09-inlegalbert_cbp_lkg_triples_finetuned_en.md new file mode 100644 index 00000000000000..003e102784eb39 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-inlegalbert_cbp_lkg_triples_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English inlegalbert_cbp_lkg_triples_finetuned BertEmbeddings from kinshuk-h +author: John Snow Labs +name: inlegalbert_cbp_lkg_triples_finetuned +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`inlegalbert_cbp_lkg_triples_finetuned` is a English model originally trained by kinshuk-h. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/inlegalbert_cbp_lkg_triples_finetuned_en_5.1.1_3.0_1694266522167.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/inlegalbert_cbp_lkg_triples_finetuned_en_5.1.1_3.0_1694266522167.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("inlegalbert_cbp_lkg_triples_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("inlegalbert_cbp_lkg_triples_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|inlegalbert_cbp_lkg_triples_finetuned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/kinshuk-h/InLegalBERT-cbp-lkg-triples-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-javabert_en.md b/docs/_posts/ahmedlone127/2023-09-09-javabert_en.md new file mode 100644 index 00000000000000..c1b144b9f1b5e9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-javabert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English javabert BertEmbeddings from CAUKiel +author: John Snow Labs +name: javabert +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`javabert` is a English model originally trained by CAUKiel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/javabert_en_5.1.1_3.0_1694266219357.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/javabert_en_5.1.1_3.0_1694266219357.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("javabert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("javabert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|javabert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.2 MB| + +## References + +https://huggingface.co/CAUKiel/JavaBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-javabert_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-09-javabert_uncased_en.md new file mode 100644 index 00000000000000..1cdbfb654cef4f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-javabert_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English javabert_uncased BertEmbeddings from CAUKiel +author: John Snow Labs +name: javabert_uncased +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`javabert_uncased` is a English model originally trained by CAUKiel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/javabert_uncased_en_5.1.1_3.0_1694266075550.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/javabert_uncased_en_5.1.1_3.0_1694266075550.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("javabert_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("javabert_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|javabert_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.2 MB| + +## References + +https://huggingface.co/CAUKiel/JavaBERT-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-jobbert_org_add_words_trial_26_12_2022_en.md b/docs/_posts/ahmedlone127/2023-09-09-jobbert_org_add_words_trial_26_12_2022_en.md new file mode 100644 index 00000000000000..1d4f21d35bdd44 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-jobbert_org_add_words_trial_26_12_2022_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English jobbert_org_add_words_trial_26_12_2022 BertEmbeddings from EslamAhmed +author: John Snow Labs +name: jobbert_org_add_words_trial_26_12_2022 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`jobbert_org_add_words_trial_26_12_2022` is a English model originally trained by EslamAhmed. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/jobbert_org_add_words_trial_26_12_2022_en_5.1.1_3.0_1694278226503.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/jobbert_org_add_words_trial_26_12_2022_en_5.1.1_3.0_1694278226503.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("jobbert_org_add_words_trial_26_12_2022","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("jobbert_org_add_words_trial_26_12_2022", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|jobbert_org_add_words_trial_26_12_2022| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|447.4 MB| + +## References + +https://huggingface.co/EslamAhmed/JOBBERT_org_add_words_trial_26-12-2022 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-jobbert_org_add_words_v2_trial_26_12_2022_en.md b/docs/_posts/ahmedlone127/2023-09-09-jobbert_org_add_words_v2_trial_26_12_2022_en.md new file mode 100644 index 00000000000000..7f42c389668eaa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-jobbert_org_add_words_v2_trial_26_12_2022_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English jobbert_org_add_words_v2_trial_26_12_2022 BertEmbeddings from EslamAhmed +author: John Snow Labs +name: jobbert_org_add_words_v2_trial_26_12_2022 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`jobbert_org_add_words_v2_trial_26_12_2022` is a English model originally trained by EslamAhmed. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/jobbert_org_add_words_v2_trial_26_12_2022_en_5.1.1_3.0_1694278342003.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/jobbert_org_add_words_v2_trial_26_12_2022_en_5.1.1_3.0_1694278342003.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("jobbert_org_add_words_v2_trial_26_12_2022","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("jobbert_org_add_words_v2_trial_26_12_2022", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|jobbert_org_add_words_v2_trial_26_12_2022| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|447.4 MB| + +## References + +https://huggingface.co/EslamAhmed/JOBBERT_org_add_words_v2_trial_26-12-2022 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-jobbert_test_org_trial_26_12_2022_en.md b/docs/_posts/ahmedlone127/2023-09-09-jobbert_test_org_trial_26_12_2022_en.md new file mode 100644 index 00000000000000..2c66afe1adbbbe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-jobbert_test_org_trial_26_12_2022_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English jobbert_test_org_trial_26_12_2022 BertEmbeddings from EslamAhmed +author: John Snow Labs +name: jobbert_test_org_trial_26_12_2022 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`jobbert_test_org_trial_26_12_2022` is a English model originally trained by EslamAhmed. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/jobbert_test_org_trial_26_12_2022_en_5.1.1_3.0_1694267094649.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/jobbert_test_org_trial_26_12_2022_en_5.1.1_3.0_1694267094649.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("jobbert_test_org_trial_26_12_2022","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("jobbert_test_org_trial_26_12_2022", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|jobbert_test_org_trial_26_12_2022| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.1 MB| + +## References + +https://huggingface.co/EslamAhmed/JOBBERT_test_org_trial_26-12-2022 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-jzmodel01_en.md b/docs/_posts/ahmedlone127/2023-09-09-jzmodel01_en.md new file mode 100644 index 00000000000000..b89266c640697d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-jzmodel01_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English jzmodel01 BertEmbeddings from jinzhan +author: John Snow Labs +name: jzmodel01 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`jzmodel01` is a English model originally trained by jinzhan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/jzmodel01_en_5.1.1_3.0_1694283002753.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/jzmodel01_en_5.1.1_3.0_1694283002753.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("jzmodel01","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("jzmodel01", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|jzmodel01| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/jinzhan/jzmodel01 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-kaz_legal_bert_5_en.md b/docs/_posts/ahmedlone127/2023-09-09-kaz_legal_bert_5_en.md new file mode 100644 index 00000000000000..f749093710f761 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-kaz_legal_bert_5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kaz_legal_bert_5 BertEmbeddings from kaisar-barlybay-sse +author: John Snow Labs +name: kaz_legal_bert_5 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kaz_legal_bert_5` is a English model originally trained by kaisar-barlybay-sse. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kaz_legal_bert_5_en_5.1.1_3.0_1694279378634.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kaz_legal_bert_5_en_5.1.1_3.0_1694279378634.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kaz_legal_bert_5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kaz_legal_bert_5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kaz_legal_bert_5| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.0 MB| + +## References + +https://huggingface.co/kaisar-barlybay-sse/kaz_legal_bert_5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-kaz_legal_bert_en.md b/docs/_posts/ahmedlone127/2023-09-09-kaz_legal_bert_en.md new file mode 100644 index 00000000000000..ddd445cde44b74 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-kaz_legal_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kaz_legal_bert BertEmbeddings from kaisar-barlybay-sse +author: John Snow Labs +name: kaz_legal_bert +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kaz_legal_bert` is a English model originally trained by kaisar-barlybay-sse. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kaz_legal_bert_en_5.1.1_3.0_1694278999873.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kaz_legal_bert_en_5.1.1_3.0_1694278999873.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kaz_legal_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kaz_legal_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kaz_legal_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.0 MB| + +## References + +https://huggingface.co/kaisar-barlybay-sse/kaz_legal_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-kbert_base_esg_e10_en.md b/docs/_posts/ahmedlone127/2023-09-09-kbert_base_esg_e10_en.md new file mode 100644 index 00000000000000..e8786b2c03fb60 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-kbert_base_esg_e10_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kbert_base_esg_e10 BertEmbeddings from jinbbong +author: John Snow Labs +name: kbert_base_esg_e10 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kbert_base_esg_e10` is a English model originally trained by jinbbong. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kbert_base_esg_e10_en_5.1.1_3.0_1694280021278.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kbert_base_esg_e10_en_5.1.1_3.0_1694280021278.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kbert_base_esg_e10","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kbert_base_esg_e10", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kbert_base_esg_e10| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|441.2 MB| + +## References + +https://huggingface.co/jinbbong/kbert_base_esg_e10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-kbert_base_esg_e3_en.md b/docs/_posts/ahmedlone127/2023-09-09-kbert_base_esg_e3_en.md new file mode 100644 index 00000000000000..842df31a86d4af --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-kbert_base_esg_e3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kbert_base_esg_e3 BertEmbeddings from jinbbong +author: John Snow Labs +name: kbert_base_esg_e3 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kbert_base_esg_e3` is a English model originally trained by jinbbong. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kbert_base_esg_e3_en_5.1.1_3.0_1694280184375.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kbert_base_esg_e3_en_5.1.1_3.0_1694280184375.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kbert_base_esg_e3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kbert_base_esg_e3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kbert_base_esg_e3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|441.2 MB| + +## References + +https://huggingface.co/jinbbong/kbert_base_esg_e3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-kbert_base_esg_e5_en.md b/docs/_posts/ahmedlone127/2023-09-09-kbert_base_esg_e5_en.md new file mode 100644 index 00000000000000..7ef1ad8d43fe97 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-kbert_base_esg_e5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kbert_base_esg_e5 BertEmbeddings from jinbbong +author: John Snow Labs +name: kbert_base_esg_e5 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kbert_base_esg_e5` is a English model originally trained by jinbbong. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kbert_base_esg_e5_en_5.1.1_3.0_1694280334055.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kbert_base_esg_e5_en_5.1.1_3.0_1694280334055.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kbert_base_esg_e5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kbert_base_esg_e5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kbert_base_esg_e5| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|441.2 MB| + +## References + +https://huggingface.co/jinbbong/kbert_base_esg_e5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-kinyabert_large_en.md b/docs/_posts/ahmedlone127/2023-09-09-kinyabert_large_en.md new file mode 100644 index 00000000000000..f4c612a45d1cd8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-kinyabert_large_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kinyabert_large BertEmbeddings from jean-paul +author: John Snow Labs +name: kinyabert_large +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kinyabert_large` is a English model originally trained by jean-paul. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kinyabert_large_en_5.1.1_3.0_1694279233724.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kinyabert_large_en_5.1.1_3.0_1694279233724.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kinyabert_large","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kinyabert_large", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kinyabert_large| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.7 MB| + +## References + +https://huggingface.co/jean-paul/KinyaBERT-large \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-kinyabert_small_en.md b/docs/_posts/ahmedlone127/2023-09-09-kinyabert_small_en.md new file mode 100644 index 00000000000000..8b3fdc3da7be2e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-kinyabert_small_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kinyabert_small BertEmbeddings from jean-paul +author: John Snow Labs +name: kinyabert_small +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kinyabert_small` is a English model originally trained by jean-paul. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kinyabert_small_en_5.1.1_3.0_1694279349541.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kinyabert_small_en_5.1.1_3.0_1694279349541.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kinyabert_small","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kinyabert_small", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kinyabert_small| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.4 MB| + +## References + +https://huggingface.co/jean-paul/KinyaBERT-small \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-knowbias_bert_base_uncased_gender_en.md b/docs/_posts/ahmedlone127/2023-09-09-knowbias_bert_base_uncased_gender_en.md new file mode 100644 index 00000000000000..394c2c5d2ab70b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-knowbias_bert_base_uncased_gender_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English knowbias_bert_base_uncased_gender BertEmbeddings from squiduu +author: John Snow Labs +name: knowbias_bert_base_uncased_gender +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`knowbias_bert_base_uncased_gender` is a English model originally trained by squiduu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/knowbias_bert_base_uncased_gender_en_5.1.1_3.0_1694279779070.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/knowbias_bert_base_uncased_gender_en_5.1.1_3.0_1694279779070.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("knowbias_bert_base_uncased_gender","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("knowbias_bert_base_uncased_gender", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|knowbias_bert_base_uncased_gender| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/squiduu/knowbias-bert-base-uncased-gender \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-korean_mbertmodel_mberttok_en.md b/docs/_posts/ahmedlone127/2023-09-09-korean_mbertmodel_mberttok_en.md new file mode 100644 index 00000000000000..f94be219287cf0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-korean_mbertmodel_mberttok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English korean_mbertmodel_mberttok BertEmbeddings from hgiyt +author: John Snow Labs +name: korean_mbertmodel_mberttok +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`korean_mbertmodel_mberttok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/korean_mbertmodel_mberttok_en_5.1.1_3.0_1694261027566.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/korean_mbertmodel_mberttok_en_5.1.1_3.0_1694261027566.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("korean_mbertmodel_mberttok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("korean_mbertmodel_mberttok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|korean_mbertmodel_mberttok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|511.4 MB| + +## References + +https://huggingface.co/hgiyt/ko-mbertmodel-mberttok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-korean_monomodel_mberttok_en.md b/docs/_posts/ahmedlone127/2023-09-09-korean_monomodel_mberttok_en.md new file mode 100644 index 00000000000000..d7698f059b151e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-korean_monomodel_mberttok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English korean_monomodel_mberttok BertEmbeddings from hgiyt +author: John Snow Labs +name: korean_monomodel_mberttok +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`korean_monomodel_mberttok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/korean_monomodel_mberttok_en_5.1.1_3.0_1694265026447.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/korean_monomodel_mberttok_en_5.1.1_3.0_1694265026447.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("korean_monomodel_mberttok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("korean_monomodel_mberttok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|korean_monomodel_mberttok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|510.1 MB| + +## References + +https://huggingface.co/hgiyt/ko-monomodel-mberttok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-kw_pubmed_10000_0.000006_en.md b/docs/_posts/ahmedlone127/2023-09-09-kw_pubmed_10000_0.000006_en.md new file mode 100644 index 00000000000000..8befc8261b3a36 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-kw_pubmed_10000_0.000006_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kw_pubmed_10000_0.000006 BertEmbeddings from enoriega +author: John Snow Labs +name: kw_pubmed_10000_0.000006 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kw_pubmed_10000_0.000006` is a English model originally trained by enoriega. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kw_pubmed_10000_0.000006_en_5.1.1_3.0_1694259822636.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kw_pubmed_10000_0.000006_en_5.1.1_3.0_1694259822636.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kw_pubmed_10000_0.000006","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kw_pubmed_10000_0.000006", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kw_pubmed_10000_0.000006| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.2 MB| + +## References + +https://huggingface.co/enoriega/kw_pubmed_10000_0.000006 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-kw_pubmed_10000_0.00006_en.md b/docs/_posts/ahmedlone127/2023-09-09-kw_pubmed_10000_0.00006_en.md new file mode 100644 index 00000000000000..af8c39513cdd63 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-kw_pubmed_10000_0.00006_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kw_pubmed_10000_0.00006 BertEmbeddings from enoriega +author: John Snow Labs +name: kw_pubmed_10000_0.00006 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kw_pubmed_10000_0.00006` is a English model originally trained by enoriega. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kw_pubmed_10000_0.00006_en_5.1.1_3.0_1694259256891.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kw_pubmed_10000_0.00006_en_5.1.1_3.0_1694259256891.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kw_pubmed_10000_0.00006","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kw_pubmed_10000_0.00006", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kw_pubmed_10000_0.00006| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/enoriega/kw_pubmed_10000_0.00006 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-kw_pubmed_10000_0.0003_en.md b/docs/_posts/ahmedlone127/2023-09-09-kw_pubmed_10000_0.0003_en.md new file mode 100644 index 00000000000000..4482261945e5ea --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-kw_pubmed_10000_0.0003_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kw_pubmed_10000_0.0003 BertEmbeddings from enoriega +author: John Snow Labs +name: kw_pubmed_10000_0.0003 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kw_pubmed_10000_0.0003` is a English model originally trained by enoriega. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kw_pubmed_10000_0.0003_en_5.1.1_3.0_1694259557823.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kw_pubmed_10000_0.0003_en_5.1.1_3.0_1694259557823.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kw_pubmed_10000_0.0003","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kw_pubmed_10000_0.0003", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kw_pubmed_10000_0.0003| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/enoriega/kw_pubmed_10000_0.0003 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-kw_pubmed_5000_0.000006_en.md b/docs/_posts/ahmedlone127/2023-09-09-kw_pubmed_5000_0.000006_en.md new file mode 100644 index 00000000000000..f8b3fa206ed2d7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-kw_pubmed_5000_0.000006_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kw_pubmed_5000_0.000006 BertEmbeddings from enoriega +author: John Snow Labs +name: kw_pubmed_5000_0.000006 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kw_pubmed_5000_0.000006` is a English model originally trained by enoriega. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kw_pubmed_5000_0.000006_en_5.1.1_3.0_1694259689852.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kw_pubmed_5000_0.000006_en_5.1.1_3.0_1694259689852.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kw_pubmed_5000_0.000006","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kw_pubmed_5000_0.000006", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kw_pubmed_5000_0.000006| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.2 MB| + +## References + +https://huggingface.co/enoriega/kw_pubmed_5000_0.000006 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-kw_pubmed_5000_0.00006_en.md b/docs/_posts/ahmedlone127/2023-09-09-kw_pubmed_5000_0.00006_en.md new file mode 100644 index 00000000000000..293575a5a53b4d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-kw_pubmed_5000_0.00006_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kw_pubmed_5000_0.00006 BertEmbeddings from enoriega +author: John Snow Labs +name: kw_pubmed_5000_0.00006 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kw_pubmed_5000_0.00006` is a English model originally trained by enoriega. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kw_pubmed_5000_0.00006_en_5.1.1_3.0_1694259408552.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kw_pubmed_5000_0.00006_en_5.1.1_3.0_1694259408552.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kw_pubmed_5000_0.00006","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kw_pubmed_5000_0.00006", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kw_pubmed_5000_0.00006| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/enoriega/kw_pubmed_5000_0.00006 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-kw_pubmed_5000_0.0003_en.md b/docs/_posts/ahmedlone127/2023-09-09-kw_pubmed_5000_0.0003_en.md new file mode 100644 index 00000000000000..992f6205d99d41 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-kw_pubmed_5000_0.0003_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kw_pubmed_5000_0.0003 BertEmbeddings from enoriega +author: John Snow Labs +name: kw_pubmed_5000_0.0003 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kw_pubmed_5000_0.0003` is a English model originally trained by enoriega. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kw_pubmed_5000_0.0003_en_5.1.1_3.0_1694259116149.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kw_pubmed_5000_0.0003_en_5.1.1_3.0_1694259116149.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kw_pubmed_5000_0.0003","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kw_pubmed_5000_0.0003", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kw_pubmed_5000_0.0003| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/enoriega/kw_pubmed_5000_0.0003 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-legal_bert_base_uncased_finetuned_ledgarscotus7_en.md b/docs/_posts/ahmedlone127/2023-09-09-legal_bert_base_uncased_finetuned_ledgarscotus7_en.md new file mode 100644 index 00000000000000..8fb546e076f123 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-legal_bert_base_uncased_finetuned_ledgarscotus7_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English legal_bert_base_uncased_finetuned_ledgarscotus7 BertEmbeddings from hatemestinbejaia +author: John Snow Labs +name: legal_bert_base_uncased_finetuned_ledgarscotus7 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legal_bert_base_uncased_finetuned_ledgarscotus7` is a English model originally trained by hatemestinbejaia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legal_bert_base_uncased_finetuned_ledgarscotus7_en_5.1.1_3.0_1694278582359.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legal_bert_base_uncased_finetuned_ledgarscotus7_en_5.1.1_3.0_1694278582359.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legal_bert_base_uncased_finetuned_ledgarscotus7","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legal_bert_base_uncased_finetuned_ledgarscotus7", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legal_bert_base_uncased_finetuned_ledgarscotus7| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/hatemestinbejaia/legal-bert-base-uncased-finetuned-ledgarscotus7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-lsg16k_italian_legal_bert_it.md b/docs/_posts/ahmedlone127/2023-09-09-lsg16k_italian_legal_bert_it.md new file mode 100644 index 00000000000000..f3256238134713 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-lsg16k_italian_legal_bert_it.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Italian lsg16k_italian_legal_bert BertEmbeddings from dlicari +author: John Snow Labs +name: lsg16k_italian_legal_bert +date: 2023-09-09 +tags: [bert, it, open_source, fill_mask, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lsg16k_italian_legal_bert` is a Italian model originally trained by dlicari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lsg16k_italian_legal_bert_it_5.1.1_3.0_1694259755707.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lsg16k_italian_legal_bert_it_5.1.1_3.0_1694259755707.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("lsg16k_italian_legal_bert","it") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("lsg16k_italian_legal_bert", "it") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lsg16k_italian_legal_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|it| +|Size:|454.6 MB| + +## References + +https://huggingface.co/dlicari/lsg16k-Italian-Legal-BERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-materialsbert_en.md b/docs/_posts/ahmedlone127/2023-09-09-materialsbert_en.md new file mode 100644 index 00000000000000..1c05d73f30f44c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-materialsbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English materialsbert BertEmbeddings from pranav-s +author: John Snow Labs +name: materialsbert +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`materialsbert` is a English model originally trained by pranav-s. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/materialsbert_en_5.1.1_3.0_1694283977428.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/materialsbert_en_5.1.1_3.0_1694283977428.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("materialsbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("materialsbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|materialsbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.9 MB| + +## References + +https://huggingface.co/pranav-s/MaterialsBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-medbert_breastcancer_en.md b/docs/_posts/ahmedlone127/2023-09-09-medbert_breastcancer_en.md new file mode 100644 index 00000000000000..018573d6cb654a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-medbert_breastcancer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English medbert_breastcancer BertEmbeddings from Gaborandi +author: John Snow Labs +name: medbert_breastcancer +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`medbert_breastcancer` is a English model originally trained by Gaborandi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/medbert_breastcancer_en_5.1.1_3.0_1694279810961.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/medbert_breastcancer_en_5.1.1_3.0_1694279810961.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("medbert_breastcancer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("medbert_breastcancer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|medbert_breastcancer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.7 MB| + +## References + +https://huggingface.co/Gaborandi/MedBERT-breastcancer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-medium_mlm_imdb_en.md b/docs/_posts/ahmedlone127/2023-09-09-medium_mlm_imdb_en.md new file mode 100644 index 00000000000000..6c47bcf27c1088 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-medium_mlm_imdb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English medium_mlm_imdb BertEmbeddings from muhtasham +author: John Snow Labs +name: medium_mlm_imdb +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`medium_mlm_imdb` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/medium_mlm_imdb_en_5.1.1_3.0_1694258626582.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/medium_mlm_imdb_en_5.1.1_3.0_1694258626582.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("medium_mlm_imdb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("medium_mlm_imdb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|medium_mlm_imdb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|154.2 MB| + +## References + +https://huggingface.co/muhtasham/medium-mlm-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-metaphor_finetuned_bert_5epochs_en.md b/docs/_posts/ahmedlone127/2023-09-09-metaphor_finetuned_bert_5epochs_en.md new file mode 100644 index 00000000000000..9ed52e130aa7a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-metaphor_finetuned_bert_5epochs_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English metaphor_finetuned_bert_5epochs BertEmbeddings from kangela +author: John Snow Labs +name: metaphor_finetuned_bert_5epochs +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`metaphor_finetuned_bert_5epochs` is a English model originally trained by kangela. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/metaphor_finetuned_bert_5epochs_en_5.1.1_3.0_1694283470378.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/metaphor_finetuned_bert_5epochs_en_5.1.1_3.0_1694283470378.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("metaphor_finetuned_bert_5epochs","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("metaphor_finetuned_bert_5epochs", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|metaphor_finetuned_bert_5epochs| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/kangela/Metaphor-FineTuned-BERT-5Epochs \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-mlm_20230427_indobertlarge_001_en.md b/docs/_posts/ahmedlone127/2023-09-09-mlm_20230427_indobertlarge_001_en.md new file mode 100644 index 00000000000000..444e875589cfe7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-mlm_20230427_indobertlarge_001_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230427_indobertlarge_001 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230427_indobertlarge_001 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230427_indobertlarge_001` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230427_indobertlarge_001_en_5.1.1_3.0_1694280023038.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230427_indobertlarge_001_en_5.1.1_3.0_1694280023038.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230427_indobertlarge_001","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230427_indobertlarge_001", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230427_indobertlarge_001| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/intanm/mlm-20230427-IndoBERTLarge-001 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-mlm_20230427_mbert_001_en.md b/docs/_posts/ahmedlone127/2023-09-09-mlm_20230427_mbert_001_en.md new file mode 100644 index 00000000000000..4d75de27e5dceb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-mlm_20230427_mbert_001_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230427_mbert_001 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230427_mbert_001 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230427_mbert_001` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230427_mbert_001_en_5.1.1_3.0_1694279570434.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230427_mbert_001_en_5.1.1_3.0_1694279570434.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230427_mbert_001","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230427_mbert_001", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230427_mbert_001| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/intanm/mlm-20230427-mBERT-001 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-mlm_20230428_indobert_base_p2_001_en.md b/docs/_posts/ahmedlone127/2023-09-09-mlm_20230428_indobert_base_p2_001_en.md new file mode 100644 index 00000000000000..02519bca0d489e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-mlm_20230428_indobert_base_p2_001_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230428_indobert_base_p2_001 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230428_indobert_base_p2_001 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230428_indobert_base_p2_001` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230428_indobert_base_p2_001_en_5.1.1_3.0_1694283914581.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230428_indobert_base_p2_001_en_5.1.1_3.0_1694283914581.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230428_indobert_base_p2_001","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230428_indobert_base_p2_001", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230428_indobert_base_p2_001| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.2 MB| + +## References + +https://huggingface.co/intanm/mlm-20230428-indobert-base-p2-001 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-mtl_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-09-mtl_bert_base_uncased_en.md new file mode 100644 index 00000000000000..4d77dabecf3c93 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-mtl_bert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mtl_bert_base_uncased BertEmbeddings from jgammack +author: John Snow Labs +name: mtl_bert_base_uncased +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mtl_bert_base_uncased` is a English model originally trained by jgammack. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mtl_bert_base_uncased_en_5.1.1_3.0_1694279621938.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mtl_bert_base_uncased_en_5.1.1_3.0_1694279621938.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mtl_bert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mtl_bert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mtl_bert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/jgammack/MTL-bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-mtl_bert_base_uncased_ww_en.md b/docs/_posts/ahmedlone127/2023-09-09-mtl_bert_base_uncased_ww_en.md new file mode 100644 index 00000000000000..323231ff9ef067 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-mtl_bert_base_uncased_ww_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mtl_bert_base_uncased_ww BertEmbeddings from jgammack +author: John Snow Labs +name: mtl_bert_base_uncased_ww +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mtl_bert_base_uncased_ww` is a English model originally trained by jgammack. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mtl_bert_base_uncased_ww_en_5.1.1_3.0_1694279504952.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mtl_bert_base_uncased_ww_en_5.1.1_3.0_1694279504952.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mtl_bert_base_uncased_ww","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mtl_bert_base_uncased_ww", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mtl_bert_base_uncased_ww| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/jgammack/MTL-bert-base-uncased-ww \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-mymodel04_illvmi_en.md b/docs/_posts/ahmedlone127/2023-09-09-mymodel04_illvmi_en.md new file mode 100644 index 00000000000000..dc704c8fa320a8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-mymodel04_illvmi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mymodel04_illvmi BertEmbeddings from illvmi +author: John Snow Labs +name: mymodel04_illvmi +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mymodel04_illvmi` is a English model originally trained by illvmi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mymodel04_illvmi_en_5.1.1_3.0_1694266908899.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mymodel04_illvmi_en_5.1.1_3.0_1694266908899.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mymodel04_illvmi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mymodel04_illvmi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mymodel04_illvmi| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/illvmi/mymodel04 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-nepali_bert_npvec1_en.md b/docs/_posts/ahmedlone127/2023-09-09-nepali_bert_npvec1_en.md new file mode 100644 index 00000000000000..effdf89c0a764f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-nepali_bert_npvec1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English nepali_bert_npvec1 BertEmbeddings from nowalab +author: John Snow Labs +name: nepali_bert_npvec1 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nepali_bert_npvec1` is a English model originally trained by nowalab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nepali_bert_npvec1_en_5.1.1_3.0_1694282954336.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nepali_bert_npvec1_en_5.1.1_3.0_1694282954336.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("nepali_bert_npvec1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("nepali_bert_npvec1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nepali_bert_npvec1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|83.5 MB| + +## References + +https://huggingface.co/nowalab/nepali-bert-npvec1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-newsbert_en.md b/docs/_posts/ahmedlone127/2023-09-09-newsbert_en.md new file mode 100644 index 00000000000000..e28d4d04e45574 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-newsbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English newsbert BertEmbeddings from uclanlp +author: John Snow Labs +name: newsbert +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`newsbert` is a English model originally trained by uclanlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/newsbert_en_5.1.1_3.0_1694259618522.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/newsbert_en_5.1.1_3.0_1694259618522.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("newsbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("newsbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|newsbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/uclanlp/newsbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-prompt_finetune_en.md b/docs/_posts/ahmedlone127/2023-09-09-prompt_finetune_en.md new file mode 100644 index 00000000000000..1ef62ad9cd0024 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-prompt_finetune_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English prompt_finetune BertEmbeddings from AndyJ +author: John Snow Labs +name: prompt_finetune +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`prompt_finetune` is a English model originally trained by AndyJ. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/prompt_finetune_en_5.1.1_3.0_1694260942500.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/prompt_finetune_en_5.1.1_3.0_1694260942500.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("prompt_finetune","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("prompt_finetune", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|prompt_finetune| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.0 MB| + +## References + +https://huggingface.co/AndyJ/prompt_finetune \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-pt_caselawbert_en.md b/docs/_posts/ahmedlone127/2023-09-09-pt_caselawbert_en.md new file mode 100644 index 00000000000000..72aedc0703d523 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-pt_caselawbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pt_caselawbert BertEmbeddings from SharedBailii +author: John Snow Labs +name: pt_caselawbert +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pt_caselawbert` is a English model originally trained by SharedBailii. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pt_caselawbert_en_5.1.1_3.0_1694266014749.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pt_caselawbert_en_5.1.1_3.0_1694266014749.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("pt_caselawbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("pt_caselawbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pt_caselawbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.7 MB| + +## References + +https://huggingface.co/SharedBailii/PT-CaseLawBert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-pt_legalbert_en.md b/docs/_posts/ahmedlone127/2023-09-09-pt_legalbert_en.md new file mode 100644 index 00000000000000..ad6c68aa3c6656 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-pt_legalbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pt_legalbert BertEmbeddings from SharedBailii +author: John Snow Labs +name: pt_legalbert +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pt_legalbert` is a English model originally trained by SharedBailii. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pt_legalbert_en_5.1.1_3.0_1694266151456.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pt_legalbert_en_5.1.1_3.0_1694266151456.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("pt_legalbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("pt_legalbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pt_legalbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/SharedBailii/PT-LegalBert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-pt_pol_en.md b/docs/_posts/ahmedlone127/2023-09-09-pt_pol_en.md new file mode 100644 index 00000000000000..7168b1dde70c1c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-pt_pol_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pt_pol BertEmbeddings from SharedBailii +author: John Snow Labs +name: pt_pol +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pt_pol` is a English model originally trained by SharedBailii. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pt_pol_en_5.1.1_3.0_1694265453085.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pt_pol_en_5.1.1_3.0_1694265453085.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("pt_pol","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("pt_pol", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pt_pol| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/SharedBailii/PT-POL \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-romanian_bert_tweet_ro.md b/docs/_posts/ahmedlone127/2023-09-09-romanian_bert_tweet_ro.md new file mode 100644 index 00000000000000..3aaa3372223950 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-romanian_bert_tweet_ro.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Moldavian, Moldovan, Romanian romanian_bert_tweet BertEmbeddings from Iulian277 +author: John Snow Labs +name: romanian_bert_tweet +date: 2023-09-09 +tags: [bert, ro, open_source, fill_mask, onnx] +task: Embeddings +language: ro +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`romanian_bert_tweet` is a Moldavian, Moldovan, Romanian model originally trained by Iulian277. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/romanian_bert_tweet_ro_5.1.1_3.0_1694283360131.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/romanian_bert_tweet_ro_5.1.1_3.0_1694283360131.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("romanian_bert_tweet","ro") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("romanian_bert_tweet", "ro") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|romanian_bert_tweet| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ro| +|Size:|466.5 MB| + +## References + +https://huggingface.co/Iulian277/ro-bert-tweet \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-s3_v1_20_epochs_en.md b/docs/_posts/ahmedlone127/2023-09-09-s3_v1_20_epochs_en.md new file mode 100644 index 00000000000000..1bbcb5a006c2c5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-s3_v1_20_epochs_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English s3_v1_20_epochs BertEmbeddings from AethiQs-Max +author: John Snow Labs +name: s3_v1_20_epochs +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`s3_v1_20_epochs` is a English model originally trained by AethiQs-Max. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/s3_v1_20_epochs_en_5.1.1_3.0_1694260577106.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/s3_v1_20_epochs_en_5.1.1_3.0_1694260577106.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("s3_v1_20_epochs","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("s3_v1_20_epochs", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|s3_v1_20_epochs| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/AethiQs-Max/s3-v1-20_epochs \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-sae_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-09-sae_bert_base_uncased_en.md new file mode 100644 index 00000000000000..b9a4fb512b7dec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-sae_bert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sae_bert_base_uncased BertEmbeddings from jgammack +author: John Snow Labs +name: sae_bert_base_uncased +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sae_bert_base_uncased` is a English model originally trained by jgammack. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sae_bert_base_uncased_en_5.1.1_3.0_1694279737863.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sae_bert_base_uncased_en_5.1.1_3.0_1694279737863.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sae_bert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sae_bert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sae_bert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/jgammack/SAE-bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-scholarbert_100_wb_en.md b/docs/_posts/ahmedlone127/2023-09-09-scholarbert_100_wb_en.md new file mode 100644 index 00000000000000..f202cd31862621 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-scholarbert_100_wb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English scholarbert_100_wb BertEmbeddings from globuslabs +author: John Snow Labs +name: scholarbert_100_wb +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scholarbert_100_wb` is a English model originally trained by globuslabs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scholarbert_100_wb_en_5.1.1_3.0_1694279655954.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scholarbert_100_wb_en_5.1.1_3.0_1694279655954.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("scholarbert_100_wb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("scholarbert_100_wb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scholarbert_100_wb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/globuslabs/ScholarBERT_100_WB \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-scholarbert_10_en.md b/docs/_posts/ahmedlone127/2023-09-09-scholarbert_10_en.md new file mode 100644 index 00000000000000..28daa3d3d42423 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-scholarbert_10_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English scholarbert_10 BertEmbeddings from globuslabs +author: John Snow Labs +name: scholarbert_10 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scholarbert_10` is a English model originally trained by globuslabs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scholarbert_10_en_5.1.1_3.0_1694279085983.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scholarbert_10_en_5.1.1_3.0_1694279085983.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("scholarbert_10","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("scholarbert_10", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scholarbert_10| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/globuslabs/ScholarBERT_10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-scholarbert_10_wb_en.md b/docs/_posts/ahmedlone127/2023-09-09-scholarbert_10_wb_en.md new file mode 100644 index 00000000000000..a3511f63b3c5e5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-scholarbert_10_wb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English scholarbert_10_wb BertEmbeddings from globuslabs +author: John Snow Labs +name: scholarbert_10_wb +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scholarbert_10_wb` is a English model originally trained by globuslabs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scholarbert_10_wb_en_5.1.1_3.0_1694279935293.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scholarbert_10_wb_en_5.1.1_3.0_1694279935293.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("scholarbert_10_wb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("scholarbert_10_wb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scholarbert_10_wb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/globuslabs/ScholarBERT_10_WB \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-scholarbert_1_en.md b/docs/_posts/ahmedlone127/2023-09-09-scholarbert_1_en.md new file mode 100644 index 00000000000000..41090287433361 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-scholarbert_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English scholarbert_1 BertEmbeddings from globuslabs +author: John Snow Labs +name: scholarbert_1 +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scholarbert_1` is a English model originally trained by globuslabs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scholarbert_1_en_5.1.1_3.0_1694279368543.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scholarbert_1_en_5.1.1_3.0_1694279368543.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("scholarbert_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("scholarbert_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scholarbert_1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/globuslabs/ScholarBERT_1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-scholarbert_en.md b/docs/_posts/ahmedlone127/2023-09-09-scholarbert_en.md new file mode 100644 index 00000000000000..4c0b4f162fd3e7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-scholarbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English scholarbert BertEmbeddings from globuslabs +author: John Snow Labs +name: scholarbert +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scholarbert` is a English model originally trained by globuslabs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scholarbert_en_5.1.1_3.0_1694278560520.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scholarbert_en_5.1.1_3.0_1694278560520.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("scholarbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("scholarbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scholarbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/globuslabs/ScholarBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-spacebert_en.md b/docs/_posts/ahmedlone127/2023-09-09-spacebert_en.md new file mode 100644 index 00000000000000..06af2bc491452a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-spacebert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English spacebert BertEmbeddings from icelab +author: John Snow Labs +name: spacebert +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`spacebert` is a English model originally trained by icelab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/spacebert_en_5.1.1_3.0_1694266124128.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/spacebert_en_5.1.1_3.0_1694266124128.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("spacebert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("spacebert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|spacebert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/icelab/spacebert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-spacescibert_en.md b/docs/_posts/ahmedlone127/2023-09-09-spacescibert_en.md new file mode 100644 index 00000000000000..97aa84a1c3942f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-spacescibert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English spacescibert BertEmbeddings from icelab +author: John Snow Labs +name: spacescibert +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`spacescibert` is a English model originally trained by icelab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/spacescibert_en_5.1.1_3.0_1694266250257.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/spacescibert_en_5.1.1_3.0_1694266250257.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("spacescibert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("spacescibert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|spacescibert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/icelab/spacescibert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-tendencias_en.md b/docs/_posts/ahmedlone127/2023-09-09-tendencias_en.md new file mode 100644 index 00000000000000..74de16a8e716da --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-tendencias_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tendencias BertEmbeddings from edwatanabe +author: John Snow Labs +name: tendencias +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tendencias` is a English model originally trained by edwatanabe. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tendencias_en_5.1.1_3.0_1694283750490.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tendencias_en_5.1.1_3.0_1694283750490.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tendencias","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tendencias", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tendencias| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/edwatanabe/tendencias \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-test_ru.md b/docs/_posts/ahmedlone127/2023-09-09-test_ru.md new file mode 100644 index 00000000000000..f38da753fdb9a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-test_ru.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Russian test BertEmbeddings from k0t1k +author: John Snow Labs +name: test +date: 2023-09-09 +tags: [bert, ru, open_source, fill_mask, onnx] +task: Embeddings +language: ru +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`test` is a Russian model originally trained by k0t1k. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/test_ru_5.1.1_3.0_1694284796336.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/test_ru_5.1.1_3.0_1694284796336.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("test","ru") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("test", "ru") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|test| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ru| +|Size:|43.8 MB| + +## References + +https://huggingface.co/k0t1k/test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-tiny_mlm_glue_cola_en.md b/docs/_posts/ahmedlone127/2023-09-09-tiny_mlm_glue_cola_en.md new file mode 100644 index 00000000000000..47a9f61e5832be --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-tiny_mlm_glue_cola_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_cola BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_cola +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_cola` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_cola_en_5.1.1_3.0_1694284657538.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_cola_en_5.1.1_3.0_1694284657538.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_cola","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_cola", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_cola| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-cola \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-tiny_mlm_glue_mnli_en.md b/docs/_posts/ahmedlone127/2023-09-09-tiny_mlm_glue_mnli_en.md new file mode 100644 index 00000000000000..72cc2d69973004 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-tiny_mlm_glue_mnli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_mnli BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_mnli +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_mnli` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_mnli_en_5.1.1_3.0_1694284719722.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_mnli_en_5.1.1_3.0_1694284719722.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_mnli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_mnli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_mnli| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-tiny_mlm_glue_mrpc_en.md b/docs/_posts/ahmedlone127/2023-09-09-tiny_mlm_glue_mrpc_en.md new file mode 100644 index 00000000000000..55ed9c9eb86c27 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-tiny_mlm_glue_mrpc_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_mrpc BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_mrpc +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_mrpc` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_mrpc_en_5.1.1_3.0_1694284793871.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_mrpc_en_5.1.1_3.0_1694284793871.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_mrpc","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_mrpc", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_mrpc| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-mrpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-tiny_mlm_glue_qnli_en.md b/docs/_posts/ahmedlone127/2023-09-09-tiny_mlm_glue_qnli_en.md new file mode 100644 index 00000000000000..23047374c2645a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-tiny_mlm_glue_qnli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_qnli BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_qnli +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_qnli` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_qnli_en_5.1.1_3.0_1694284869990.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_qnli_en_5.1.1_3.0_1694284869990.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_qnli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_qnli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_qnli| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-qnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-tiny_mlm_glue_qqp_en.md b/docs/_posts/ahmedlone127/2023-09-09-tiny_mlm_glue_qqp_en.md new file mode 100644 index 00000000000000..be40c3d4003454 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-tiny_mlm_glue_qqp_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_qqp BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_qqp +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_qqp` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_qqp_en_5.1.1_3.0_1694284948621.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_qqp_en_5.1.1_3.0_1694284948621.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_qqp","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_qqp", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_qqp| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-qqp \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-tiny_mlm_snli_en.md b/docs/_posts/ahmedlone127/2023-09-09-tiny_mlm_snli_en.md new file mode 100644 index 00000000000000..ca327f395c682b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-tiny_mlm_snli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_snli BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_snli +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_snli` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_snli_en_5.1.1_3.0_1694283488441.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_snli_en_5.1.1_3.0_1694283488441.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_snli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_snli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_snli| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-snli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-tod_bert_jnt_en.md b/docs/_posts/ahmedlone127/2023-09-09-tod_bert_jnt_en.md new file mode 100644 index 00000000000000..ce799c56d6f12b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-tod_bert_jnt_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tod_bert_jnt BertEmbeddings from jasonwu +author: John Snow Labs +name: tod_bert_jnt +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tod_bert_jnt` is a English model originally trained by jasonwu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tod_bert_jnt_en_5.1.1_3.0_1694278507559.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tod_bert_jnt_en_5.1.1_3.0_1694278507559.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tod_bert_jnt","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tod_bert_jnt", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tod_bert_jnt| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/jasonwu/ToD-BERT-jnt \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-turkish_mbertmodel_mberttok_en.md b/docs/_posts/ahmedlone127/2023-09-09-turkish_mbertmodel_mberttok_en.md new file mode 100644 index 00000000000000..56d140915691c3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-turkish_mbertmodel_mberttok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English turkish_mbertmodel_mberttok BertEmbeddings from hgiyt +author: John Snow Labs +name: turkish_mbertmodel_mberttok +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`turkish_mbertmodel_mberttok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/turkish_mbertmodel_mberttok_en_5.1.1_3.0_1694265250672.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/turkish_mbertmodel_mberttok_en_5.1.1_3.0_1694265250672.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("turkish_mbertmodel_mberttok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("turkish_mbertmodel_mberttok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|turkish_mbertmodel_mberttok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|525.2 MB| + +## References + +https://huggingface.co/hgiyt/tr-mbertmodel-mberttok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-turkish_mbertmodel_monotok_adapter_en.md b/docs/_posts/ahmedlone127/2023-09-09-turkish_mbertmodel_monotok_adapter_en.md new file mode 100644 index 00000000000000..fde120f67aa5b2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-turkish_mbertmodel_monotok_adapter_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English turkish_mbertmodel_monotok_adapter BertEmbeddings from hgiyt +author: John Snow Labs +name: turkish_mbertmodel_monotok_adapter +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`turkish_mbertmodel_monotok_adapter` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/turkish_mbertmodel_monotok_adapter_en_5.1.1_3.0_1694265394331.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/turkish_mbertmodel_monotok_adapter_en_5.1.1_3.0_1694265394331.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("turkish_mbertmodel_monotok_adapter","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("turkish_mbertmodel_monotok_adapter", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|turkish_mbertmodel_monotok_adapter| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.6 MB| + +## References + +https://huggingface.co/hgiyt/tr-mbertmodel-monotok-adapter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-turkish_mbertmodel_monotok_en.md b/docs/_posts/ahmedlone127/2023-09-09-turkish_mbertmodel_monotok_en.md new file mode 100644 index 00000000000000..44c25a569f9151 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-turkish_mbertmodel_monotok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English turkish_mbertmodel_monotok BertEmbeddings from hgiyt +author: John Snow Labs +name: turkish_mbertmodel_monotok +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`turkish_mbertmodel_monotok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/turkish_mbertmodel_monotok_en_5.1.1_3.0_1694265535952.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/turkish_mbertmodel_monotok_en_5.1.1_3.0_1694265535952.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("turkish_mbertmodel_monotok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("turkish_mbertmodel_monotok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|turkish_mbertmodel_monotok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.6 MB| + +## References + +https://huggingface.co/hgiyt/tr-mbertmodel-monotok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-turkish_monomodel_mberttok_en.md b/docs/_posts/ahmedlone127/2023-09-09-turkish_monomodel_mberttok_en.md new file mode 100644 index 00000000000000..b9a1599559d93c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-turkish_monomodel_mberttok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English turkish_monomodel_mberttok BertEmbeddings from hgiyt +author: John Snow Labs +name: turkish_monomodel_mberttok +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`turkish_monomodel_mberttok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/turkish_monomodel_mberttok_en_5.1.1_3.0_1694265682717.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/turkish_monomodel_mberttok_en_5.1.1_3.0_1694265682717.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("turkish_monomodel_mberttok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("turkish_monomodel_mberttok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|turkish_monomodel_mberttok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|524.2 MB| + +## References + +https://huggingface.co/hgiyt/tr-monomodel-mberttok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-turkish_monomodel_monotok_en.md b/docs/_posts/ahmedlone127/2023-09-09-turkish_monomodel_monotok_en.md new file mode 100644 index 00000000000000..663567c4c5c30a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-turkish_monomodel_monotok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English turkish_monomodel_monotok BertEmbeddings from hgiyt +author: John Snow Labs +name: turkish_monomodel_monotok +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`turkish_monomodel_monotok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/turkish_monomodel_monotok_en_5.1.1_3.0_1694265818291.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/turkish_monomodel_monotok_en_5.1.1_3.0_1694265818291.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("turkish_monomodel_monotok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("turkish_monomodel_monotok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|turkish_monomodel_monotok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.2 MB| + +## References + +https://huggingface.co/hgiyt/tr-monomodel-monotok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-vbert_2021_large_en.md b/docs/_posts/ahmedlone127/2023-09-09-vbert_2021_large_en.md new file mode 100644 index 00000000000000..64fbe01a0a7703 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-vbert_2021_large_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English vbert_2021_large BertEmbeddings from VMware +author: John Snow Labs +name: vbert_2021_large +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`vbert_2021_large` is a English model originally trained by VMware. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/vbert_2021_large_en_5.1.1_3.0_1694258767377.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/vbert_2021_large_en_5.1.1_3.0_1694258767377.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("vbert_2021_large","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("vbert_2021_large", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|vbert_2021_large| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/VMware/vbert-2021-large \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-wordpred_arabert_en.md b/docs/_posts/ahmedlone127/2023-09-09-wordpred_arabert_en.md new file mode 100644 index 00000000000000..3de4403923a8f3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-wordpred_arabert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English wordpred_arabert BertEmbeddings from rowidabelaal +author: John Snow Labs +name: wordpred_arabert +date: 2023-09-09 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`wordpred_arabert` is a English model originally trained by rowidabelaal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/wordpred_arabert_en_5.1.1_3.0_1694260416937.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/wordpred_arabert_en_5.1.1_3.0_1694260416937.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("wordpred_arabert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("wordpred_arabert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|wordpred_arabert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.4 GB| + +## References + +https://huggingface.co/rowidabelaal/wordpred_arabert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-09-xtreme_squad_bert_base_multilingual_cased_xx.md b/docs/_posts/ahmedlone127/2023-09-09-xtreme_squad_bert_base_multilingual_cased_xx.md new file mode 100644 index 00000000000000..25d1e020185df7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-09-xtreme_squad_bert_base_multilingual_cased_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual xtreme_squad_bert_base_multilingual_cased BertEmbeddings from dyyyyyyyy +author: John Snow Labs +name: xtreme_squad_bert_base_multilingual_cased +date: 2023-09-09 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`xtreme_squad_bert_base_multilingual_cased` is a Multilingual model originally trained by dyyyyyyyy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xtreme_squad_bert_base_multilingual_cased_xx_5.1.1_3.0_1694266754297.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xtreme_squad_bert_base_multilingual_cased_xx_5.1.1_3.0_1694266754297.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("xtreme_squad_bert_base_multilingual_cased","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("xtreme_squad_bert_base_multilingual_cased", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xtreme_squad_bert_base_multilingual_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|665.0 MB| + +## References + +https://huggingface.co/dyyyyyyyy/XTREME_squad_BERT-base-multilingual-cased \ No newline at end of file From f3c878e67fe5daf1bff47b9ad82776750000ea34 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Wed, 13 Sep 2023 14:01:31 +0700 Subject: [PATCH 41/55] 2023-09-12-tiny_mlm_glue_rte_en (#13975) * Add model 2023-09-13-bert_base_finnish_cased_v1_fi * Add model 2023-09-13-bert_tiny_finetuned_nan_labels_nepal_bhasa_longer_en * Add model 2023-09-12-dlub_2022_mlm_en * Add model 2023-09-13-retbert_en * Add model 2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_6_en * Add model 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2023-09-13-bert_base_uncased_noisy_orcas_1.0positive_0.5_negative_margin1.0_cosine_en * Add model 2023-09-13-improvedabg_20_epochs_en * Add model 2023-09-13-labse_english_russian_erzya_v1_ru * Add model 2023-09-13-inlegalbert_en * Add model 2023-09-13-bert_base_uncased_test_en * Add model 2023-09-13-hindi_tweets_bert_hateful_hi * Add model 2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_3_en * Add model 2023-09-13-gujibert_fan_en * Add model 2023-09-13-dictbert_en * Add model 2023-09-13-mabepa_sts_es * Add model 2023-09-13-hindi_least_haitian_1m_hi * Add model 2023-09-12-small_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab_en * Add model 2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_2_en * Add model 2023-09-13-hebert_en * Add model 2023-09-12-cro_cov_csebert_en * Add model 2023-09-13-paraphraserplus_1epoch_en * Add model 2023-09-13-nl1_en * Add model 2023-09-13-parlamint_en * Add model 2023-09-13-bert_base_finetuned_wellness_en * Add model 2023-09-13-ern3_en * Add model 2023-09-13-splade_cocondenser_selfdistil_baseplate_en * Add model 2023-09-12-akeylegalbert_inscotus_and_ledgar_en * Add model 2023-09-13-bert_base_dutch_cased_en * Add model 2023-09-12-bert_base_english_french_chinese_cased_en * Add model 2023-09-13-kpfbert_base_en * Add model 2023-09-13-kcbert_base_petition_en * Add model 2023-09-13-batterybert_uncased_en * Add model 2023-09-13-berel_sivan22_he * Add model 2023-09-13-first_model_en * Add model 2023-09-13-bert_base_uncased_bert_mask_complete_word_updated_vocab_en * Add model 2023-09-13-bert_base_120_en * Add model 2023-09-13-nepalibert_ne * Add model 2023-09-13-bert_cn_wudi7758521521_en * Add model 2023-09-12-bert_c2_english_german_en * Add model 2023-09-13-bert_small_finetuned_eurlex_longer_en * Add model 2023-09-13-bert_base_uncased_finetuned_gap_en * Add model 2023-09-13-clr_finetuned_bert_base_uncased_en * Add model 2023-09-13-biblitbert_1_en * Add model 2023-09-13-model_ankai_en * Add model 2023-09-13-bert_base_standard_bahasa_cased_en * Add model 2023-09-13-bert_portuguese_institutional_corpus_v.1_en * Add model 2023-09-13-bert_base_uncased_issues_128_twidfeel_en * Add model 2023-09-13-bert_base_vn_finetuned_portuguese_en * Add model 2023-09-13-bert_model_nyashavision22_en * Add model 2023-09-13-kcbert_base_dev_en * Add model 2023-09-13-bert_finetuning_test_xiejiafang_en * Add model 2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_13_en * Add model 2023-09-13-bert_base_uncased_dstc9_en * Add model 2023-09-13-bert_embding_finetuned_spmlm_02_en * Add model 2023-09-13-sentmae_en * Add model 2023-09-13-bert_base_uncased_issues_128_munsu_en * Add model 2023-09-13-pcscibert_cased_en * Add model 2023-09-13-bert_base_uncased_2022_habana_test_1_en * Add model 2023-09-13-kcbert_large_dev_en * Add model 2023-09-13-bert_truncate_en * Add model 2023-09-13-bert_large_arabertv2_ar * Add model 2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_0_en * Add model 2023-09-13-kcbert_large_en * Add model 2023-09-12-cysecbert_en * Add model 2023-09-13-scibert_scivocab_uncased_long_4096_en * Add model 2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_1_en * Add model 2023-09-13-bert_finetuning_test1_en * Add model 2023-09-13-bert_concat_3_en * Add model 2023-09-13-bert_base_spanish_wwm_cased_finetuned_literature_pro_en * Add model 2023-09-13-bioptimus_en * Add model 2023-09-13-bert_base_nli_stsb_en * Add model 2023-09-13-batterybert_cased_en * Add model 2023-09-13-bert_finetune_simcse_truncate_en * Add model 2023-09-13-bert_base_nli_en * Add model 2023-09-13-further_train_original_10_en * Add model 2023-09-13-bert_concat_3_finetune_simcse_truncate_en * Add model 2023-09-13-bert_base_uncased_binwang_en * Add model 2023-09-13-legalbert_adept_en * Add model 2023-09-13-tlm_ag_medium_scale_en * Add model 2023-09-13-bert_concat_2_finetune_simcse_truncate_en --------- Co-authored-by: ahmedlone127 --- .../2023-09-12-20split_dataset_en.md | 93 +++++++++++++++++++ ...3-09-12-abena_base_akuapem_twi_cased_en.md | 93 +++++++++++++++++++ ...-09-12-abena_base_asante_twi_uncased_en.md | 93 +++++++++++++++++++ ...2023-09-12-abstract_sim_query_pubmed_en.md | 93 +++++++++++++++++++ ...3-09-12-abstract_sim_sentence_pubmed_en.md | 93 +++++++++++++++++++ .../ahmedlone127/2023-09-12-ai12_mackei_en.md | 93 +++++++++++++++++++ .../2023-09-12-akeylegalbert_en.md | 93 +++++++++++++++++++ ...egalbert_inscotus_and_ledgar_14epoch_en.md | 93 +++++++++++++++++++ ...12-akeylegalbert_inscotus_and_ledgar_en.md | 93 +++++++++++++++++++ ...9-12-algarlegalbert1_large_arabertv2_en.md | 93 +++++++++++++++++++ .../2023-09-12-arabert256_flickr8k_en.md | 93 +++++++++++++++++++ .../2023-09-12-arabert32_coco_en.md | 93 +++++++++++++++++++ .../ahmedlone127/2023-09-12-arabert_c19_ar.md | 93 +++++++++++++++++++ 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docs/_posts/ahmedlone127/2023-09-13-tulio_bert_es.md create mode 100644 docs/_posts/ahmedlone127/2023-09-13-wineberto_italian_cased_it.md create mode 100644 docs/_posts/ahmedlone127/2023-09-13-wobert_chinese_plus_zh.md diff --git a/docs/_posts/ahmedlone127/2023-09-12-20split_dataset_en.md b/docs/_posts/ahmedlone127/2023-09-12-20split_dataset_en.md new file mode 100644 index 00000000000000..780390afc444b0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-20split_dataset_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English 20split_dataset BertEmbeddings from Billwzl +author: John Snow Labs +name: 20split_dataset +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`20split_dataset` is a English model originally trained by Billwzl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/20split_dataset_en_5.1.1_3.0_1694558868282.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/20split_dataset_en_5.1.1_3.0_1694558868282.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("20split_dataset","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("20split_dataset", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|20split_dataset| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/Billwzl/20split_dataset \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-abena_base_akuapem_twi_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-abena_base_akuapem_twi_cased_en.md new file mode 100644 index 00000000000000..3b52b9a141e7c2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-abena_base_akuapem_twi_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English abena_base_akuapem_twi_cased BertEmbeddings from Ghana-NLP +author: John Snow Labs +name: abena_base_akuapem_twi_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`abena_base_akuapem_twi_cased` is a English model originally trained by Ghana-NLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/abena_base_akuapem_twi_cased_en_5.1.1_3.0_1694558470329.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/abena_base_akuapem_twi_cased_en_5.1.1_3.0_1694558470329.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("abena_base_akuapem_twi_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("abena_base_akuapem_twi_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|abena_base_akuapem_twi_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|664.5 MB| + +## References + +https://huggingface.co/Ghana-NLP/abena-base-akuapem-twi-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-abena_base_asante_twi_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-12-abena_base_asante_twi_uncased_en.md new file mode 100644 index 00000000000000..f196600033a6cc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-abena_base_asante_twi_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English abena_base_asante_twi_uncased BertEmbeddings from Ghana-NLP +author: John Snow Labs +name: abena_base_asante_twi_uncased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`abena_base_asante_twi_uncased` is a English model originally trained by Ghana-NLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/abena_base_asante_twi_uncased_en_5.1.1_3.0_1694558682719.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/abena_base_asante_twi_uncased_en_5.1.1_3.0_1694558682719.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("abena_base_asante_twi_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("abena_base_asante_twi_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|abena_base_asante_twi_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|664.3 MB| + +## References + +https://huggingface.co/Ghana-NLP/abena-base-asante-twi-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-abstract_sim_query_pubmed_en.md b/docs/_posts/ahmedlone127/2023-09-12-abstract_sim_query_pubmed_en.md new file mode 100644 index 00000000000000..934d2783451faf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-abstract_sim_query_pubmed_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English abstract_sim_query_pubmed BertEmbeddings from biu-nlp +author: John Snow Labs +name: abstract_sim_query_pubmed +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`abstract_sim_query_pubmed` is a English model originally trained by biu-nlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/abstract_sim_query_pubmed_en_5.1.1_3.0_1694561530585.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/abstract_sim_query_pubmed_en_5.1.1_3.0_1694561530585.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("abstract_sim_query_pubmed","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("abstract_sim_query_pubmed", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|abstract_sim_query_pubmed| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.2 MB| + +## References + +https://huggingface.co/biu-nlp/abstract-sim-query-pubmed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-abstract_sim_sentence_pubmed_en.md b/docs/_posts/ahmedlone127/2023-09-12-abstract_sim_sentence_pubmed_en.md new file mode 100644 index 00000000000000..67b65af41b7b7a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-abstract_sim_sentence_pubmed_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English abstract_sim_sentence_pubmed BertEmbeddings from biu-nlp +author: John Snow Labs +name: abstract_sim_sentence_pubmed +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`abstract_sim_sentence_pubmed` is a English model originally trained by biu-nlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/abstract_sim_sentence_pubmed_en_5.1.1_3.0_1694561658345.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/abstract_sim_sentence_pubmed_en_5.1.1_3.0_1694561658345.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("abstract_sim_sentence_pubmed","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("abstract_sim_sentence_pubmed", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|abstract_sim_sentence_pubmed| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.3 MB| + +## References + +https://huggingface.co/biu-nlp/abstract-sim-sentence-pubmed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-ai12_mackei_en.md b/docs/_posts/ahmedlone127/2023-09-12-ai12_mackei_en.md new file mode 100644 index 00000000000000..dd2be1f5865cf6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-ai12_mackei_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ai12_mackei BertEmbeddings from mackei +author: John Snow Labs +name: ai12_mackei +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ai12_mackei` is a English model originally trained by mackei. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ai12_mackei_en_5.1.1_3.0_1694551191898.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ai12_mackei_en_5.1.1_3.0_1694551191898.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ai12_mackei","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ai12_mackei", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ai12_mackei| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/mackei/ai12 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-akeylegalbert_en.md b/docs/_posts/ahmedlone127/2023-09-12-akeylegalbert_en.md new file mode 100644 index 00000000000000..787d76eff64468 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-akeylegalbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English akeylegalbert BertEmbeddings from hatemestinbejaia +author: John Snow Labs +name: akeylegalbert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`akeylegalbert` is a English model originally trained by hatemestinbejaia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/akeylegalbert_en_5.1.1_3.0_1694557063708.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/akeylegalbert_en_5.1.1_3.0_1694557063708.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("akeylegalbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("akeylegalbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|akeylegalbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/hatemestinbejaia/AkeyLegalBert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-akeylegalbert_inscotus_and_ledgar_14epoch_en.md b/docs/_posts/ahmedlone127/2023-09-12-akeylegalbert_inscotus_and_ledgar_14epoch_en.md new file mode 100644 index 00000000000000..6887e299659818 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-akeylegalbert_inscotus_and_ledgar_14epoch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English akeylegalbert_inscotus_and_ledgar_14epoch BertEmbeddings from hatemestinbejaia +author: John Snow Labs +name: akeylegalbert_inscotus_and_ledgar_14epoch +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`akeylegalbert_inscotus_and_ledgar_14epoch` is a English model originally trained by hatemestinbejaia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/akeylegalbert_inscotus_and_ledgar_14epoch_en_5.1.1_3.0_1694554005394.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/akeylegalbert_inscotus_and_ledgar_14epoch_en_5.1.1_3.0_1694554005394.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("akeylegalbert_inscotus_and_ledgar_14epoch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("akeylegalbert_inscotus_and_ledgar_14epoch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|akeylegalbert_inscotus_and_ledgar_14epoch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/hatemestinbejaia/AkeyLegalBert_inScotus_and_Ledgar_14epoch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-akeylegalbert_inscotus_and_ledgar_en.md b/docs/_posts/ahmedlone127/2023-09-12-akeylegalbert_inscotus_and_ledgar_en.md new file mode 100644 index 00000000000000..9310c0c7819043 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-akeylegalbert_inscotus_and_ledgar_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English akeylegalbert_inscotus_and_ledgar BertEmbeddings from hatemestinbejaia +author: John Snow Labs +name: akeylegalbert_inscotus_and_ledgar +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`akeylegalbert_inscotus_and_ledgar` is a English model originally trained by hatemestinbejaia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/akeylegalbert_inscotus_and_ledgar_en_5.1.1_3.0_1694553675259.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/akeylegalbert_inscotus_and_ledgar_en_5.1.1_3.0_1694553675259.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("akeylegalbert_inscotus_and_ledgar","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("akeylegalbert_inscotus_and_ledgar", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|akeylegalbert_inscotus_and_ledgar| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/hatemestinbejaia/AkeyLegalBert_inScotus_and_Ledgar \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-algarlegalbert1_large_arabertv2_en.md b/docs/_posts/ahmedlone127/2023-09-12-algarlegalbert1_large_arabertv2_en.md new file mode 100644 index 00000000000000..d1a780f6d78143 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-algarlegalbert1_large_arabertv2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English algarlegalbert1_large_arabertv2 BertEmbeddings from hatemestinbejaia +author: John Snow Labs +name: algarlegalbert1_large_arabertv2 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`algarlegalbert1_large_arabertv2` is a English model originally trained by hatemestinbejaia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/algarlegalbert1_large_arabertv2_en_5.1.1_3.0_1694555945058.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/algarlegalbert1_large_arabertv2_en_5.1.1_3.0_1694555945058.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("algarlegalbert1_large_arabertv2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("algarlegalbert1_large_arabertv2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|algarlegalbert1_large_arabertv2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.4 GB| + +## References + +https://huggingface.co/hatemestinbejaia/AlgArLegalBert1-large-arabertv2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-arabert256_flickr8k_en.md b/docs/_posts/ahmedlone127/2023-09-12-arabert256_flickr8k_en.md new file mode 100644 index 00000000000000..e38d9b909ca658 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-arabert256_flickr8k_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English arabert256_flickr8k BertEmbeddings from jontooy +author: John Snow Labs +name: arabert256_flickr8k +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabert256_flickr8k` is a English model originally trained by jontooy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabert256_flickr8k_en_5.1.1_3.0_1694510056959.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabert256_flickr8k_en_5.1.1_3.0_1694510056959.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabert256_flickr8k","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabert256_flickr8k", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabert256_flickr8k| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|505.1 MB| + +## References + +https://huggingface.co/jontooy/AraBERT256-Flickr8k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-arabert32_coco_en.md b/docs/_posts/ahmedlone127/2023-09-12-arabert32_coco_en.md new file mode 100644 index 00000000000000..c953572c2f84fa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-arabert32_coco_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English arabert32_coco BertEmbeddings from jontooy +author: John Snow Labs +name: arabert32_coco +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabert32_coco` is a English model originally trained by jontooy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabert32_coco_en_5.1.1_3.0_1694509854276.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabert32_coco_en_5.1.1_3.0_1694509854276.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabert32_coco","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabert32_coco", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabert32_coco| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|505.1 MB| + +## References + +https://huggingface.co/jontooy/AraBERT32-COCO \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-arabert_c19_ar.md b/docs/_posts/ahmedlone127/2023-09-12-arabert_c19_ar.md new file mode 100644 index 00000000000000..3cff9a535af3ea --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-arabert_c19_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic arabert_c19 BertEmbeddings from moha +author: John Snow Labs +name: arabert_c19 +date: 2023-09-12 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabert_c19` is a Arabic model originally trained by moha. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabert_c19_ar_5.1.1_3.0_1694554366327.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabert_c19_ar_5.1.1_3.0_1694554366327.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabert_c19","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabert_c19", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabert_c19| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|504.9 MB| + +## References + +https://huggingface.co/moha/arabert_c19 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v1_ar.md b/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v1_ar.md new file mode 100644 index 00000000000000..adf89169ecb019 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v1_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic arabertmo_base_v1 BertEmbeddings from Ebtihal +author: John Snow Labs +name: arabertmo_base_v1 +date: 2023-09-12 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabertmo_base_v1` is a Arabic model originally trained by Ebtihal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabertmo_base_v1_ar_5.1.1_3.0_1694509594398.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabertmo_base_v1_ar_5.1.1_3.0_1694509594398.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabertmo_base_v1","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabertmo_base_v1", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabertmo_base_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|407.8 MB| + +## References + +https://huggingface.co/Ebtihal/AraBertMo_base_V1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v2_ar.md b/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v2_ar.md new file mode 100644 index 00000000000000..3d49800c8ab1f1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v2_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic arabertmo_base_v2 BertEmbeddings from Ebtihal +author: John Snow Labs +name: arabertmo_base_v2 +date: 2023-09-12 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabertmo_base_v2` is a Arabic model originally trained by Ebtihal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabertmo_base_v2_ar_5.1.1_3.0_1694509720231.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabertmo_base_v2_ar_5.1.1_3.0_1694509720231.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabertmo_base_v2","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabertmo_base_v2", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabertmo_base_v2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|407.8 MB| + +## References + +https://huggingface.co/Ebtihal/AraBertMo_base_V2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v3_ar.md b/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v3_ar.md new file mode 100644 index 00000000000000..50e019c20dcb7c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v3_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic arabertmo_base_v3 BertEmbeddings from Ebtihal +author: John Snow Labs +name: arabertmo_base_v3 +date: 2023-09-12 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabertmo_base_v3` is a Arabic model originally trained by Ebtihal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabertmo_base_v3_ar_5.1.1_3.0_1694509862023.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabertmo_base_v3_ar_5.1.1_3.0_1694509862023.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabertmo_base_v3","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabertmo_base_v3", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabertmo_base_v3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|407.8 MB| + +## References + +https://huggingface.co/Ebtihal/AraBertMo_base_V3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v4_ar.md b/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v4_ar.md new file mode 100644 index 00000000000000..2fa8ff3e650214 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v4_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic arabertmo_base_v4 BertEmbeddings from Ebtihal +author: John Snow Labs +name: arabertmo_base_v4 +date: 2023-09-12 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabertmo_base_v4` is a Arabic model originally trained by Ebtihal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabertmo_base_v4_ar_5.1.1_3.0_1694510028864.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabertmo_base_v4_ar_5.1.1_3.0_1694510028864.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabertmo_base_v4","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabertmo_base_v4", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabertmo_base_v4| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|407.9 MB| + +## References + +https://huggingface.co/Ebtihal/AraBertMo_base_V4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v5_ar.md b/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v5_ar.md new file mode 100644 index 00000000000000..4525a6a58b56fd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v5_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic arabertmo_base_v5 BertEmbeddings from Ebtihal +author: John Snow Labs +name: arabertmo_base_v5 +date: 2023-09-12 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabertmo_base_v5` is a Arabic model originally trained by Ebtihal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabertmo_base_v5_ar_5.1.1_3.0_1694510170817.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabertmo_base_v5_ar_5.1.1_3.0_1694510170817.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabertmo_base_v5","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabertmo_base_v5", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabertmo_base_v5| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|407.9 MB| + +## References + +https://huggingface.co/Ebtihal/AraBertMo_base_V5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v6_ar.md b/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v6_ar.md new file mode 100644 index 00000000000000..737813848f622d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v6_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic arabertmo_base_v6 BertEmbeddings from Ebtihal +author: John Snow Labs +name: arabertmo_base_v6 +date: 2023-09-12 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabertmo_base_v6` is a Arabic model originally trained by Ebtihal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabertmo_base_v6_ar_5.1.1_3.0_1694510331239.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabertmo_base_v6_ar_5.1.1_3.0_1694510331239.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabertmo_base_v6","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabertmo_base_v6", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabertmo_base_v6| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|407.9 MB| + +## References + +https://huggingface.co/Ebtihal/AraBertMo_base_V6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v7_en.md b/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v7_en.md new file mode 100644 index 00000000000000..76058d595f77cb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v7_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English arabertmo_base_v7 BertEmbeddings from Ebtihal +author: John Snow Labs +name: arabertmo_base_v7 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabertmo_base_v7` is a English model originally trained by Ebtihal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabertmo_base_v7_en_5.1.1_3.0_1694510465368.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabertmo_base_v7_en_5.1.1_3.0_1694510465368.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabertmo_base_v7","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabertmo_base_v7", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabertmo_base_v7| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.9 MB| + +## References + +https://huggingface.co/Ebtihal/AraBertMo_base_V7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v8_en.md b/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v8_en.md new file mode 100644 index 00000000000000..71772712dcccf2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v8_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English arabertmo_base_v8 BertEmbeddings from Ebtihal +author: John Snow Labs +name: arabertmo_base_v8 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabertmo_base_v8` is a English model originally trained by Ebtihal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabertmo_base_v8_en_5.1.1_3.0_1694510601521.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabertmo_base_v8_en_5.1.1_3.0_1694510601521.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabertmo_base_v8","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabertmo_base_v8", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabertmo_base_v8| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.9 MB| + +## References + +https://huggingface.co/Ebtihal/AraBertMo_base_V8 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v9_en.md b/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v9_en.md new file mode 100644 index 00000000000000..56b5aea49b96a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-arabertmo_base_v9_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English arabertmo_base_v9 BertEmbeddings from Ebtihal +author: John Snow Labs +name: arabertmo_base_v9 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabertmo_base_v9` is a English model originally trained by Ebtihal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabertmo_base_v9_en_5.1.1_3.0_1694510724445.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabertmo_base_v9_en_5.1.1_3.0_1694510724445.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabertmo_base_v9","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabertmo_base_v9", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabertmo_base_v9| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.9 MB| + +## References + +https://huggingface.co/Ebtihal/AraBertMo_base_V9 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-arabic_quran_nahj_sahife_ar.md b/docs/_posts/ahmedlone127/2023-09-12-arabic_quran_nahj_sahife_ar.md new file mode 100644 index 00000000000000..f0a31f7ef38fa0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-arabic_quran_nahj_sahife_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic arabic_quran_nahj_sahife BertEmbeddings from pourmand1376 +author: John Snow Labs +name: arabic_quran_nahj_sahife +date: 2023-09-12 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabic_quran_nahj_sahife` is a Arabic model originally trained by pourmand1376. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabic_quran_nahj_sahife_ar_5.1.1_3.0_1694547794347.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabic_quran_nahj_sahife_ar_5.1.1_3.0_1694547794347.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabic_quran_nahj_sahife","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabic_quran_nahj_sahife", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabic_quran_nahj_sahife| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|412.0 MB| + +## References + +https://huggingface.co/pourmand1376/arabic-quran-nahj-sahife \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-aristoberto_en.md b/docs/_posts/ahmedlone127/2023-09-12-aristoberto_en.md new file mode 100644 index 00000000000000..10d30d7d6fd037 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-aristoberto_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English aristoberto BertEmbeddings from Jacobo +author: John Snow Labs +name: aristoberto +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`aristoberto` is a English model originally trained by Jacobo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/aristoberto_en_5.1.1_3.0_1694561096224.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/aristoberto_en_5.1.1_3.0_1694561096224.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("aristoberto","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("aristoberto", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|aristoberto| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|420.1 MB| + +## References + +https://huggingface.co/Jacobo/aristoBERTo \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-astrobert_en.md b/docs/_posts/ahmedlone127/2023-09-12-astrobert_en.md new file mode 100644 index 00000000000000..ce4bf012e03b87 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-astrobert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English astrobert BertEmbeddings from adsabs +author: John Snow Labs +name: astrobert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`astrobert` is a English model originally trained by adsabs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/astrobert_en_5.1.1_3.0_1694554976289.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/astrobert_en_5.1.1_3.0_1694554976289.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("astrobert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("astrobert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|astrobert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.6 MB| + +## References + +https://huggingface.co/adsabs/astroBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-baby_model_en.md b/docs/_posts/ahmedlone127/2023-09-12-baby_model_en.md new file mode 100644 index 00000000000000..10d7b3bacb6524 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-baby_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English baby_model BertEmbeddings from patNike +author: John Snow Labs +name: baby_model +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`baby_model` is a English model originally trained by patNike. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/baby_model_en_5.1.1_3.0_1694562898992.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/baby_model_en_5.1.1_3.0_1694562898992.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("baby_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("baby_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|baby_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/patNike/baby_model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-berel_base_en.md b/docs/_posts/ahmedlone127/2023-09-12-berel_base_en.md new file mode 100644 index 00000000000000..8560c91596e653 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-berel_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English berel_base BertEmbeddings from t4-project +author: John Snow Labs +name: berel_base +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`berel_base` is a English model originally trained by t4-project. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/berel_base_en_5.1.1_3.0_1694559343111.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/berel_base_en_5.1.1_3.0_1694559343111.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("berel_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("berel_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|berel_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|690.1 MB| + +## References + +https://huggingface.co/t4-project/BEREL-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_1760_1850_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_1760_1850_en.md new file mode 100644 index 00000000000000..520ec650e3ca92 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_1760_1850_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_1760_1850 BertEmbeddings from Livingwithmachines +author: John Snow Labs +name: bert_1760_1850 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_1760_1850` is a English model originally trained by Livingwithmachines. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_1760_1850_en_5.1.1_3.0_1694561341595.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_1760_1850_en_5.1.1_3.0_1694561341595.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_1760_1850","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_1760_1850", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_1760_1850| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Livingwithmachines/bert_1760_1850 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_1760_1900_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_1760_1900_en.md new file mode 100644 index 00000000000000..452403dad636d9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_1760_1900_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_1760_1900 BertEmbeddings from Livingwithmachines +author: John Snow Labs +name: bert_1760_1900 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_1760_1900` is a English model originally trained by Livingwithmachines. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_1760_1900_en_5.1.1_3.0_1694561483109.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_1760_1900_en_5.1.1_3.0_1694561483109.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_1760_1900","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_1760_1900", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_1760_1900| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/Livingwithmachines/bert_1760_1900 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_1850_1875_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_1850_1875_en.md new file mode 100644 index 00000000000000..82f7734b491242 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_1850_1875_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_1850_1875 BertEmbeddings from Livingwithmachines +author: John Snow Labs +name: bert_1850_1875 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_1850_1875` is a English model originally trained by Livingwithmachines. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_1850_1875_en_5.1.1_3.0_1694561623490.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_1850_1875_en_5.1.1_3.0_1694561623490.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_1850_1875","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_1850_1875", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_1850_1875| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/Livingwithmachines/bert_1850_1875 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_1875_1890_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_1875_1890_en.md new file mode 100644 index 00000000000000..f4f5507f9b6045 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_1875_1890_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_1875_1890 BertEmbeddings from Livingwithmachines +author: John Snow Labs +name: bert_1875_1890 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_1875_1890` is a English model originally trained by Livingwithmachines. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_1875_1890_en_5.1.1_3.0_1694561751322.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_1875_1890_en_5.1.1_3.0_1694561751322.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_1875_1890","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_1875_1890", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_1875_1890| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/Livingwithmachines/bert_1875_1890 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_1890_1900_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_1890_1900_en.md new file mode 100644 index 00000000000000..9aab57cf704483 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_1890_1900_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_1890_1900 BertEmbeddings from Livingwithmachines +author: John Snow Labs +name: bert_1890_1900 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_1890_1900` is a English model originally trained by Livingwithmachines. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_1890_1900_en_5.1.1_3.0_1694561889659.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_1890_1900_en_5.1.1_3.0_1694561889659.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_1890_1900","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_1890_1900", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_1890_1900| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/Livingwithmachines/bert_1890_1900 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_ancient_chinese_zh.md b/docs/_posts/ahmedlone127/2023-09-12-bert_ancient_chinese_zh.md new file mode 100644 index 00000000000000..3bfe0f08bfacef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_ancient_chinese_zh.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Chinese bert_ancient_chinese BertEmbeddings from Jihuai +author: John Snow Labs +name: bert_ancient_chinese +date: 2023-09-12 +tags: [bert, zh, open_source, fill_mask, onnx] +task: Embeddings +language: zh +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ancient_chinese` is a Chinese model originally trained by Jihuai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ancient_chinese_zh_5.1.1_3.0_1694547641835.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ancient_chinese_zh_5.1.1_3.0_1694547641835.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_ancient_chinese","zh") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_ancient_chinese", "zh") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ancient_chinese| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|zh| +|Size:|430.5 MB| + +## References + +https://huggingface.co/Jihuai/bert-ancient-chinese \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_10lang_cased_xx.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_10lang_cased_xx.md new file mode 100644 index 00000000000000..630eef5ec4eeb3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_10lang_cased_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_10lang_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_10lang_cased +date: 2023-09-12 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_10lang_cased` is a Multilingual model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_10lang_cased_xx_5.1.1_3.0_1694549523236.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_10lang_cased_xx_5.1.1_3.0_1694549523236.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_10lang_cased","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_10lang_cased", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_10lang_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|514.3 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-10lang-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_15lang_cased_xx.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_15lang_cased_xx.md new file mode 100644 index 00000000000000..63be7c86770505 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_15lang_cased_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_15lang_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_15lang_cased +date: 2023-09-12 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_15lang_cased` is a Multilingual model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_15lang_cased_xx_5.1.1_3.0_1694549707123.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_15lang_cased_xx_5.1.1_3.0_1694549707123.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_15lang_cased","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_15lang_cased", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_15lang_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|526.6 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-15lang-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_25lang_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_25lang_cased_en.md new file mode 100644 index 00000000000000..c84718c61a1780 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_25lang_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_25lang_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_25lang_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_25lang_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_25lang_cased_en_5.1.1_3.0_1694549898376.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_25lang_cased_en_5.1.1_3.0_1694549898376.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_25lang_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_25lang_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_25lang_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|565.3 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-25lang-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_cased_divehi_v2_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_cased_divehi_v2_en.md new file mode 100644 index 00000000000000..5f0da46f695097 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_cased_divehi_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_divehi_v2 BertEmbeddings from mahfooz +author: John Snow Labs +name: bert_base_cased_divehi_v2 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_divehi_v2` is a English model originally trained by mahfooz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_divehi_v2_en_5.1.1_3.0_1694549387188.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_divehi_v2_en_5.1.1_3.0_1694549387188.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_divehi_v2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_divehi_v2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_divehi_v2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.5 MB| + +## References + +https://huggingface.co/mahfooz/bert-base-cased-dv-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_cased_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_cased_finetuned_en.md new file mode 100644 index 00000000000000..612e93eb431eda --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_cased_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned BertEmbeddings from GusNicho +author: John Snow Labs +name: bert_base_cased_finetuned +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned` is a English model originally trained by GusNicho. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_en_5.1.1_3.0_1694559327612.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_en_5.1.1_3.0_1694559327612.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/GusNicho/bert-base-cased-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_cased_test_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_cased_test_en.md new file mode 100644 index 00000000000000..6257d2040102f0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_cased_test_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_test BertEmbeddings from chenyu313 +author: John Snow Labs +name: bert_base_cased_test +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_test` is a English model originally trained by chenyu313. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_test_en_5.1.1_3.0_1694554576959.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_test_en_5.1.1_3.0_1694554576959.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_test","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_test", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_test| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/chenyu313/bert-base-cased-test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_dutch_cased_finetuned_germanic_languages_nl.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_dutch_cased_finetuned_germanic_languages_nl.md new file mode 100644 index 00000000000000..85066ea191d1c5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_dutch_cased_finetuned_germanic_languages_nl.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Dutch, Flemish bert_base_dutch_cased_finetuned_germanic_languages BertEmbeddings from GeniusVoice +author: John Snow Labs +name: bert_base_dutch_cased_finetuned_germanic_languages +date: 2023-09-12 +tags: [bert, nl, open_source, fill_mask, onnx] +task: Embeddings +language: nl +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_dutch_cased_finetuned_germanic_languages` is a Dutch, Flemish model originally trained by GeniusVoice. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_dutch_cased_finetuned_germanic_languages_nl_5.1.1_3.0_1694549349357.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_dutch_cased_finetuned_germanic_languages_nl_5.1.1_3.0_1694549349357.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_dutch_cased_finetuned_germanic_languages","nl") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_dutch_cased_finetuned_germanic_languages", "nl") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_dutch_cased_finetuned_germanic_languages| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|nl| +|Size:|406.8 MB| + +## References + +https://huggingface.co/GeniusVoice/bert-base-dutch-cased-finetuned-gem \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_dutch_cased_gronlp_nl.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_dutch_cased_gronlp_nl.md new file mode 100644 index 00000000000000..b8755890f4c0e0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_dutch_cased_gronlp_nl.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Dutch, Flemish bert_base_dutch_cased_gronlp BertEmbeddings from GroNLP +author: John Snow Labs +name: bert_base_dutch_cased_gronlp +date: 2023-09-12 +tags: [bert, nl, open_source, fill_mask, onnx] +task: Embeddings +language: nl +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_dutch_cased_gronlp` is a Dutch, Flemish model originally trained by GroNLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_dutch_cased_gronlp_nl_5.1.1_3.0_1694559021052.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_dutch_cased_gronlp_nl_5.1.1_3.0_1694559021052.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_dutch_cased_gronlp","nl") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_dutch_cased_gronlp", "nl") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_dutch_cased_gronlp| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|nl| +|Size:|406.8 MB| + +## References + +https://huggingface.co/GroNLP/bert-base-dutch-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_arabic_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_arabic_cased_en.md new file mode 100644 index 00000000000000..0da02178e7a8f4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_arabic_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_arabic_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_arabic_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_arabic_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_arabic_cased_en_5.1.1_3.0_1694550500308.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_arabic_cased_en_5.1.1_3.0_1694550500308.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_arabic_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_arabic_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_arabic_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.4 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-ar-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_bulgarian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_bulgarian_cased_en.md new file mode 100644 index 00000000000000..498ecc8acdce6d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_bulgarian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_bulgarian_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_bulgarian_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_bulgarian_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_bulgarian_cased_en_5.1.1_3.0_1694550674451.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_bulgarian_cased_en_5.1.1_3.0_1694550674451.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_bulgarian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_bulgarian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_bulgarian_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|420.0 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-bg-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_chinese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_chinese_cased_en.md new file mode 100644 index 00000000000000..109b67366824be --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_chinese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_chinese_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_chinese_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_chinese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_chinese_cased_en_5.1.1_3.0_1694556350417.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_chinese_cased_en_5.1.1_3.0_1694556350417.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_chinese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_chinese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_chinese_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|421.8 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-zh-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_chinese_hindi_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_chinese_hindi_cased_en.md new file mode 100644 index 00000000000000..5d9a398859b97b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_chinese_hindi_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_chinese_hindi_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_chinese_hindi_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_chinese_hindi_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_chinese_hindi_cased_en_5.1.1_3.0_1694556485723.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_chinese_hindi_cased_en_5.1.1_3.0_1694556485723.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_chinese_hindi_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_chinese_hindi_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_chinese_hindi_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|426.3 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-zh-hi-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_danish_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_danish_cased_en.md new file mode 100644 index 00000000000000..73ec5ce0f00e26 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_danish_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_danish_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_danish_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_danish_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_danish_cased_en_5.1.1_3.0_1694550943639.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_danish_cased_en_5.1.1_3.0_1694550943639.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_danish_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_danish_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_danish_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|414.6 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-da-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_arabic_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_arabic_cased_en.md new file mode 100644 index 00000000000000..bc901d17436626 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_arabic_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_french_arabic_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_french_arabic_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_french_arabic_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_french_arabic_cased_en_5.1.1_3.0_1694551872020.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_french_arabic_cased_en_5.1.1_3.0_1694551872020.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_french_arabic_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_french_arabic_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_french_arabic_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|426.0 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-fr-ar-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_cased_en.md new file mode 100644 index 00000000000000..505fc85204dfd4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_french_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_french_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_french_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_french_cased_en_5.1.1_3.0_1694552022056.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_french_cased_en_5.1.1_3.0_1694552022056.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_french_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_french_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_french_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|416.5 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-fr-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_chinese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_chinese_cased_en.md new file mode 100644 index 00000000000000..90a546f334b71d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_chinese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_french_chinese_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_french_chinese_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_french_chinese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_french_chinese_cased_en_5.1.1_3.0_1694553556344.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_french_chinese_cased_en_5.1.1_3.0_1694553556344.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_french_chinese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_french_chinese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_french_chinese_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|435.3 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-fr-zh-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_chinese_japanese_vietnamese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_chinese_japanese_vietnamese_cased_en.md new file mode 100644 index 00000000000000..b30cb7c2282886 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_chinese_japanese_vietnamese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_french_chinese_japanese_vietnamese_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_french_chinese_japanese_vietnamese_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_french_chinese_japanese_vietnamese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_french_chinese_japanese_vietnamese_cased_en_5.1.1_3.0_1694553703735.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_french_chinese_japanese_vietnamese_cased_en_5.1.1_3.0_1694553703735.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_french_chinese_japanese_vietnamese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_french_chinese_japanese_vietnamese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_french_chinese_japanese_vietnamese_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|446.6 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-fr-zh-ja-vi-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_danish_japanese_vietnamese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_danish_japanese_vietnamese_cased_en.md new file mode 100644 index 00000000000000..c9f7fefe5c89ad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_danish_japanese_vietnamese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_french_danish_japanese_vietnamese_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_french_danish_japanese_vietnamese_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_french_danish_japanese_vietnamese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_french_danish_japanese_vietnamese_cased_en_5.1.1_3.0_1694552158682.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_french_danish_japanese_vietnamese_cased_en_5.1.1_3.0_1694552158682.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_french_danish_japanese_vietnamese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_french_danish_japanese_vietnamese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_french_danish_japanese_vietnamese_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|446.6 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-fr-da-ja-vi-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_dutch_russian_arabic_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_dutch_russian_arabic_cased_en.md new file mode 100644 index 00000000000000..d3d6b817004f00 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_dutch_russian_arabic_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_french_dutch_russian_arabic_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_french_dutch_russian_arabic_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_french_dutch_russian_arabic_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_french_dutch_russian_arabic_cased_en_5.1.1_3.0_1694553406628.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_french_dutch_russian_arabic_cased_en_5.1.1_3.0_1694553406628.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_french_dutch_russian_arabic_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_french_dutch_russian_arabic_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_french_dutch_russian_arabic_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|461.5 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-fr-nl-ru-ar-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_german_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_german_cased_en.md new file mode 100644 index 00000000000000..9b87ae583b200f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_german_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_french_german_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_french_german_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_french_german_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_french_german_cased_en_5.1.1_3.0_1694552319403.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_french_german_cased_en_5.1.1_3.0_1694552319403.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_french_german_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_french_german_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_french_german_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|432.7 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-fr-de-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_german_norwegian_danish_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_german_norwegian_danish_cased_en.md new file mode 100644 index 00000000000000..4673eebea8eaab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_german_norwegian_danish_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_french_german_norwegian_danish_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_french_german_norwegian_danish_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_french_german_norwegian_danish_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_french_german_norwegian_danish_cased_en_5.1.1_3.0_1694552478365.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_french_german_norwegian_danish_cased_en_5.1.1_3.0_1694552478365.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_french_german_norwegian_danish_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_french_german_norwegian_danish_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_french_german_norwegian_danish_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|440.5 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-fr-de-no-da-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_italian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_italian_cased_en.md new file mode 100644 index 00000000000000..f2fc131d2f6b9a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_italian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_french_italian_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_french_italian_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_french_italian_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_french_italian_cased_en_5.1.1_3.0_1694553098457.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_french_italian_cased_en_5.1.1_3.0_1694553098457.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_french_italian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_french_italian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_french_italian_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|428.1 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-fr-it-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_lithuanian_norwegian_polish_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_lithuanian_norwegian_polish_cased_en.md new file mode 100644 index 00000000000000..b95580bffaeb7f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_lithuanian_norwegian_polish_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_french_lithuanian_norwegian_polish_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_french_lithuanian_norwegian_polish_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_french_lithuanian_norwegian_polish_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_french_lithuanian_norwegian_polish_cased_en_5.1.1_3.0_1694553253099.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_french_lithuanian_norwegian_polish_cased_en_5.1.1_3.0_1694553253099.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_french_lithuanian_norwegian_polish_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_french_lithuanian_norwegian_polish_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_french_lithuanian_norwegian_polish_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|441.9 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-fr-lt-no-pl-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_spanish_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_spanish_cased_en.md new file mode 100644 index 00000000000000..09ad6305d4fe16 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_spanish_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_french_spanish_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_french_spanish_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_french_spanish_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_french_spanish_cased_en_5.1.1_3.0_1694552634510.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_french_spanish_cased_en_5.1.1_3.0_1694552634510.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_french_spanish_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_french_spanish_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_french_spanish_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|433.1 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-fr-es-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_spanish_german_chinese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_spanish_german_chinese_cased_en.md new file mode 100644 index 00000000000000..164b1bfa67175d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_spanish_german_chinese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_french_spanish_german_chinese_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_french_spanish_german_chinese_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_french_spanish_german_chinese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_french_spanish_german_chinese_cased_en_5.1.1_3.0_1694552792508.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_french_spanish_german_chinese_cased_en_5.1.1_3.0_1694552792508.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_french_spanish_german_chinese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_french_spanish_german_chinese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_french_spanish_german_chinese_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|466.7 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-fr-es-de-zh-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_spanish_portuguese_italian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_spanish_portuguese_italian_cased_en.md new file mode 100644 index 00000000000000..4de6c38b51eb29 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_french_spanish_portuguese_italian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_french_spanish_portuguese_italian_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_french_spanish_portuguese_italian_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_french_spanish_portuguese_italian_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_french_spanish_portuguese_italian_cased_en_5.1.1_3.0_1694552951990.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_french_spanish_portuguese_italian_cased_en_5.1.1_3.0_1694552951990.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_french_spanish_portuguese_italian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_french_spanish_portuguese_italian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_french_spanish_portuguese_italian_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|444.6 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-fr-es-pt-it-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_german_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_german_cased_en.md new file mode 100644 index 00000000000000..ab954c6eec1a23 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_german_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_german_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_german_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_german_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_german_cased_en_5.1.1_3.0_1694551087783.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_german_cased_en_5.1.1_3.0_1694551087783.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_german_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_german_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_german_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|421.8 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-de-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_hindi_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_hindi_cased_en.md new file mode 100644 index 00000000000000..dae53a7b0be28f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_hindi_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_hindi_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_hindi_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_hindi_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_hindi_cased_en_5.1.1_3.0_1694553855877.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_hindi_cased_en_5.1.1_3.0_1694553855877.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_hindi_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_hindi_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_hindi_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-hi-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_italian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_italian_cased_en.md new file mode 100644 index 00000000000000..c2e9d25b892aaa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_italian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_italian_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_italian_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_italian_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_italian_cased_en_5.1.1_3.0_1694553994811.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_italian_cased_en_5.1.1_3.0_1694553994811.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_italian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_italian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_italian_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|417.9 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-it-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_japanese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_japanese_cased_en.md new file mode 100644 index 00000000000000..0f0c199d37599b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_japanese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_japanese_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_japanese_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_japanese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_japanese_cased_en_5.1.1_3.0_1694554115375.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_japanese_cased_en_5.1.1_3.0_1694554115375.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_japanese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_japanese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_japanese_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|416.3 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-ja-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_lithuanian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_lithuanian_cased_en.md new file mode 100644 index 00000000000000..6d3dc265adafc6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_lithuanian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_lithuanian_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_lithuanian_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_lithuanian_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_lithuanian_cased_en_5.1.1_3.0_1694554240405.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_lithuanian_cased_en_5.1.1_3.0_1694554240405.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_lithuanian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_lithuanian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_lithuanian_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|411.9 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-lt-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_norwegian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_norwegian_cased_en.md new file mode 100644 index 00000000000000..73cb0747dbf2bd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_norwegian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_norwegian_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_norwegian_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_norwegian_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_norwegian_cased_en_5.1.1_3.0_1694554556744.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_norwegian_cased_en_5.1.1_3.0_1694554556744.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_norwegian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_norwegian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_norwegian_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|415.6 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-no-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_polish_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_polish_cased_en.md new file mode 100644 index 00000000000000..372a14b4d7dd63 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_polish_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_polish_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_polish_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_polish_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_polish_cased_en_5.1.1_3.0_1694554731414.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_polish_cased_en_5.1.1_3.0_1694554731414.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_polish_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_polish_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_polish_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|417.9 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-pl-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_portuguese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_portuguese_cased_en.md new file mode 100644 index 00000000000000..b378b9fc4d462f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_portuguese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_portuguese_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_portuguese_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_portuguese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_portuguese_cased_en_5.1.1_3.0_1694554858787.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_portuguese_cased_en_5.1.1_3.0_1694554858787.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_portuguese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_portuguese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_portuguese_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|419.2 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-pt-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_romanian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_romanian_cased_en.md new file mode 100644 index 00000000000000..cc7304f533b9b5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_romanian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_romanian_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_romanian_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_romanian_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_romanian_cased_en_5.1.1_3.0_1694555022603.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_romanian_cased_en_5.1.1_3.0_1694555022603.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_romanian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_romanian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_romanian_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|413.3 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-ro-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_spanish_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_spanish_cased_en.md new file mode 100644 index 00000000000000..1053c12c4e75ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_spanish_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_spanish_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_spanish_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_spanish_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_spanish_cased_en_5.1.1_3.0_1694551237571.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_spanish_cased_en_5.1.1_3.0_1694551237571.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_spanish_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_spanish_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_spanish_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|422.2 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-es-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_spanish_chinese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_spanish_chinese_cased_en.md new file mode 100644 index 00000000000000..a45780d7531714 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_spanish_chinese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_spanish_chinese_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_spanish_chinese_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_spanish_chinese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_spanish_chinese_cased_en_5.1.1_3.0_1694551698031.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_spanish_chinese_cased_en_5.1.1_3.0_1694551698031.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_spanish_chinese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_spanish_chinese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_spanish_chinese_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|441.3 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-es-zh-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_spanish_italian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_spanish_italian_cased_en.md new file mode 100644 index 00000000000000..b6b4c68f82395d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_spanish_italian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_spanish_italian_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_spanish_italian_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_spanish_italian_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_spanish_italian_cased_en_5.1.1_3.0_1694551412882.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_spanish_italian_cased_en_5.1.1_3.0_1694551412882.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_spanish_italian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_spanish_italian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_spanish_italian_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|431.6 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-es-it-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_spanish_portuguese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_spanish_portuguese_cased_en.md new file mode 100644 index 00000000000000..bd6389c9cac801 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_spanish_portuguese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_spanish_portuguese_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_spanish_portuguese_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_spanish_portuguese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_spanish_portuguese_cased_en_5.1.1_3.0_1694551563082.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_spanish_portuguese_cased_en_5.1.1_3.0_1694551563082.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_spanish_portuguese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_spanish_portuguese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_spanish_portuguese_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|428.0 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-es-pt-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_swahili_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_swahili_cased_en.md new file mode 100644 index 00000000000000..e87b3d8cf83294 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_swahili_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_swahili_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_swahili_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_swahili_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_swahili_cased_en_5.1.1_3.0_1694555358059.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_swahili_cased_en_5.1.1_3.0_1694555358059.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_swahili_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_swahili_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_swahili_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.7 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-sw-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_thai_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_thai_cased_en.md new file mode 100644 index 00000000000000..f6aa44acd672a9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_thai_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_thai_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_thai_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_thai_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_thai_cased_en_5.1.1_3.0_1694555543000.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_thai_cased_en_5.1.1_3.0_1694555543000.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_thai_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_thai_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_thai_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|404.3 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-th-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_ukrainian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_ukrainian_cased_en.md new file mode 100644 index 00000000000000..95024470e16b7f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_ukrainian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_ukrainian_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_ukrainian_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_ukrainian_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_ukrainian_cased_en_5.1.1_3.0_1694555899898.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_ukrainian_cased_en_5.1.1_3.0_1694555899898.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_ukrainian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_ukrainian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_ukrainian_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|422.5 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-uk-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_urdu_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_urdu_cased_en.md new file mode 100644 index 00000000000000..a7f82d7e7ca9a2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_urdu_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_urdu_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_urdu_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_urdu_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_urdu_cased_en_5.1.1_3.0_1694556060152.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_urdu_cased_en_5.1.1_3.0_1694556060152.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_urdu_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_urdu_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_urdu_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.7 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-ur-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_vietnamese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_vietnamese_cased_en.md new file mode 100644 index 00000000000000..d28523b7e7bd4e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_vietnamese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_vietnamese_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_vietnamese_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_vietnamese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_vietnamese_cased_en_5.1.1_3.0_1694556202106.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_vietnamese_cased_en_5.1.1_3.0_1694556202106.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_vietnamese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_vietnamese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_vietnamese_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.8 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-vi-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_galician_cased_gl.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_galician_cased_gl.md new file mode 100644 index 00000000000000..3683260ef29e77 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_galician_cased_gl.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Galician bert_base_galician_cased BertEmbeddings from marcosgg +author: John Snow Labs +name: bert_base_galician_cased +date: 2023-09-12 +tags: [bert, gl, open_source, fill_mask, onnx] +task: Embeddings +language: gl +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_galician_cased` is a Galician model originally trained by marcosgg. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_galician_cased_gl_5.1.1_3.0_1694551516731.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_galician_cased_gl_5.1.1_3.0_1694551516731.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_galician_cased","gl") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_galician_cased", "gl") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_galician_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|gl| +|Size:|664.5 MB| + +## References + +https://huggingface.co/marcosgg/bert-base-gl-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_german_dbmdz_uncased_german_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_german_dbmdz_uncased_german_en.md new file mode 100644 index 00000000000000..3e20cf82c3cde5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_german_dbmdz_uncased_german_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_german_dbmdz_uncased_german BertEmbeddings from koala +author: John Snow Labs +name: bert_base_german_dbmdz_uncased_german +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_german_dbmdz_uncased_german` is a English model originally trained by koala. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_german_dbmdz_uncased_german_en_5.1.1_3.0_1694508300297.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_german_dbmdz_uncased_german_en_5.1.1_3.0_1694508300297.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_german_dbmdz_uncased_german","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_german_dbmdz_uncased_german", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_german_dbmdz_uncased_german| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/koala/bert-base-german-dbmdz-uncased-de \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_greek_uncased_v1_el.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_greek_uncased_v1_el.md new file mode 100644 index 00000000000000..2a200fdac65287 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_greek_uncased_v1_el.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Modern Greek (1453-) bert_base_greek_uncased_v1 BertEmbeddings from nlpaueb +author: John Snow Labs +name: bert_base_greek_uncased_v1 +date: 2023-09-12 +tags: [bert, el, open_source, fill_mask, onnx] +task: Embeddings +language: el +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_greek_uncased_v1` is a Modern Greek (1453-) model originally trained by nlpaueb. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_greek_uncased_v1_el_5.1.1_3.0_1694561091912.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_greek_uncased_v1_el_5.1.1_3.0_1694561091912.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_greek_uncased_v1","el") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_greek_uncased_v1", "el") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_greek_uncased_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|el| +|Size:|421.1 MB| + +## References + +https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_ko.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_ko.md new file mode 100644 index 00000000000000..97429141244fd0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_ko.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Korean bert_base BertEmbeddings from klue +author: John Snow Labs +name: bert_base +date: 2023-09-12 +tags: [bert, ko, open_source, fill_mask, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base` is a Korean model originally trained by klue. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_ko_5.1.1_3.0_1694508175577.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_ko_5.1.1_3.0_1694508175577.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base","ko") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base", "ko") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ko| +|Size:|412.4 MB| + +## References + +https://huggingface.co/klue/bert-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_parsbert_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_parsbert_uncased_en.md new file mode 100644 index 00000000000000..79a8300797a022 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_parsbert_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_parsbert_uncased BertEmbeddings from HooshvareLab +author: John Snow Labs +name: bert_base_parsbert_uncased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_parsbert_uncased` is a English model originally trained by HooshvareLab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_parsbert_uncased_en_5.1.1_3.0_1694559679646.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_parsbert_uncased_en_5.1.1_3.0_1694559679646.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_parsbert_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_parsbert_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_parsbert_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|606.4 MB| + +## References + +https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_parsbert_uncased_finetuned_conditioned_khorshid_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_parsbert_uncased_finetuned_conditioned_khorshid_en.md new file mode 100644 index 00000000000000..e117fc540fd68e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_parsbert_uncased_finetuned_conditioned_khorshid_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_parsbert_uncased_finetuned_conditioned_khorshid BertEmbeddings from Hamid-reza +author: John Snow Labs +name: bert_base_parsbert_uncased_finetuned_conditioned_khorshid +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_parsbert_uncased_finetuned_conditioned_khorshid` is a English model originally trained by Hamid-reza. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_parsbert_uncased_finetuned_conditioned_khorshid_en_5.1.1_3.0_1694552930772.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_parsbert_uncased_finetuned_conditioned_khorshid_en_5.1.1_3.0_1694552930772.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_parsbert_uncased_finetuned_conditioned_khorshid","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_parsbert_uncased_finetuned_conditioned_khorshid", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_parsbert_uncased_finetuned_conditioned_khorshid| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|606.3 MB| + +## References + +https://huggingface.co/Hamid-reza/bert-base-parsbert-uncased-finetuned-conditioned-khorshid \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_parsbert_uncased_finetuned_khorshid_accelerate_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_parsbert_uncased_finetuned_khorshid_accelerate_en.md new file mode 100644 index 00000000000000..854f5c741285c9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_parsbert_uncased_finetuned_khorshid_accelerate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_parsbert_uncased_finetuned_khorshid_accelerate BertEmbeddings from Hamid-reza +author: John Snow Labs +name: bert_base_parsbert_uncased_finetuned_khorshid_accelerate +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_parsbert_uncased_finetuned_khorshid_accelerate` is a English model originally trained by Hamid-reza. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_parsbert_uncased_finetuned_khorshid_accelerate_en_5.1.1_3.0_1694551419310.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_parsbert_uncased_finetuned_khorshid_accelerate_en_5.1.1_3.0_1694551419310.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_parsbert_uncased_finetuned_khorshid_accelerate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_parsbert_uncased_finetuned_khorshid_accelerate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_parsbert_uncased_finetuned_khorshid_accelerate| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|606.3 MB| + +## References + +https://huggingface.co/Hamid-reza/bert-base-parsbert-uncased-finetuned-khorshid-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_parsbert_uncased_finetuned_khorshid_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_parsbert_uncased_finetuned_khorshid_en.md new file mode 100644 index 00000000000000..dd74fe05f010f9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_parsbert_uncased_finetuned_khorshid_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_parsbert_uncased_finetuned_khorshid BertEmbeddings from Hamid-reza +author: John Snow Labs +name: bert_base_parsbert_uncased_finetuned_khorshid +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_parsbert_uncased_finetuned_khorshid` is a English model originally trained by Hamid-reza. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_parsbert_uncased_finetuned_khorshid_en_5.1.1_3.0_1694551198123.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_parsbert_uncased_finetuned_khorshid_en_5.1.1_3.0_1694551198123.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_parsbert_uncased_finetuned_khorshid","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_parsbert_uncased_finetuned_khorshid", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_parsbert_uncased_finetuned_khorshid| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|606.4 MB| + +## References + +https://huggingface.co/Hamid-reza/bert-base-parsbert-uncased-finetuned-khorshid \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_portuguese_cased_finetuned_acordao_v2_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_portuguese_cased_finetuned_acordao_v2_en.md new file mode 100644 index 00000000000000..44b9287425c3f5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_portuguese_cased_finetuned_acordao_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_portuguese_cased_finetuned_acordao_v2 BertEmbeddings from ederkamphorst +author: John Snow Labs +name: bert_base_portuguese_cased_finetuned_acordao_v2 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_portuguese_cased_finetuned_acordao_v2` is a English model originally trained by ederkamphorst. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_portuguese_cased_finetuned_acordao_v2_en_5.1.1_3.0_1694508805037.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_portuguese_cased_finetuned_acordao_v2_en_5.1.1_3.0_1694508805037.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_portuguese_cased_finetuned_acordao_v2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_portuguese_cased_finetuned_acordao_v2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_portuguese_cased_finetuned_acordao_v2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/ederkamphorst/bert-base-portuguese-cased-finetuned-acordao_v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_portuguese_cased_neuralmind_pt.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_portuguese_cased_neuralmind_pt.md new file mode 100644 index 00000000000000..edd95378a27a10 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_portuguese_cased_neuralmind_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese bert_base_portuguese_cased_neuralmind BertEmbeddings from neuralmind +author: John Snow Labs +name: bert_base_portuguese_cased_neuralmind +date: 2023-09-12 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_portuguese_cased_neuralmind` is a Portuguese model originally trained by neuralmind. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_portuguese_cased_neuralmind_pt_5.1.1_3.0_1694557881484.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_portuguese_cased_neuralmind_pt_5.1.1_3.0_1694557881484.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_portuguese_cased_neuralmind","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_portuguese_cased_neuralmind", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_portuguese_cased_neuralmind| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|405.9 MB| + +## References + +https://huggingface.co/neuralmind/bert-base-portuguese-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_swedish_cased_kb_sv.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_swedish_cased_kb_sv.md new file mode 100644 index 00000000000000..23250a730a4ea3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_swedish_cased_kb_sv.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Swedish bert_base_swedish_cased_kb BertEmbeddings from KB +author: John Snow Labs +name: bert_base_swedish_cased_kb +date: 2023-09-12 +tags: [bert, sv, open_source, fill_mask, onnx] +task: Embeddings +language: sv +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_swedish_cased_kb` is a Swedish model originally trained by KB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_swedish_cased_kb_sv_5.1.1_3.0_1694510194246.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_swedish_cased_kb_sv_5.1.1_3.0_1694510194246.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_swedish_cased_kb","sv") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_swedish_cased_kb", "sv") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_swedish_cased_kb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|sv| +|Size:|465.2 MB| + +## References + +https://huggingface.co/KB/bert-base-swedish-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_tapt_govreport_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_tapt_govreport_en.md new file mode 100644 index 00000000000000..a31f17cef5f6b8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_tapt_govreport_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_tapt_govreport BertEmbeddings from eliolio +author: John Snow Labs +name: bert_base_tapt_govreport +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_tapt_govreport` is a English model originally trained by eliolio. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_tapt_govreport_en_5.1.1_3.0_1694562890971.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_tapt_govreport_en_5.1.1_3.0_1694562890971.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_tapt_govreport","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_tapt_govreport", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_tapt_govreport| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/eliolio/bert-base-tapt-govreport \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_echr_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_echr_en.md new file mode 100644 index 00000000000000..fc5fa653d2dea5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_echr_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_echr BertEmbeddings from nlpaueb +author: John Snow Labs +name: bert_base_uncased_echr +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_echr` is a English model originally trained by nlpaueb. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_echr_en_5.1.1_3.0_1694561249416.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_echr_en_5.1.1_3.0_1694561249416.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_echr","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_echr", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_echr| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/nlpaueb/bert-base-uncased-echr \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_englishlawai_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_englishlawai_en.md new file mode 100644 index 00000000000000..02d277b3a73897 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_englishlawai_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_englishlawai BertEmbeddings from Makabaka +author: John Snow Labs +name: bert_base_uncased_englishlawai +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_englishlawai` is a English model originally trained by Makabaka. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_englishlawai_en_5.1.1_3.0_1694550163867.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_englishlawai_en_5.1.1_3.0_1694550163867.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_englishlawai","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_englishlawai", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_englishlawai| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Makabaka/bert-base-uncased-EnglishLawAI \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_eurlex_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_eurlex_en.md new file mode 100644 index 00000000000000..9460a87cc03e1c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_eurlex_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_eurlex BertEmbeddings from nlpaueb +author: John Snow Labs +name: bert_base_uncased_eurlex +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_eurlex` is a English model originally trained by nlpaueb. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_eurlex_en_5.1.1_3.0_1694561402834.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_eurlex_en_5.1.1_3.0_1694561402834.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_eurlex","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_eurlex", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_eurlex| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/nlpaueb/bert-base-uncased-eurlex \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_finetuned_academic_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_finetuned_academic_en.md new file mode 100644 index 00000000000000..00b7d46ae5899b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_finetuned_academic_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_academic BertEmbeddings from egumasa +author: John Snow Labs +name: bert_base_uncased_finetuned_academic +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_academic` is a English model originally trained by egumasa. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_academic_en_5.1.1_3.0_1694555134262.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_academic_en_5.1.1_3.0_1694555134262.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_academic","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_academic", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_academic| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/egumasa/bert-base-uncased-finetuned-academic \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_finetuned_crypto_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_finetuned_crypto_en.md new file mode 100644 index 00000000000000..5d06344969a462 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_finetuned_crypto_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_crypto BertEmbeddings from smarquie +author: John Snow Labs +name: bert_base_uncased_finetuned_crypto +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_crypto` is a English model originally trained by smarquie. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_crypto_en_5.1.1_3.0_1694562492423.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_crypto_en_5.1.1_3.0_1694562492423.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_crypto","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_crypto", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_crypto| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/smarquie/bert-base-uncased-finetuned-crypto \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_gujarati_128_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_gujarati_128_en.md new file mode 100644 index 00000000000000..c74ee99a926635 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_gujarati_128_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_gujarati_128 BertEmbeddings from mischi001 +author: John Snow Labs +name: bert_base_uncased_gujarati_128 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_gujarati_128` is a English model originally trained by mischi001. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_gujarati_128_en_5.1.1_3.0_1694510645737.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_gujarati_128_en_5.1.1_3.0_1694510645737.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_gujarati_128","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_gujarati_128", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_gujarati_128| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/mischi001/bert-base-uncased-gu-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_dongyeop_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_dongyeop_en.md new file mode 100644 index 00000000000000..7ed36aba92f77e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_dongyeop_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_dongyeop BertEmbeddings from Dongyeop +author: John Snow Labs +name: bert_base_uncased_issues_128_dongyeop +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_dongyeop` is a English model originally trained by Dongyeop. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_dongyeop_en_5.1.1_3.0_1694510297896.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_dongyeop_en_5.1.1_3.0_1694510297896.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_dongyeop","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_dongyeop", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_dongyeop| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Dongyeop/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_hrayrmsint_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_hrayrmsint_en.md new file mode 100644 index 00000000000000..27bf3aaa32a0d2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_hrayrmsint_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_hrayrmsint BertEmbeddings from HrayrMSint +author: John Snow Labs +name: bert_base_uncased_issues_128_hrayrmsint +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_hrayrmsint` is a English model originally trained by HrayrMSint. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_hrayrmsint_en_5.1.1_3.0_1694549539463.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_hrayrmsint_en_5.1.1_3.0_1694549539463.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_hrayrmsint","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_hrayrmsint", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_hrayrmsint| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/HrayrMSint/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_isaacp_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_isaacp_en.md new file mode 100644 index 00000000000000..e1cc7c79e1f8da --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_isaacp_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_isaacp BertEmbeddings from Isaacp +author: John Snow Labs +name: bert_base_uncased_issues_128_isaacp +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_isaacp` is a English model originally trained by Isaacp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_isaacp_en_5.1.1_3.0_1694508451060.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_isaacp_en_5.1.1_3.0_1694508451060.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_isaacp","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_isaacp", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_isaacp| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Isaacp/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_issues_128_lvwerra_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_issues_128_lvwerra_en.md new file mode 100644 index 00000000000000..38347519819ad8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_issues_128_lvwerra_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_issues_128_lvwerra BertEmbeddings from lvwerra +author: John Snow Labs +name: bert_base_uncased_issues_128_issues_128_lvwerra +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_issues_128_lvwerra` is a English model originally trained by lvwerra. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_issues_128_lvwerra_en_5.1.1_3.0_1694550270689.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_issues_128_lvwerra_en_5.1.1_3.0_1694550270689.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_issues_128_lvwerra","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_issues_128_lvwerra", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_issues_128_lvwerra| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/lvwerra/bert-base-uncased-issues-128-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_jangmin_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_jangmin_en.md new file mode 100644 index 00000000000000..f1ad86721b3e5f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_jangmin_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_jangmin BertEmbeddings from jangmin +author: John Snow Labs +name: bert_base_uncased_issues_128_jangmin +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_jangmin` is a English model originally trained by jangmin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_jangmin_en_5.1.1_3.0_1694554761560.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_jangmin_en_5.1.1_3.0_1694554761560.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_jangmin","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_jangmin", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_jangmin| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/jangmin/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_junghun_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_junghun_en.md new file mode 100644 index 00000000000000..e259aa105c6eb1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_junghun_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_junghun BertEmbeddings from JungHun +author: John Snow Labs +name: bert_base_uncased_issues_128_junghun +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_junghun` is a English model originally trained by JungHun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_junghun_en_5.1.1_3.0_1694556714538.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_junghun_en_5.1.1_3.0_1694556714538.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_junghun","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_junghun", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_junghun| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/JungHun/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_jx7789_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_jx7789_en.md new file mode 100644 index 00000000000000..315c9204483781 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_jx7789_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_jx7789 BertEmbeddings from jx7789 +author: John Snow Labs +name: bert_base_uncased_issues_128_jx7789 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_jx7789` is a English model originally trained by jx7789. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_jx7789_en_5.1.1_3.0_1694555592115.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_jx7789_en_5.1.1_3.0_1694555592115.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_jx7789","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_jx7789", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_jx7789| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/jx7789/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_transll_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_transll_en.md new file mode 100644 index 00000000000000..48de853cec6289 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_transll_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_transll BertEmbeddings from TransLL +author: John Snow Labs +name: bert_base_uncased_issues_128_transll +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_transll` is a English model originally trained by TransLL. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_transll_en_5.1.1_3.0_1694550129029.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_transll_en_5.1.1_3.0_1694550129029.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_transll","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_transll", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_transll| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/TransLL/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_mnli_sparse_70_unstructured_norwegian_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_mnli_sparse_70_unstructured_norwegian_classifier_en.md new file mode 100644 index 00000000000000..abe6f9dcf72fe4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_mnli_sparse_70_unstructured_norwegian_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_mnli_sparse_70_unstructured_norwegian_classifier BertEmbeddings from Intel +author: John Snow Labs +name: bert_base_uncased_mnli_sparse_70_unstructured_norwegian_classifier +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_mnli_sparse_70_unstructured_norwegian_classifier` is a English model originally trained by Intel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mnli_sparse_70_unstructured_norwegian_classifier_en_5.1.1_3.0_1694559998864.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mnli_sparse_70_unstructured_norwegian_classifier_en_5.1.1_3.0_1694559998864.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_mnli_sparse_70_unstructured_norwegian_classifier","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_mnli_sparse_70_unstructured_norwegian_classifier", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_mnli_sparse_70_unstructured_norwegian_classifier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|225.9 MB| + +## References + +https://huggingface.co/Intel/bert-base-uncased-mnli-sparse-70-unstructured-no-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_rahuldave_issues_128_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_rahuldave_issues_128_en.md new file mode 100644 index 00000000000000..9357261bb64a02 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_rahuldave_issues_128_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_rahuldave_issues_128 BertEmbeddings from rahuldave +author: John Snow Labs +name: bert_base_uncased_rahuldave_issues_128 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_rahuldave_issues_128` is a English model originally trained by rahuldave. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_rahuldave_issues_128_en_5.1.1_3.0_1694553801165.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_rahuldave_issues_128_en_5.1.1_3.0_1694553801165.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_rahuldave_issues_128","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_rahuldave_issues_128", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_rahuldave_issues_128| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/rahuldave/bert-base-uncased-rahuldave-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_sparse_70_unstructured_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_sparse_70_unstructured_en.md new file mode 100644 index 00000000000000..f8578c727b7524 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_sparse_70_unstructured_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_sparse_70_unstructured BertEmbeddings from Intel +author: John Snow Labs +name: bert_base_uncased_sparse_70_unstructured +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sparse_70_unstructured` is a English model originally trained by Intel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sparse_70_unstructured_en_5.1.1_3.0_1694560206490.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sparse_70_unstructured_en_5.1.1_3.0_1694560206490.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_sparse_70_unstructured","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_sparse_70_unstructured", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sparse_70_unstructured| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|225.8 MB| + +## References + +https://huggingface.co/Intel/bert-base-uncased-sparse-70-unstructured \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_sparse_85_unstructured_pruneofa_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_sparse_85_unstructured_pruneofa_en.md new file mode 100644 index 00000000000000..6f7d439896013e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_sparse_85_unstructured_pruneofa_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_sparse_85_unstructured_pruneofa BertEmbeddings from Intel +author: John Snow Labs +name: bert_base_uncased_sparse_85_unstructured_pruneofa +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sparse_85_unstructured_pruneofa` is a English model originally trained by Intel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sparse_85_unstructured_pruneofa_en_5.1.1_3.0_1694560356013.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sparse_85_unstructured_pruneofa_en_5.1.1_3.0_1694560356013.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_sparse_85_unstructured_pruneofa","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_sparse_85_unstructured_pruneofa", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sparse_85_unstructured_pruneofa| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|175.6 MB| + +## References + +https://huggingface.co/Intel/bert-base-uncased-sparse-85-unstructured-pruneofa \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_sparse_90_unstructured_pruneofa_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_sparse_90_unstructured_pruneofa_en.md new file mode 100644 index 00000000000000..4a7bed5ce732b9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_sparse_90_unstructured_pruneofa_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_sparse_90_unstructured_pruneofa BertEmbeddings from Intel +author: John Snow Labs +name: bert_base_uncased_sparse_90_unstructured_pruneofa +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sparse_90_unstructured_pruneofa` is a English model originally trained by Intel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sparse_90_unstructured_pruneofa_en_5.1.1_3.0_1694560534251.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sparse_90_unstructured_pruneofa_en_5.1.1_3.0_1694560534251.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_sparse_90_unstructured_pruneofa","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_sparse_90_unstructured_pruneofa", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sparse_90_unstructured_pruneofa| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|157.7 MB| + +## References + +https://huggingface.co/Intel/bert-base-uncased-sparse-90-unstructured-pruneofa \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_transformers_github_128_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_transformers_github_128_en.md new file mode 100644 index 00000000000000..7d61bfb913f5c2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_transformers_github_128_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_transformers_github_128 BertEmbeddings from GV05 +author: John Snow Labs +name: bert_base_uncased_transformers_github_128 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_transformers_github_128` is a English model originally trained by GV05. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_transformers_github_128_en_5.1.1_3.0_1694554427020.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_transformers_github_128_en_5.1.1_3.0_1694554427020.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_transformers_github_128","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_transformers_github_128", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_transformers_github_128| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/GV05/bert-base-uncased-transformers-github-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_vietnamese_uncased_vi.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_vietnamese_uncased_vi.md new file mode 100644 index 00000000000000..a399d814a7b57f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_vietnamese_uncased_vi.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Vietnamese bert_base_vietnamese_uncased BertEmbeddings from tintnguyen +author: John Snow Labs +name: bert_base_vietnamese_uncased +date: 2023-09-12 +tags: [bert, vi, open_source, fill_mask, onnx] +task: Embeddings +language: vi +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_vietnamese_uncased` is a Vietnamese model originally trained by tintnguyen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_vietnamese_uncased_vi_5.1.1_3.0_1694551029030.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_vietnamese_uncased_vi_5.1.1_3.0_1694551029030.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_vietnamese_uncased","vi") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_vietnamese_uncased", "vi") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_vietnamese_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|vi| +|Size:|536.8 MB| + +## References + +https://huggingface.co/tintnguyen/bert-base-vi-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_wikihow_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_wikihow_en.md new file mode 100644 index 00000000000000..fe2333ebca4769 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_wikihow_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_wikihow BertEmbeddings from Aktsvigun +author: John Snow Labs +name: bert_base_wikihow +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_wikihow` is a English model originally trained by Aktsvigun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_wikihow_en_5.1.1_3.0_1694553949077.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_wikihow_en_5.1.1_3.0_1694553949077.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_wikihow","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_wikihow", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_wikihow| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Aktsvigun/bert-base-wikihow \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_c1_english_german_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_c1_english_german_en.md new file mode 100644 index 00000000000000..d96fe467741491 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_c1_english_german_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_c1_english_german BertEmbeddings from OpenSemShift +author: John Snow Labs +name: bert_c1_english_german +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_c1_english_german` is a English model originally trained by OpenSemShift. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_c1_english_german_en_5.1.1_3.0_1694558764674.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_c1_english_german_en_5.1.1_3.0_1694558764674.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_c1_english_german","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_c1_english_german", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_c1_english_german| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.1 MB| + +## References + +https://huggingface.co/OpenSemShift/bert-c1-en-de \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_c2_english_german_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_c2_english_german_en.md new file mode 100644 index 00000000000000..f9bb973d3548b6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_c2_english_german_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_c2_english_german BertEmbeddings from OpenSemShift +author: John Snow Labs +name: bert_c2_english_german +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_c2_english_german` is a English model originally trained by OpenSemShift. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_c2_english_german_en_5.1.1_3.0_1694558981307.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_c2_english_german_en_5.1.1_3.0_1694558981307.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_c2_english_german","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_c2_english_german", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_c2_english_german| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.1 MB| + +## References + +https://huggingface.co/OpenSemShift/bert-c2-en-de \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_c2_english_only_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_c2_english_only_en.md new file mode 100644 index 00000000000000..4ae205f09173fc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_c2_english_only_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_c2_english_only BertEmbeddings from OpenSemShift +author: John Snow Labs +name: bert_c2_english_only +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_c2_english_only` is a English model originally trained by OpenSemShift. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_c2_english_only_en_5.1.1_3.0_1694560907370.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_c2_english_only_en_5.1.1_3.0_1694560907370.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_c2_english_only","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_c2_english_only", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_c2_english_only| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.1 MB| + +## References + +https://huggingface.co/OpenSemShift/bert-c2-en-only \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_clinical_scratch_wl_spanish_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_clinical_scratch_wl_spanish_en.md new file mode 100644 index 00000000000000..bdee3e9038fe12 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_clinical_scratch_wl_spanish_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_clinical_scratch_wl_spanish BertEmbeddings from plncmm +author: John Snow Labs +name: bert_clinical_scratch_wl_spanish +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_clinical_scratch_wl_spanish` is a English model originally trained by plncmm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_clinical_scratch_wl_spanish_en_5.1.1_3.0_1694549252966.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_clinical_scratch_wl_spanish_en_5.1.1_3.0_1694549252966.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_clinical_scratch_wl_spanish","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_clinical_scratch_wl_spanish", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_clinical_scratch_wl_spanish| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/plncmm/bert-clinical-scratch-wl-es \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_e_base_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_e_base_mlm_en.md new file mode 100644 index 00000000000000..7516ad99fa0fdc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_e_base_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_e_base_mlm BertEmbeddings from nasa-impact +author: John Snow Labs +name: bert_e_base_mlm +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_e_base_mlm` is a English model originally trained by nasa-impact. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_e_base_mlm_en_5.1.1_3.0_1694557364630.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_e_base_mlm_en_5.1.1_3.0_1694557364630.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_e_base_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_e_base_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_e_base_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/nasa-impact/bert-e-base-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_emoji_latvian_twitter_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_emoji_latvian_twitter_en.md new file mode 100644 index 00000000000000..ead21b98623a7f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_emoji_latvian_twitter_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_emoji_latvian_twitter BertEmbeddings from FFZG-cleopatra +author: John Snow Labs +name: bert_emoji_latvian_twitter +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_emoji_latvian_twitter` is a English model originally trained by FFZG-cleopatra. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_emoji_latvian_twitter_en_5.1.1_3.0_1694548098064.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_emoji_latvian_twitter_en_5.1.1_3.0_1694548098064.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_emoji_latvian_twitter","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_emoji_latvian_twitter", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_emoji_latvian_twitter| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|664.2 MB| + +## References + +https://huggingface.co/FFZG-cleopatra/bert-emoji-latvian-twitter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_finetuning_test_lian01110_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_finetuning_test_lian01110_en.md new file mode 100644 index 00000000000000..a6be7241b26233 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_finetuning_test_lian01110_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_finetuning_test_lian01110 BertEmbeddings from lian01110 +author: John Snow Labs +name: bert_finetuning_test_lian01110 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuning_test_lian01110` is a English model originally trained by lian01110. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuning_test_lian01110_en_5.1.1_3.0_1694548658763.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuning_test_lian01110_en_5.1.1_3.0_1694548658763.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_finetuning_test_lian01110","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_finetuning_test_lian01110", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuning_test_lian01110| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/lian01110/bert_finetuning_test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_for_finacial_triples_completion_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_for_finacial_triples_completion_en.md new file mode 100644 index 00000000000000..10a56529d1883d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_for_finacial_triples_completion_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_for_finacial_triples_completion BertEmbeddings from reginaboateng +author: John Snow Labs +name: bert_for_finacial_triples_completion +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_for_finacial_triples_completion` is a English model originally trained by reginaboateng. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_for_finacial_triples_completion_en_5.1.1_3.0_1694549182533.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_for_finacial_triples_completion_en_5.1.1_3.0_1694549182533.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_for_finacial_triples_completion","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_for_finacial_triples_completion", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_for_finacial_triples_completion| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/reginaboateng/bert_for_finacial_triples_completion \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_funting_test_ai10_niepan_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_funting_test_ai10_niepan_en.md new file mode 100644 index 00000000000000..2ac0ed5a6a7c05 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_funting_test_ai10_niepan_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_funting_test_ai10_niepan BertEmbeddings from niepan +author: John Snow Labs +name: bert_funting_test_ai10_niepan +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_funting_test_ai10_niepan` is a English model originally trained by niepan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_funting_test_ai10_niepan_en_5.1.1_3.0_1694558288937.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_funting_test_ai10_niepan_en_5.1.1_3.0_1694558288937.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_funting_test_ai10_niepan","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_funting_test_ai10_niepan", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_funting_test_ai10_niepan| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/niepan/bert_funting_test_ai10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_galician_gl.md b/docs/_posts/ahmedlone127/2023-09-12-bert_galician_gl.md new file mode 100644 index 00000000000000..c45ebe51884458 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_galician_gl.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Galician bert_galician BertEmbeddings from fpuentes +author: John Snow Labs +name: bert_galician +date: 2023-09-12 +tags: [bert, gl, open_source, fill_mask, onnx] +task: Embeddings +language: gl +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_galician` is a Galician model originally trained by fpuentes. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_galician_gl_5.1.1_3.0_1694550784049.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_galician_gl_5.1.1_3.0_1694550784049.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_galician","gl") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_galician", "gl") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_galician| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|gl| +|Size:|409.0 MB| + +## References + +https://huggingface.co/fpuentes/bert-galician \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_hateful_memes_expanded_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_hateful_memes_expanded_en.md new file mode 100644 index 00000000000000..59b1251b4d0c10 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_hateful_memes_expanded_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_hateful_memes_expanded BertEmbeddings from limjiayi +author: John Snow Labs +name: bert_hateful_memes_expanded +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_hateful_memes_expanded` is a English model originally trained by limjiayi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_hateful_memes_expanded_en_5.1.1_3.0_1694548862078.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_hateful_memes_expanded_en_5.1.1_3.0_1694548862078.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_hateful_memes_expanded","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_hateful_memes_expanded", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_hateful_memes_expanded| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/limjiayi/bert-hateful-memes-expanded \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_java_bfp_combined_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_java_bfp_combined_en.md new file mode 100644 index 00000000000000..ef3ec9b7562b4d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_java_bfp_combined_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_java_bfp_combined BertEmbeddings from up201806461 +author: John Snow Labs +name: bert_java_bfp_combined +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_java_bfp_combined` is a English model originally trained by up201806461. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_java_bfp_combined_en_5.1.1_3.0_1694562355805.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_java_bfp_combined_en_5.1.1_3.0_1694562355805.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_java_bfp_combined","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_java_bfp_combined", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_java_bfp_combined| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/up201806461/bert-java-bfp_combined \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_khmer_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_khmer_base_uncased_en.md new file mode 100644 index 00000000000000..09a77ee8a25bf0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_khmer_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_khmer_base_uncased BertEmbeddings from GKLMIP +author: John Snow Labs +name: bert_khmer_base_uncased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_khmer_base_uncased` is a English model originally trained by GKLMIP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_khmer_base_uncased_en_5.1.1_3.0_1694548567530.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_khmer_base_uncased_en_5.1.1_3.0_1694548567530.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_khmer_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_khmer_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_khmer_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|413.0 MB| + +## References + +https://huggingface.co/GKLMIP/bert-khmer-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_khmer_base_uncased_tokenized_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_khmer_base_uncased_tokenized_en.md new file mode 100644 index 00000000000000..065f90f832fbd7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_khmer_base_uncased_tokenized_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_khmer_base_uncased_tokenized BertEmbeddings from GKLMIP +author: John Snow Labs +name: bert_khmer_base_uncased_tokenized +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_khmer_base_uncased_tokenized` is a English model originally trained by GKLMIP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_khmer_base_uncased_tokenized_en_5.1.1_3.0_1694548424527.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_khmer_base_uncased_tokenized_en_5.1.1_3.0_1694548424527.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_khmer_base_uncased_tokenized","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_khmer_base_uncased_tokenized", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_khmer_base_uncased_tokenized| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.9 MB| + +## References + +https://huggingface.co/GKLMIP/bert-khmer-base-uncased-tokenized \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_khmer_small_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_khmer_small_uncased_en.md new file mode 100644 index 00000000000000..5c0910e0b64983 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_khmer_small_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_khmer_small_uncased BertEmbeddings from GKLMIP +author: John Snow Labs +name: bert_khmer_small_uncased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_khmer_small_uncased` is a English model originally trained by GKLMIP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_khmer_small_uncased_en_5.1.1_3.0_1694548748669.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_khmer_small_uncased_en_5.1.1_3.0_1694548748669.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_khmer_small_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_khmer_small_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_khmer_small_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|110.2 MB| + +## References + +https://huggingface.co/GKLMIP/bert-khmer-small-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_khmer_small_uncased_tokenized_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_khmer_small_uncased_tokenized_en.md new file mode 100644 index 00000000000000..b1106d77e59c95 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_khmer_small_uncased_tokenized_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_khmer_small_uncased_tokenized BertEmbeddings from GKLMIP +author: John Snow Labs +name: bert_khmer_small_uncased_tokenized +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_khmer_small_uncased_tokenized` is a English model originally trained by GKLMIP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_khmer_small_uncased_tokenized_en_5.1.1_3.0_1694548658102.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_khmer_small_uncased_tokenized_en_5.1.1_3.0_1694548658102.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_khmer_small_uncased_tokenized","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_khmer_small_uncased_tokenized", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_khmer_small_uncased_tokenized| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|110.2 MB| + +## References + +https://huggingface.co/GKLMIP/bert-khmer-small-uncased-tokenized \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_korean_base_ko.md b/docs/_posts/ahmedlone127/2023-09-12-bert_korean_base_ko.md new file mode 100644 index 00000000000000..7bbf1ef5e79a2a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_korean_base_ko.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Korean bert_korean_base BertEmbeddings from lassl +author: John Snow Labs +name: bert_korean_base +date: 2023-09-12 +tags: [bert, ko, open_source, fill_mask, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_korean_base` is a Korean model originally trained by lassl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_korean_base_ko_5.1.1_3.0_1694547622935.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_korean_base_ko_5.1.1_3.0_1694547622935.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_korean_base","ko") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_korean_base", "ko") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_korean_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ko| +|Size:|467.8 MB| + +## References + +https://huggingface.co/lassl/bert-ko-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_korean_small_ko.md b/docs/_posts/ahmedlone127/2023-09-12-bert_korean_small_ko.md new file mode 100644 index 00000000000000..1f09b7aebc3cb3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_korean_small_ko.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Korean bert_korean_small BertEmbeddings from lassl +author: John Snow Labs +name: bert_korean_small +date: 2023-09-12 +tags: [bert, ko, open_source, fill_mask, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_korean_small` is a Korean model originally trained by lassl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_korean_small_ko_5.1.1_3.0_1694547734588.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_korean_small_ko_5.1.1_3.0_1694547734588.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_korean_small","ko") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_korean_small", "ko") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_korean_small| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ko| +|Size:|85.6 MB| + +## References + +https://huggingface.co/lassl/bert-ko-small \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_english_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_english_en.md new file mode 100644 index 00000000000000..5e62b1afb3b957 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_english_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_english BertEmbeddings from koala +author: John Snow Labs +name: bert_large_cased_english +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_english` is a English model originally trained by koala. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_english_en_5.1.1_3.0_1694508535175.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_english_en_5.1.1_3.0_1694508535175.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_english","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_english", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_english| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/koala/bert-large-cased-en \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_finetuned_prompt_20_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_finetuned_prompt_20_en.md new file mode 100644 index 00000000000000..ba14f69c18358a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_finetuned_prompt_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_finetuned_prompt_20 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_finetuned_prompt_20 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_finetuned_prompt_20` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_prompt_20_en_5.1.1_3.0_1694560618058.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_prompt_20_en_5.1.1_3.0_1694560618058.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_finetuned_prompt_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_finetuned_prompt_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_finetuned_prompt_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-finetuned-prompt-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_portuguese_law_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_portuguese_law_en.md new file mode 100644 index 00000000000000..6bef6ea8d03272 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_portuguese_law_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_portuguese_law BertEmbeddings from edwatanabe +author: John Snow Labs +name: bert_large_cased_portuguese_law +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_portuguese_law` is a English model originally trained by edwatanabe. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_portuguese_law_en_5.1.1_3.0_1694548002360.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_portuguese_law_en_5.1.1_3.0_1694548002360.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_portuguese_law","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_portuguese_law", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_portuguese_law| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/edwatanabe/bert-large-cased-pt-law \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sclarge_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sclarge_en.md new file mode 100644 index 00000000000000..825ce080100fc7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sclarge_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sclarge BertEmbeddings from ZongqianLi +author: John Snow Labs +name: bert_large_cased_sclarge +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sclarge` is a English model originally trained by ZongqianLi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sclarge_en_5.1.1_3.0_1694562199422.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sclarge_en_5.1.1_3.0_1694562199422.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sclarge","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sclarge", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sclarge| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/ZongqianLi/bert_large_cased_sclarge \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_10_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_10_en.md new file mode 100644 index 00000000000000..8876631c16104c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_10_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_10 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_10 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_10` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_10_en_5.1.1_3.0_1694508360095.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_10_en_5.1.1_3.0_1694508360095.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_10","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_10", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_10| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_11_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_11_en.md new file mode 100644 index 00000000000000..8136d0ebbe7620 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_11_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_11 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_11 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_11` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_11_en_5.1.1_3.0_1694509499566.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_11_en_5.1.1_3.0_1694509499566.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_11","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_11", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_11| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-11 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_12_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_12_en.md new file mode 100644 index 00000000000000..1319cc27ddebfc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_12_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_12 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_12 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_12` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_12_en_5.1.1_3.0_1694510143392.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_12_en_5.1.1_3.0_1694510143392.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_12","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_12", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_12| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-12 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_13_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_13_en.md new file mode 100644 index 00000000000000..638809f31231ff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_13_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_13 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_13 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_13` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_13_en_5.1.1_3.0_1694510416192.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_13_en_5.1.1_3.0_1694510416192.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_13","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_13", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_13| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-13 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_14_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_14_en.md new file mode 100644 index 00000000000000..3ac5584420fff5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_14_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_14 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_14 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_14` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_14_en_5.1.1_3.0_1694510686666.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_14_en_5.1.1_3.0_1694510686666.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_14","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_14", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_14| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-14 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_15_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_15_en.md new file mode 100644 index 00000000000000..ccb7015fb49c59 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_15_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_15 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_15 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_15` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_15_en_5.1.1_3.0_1694547583222.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_15_en_5.1.1_3.0_1694547583222.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_15","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_15", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_15| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-15 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_16_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_16_en.md new file mode 100644 index 00000000000000..569a02cfdcf537 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_16_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_16 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_16 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_16` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_16_en_5.1.1_3.0_1694547909959.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_16_en_5.1.1_3.0_1694547909959.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_16","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_16", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_16| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-16 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_17_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_17_en.md new file mode 100644 index 00000000000000..efe1f33b166e14 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_17_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_17 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_17 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_17` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_17_en_5.1.1_3.0_1694548216024.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_17_en_5.1.1_3.0_1694548216024.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_17","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_17", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_17| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-17 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_18_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_18_en.md new file mode 100644 index 00000000000000..41a50555598393 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_18_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_18 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_18 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_18` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_18_en_5.1.1_3.0_1694548498129.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_18_en_5.1.1_3.0_1694548498129.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_18","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_18", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_18| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-18 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_19_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_19_en.md new file mode 100644 index 00000000000000..d66544b26af1ef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_19_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_19 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_19 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_19` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_19_en_5.1.1_3.0_1694548783818.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_19_en_5.1.1_3.0_1694548783818.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_19","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_19", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_19| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-19 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_9_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_9_en.md new file mode 100644 index 00000000000000..267efcba9435ce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_9_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_9 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_9 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_9` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_9_en_5.1.1_3.0_1694508105221.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_9_en_5.1.1_3.0_1694508105221.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_9","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_9", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_9| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-9 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_portuguese_cased_pt.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_portuguese_cased_pt.md new file mode 100644 index 00000000000000..3b5fd7ce34bba3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_portuguese_cased_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese bert_large_portuguese_cased BertEmbeddings from neuralmind +author: John Snow Labs +name: bert_large_portuguese_cased +date: 2023-09-12 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_portuguese_cased` is a Portuguese model originally trained by neuralmind. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_portuguese_cased_pt_5.1.1_3.0_1694558116929.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_portuguese_cased_pt_5.1.1_3.0_1694558116929.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_portuguese_cased","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_portuguese_cased", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_portuguese_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|1.2 GB| + +## References + +https://huggingface.co/neuralmind/bert-large-portuguese-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_bengali_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_bengali_en.md new file mode 100644 index 00000000000000..b839e31e6a0881 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_bengali_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_bengali BertEmbeddings from koala +author: John Snow Labs +name: bert_large_uncased_bengali +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_bengali` is a English model originally trained by koala. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_bengali_en_5.1.1_3.0_1694508782600.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_bengali_en_5.1.1_3.0_1694508782600.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_bengali","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_bengali", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_bengali| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/koala/bert-large-uncased-bn \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_chinese_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_chinese_en.md new file mode 100644 index 00000000000000..4635e55be80a82 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_chinese_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_chinese BertEmbeddings from koala +author: John Snow Labs +name: bert_large_uncased_chinese +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_chinese` is a English model originally trained by koala. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_chinese_en_5.1.1_3.0_1694510115547.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_chinese_en_5.1.1_3.0_1694510115547.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_chinese","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_chinese", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_chinese| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/koala/bert-large-uncased-zh \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_english_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_english_en.md new file mode 100644 index 00000000000000..817bdf437e90dd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_english_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_english BertEmbeddings from koala +author: John Snow Labs +name: bert_large_uncased_english +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_english` is a English model originally trained by koala. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_english_en_5.1.1_3.0_1694509318989.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_english_en_5.1.1_3.0_1694509318989.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_english","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_english", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_english| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/koala/bert-large-uncased-en \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_finetuned_clinc150_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_finetuned_clinc150_en.md new file mode 100644 index 00000000000000..9bd92193e94568 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_finetuned_clinc150_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_finetuned_clinc150 BertEmbeddings from FilippoComastri +author: John Snow Labs +name: bert_large_uncased_finetuned_clinc150 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_finetuned_clinc150` is a English model originally trained by FilippoComastri. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_finetuned_clinc150_en_5.1.1_3.0_1694552611395.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_finetuned_clinc150_en_5.1.1_3.0_1694552611395.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_finetuned_clinc150","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_finetuned_clinc150", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_finetuned_clinc150| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/FilippoComastri/bert-large-uncased-finetuned-clinc150 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_finetuned_da_zero_shot_20_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_finetuned_da_zero_shot_20_en.md new file mode 100644 index 00000000000000..320e405c61ac4d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_finetuned_da_zero_shot_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_finetuned_da_zero_shot_20 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_uncased_finetuned_da_zero_shot_20 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_finetuned_da_zero_shot_20` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_finetuned_da_zero_shot_20_en_5.1.1_3.0_1694560241206.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_finetuned_da_zero_shot_20_en_5.1.1_3.0_1694560241206.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_finetuned_da_zero_shot_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_finetuned_da_zero_shot_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_finetuned_da_zero_shot_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-uncased-finetuned-DA-Zero-shot-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_finetuned_da_zero_shot_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_finetuned_da_zero_shot_en.md new file mode 100644 index 00000000000000..d519bada948a6b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_finetuned_da_zero_shot_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_finetuned_da_zero_shot BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_uncased_finetuned_da_zero_shot +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_finetuned_da_zero_shot` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_finetuned_da_zero_shot_en_5.1.1_3.0_1694559743511.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_finetuned_da_zero_shot_en_5.1.1_3.0_1694559743511.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_finetuned_da_zero_shot","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_finetuned_da_zero_shot", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_finetuned_da_zero_shot| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-uncased-finetuned-DA-Zero-shot \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_finetuned_imdb_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_finetuned_imdb_en.md new file mode 100644 index 00000000000000..77acfb49b7ec77 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_finetuned_imdb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_finetuned_imdb BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_uncased_finetuned_imdb +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_finetuned_imdb` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_finetuned_imdb_en_5.1.1_3.0_1694559416760.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_finetuned_imdb_en_5.1.1_3.0_1694559416760.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_finetuned_imdb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_finetuned_imdb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_finetuned_imdb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_finetuned_youcook_4_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_finetuned_youcook_4_en.md new file mode 100644 index 00000000000000..50c15d66752ada --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_finetuned_youcook_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_finetuned_youcook_4 BertEmbeddings from CennetOguz +author: John Snow Labs +name: bert_large_uncased_finetuned_youcook_4 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_finetuned_youcook_4` is a English model originally trained by CennetOguz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_finetuned_youcook_4_en_5.1.1_3.0_1694560592308.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_finetuned_youcook_4_en_5.1.1_3.0_1694560592308.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_finetuned_youcook_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_finetuned_youcook_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_finetuned_youcook_4| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/CennetOguz/bert-large-uncased-finetuned-youcook_4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_german_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_german_en.md new file mode 100644 index 00000000000000..4df357b4bde208 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_german_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_german BertEmbeddings from koala +author: John Snow Labs +name: bert_large_uncased_german +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_german` is a English model originally trained by koala. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_german_en_5.1.1_3.0_1694509061010.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_german_en_5.1.1_3.0_1694509061010.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_german","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_german", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_german| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/koala/bert-large-uncased-de \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_hindi_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_hindi_en.md new file mode 100644 index 00000000000000..d47ae7e623ad40 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_hindi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_hindi BertEmbeddings from koala +author: John Snow Labs +name: bert_large_uncased_hindi +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_hindi` is a English model originally trained by koala. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_hindi_en_5.1.1_3.0_1694509561973.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_hindi_en_5.1.1_3.0_1694509561973.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_hindi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_hindi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_hindi| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/koala/bert-large-uncased-hi \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_korean_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_korean_en.md new file mode 100644 index 00000000000000..e450ff959c84e9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_large_uncased_korean_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_korean BertEmbeddings from koala +author: John Snow Labs +name: bert_large_uncased_korean +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_korean` is a English model originally trained by koala. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_korean_en_5.1.1_3.0_1694509833455.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_korean_en_5.1.1_3.0_1694509833455.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_korean","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_korean", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_korean| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/koala/bert-large-uncased-ko \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_ltrc_telugu_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_ltrc_telugu_en.md new file mode 100644 index 00000000000000..009d029dcf02f1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_ltrc_telugu_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_ltrc_telugu BertEmbeddings from ltrctelugu +author: John Snow Labs +name: bert_ltrc_telugu +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ltrc_telugu` is a English model originally trained by ltrctelugu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ltrc_telugu_en_5.1.1_3.0_1694549907705.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ltrc_telugu_en_5.1.1_3.0_1694549907705.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_ltrc_telugu","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_ltrc_telugu", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ltrc_telugu| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.4 MB| + +## References + +https://huggingface.co/ltrctelugu/bert_ltrc_telugu \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_persian_poetry_fa.md b/docs/_posts/ahmedlone127/2023-09-12-bert_persian_poetry_fa.md new file mode 100644 index 00000000000000..9155037bc88afe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_persian_poetry_fa.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Persian bert_persian_poetry BertEmbeddings from mitra-mir +author: John Snow Labs +name: bert_persian_poetry +date: 2023-09-12 +tags: [bert, fa, open_source, fill_mask, onnx] +task: Embeddings +language: fa +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_persian_poetry` is a Persian model originally trained by mitra-mir. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_persian_poetry_fa_5.1.1_3.0_1694553964697.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_persian_poetry_fa_5.1.1_3.0_1694553964697.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_persian_poetry","fa") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_persian_poetry", "fa") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_persian_poetry| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|fa| +|Size:|441.4 MB| + +## References + +https://huggingface.co/mitra-mir/BERT-Persian-Poetry \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_political_election2020_twitter_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_political_election2020_twitter_mlm_en.md new file mode 100644 index 00000000000000..0a2651b7446a9a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_political_election2020_twitter_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_political_election2020_twitter_mlm BertEmbeddings from kornosk +author: John Snow Labs +name: bert_political_election2020_twitter_mlm +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_political_election2020_twitter_mlm` is a English model originally trained by kornosk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_political_election2020_twitter_mlm_en_5.1.1_3.0_1694510414367.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_political_election2020_twitter_mlm_en_5.1.1_3.0_1694510414367.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_political_election2020_twitter_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_political_election2020_twitter_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_political_election2020_twitter_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.6 MB| + +## References + +https://huggingface.co/kornosk/bert-political-election2020-twitter-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_tagalog_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_tagalog_base_uncased_en.md new file mode 100644 index 00000000000000..f2cdce9badceed --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_tagalog_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_tagalog_base_uncased BertEmbeddings from GKLMIP +author: John Snow Labs +name: bert_tagalog_base_uncased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tagalog_base_uncased` is a English model originally trained by GKLMIP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tagalog_base_uncased_en_5.1.1_3.0_1694549199633.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tagalog_base_uncased_en_5.1.1_3.0_1694549199633.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_tagalog_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_tagalog_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tagalog_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|469.7 MB| + +## References + +https://huggingface.co/GKLMIP/bert-tagalog-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_test_andychiang_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_test_andychiang_en.md new file mode 100644 index 00000000000000..3cb148360a70e6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_test_andychiang_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_test_andychiang BertEmbeddings from AndyChiang +author: John Snow Labs +name: bert_test_andychiang +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_test_andychiang` is a English model originally trained by AndyChiang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_test_andychiang_en_5.1.1_3.0_1694559088930.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_test_andychiang_en_5.1.1_3.0_1694559088930.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_test_andychiang","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_test_andychiang", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_test_andychiang| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/AndyChiang/bert-test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_uncased_finetuned_model_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_uncased_finetuned_model_en.md new file mode 100644 index 00000000000000..83cc82471ed04c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_uncased_finetuned_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_uncased_finetuned_model BertEmbeddings from harvinder676 +author: John Snow Labs +name: bert_uncased_finetuned_model +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_uncased_finetuned_model` is a English model originally trained by harvinder676. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_uncased_finetuned_model_en_5.1.1_3.0_1694556527374.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_uncased_finetuned_model_en_5.1.1_3.0_1694556527374.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_uncased_finetuned_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_uncased_finetuned_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_uncased_finetuned_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/harvinder676/bert-uncased-finetuned-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_uncased_l_12_h_768_a_12_italian_alb3rt0_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_uncased_l_12_h_768_a_12_italian_alb3rt0_en.md new file mode 100644 index 00000000000000..df43c41eaccefa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_uncased_l_12_h_768_a_12_italian_alb3rt0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_uncased_l_12_h_768_a_12_italian_alb3rt0 BertEmbeddings from m-polignano-uniba +author: John Snow Labs +name: bert_uncased_l_12_h_768_a_12_italian_alb3rt0 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_uncased_l_12_h_768_a_12_italian_alb3rt0` is a English model originally trained by m-polignano-uniba. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_uncased_l_12_h_768_a_12_italian_alb3rt0_en_5.1.1_3.0_1694550811678.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_uncased_l_12_h_768_a_12_italian_alb3rt0_en_5.1.1_3.0_1694550811678.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_uncased_l_12_h_768_a_12_italian_alb3rt0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_uncased_l_12_h_768_a_12_italian_alb3rt0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_uncased_l_12_h_768_a_12_italian_alb3rt0| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|689.7 MB| + +## References + +https://huggingface.co/m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertabaporu_base_uncased_pt.md b/docs/_posts/ahmedlone127/2023-09-12-bertabaporu_base_uncased_pt.md new file mode 100644 index 00000000000000..0140312ebe2270 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertabaporu_base_uncased_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese bertabaporu_base_uncased BertEmbeddings from pablocosta +author: John Snow Labs +name: bertabaporu_base_uncased +date: 2023-09-12 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertabaporu_base_uncased` is a Portuguese model originally trained by pablocosta. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertabaporu_base_uncased_pt_5.1.1_3.0_1694556656111.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertabaporu_base_uncased_pt_5.1.1_3.0_1694556656111.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertabaporu_base_uncased","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertabaporu_base_uncased", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertabaporu_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|504.8 MB| + +## References + +https://huggingface.co/pablocosta/bertabaporu-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertabaporu_large_uncased_pt.md b/docs/_posts/ahmedlone127/2023-09-12-bertabaporu_large_uncased_pt.md new file mode 100644 index 00000000000000..f530999d960781 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertabaporu_large_uncased_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese bertabaporu_large_uncased BertEmbeddings from pablocosta +author: John Snow Labs +name: bertabaporu_large_uncased +date: 2023-09-12 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertabaporu_large_uncased` is a Portuguese model originally trained by pablocosta. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertabaporu_large_uncased_pt_5.1.1_3.0_1694556951243.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertabaporu_large_uncased_pt_5.1.1_3.0_1694556951243.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertabaporu_large_uncased","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertabaporu_large_uncased", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertabaporu_large_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|1.4 GB| + +## References + +https://huggingface.co/pablocosta/bertabaporu-large-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertbase_ug_1e_en.md b/docs/_posts/ahmedlone127/2023-09-12-bertbase_ug_1e_en.md new file mode 100644 index 00000000000000..c433c7b1c1cdf9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertbase_ug_1e_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertbase_ug_1e BertEmbeddings from TurkLangsTeamURFU +author: John Snow Labs +name: bertbase_ug_1e +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertbase_ug_1e` is a English model originally trained by TurkLangsTeamURFU. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertbase_ug_1e_en_5.1.1_3.0_1694559625780.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertbase_ug_1e_en_5.1.1_3.0_1694559625780.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertbase_ug_1e","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertbase_ug_1e", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertbase_ug_1e| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|494.1 MB| + +## References + +https://huggingface.co/TurkLangsTeamURFU/BertBase_UG_1e \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertbase_ug_2e_en.md b/docs/_posts/ahmedlone127/2023-09-12-bertbase_ug_2e_en.md new file mode 100644 index 00000000000000..7bdf0ba53b5e80 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertbase_ug_2e_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertbase_ug_2e BertEmbeddings from TurkLangsTeamURFU +author: John Snow Labs +name: bertbase_ug_2e +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertbase_ug_2e` is a English model originally trained by TurkLangsTeamURFU. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertbase_ug_2e_en_5.1.1_3.0_1694559815371.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertbase_ug_2e_en_5.1.1_3.0_1694559815371.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertbase_ug_2e","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertbase_ug_2e", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertbase_ug_2e| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|493.6 MB| + +## References + +https://huggingface.co/TurkLangsTeamURFU/BertBase_UG_2e \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertbase_ug_3e_en.md b/docs/_posts/ahmedlone127/2023-09-12-bertbase_ug_3e_en.md new file mode 100644 index 00000000000000..064c1de630ce77 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertbase_ug_3e_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertbase_ug_3e BertEmbeddings from TurkLangsTeamURFU +author: John Snow Labs +name: bertbase_ug_3e +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertbase_ug_3e` is a English model originally trained by TurkLangsTeamURFU. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertbase_ug_3e_en_5.1.1_3.0_1694559989794.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertbase_ug_3e_en_5.1.1_3.0_1694559989794.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertbase_ug_3e","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertbase_ug_3e", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertbase_ug_3e| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|492.9 MB| + +## References + +https://huggingface.co/TurkLangsTeamURFU/BertBase_UG_3e \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertbasekk_1e_en.md b/docs/_posts/ahmedlone127/2023-09-12-bertbasekk_1e_en.md new file mode 100644 index 00000000000000..adfb015f86eb3a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertbasekk_1e_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertbasekk_1e BertEmbeddings from TurkLangsTeamURFU +author: John Snow Labs +name: bertbasekk_1e +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertbasekk_1e` is a English model originally trained by TurkLangsTeamURFU. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertbasekk_1e_en_5.1.1_3.0_1694553907199.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertbasekk_1e_en_5.1.1_3.0_1694553907199.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertbasekk_1e","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertbasekk_1e", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertbasekk_1e| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|494.1 MB| + +## References + +https://huggingface.co/TurkLangsTeamURFU/BertBaseKK_1e \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertbasekk_2e_en.md b/docs/_posts/ahmedlone127/2023-09-12-bertbasekk_2e_en.md new file mode 100644 index 00000000000000..cd439282d3971e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertbasekk_2e_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertbasekk_2e BertEmbeddings from TurkLangsTeamURFU +author: John Snow Labs +name: bertbasekk_2e +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertbasekk_2e` is a English model originally trained by TurkLangsTeamURFU. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertbasekk_2e_en_5.1.1_3.0_1694558294650.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertbasekk_2e_en_5.1.1_3.0_1694558294650.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertbasekk_2e","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertbasekk_2e", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertbasekk_2e| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|493.5 MB| + +## References + +https://huggingface.co/TurkLangsTeamURFU/BertBaseKK_2e \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertbasekk_3e_en.md b/docs/_posts/ahmedlone127/2023-09-12-bertbasekk_3e_en.md new file mode 100644 index 00000000000000..664ccdcda5be16 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertbasekk_3e_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertbasekk_3e BertEmbeddings from TurkLangsTeamURFU +author: John Snow Labs +name: bertbasekk_3e +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertbasekk_3e` is a English model originally trained by TurkLangsTeamURFU. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertbasekk_3e_en_5.1.1_3.0_1694558506984.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertbasekk_3e_en_5.1.1_3.0_1694558506984.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertbasekk_3e","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertbasekk_3e", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertbasekk_3e| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|492.9 MB| + +## References + +https://huggingface.co/TurkLangsTeamURFU/BertBaseKK_3e \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertimbaulaw_base_portuguese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bertimbaulaw_base_portuguese_cased_en.md new file mode 100644 index 00000000000000..2860b20b5c3843 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertimbaulaw_base_portuguese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertimbaulaw_base_portuguese_cased BertEmbeddings from alfaneo +author: John Snow Labs +name: bertimbaulaw_base_portuguese_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertimbaulaw_base_portuguese_cased` is a English model originally trained by alfaneo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertimbaulaw_base_portuguese_cased_en_5.1.1_3.0_1694557285783.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertimbaulaw_base_portuguese_cased_en_5.1.1_3.0_1694557285783.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertimbaulaw_base_portuguese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertimbaulaw_base_portuguese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertimbaulaw_base_portuguese_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.8 MB| + +## References + +https://huggingface.co/alfaneo/bertimbaulaw-base-portuguese-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdata_en.md b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdata_en.md new file mode 100644 index 00000000000000..5f86509ecf8c0a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdata_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertjewdialdata BertEmbeddings from Jeska +author: John Snow Labs +name: bertjewdialdata +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertjewdialdata` is a English model originally trained by Jeska. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertjewdialdata_en_5.1.1_3.0_1694561421127.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertjewdialdata_en_5.1.1_3.0_1694561421127.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertjewdialdata","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertjewdialdata", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertjewdialdata| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/Jeska/BertjeWDialData \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataall03_en.md b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataall03_en.md new file mode 100644 index 00000000000000..c3596f32f058aa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataall03_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertjewdialdataall03 BertEmbeddings from Jeska +author: John Snow Labs +name: bertjewdialdataall03 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertjewdialdataall03` is a English model originally trained by Jeska. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertjewdialdataall03_en_5.1.1_3.0_1694561834775.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertjewdialdataall03_en_5.1.1_3.0_1694561834775.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertjewdialdataall03","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertjewdialdataall03", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertjewdialdataall03| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/Jeska/BertjeWDialDataALL03 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly02_en.md b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly02_en.md new file mode 100644 index 00000000000000..746f768027b890 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly02_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertjewdialdataallqonly02 BertEmbeddings from Jeska +author: John Snow Labs +name: bertjewdialdataallqonly02 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertjewdialdataallqonly02` is a English model originally trained by Jeska. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly02_en_5.1.1_3.0_1694562283503.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly02_en_5.1.1_3.0_1694562283503.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertjewdialdataallqonly02","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertjewdialdataallqonly02", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertjewdialdataallqonly02| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/Jeska/BertjeWDialDataALLQonly02 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly03_en.md b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly03_en.md new file mode 100644 index 00000000000000..a6fa8c74e184dc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly03_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertjewdialdataallqonly03 BertEmbeddings from Jeska +author: John Snow Labs +name: bertjewdialdataallqonly03 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertjewdialdataallqonly03` is a English model originally trained by Jeska. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly03_en_5.1.1_3.0_1694562426971.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly03_en_5.1.1_3.0_1694562426971.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertjewdialdataallqonly03","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertjewdialdataallqonly03", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertjewdialdataallqonly03| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/Jeska/BertjeWDialDataALLQonly03 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly05_en.md b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly05_en.md new file mode 100644 index 00000000000000..189dd7a9d07187 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly05_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertjewdialdataallqonly05 BertEmbeddings from Jeska +author: John Snow Labs +name: bertjewdialdataallqonly05 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertjewdialdataallqonly05` is a English model originally trained by Jeska. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly05_en_5.1.1_3.0_1694562715391.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly05_en_5.1.1_3.0_1694562715391.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertjewdialdataallqonly05","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertjewdialdataallqonly05", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertjewdialdataallqonly05| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/Jeska/BertjeWDialDataALLQonly05 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly06_en.md b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly06_en.md new file mode 100644 index 00000000000000..b4e3b5cb261d20 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly06_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertjewdialdataallqonly06 BertEmbeddings from Jeska +author: John Snow Labs +name: bertjewdialdataallqonly06 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertjewdialdataallqonly06` is a English model originally trained by Jeska. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly06_en_5.1.1_3.0_1694562884246.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly06_en_5.1.1_3.0_1694562884246.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertjewdialdataallqonly06","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertjewdialdataallqonly06", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertjewdialdataallqonly06| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/Jeska/BertjeWDialDataALLQonly06 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly07_en.md b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly07_en.md new file mode 100644 index 00000000000000..2dfc8a0c42b127 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly07_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertjewdialdataallqonly07 BertEmbeddings from Jeska +author: John Snow Labs +name: bertjewdialdataallqonly07 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertjewdialdataallqonly07` is a English model originally trained by Jeska. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly07_en_5.1.1_3.0_1694563003244.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly07_en_5.1.1_3.0_1694563003244.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertjewdialdataallqonly07","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertjewdialdataallqonly07", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertjewdialdataallqonly07| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.7 MB| + +## References + +https://huggingface.co/Jeska/BertjeWDialDataALLQonly07 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly08_en.md b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly08_en.md new file mode 100644 index 00000000000000..d82bd962260441 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly08_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertjewdialdataallqonly08 BertEmbeddings from Jeska +author: John Snow Labs +name: bertjewdialdataallqonly08 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertjewdialdataallqonly08` is a English model originally trained by Jeska. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly08_en_5.1.1_3.0_1694563138956.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly08_en_5.1.1_3.0_1694563138956.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertjewdialdataallqonly08","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertjewdialdataallqonly08", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertjewdialdataallqonly08| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/Jeska/BertjeWDialDataALLQonly08 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly_en.md b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly_en.md new file mode 100644 index 00000000000000..80c0ad1a0239c7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertjewdialdataallqonly BertEmbeddings from Jeska +author: John Snow Labs +name: bertjewdialdataallqonly +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertjewdialdataallqonly` is a English model originally trained by Jeska. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly_en_5.1.1_3.0_1694562127211.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly_en_5.1.1_3.0_1694562127211.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertjewdialdataallqonly","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertjewdialdataallqonly", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertjewdialdataallqonly| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/Jeska/BertjeWDialDataALLQonly \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-beto_clinical_wl_spanish_es.md b/docs/_posts/ahmedlone127/2023-09-12-beto_clinical_wl_spanish_es.md new file mode 100644 index 00000000000000..a8541213471dab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-beto_clinical_wl_spanish_es.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Castilian, Spanish beto_clinical_wl_spanish BertEmbeddings from plncmm +author: John Snow Labs +name: beto_clinical_wl_spanish +date: 2023-09-12 +tags: [bert, es, open_source, fill_mask, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`beto_clinical_wl_spanish` is a Castilian, Spanish model originally trained by plncmm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/beto_clinical_wl_spanish_es_5.1.1_3.0_1694510336701.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/beto_clinical_wl_spanish_es_5.1.1_3.0_1694510336701.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("beto_clinical_wl_spanish","es") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("beto_clinical_wl_spanish", "es") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|beto_clinical_wl_spanish| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|es| +|Size:|409.7 MB| + +## References + +https://huggingface.co/plncmm/beto-clinical-wl-es \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bio_bert_base_spanish_wwm_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bio_bert_base_spanish_wwm_cased_en.md new file mode 100644 index 00000000000000..bbc180b3cb44a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bio_bert_base_spanish_wwm_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bio_bert_base_spanish_wwm_cased BertEmbeddings from mrojas +author: John Snow Labs +name: bio_bert_base_spanish_wwm_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bio_bert_base_spanish_wwm_cased` is a English model originally trained by mrojas. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bio_bert_base_spanish_wwm_cased_en_5.1.1_3.0_1694556544912.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bio_bert_base_spanish_wwm_cased_en_5.1.1_3.0_1694556544912.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bio_bert_base_spanish_wwm_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bio_bert_base_spanish_wwm_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bio_bert_base_spanish_wwm_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.0 MB| + +## References + +https://huggingface.co/mrojas/bio-bert-base-spanish-wwm-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-biobert_italian_en.md b/docs/_posts/ahmedlone127/2023-09-12-biobert_italian_en.md new file mode 100644 index 00000000000000..a0dc73da5299ec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-biobert_italian_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English biobert_italian BertEmbeddings from marcopost-it +author: John Snow Labs +name: biobert_italian +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biobert_italian` is a English model originally trained by marcopost-it. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biobert_italian_en_5.1.1_3.0_1694551323419.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biobert_italian_en_5.1.1_3.0_1694551323419.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biobert_italian","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biobert_italian", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biobert_italian| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.2 MB| + +## References + +https://huggingface.co/marcopost-it/biobert-it \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-biobert_patent_reference_extraction_en.md b/docs/_posts/ahmedlone127/2023-09-12-biobert_patent_reference_extraction_en.md new file mode 100644 index 00000000000000..3b583aca84bfbd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-biobert_patent_reference_extraction_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English biobert_patent_reference_extraction BertEmbeddings from kaesve +author: John Snow Labs +name: biobert_patent_reference_extraction +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biobert_patent_reference_extraction` is a English model originally trained by kaesve. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biobert_patent_reference_extraction_en_5.1.1_3.0_1694507994597.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biobert_patent_reference_extraction_en_5.1.1_3.0_1694507994597.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biobert_patent_reference_extraction","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biobert_patent_reference_extraction", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biobert_patent_reference_extraction| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.1 MB| + +## References + +https://huggingface.co/kaesve/BioBERT_patent_reference_extraction \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-biobert_v1.1_pubmed_en.md b/docs/_posts/ahmedlone127/2023-09-12-biobert_v1.1_pubmed_en.md new file mode 100644 index 00000000000000..b6795b741d65a2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-biobert_v1.1_pubmed_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English biobert_v1.1_pubmed BertEmbeddings from monologg +author: John Snow Labs +name: biobert_v1.1_pubmed +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biobert_v1.1_pubmed` is a English model originally trained by monologg. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biobert_v1.1_pubmed_en_5.1.1_3.0_1694554769599.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biobert_v1.1_pubmed_en_5.1.1_3.0_1694554769599.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biobert_v1.1_pubmed","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biobert_v1.1_pubmed", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biobert_v1.1_pubmed| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.1 MB| + +## References + +https://huggingface.co/monologg/biobert_v1.1_pubmed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bioclinicalbert_finetuned_covid_papers_en.md b/docs/_posts/ahmedlone127/2023-09-12-bioclinicalbert_finetuned_covid_papers_en.md new file mode 100644 index 00000000000000..512beedd3752f1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bioclinicalbert_finetuned_covid_papers_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bioclinicalbert_finetuned_covid_papers BertEmbeddings from mrm8488 +author: John Snow Labs +name: bioclinicalbert_finetuned_covid_papers +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bioclinicalbert_finetuned_covid_papers` is a English model originally trained by mrm8488. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bioclinicalbert_finetuned_covid_papers_en_5.1.1_3.0_1694556257175.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bioclinicalbert_finetuned_covid_papers_en_5.1.1_3.0_1694556257175.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bioclinicalbert_finetuned_covid_papers","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bioclinicalbert_finetuned_covid_papers", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bioclinicalbert_finetuned_covid_papers| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.2 MB| + +## References + +https://huggingface.co/mrm8488/bioclinicalBERT-finetuned-covid-papers \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-biomednlp_pubmedbert_base_uncased_abstract_en.md b/docs/_posts/ahmedlone127/2023-09-12-biomednlp_pubmedbert_base_uncased_abstract_en.md new file mode 100644 index 00000000000000..423700e0cb5575 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-biomednlp_pubmedbert_base_uncased_abstract_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English biomednlp_pubmedbert_base_uncased_abstract BertEmbeddings from microsoft +author: John Snow Labs +name: biomednlp_pubmedbert_base_uncased_abstract +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biomednlp_pubmedbert_base_uncased_abstract` is a English model originally trained by microsoft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biomednlp_pubmedbert_base_uncased_abstract_en_5.1.1_3.0_1694553564368.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biomednlp_pubmedbert_base_uncased_abstract_en_5.1.1_3.0_1694553564368.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biomednlp_pubmedbert_base_uncased_abstract","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biomednlp_pubmedbert_base_uncased_abstract", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biomednlp_pubmedbert_base_uncased_abstract| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bodo_bert_mlm_base_en.md b/docs/_posts/ahmedlone127/2023-09-12-bodo_bert_mlm_base_en.md new file mode 100644 index 00000000000000..b20ee811c39217 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bodo_bert_mlm_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bodo_bert_mlm_base BertEmbeddings from alayaran +author: John Snow Labs +name: bodo_bert_mlm_base +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bodo_bert_mlm_base` is a English model originally trained by alayaran. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bodo_bert_mlm_base_en_5.1.1_3.0_1694509384576.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bodo_bert_mlm_base_en_5.1.1_3.0_1694509384576.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bodo_bert_mlm_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bodo_bert_mlm_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bodo_bert_mlm_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.6 MB| + +## References + +https://huggingface.co/alayaran/bodo-bert-mlm-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-burmese_bert_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-12-burmese_bert_mlm_en.md new file mode 100644 index 00000000000000..9cbed457538a52 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-burmese_bert_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English burmese_bert_mlm BertEmbeddings from minn +author: John Snow Labs +name: burmese_bert_mlm +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`burmese_bert_mlm` is a English model originally trained by minn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/burmese_bert_mlm_en_5.1.1_3.0_1694553778330.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/burmese_bert_mlm_en_5.1.1_3.0_1694553778330.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("burmese_bert_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("burmese_bert_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|burmese_bert_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.2 MB| + +## References + +https://huggingface.co/minn/my-bert-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-clinicaladaptation_pubmedbert_base_uncased_mimic_note_en.md b/docs/_posts/ahmedlone127/2023-09-12-clinicaladaptation_pubmedbert_base_uncased_mimic_note_en.md new file mode 100644 index 00000000000000..ffb6856020bd46 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-clinicaladaptation_pubmedbert_base_uncased_mimic_note_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English clinicaladaptation_pubmedbert_base_uncased_mimic_note BertEmbeddings from jhliu +author: John Snow Labs +name: clinicaladaptation_pubmedbert_base_uncased_mimic_note +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`clinicaladaptation_pubmedbert_base_uncased_mimic_note` is a English model originally trained by jhliu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clinicaladaptation_pubmedbert_base_uncased_mimic_note_en_5.1.1_3.0_1694550817266.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clinicaladaptation_pubmedbert_base_uncased_mimic_note_en_5.1.1_3.0_1694550817266.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("clinicaladaptation_pubmedbert_base_uncased_mimic_note","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("clinicaladaptation_pubmedbert_base_uncased_mimic_note", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinicaladaptation_pubmedbert_base_uncased_mimic_note| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/jhliu/ClinicalAdaptation-PubMedBERT-base-uncased-MIMIC-note \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-clinicaladaptation_pubmedbert_base_uncased_mimic_segment_en.md b/docs/_posts/ahmedlone127/2023-09-12-clinicaladaptation_pubmedbert_base_uncased_mimic_segment_en.md new file mode 100644 index 00000000000000..9b4dc43c91f411 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-clinicaladaptation_pubmedbert_base_uncased_mimic_segment_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English clinicaladaptation_pubmedbert_base_uncased_mimic_segment BertEmbeddings from jhliu +author: John Snow Labs +name: clinicaladaptation_pubmedbert_base_uncased_mimic_segment +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`clinicaladaptation_pubmedbert_base_uncased_mimic_segment` is a English model originally trained by jhliu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clinicaladaptation_pubmedbert_base_uncased_mimic_segment_en_5.1.1_3.0_1694550645740.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clinicaladaptation_pubmedbert_base_uncased_mimic_segment_en_5.1.1_3.0_1694550645740.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("clinicaladaptation_pubmedbert_base_uncased_mimic_segment","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("clinicaladaptation_pubmedbert_base_uncased_mimic_segment", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinicaladaptation_pubmedbert_base_uncased_mimic_segment| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.0 MB| + +## References + +https://huggingface.co/jhliu/ClinicalAdaptation-PubMedBERT-base-uncased-MIMIC-segment \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-clinicaladaptation_pubmedbert_base_uncased_mimic_sentence_en.md b/docs/_posts/ahmedlone127/2023-09-12-clinicaladaptation_pubmedbert_base_uncased_mimic_sentence_en.md new file mode 100644 index 00000000000000..e4b370235616db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-clinicaladaptation_pubmedbert_base_uncased_mimic_sentence_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English clinicaladaptation_pubmedbert_base_uncased_mimic_sentence BertEmbeddings from jhliu +author: John Snow Labs +name: clinicaladaptation_pubmedbert_base_uncased_mimic_sentence +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`clinicaladaptation_pubmedbert_base_uncased_mimic_sentence` is a English model originally trained by jhliu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clinicaladaptation_pubmedbert_base_uncased_mimic_sentence_en_5.1.1_3.0_1694550482919.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clinicaladaptation_pubmedbert_base_uncased_mimic_sentence_en_5.1.1_3.0_1694550482919.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("clinicaladaptation_pubmedbert_base_uncased_mimic_sentence","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("clinicaladaptation_pubmedbert_base_uncased_mimic_sentence", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinicaladaptation_pubmedbert_base_uncased_mimic_sentence| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.9 MB| + +## References + +https://huggingface.co/jhliu/ClinicalAdaptation-PubMedBERT-base-uncased-MIMIC-sentence \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-clinicalnotebert_base_uncased_mimic_segment_note_en.md b/docs/_posts/ahmedlone127/2023-09-12-clinicalnotebert_base_uncased_mimic_segment_note_en.md new file mode 100644 index 00000000000000..b8f6d52173905d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-clinicalnotebert_base_uncased_mimic_segment_note_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English clinicalnotebert_base_uncased_mimic_segment_note BertEmbeddings from jhliu +author: John Snow Labs +name: clinicalnotebert_base_uncased_mimic_segment_note +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`clinicalnotebert_base_uncased_mimic_segment_note` is a English model originally trained by jhliu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clinicalnotebert_base_uncased_mimic_segment_note_en_5.1.1_3.0_1694550310365.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clinicalnotebert_base_uncased_mimic_segment_note_en_5.1.1_3.0_1694550310365.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("clinicalnotebert_base_uncased_mimic_segment_note","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("clinicalnotebert_base_uncased_mimic_segment_note", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinicalnotebert_base_uncased_mimic_segment_note| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.0 MB| + +## References + +https://huggingface.co/jhliu/ClinicalNoteBERT-base-uncased-MIMIC-segment-note \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-covid19_fake_news_bert_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-12-covid19_fake_news_bert_uncased_en.md new file mode 100644 index 00000000000000..a6af0e72f2bd6c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-covid19_fake_news_bert_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English covid19_fake_news_bert_uncased BertEmbeddings from Jawaher +author: John Snow Labs +name: covid19_fake_news_bert_uncased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`covid19_fake_news_bert_uncased` is a English model originally trained by Jawaher. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/covid19_fake_news_bert_uncased_en_5.1.1_3.0_1694510515872.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/covid19_fake_news_bert_uncased_en_5.1.1_3.0_1694510515872.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("covid19_fake_news_bert_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("covid19_fake_news_bert_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|covid19_fake_news_bert_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Jawaher/Covid19-fake-news-bert-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-covid_scibert_en.md b/docs/_posts/ahmedlone127/2023-09-12-covid_scibert_en.md new file mode 100644 index 00000000000000..75b293db51c7a0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-covid_scibert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English covid_scibert BertEmbeddings from lordtt13 +author: John Snow Labs +name: covid_scibert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`covid_scibert` is a English model originally trained by lordtt13. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/covid_scibert_en_5.1.1_3.0_1694549378978.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/covid_scibert_en_5.1.1_3.0_1694549378978.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("covid_scibert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("covid_scibert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|covid_scibert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.4 MB| + +## References + +https://huggingface.co/lordtt13/COVID-SciBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-covid_vaccine_twitter_bert_en.md b/docs/_posts/ahmedlone127/2023-09-12-covid_vaccine_twitter_bert_en.md new file mode 100644 index 00000000000000..2189a1b689145b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-covid_vaccine_twitter_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English covid_vaccine_twitter_bert BertEmbeddings from GateNLP +author: John Snow Labs +name: covid_vaccine_twitter_bert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`covid_vaccine_twitter_bert` is a English model originally trained by GateNLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/covid_vaccine_twitter_bert_en_5.1.1_3.0_1694553221558.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/covid_vaccine_twitter_bert_en_5.1.1_3.0_1694553221558.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("covid_vaccine_twitter_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("covid_vaccine_twitter_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|covid_vaccine_twitter_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/GateNLP/covid-vaccine-twitter-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-cro_cov_csebert_en.md b/docs/_posts/ahmedlone127/2023-09-12-cro_cov_csebert_en.md new file mode 100644 index 00000000000000..a82e91d1fe4222 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-cro_cov_csebert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cro_cov_csebert BertEmbeddings from InfoCoV +author: John Snow Labs +name: cro_cov_csebert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cro_cov_csebert` is a English model originally trained by InfoCoV. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cro_cov_csebert_en_5.1.1_3.0_1694559840041.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cro_cov_csebert_en_5.1.1_3.0_1694559840041.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cro_cov_csebert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cro_cov_csebert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cro_cov_csebert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|463.4 MB| + +## References + +https://huggingface.co/InfoCoV/Cro-CoV-cseBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-crosloengual_bert_hr.md b/docs/_posts/ahmedlone127/2023-09-12-crosloengual_bert_hr.md new file mode 100644 index 00000000000000..f3a4c1d30af803 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-crosloengual_bert_hr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Croatian crosloengual_bert BertEmbeddings from EMBEDDIA +author: John Snow Labs +name: crosloengual_bert +date: 2023-09-12 +tags: [bert, hr, open_source, fill_mask, onnx] +task: Embeddings +language: hr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`crosloengual_bert` is a Croatian model originally trained by EMBEDDIA. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/crosloengual_bert_hr_5.1.1_3.0_1694509284745.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/crosloengual_bert_hr_5.1.1_3.0_1694509284745.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("crosloengual_bert","hr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("crosloengual_bert", "hr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|crosloengual_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|hr| +|Size:|463.4 MB| + +## References + +https://huggingface.co/EMBEDDIA/crosloengual-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-custominlawbert_en.md b/docs/_posts/ahmedlone127/2023-09-12-custominlawbert_en.md new file mode 100644 index 00000000000000..7cfed09f199915 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-custominlawbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English custominlawbert BertEmbeddings from law-ai +author: John Snow Labs +name: custominlawbert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`custominlawbert` is a English model originally trained by law-ai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/custominlawbert_en_5.1.1_3.0_1694553237351.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/custominlawbert_en_5.1.1_3.0_1694553237351.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("custominlawbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("custominlawbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|custominlawbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.5 MB| + +## References + +https://huggingface.co/law-ai/CustomInLawBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-cysecbert_en.md b/docs/_posts/ahmedlone127/2023-09-12-cysecbert_en.md new file mode 100644 index 00000000000000..124d42903439a8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-cysecbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cysecbert BertEmbeddings from markusbayer +author: John Snow Labs +name: cysecbert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cysecbert` is a English model originally trained by markusbayer. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cysecbert_en_5.1.1_3.0_1694559903370.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cysecbert_en_5.1.1_3.0_1694559903370.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cysecbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cysecbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cysecbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/markusbayer/CySecBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-dal_bert_finetuned_address_v1_en.md b/docs/_posts/ahmedlone127/2023-09-12-dal_bert_finetuned_address_v1_en.md new file mode 100644 index 00000000000000..7352a07970a0f8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-dal_bert_finetuned_address_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dal_bert_finetuned_address_v1 BertEmbeddings from IRI2070 +author: John Snow Labs +name: dal_bert_finetuned_address_v1 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dal_bert_finetuned_address_v1` is a English model originally trained by IRI2070. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dal_bert_finetuned_address_v1_en_5.1.1_3.0_1694554981931.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dal_bert_finetuned_address_v1_en_5.1.1_3.0_1694554981931.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dal_bert_finetuned_address_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dal_bert_finetuned_address_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dal_bert_finetuned_address_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|432.1 MB| + +## References + +https://huggingface.co/IRI2070/dal-bert-finetuned-address-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-deberta_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-12-deberta_base_uncased_en.md new file mode 100644 index 00000000000000..44870658d01905 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-deberta_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English deberta_base_uncased BertEmbeddings from mlcorelib +author: John Snow Labs +name: deberta_base_uncased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`deberta_base_uncased` is a English model originally trained by mlcorelib. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/deberta_base_uncased_en_5.1.1_3.0_1694554096071.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_base_uncased_en_5.1.1_3.0_1694554096071.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("deberta_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("deberta_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|deberta_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/mlcorelib/deberta-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-divehi_labse_dv.md b/docs/_posts/ahmedlone127/2023-09-12-divehi_labse_dv.md new file mode 100644 index 00000000000000..7019823ece87a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-divehi_labse_dv.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Dhivehi, Divehi, Maldivian divehi_labse BertEmbeddings from monsoon-nlp +author: John Snow Labs +name: divehi_labse +date: 2023-09-12 +tags: [bert, dv, open_source, fill_mask, onnx] +task: Embeddings +language: dv +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`divehi_labse` is a Dhivehi, Divehi, Maldivian model originally trained by monsoon-nlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/divehi_labse_dv_5.1.1_3.0_1694555287990.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/divehi_labse_dv_5.1.1_3.0_1694555287990.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("divehi_labse","dv") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("divehi_labse", "dv") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|divehi_labse| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|dv| +|Size:|1.9 GB| + +## References + +https://huggingface.co/monsoon-nlp/dv-labse \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-divehi_muril_dv.md b/docs/_posts/ahmedlone127/2023-09-12-divehi_muril_dv.md new file mode 100644 index 00000000000000..7b13bb38cf35a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-divehi_muril_dv.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Dhivehi, Divehi, Maldivian divehi_muril BertEmbeddings from monsoon-nlp +author: John Snow Labs +name: divehi_muril +date: 2023-09-12 +tags: [bert, dv, open_source, fill_mask, onnx] +task: Embeddings +language: dv +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`divehi_muril` is a Dhivehi, Divehi, Maldivian model originally trained by monsoon-nlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/divehi_muril_dv_5.1.1_3.0_1694555552470.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/divehi_muril_dv_5.1.1_3.0_1694555552470.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("divehi_muril","dv") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("divehi_muril", "dv") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|divehi_muril| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|dv| +|Size:|919.1 MB| + +## References + +https://huggingface.co/monsoon-nlp/dv-muril \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-dlub_2022_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-12-dlub_2022_mlm_en.md new file mode 100644 index 00000000000000..3d73ff9eaffdb5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-dlub_2022_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dlub_2022_mlm BertEmbeddings from bayartsogt +author: John Snow Labs +name: dlub_2022_mlm +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dlub_2022_mlm` is a English model originally trained by bayartsogt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dlub_2022_mlm_en_5.1.1_3.0_1694555892646.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dlub_2022_mlm_en_5.1.1_3.0_1694555892646.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dlub_2022_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dlub_2022_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dlub_2022_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.6 MB| + +## References + +https://huggingface.co/bayartsogt/dlub-2022-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-dlub_2022_mlm_full_gansukh_en.md b/docs/_posts/ahmedlone127/2023-09-12-dlub_2022_mlm_full_gansukh_en.md new file mode 100644 index 00000000000000..c1b12f7c896339 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-dlub_2022_mlm_full_gansukh_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dlub_2022_mlm_full_gansukh BertEmbeddings from Gansukh +author: John Snow Labs +name: dlub_2022_mlm_full_gansukh +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dlub_2022_mlm_full_gansukh` is a English model originally trained by Gansukh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dlub_2022_mlm_full_gansukh_en_5.1.1_3.0_1694555350358.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dlub_2022_mlm_full_gansukh_en_5.1.1_3.0_1694555350358.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dlub_2022_mlm_full_gansukh","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dlub_2022_mlm_full_gansukh", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dlub_2022_mlm_full_gansukh| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.6 MB| + +## References + +https://huggingface.co/Gansukh/dlub-2022-mlm-full \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-dlub_2022_mlm_full_ganzorig_en.md b/docs/_posts/ahmedlone127/2023-09-12-dlub_2022_mlm_full_ganzorig_en.md new file mode 100644 index 00000000000000..8b063a7d5ded53 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-dlub_2022_mlm_full_ganzorig_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dlub_2022_mlm_full_ganzorig BertEmbeddings from ganzorig +author: John Snow Labs +name: dlub_2022_mlm_full_ganzorig +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dlub_2022_mlm_full_ganzorig` is a English model originally trained by ganzorig. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dlub_2022_mlm_full_ganzorig_en_5.1.1_3.0_1694555674512.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dlub_2022_mlm_full_ganzorig_en_5.1.1_3.0_1694555674512.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dlub_2022_mlm_full_ganzorig","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dlub_2022_mlm_full_ganzorig", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dlub_2022_mlm_full_ganzorig| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.8 MB| + +## References + +https://huggingface.co/ganzorig/dlub-2022-mlm-full \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-dlub_2022_mlm_full_omunkhuush_en.md b/docs/_posts/ahmedlone127/2023-09-12-dlub_2022_mlm_full_omunkhuush_en.md new file mode 100644 index 00000000000000..9085341fd867d0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-dlub_2022_mlm_full_omunkhuush_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dlub_2022_mlm_full_omunkhuush BertEmbeddings from omunkhuush +author: John Snow Labs +name: dlub_2022_mlm_full_omunkhuush +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dlub_2022_mlm_full_omunkhuush` is a English model originally trained by omunkhuush. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dlub_2022_mlm_full_omunkhuush_en_5.1.1_3.0_1694555506851.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dlub_2022_mlm_full_omunkhuush_en_5.1.1_3.0_1694555506851.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dlub_2022_mlm_full_omunkhuush","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dlub_2022_mlm_full_omunkhuush", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dlub_2022_mlm_full_omunkhuush| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.7 MB| + +## References + +https://huggingface.co/omunkhuush/dlub-2022-mlm-full \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-dmis_bertpubmed_en.md b/docs/_posts/ahmedlone127/2023-09-12-dmis_bertpubmed_en.md new file mode 100644 index 00000000000000..d08f5b31244e6b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-dmis_bertpubmed_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dmis_bertpubmed BertEmbeddings from abnuel +author: John Snow Labs +name: dmis_bertpubmed +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dmis_bertpubmed` is a English model originally trained by abnuel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dmis_bertpubmed_en_5.1.1_3.0_1694561812368.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dmis_bertpubmed_en_5.1.1_3.0_1694561812368.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dmis_bertpubmed","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dmis_bertpubmed", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dmis_bertpubmed| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/abnuel/dmis_bertpubmed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-dpr_passage_uned_en.md b/docs/_posts/ahmedlone127/2023-09-12-dpr_passage_uned_en.md new file mode 100644 index 00000000000000..e7ce09605c4ebe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-dpr_passage_uned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_passage_uned BertEmbeddings from avacaondata +author: John Snow Labs +name: dpr_passage_uned +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_passage_uned` is a English model originally trained by avacaondata. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_passage_uned_en_5.1.1_3.0_1694554318636.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_passage_uned_en_5.1.1_3.0_1694554318636.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_passage_uned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_passage_uned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_passage_uned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/avacaondata/dpr-passage-uned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-dpr_query_uned_en.md b/docs/_posts/ahmedlone127/2023-09-12-dpr_query_uned_en.md new file mode 100644 index 00000000000000..1e3272bd6f9bcd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-dpr_query_uned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_query_uned BertEmbeddings from avacaondata +author: John Snow Labs +name: dpr_query_uned +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_query_uned` is a English model originally trained by avacaondata. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_query_uned_en_5.1.1_3.0_1694554164070.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_query_uned_en_5.1.1_3.0_1694554164070.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_query_uned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_query_uned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_query_uned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/avacaondata/dpr-query-uned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-drbert_4gb_cp_pubmedbert_fr.md b/docs/_posts/ahmedlone127/2023-09-12-drbert_4gb_cp_pubmedbert_fr.md new file mode 100644 index 00000000000000..50d27a903f93f0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-drbert_4gb_cp_pubmedbert_fr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: French drbert_4gb_cp_pubmedbert BertEmbeddings from Dr-BERT +author: John Snow Labs +name: drbert_4gb_cp_pubmedbert +date: 2023-09-12 +tags: [bert, fr, open_source, fill_mask, onnx] +task: Embeddings +language: fr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`drbert_4gb_cp_pubmedbert` is a French model originally trained by Dr-BERT. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/drbert_4gb_cp_pubmedbert_fr_5.1.1_3.0_1694550608539.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/drbert_4gb_cp_pubmedbert_fr_5.1.1_3.0_1694550608539.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("drbert_4gb_cp_pubmedbert","fr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("drbert_4gb_cp_pubmedbert", "fr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|drbert_4gb_cp_pubmedbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|fr| +|Size:|408.2 MB| + +## References + +https://huggingface.co/Dr-BERT/DrBERT-4GB-CP-PubMedBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-drclips_en.md b/docs/_posts/ahmedlone127/2023-09-12-drclips_en.md new file mode 100644 index 00000000000000..4a0d7ba74ff95d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-drclips_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English drclips BertEmbeddings from maximedb +author: John Snow Labs +name: drclips +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`drclips` is a English model originally trained by maximedb. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/drclips_en_5.1.1_3.0_1694552359667.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/drclips_en_5.1.1_3.0_1694552359667.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("drclips","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("drclips", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|drclips| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/maximedb/drclips \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-dummy_model_arthuerwang_en.md b/docs/_posts/ahmedlone127/2023-09-12-dummy_model_arthuerwang_en.md new file mode 100644 index 00000000000000..e21a27e6031c61 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-dummy_model_arthuerwang_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dummy_model_arthuerwang BertEmbeddings from Arthuerwang +author: John Snow Labs +name: dummy_model_arthuerwang +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dummy_model_arthuerwang` is a English model originally trained by Arthuerwang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dummy_model_arthuerwang_en_5.1.1_3.0_1694509846009.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dummy_model_arthuerwang_en_5.1.1_3.0_1694509846009.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dummy_model_arthuerwang","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dummy_model_arthuerwang", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dummy_model_arthuerwang| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Arthuerwang/dummy-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-europarl_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-12-europarl_mlm_en.md new file mode 100644 index 00000000000000..aba29d2d6dfab6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-europarl_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English europarl_mlm BertEmbeddings from lukabor +author: John Snow Labs +name: europarl_mlm +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`europarl_mlm` is a English model originally trained by lukabor. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/europarl_mlm_en_5.1.1_3.0_1694550101034.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/europarl_mlm_en_5.1.1_3.0_1694550101034.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("europarl_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("europarl_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|europarl_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/lukabor/europarl-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-finest_bert_fi.md b/docs/_posts/ahmedlone127/2023-09-12-finest_bert_fi.md new file mode 100644 index 00000000000000..2a96b2bf931a91 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-finest_bert_fi.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Finnish finest_bert BertEmbeddings from EMBEDDIA +author: John Snow Labs +name: finest_bert +date: 2023-09-12 +tags: [bert, fi, open_source, fill_mask, onnx] +task: Embeddings +language: fi +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finest_bert` is a Finnish model originally trained by EMBEDDIA. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finest_bert_fi_5.1.1_3.0_1694509453012.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finest_bert_fi_5.1.1_3.0_1694509453012.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("finest_bert","fi") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("finest_bert", "fi") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finest_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|fi| +|Size:|535.1 MB| + +## References + +https://huggingface.co/EMBEDDIA/finest-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-finetuned_bert_base_multilingual_cased_noisy_english_malay_xx.md b/docs/_posts/ahmedlone127/2023-09-12-finetuned_bert_base_multilingual_cased_noisy_english_malay_xx.md new file mode 100644 index 00000000000000..e2d475104f3bde --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-finetuned_bert_base_multilingual_cased_noisy_english_malay_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual finetuned_bert_base_multilingual_cased_noisy_english_malay BertEmbeddings from mesolitica +author: John Snow Labs +name: finetuned_bert_base_multilingual_cased_noisy_english_malay +date: 2023-09-12 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finetuned_bert_base_multilingual_cased_noisy_english_malay` is a Multilingual model originally trained by mesolitica. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finetuned_bert_base_multilingual_cased_noisy_english_malay_xx_5.1.1_3.0_1694556066679.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finetuned_bert_base_multilingual_cased_noisy_english_malay_xx_5.1.1_3.0_1694556066679.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("finetuned_bert_base_multilingual_cased_noisy_english_malay","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("finetuned_bert_base_multilingual_cased_noisy_english_malay", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finetuned_bert_base_multilingual_cased_noisy_english_malay| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|557.7 MB| + +## References + +https://huggingface.co/mesolitica/finetuned-bert-base-multilingual-cased-noisy-en-ms \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-firmanbrilianbert_en.md b/docs/_posts/ahmedlone127/2023-09-12-firmanbrilianbert_en.md new file mode 100644 index 00000000000000..67e9989d6439a2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-firmanbrilianbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English firmanbrilianbert BertEmbeddings from FirmanBr +author: John Snow Labs +name: firmanbrilianbert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`firmanbrilianbert` is a English model originally trained by FirmanBr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/firmanbrilianbert_en_5.1.1_3.0_1694548253279.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/firmanbrilianbert_en_5.1.1_3.0_1694548253279.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("firmanbrilianbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("firmanbrilianbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|firmanbrilianbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.4 MB| + +## References + +https://huggingface.co/FirmanBr/FirmanBrilianBert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-flang_bert_en.md b/docs/_posts/ahmedlone127/2023-09-12-flang_bert_en.md new file mode 100644 index 00000000000000..d804ac64ea7551 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-flang_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English flang_bert BertEmbeddings from SALT-NLP +author: John Snow Labs +name: flang_bert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`flang_bert` is a English model originally trained by SALT-NLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/flang_bert_en_5.1.1_3.0_1694553487024.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/flang_bert_en_5.1.1_3.0_1694553487024.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("flang_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("flang_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|flang_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/SALT-NLP/FLANG-BERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-flang_spanbert_en.md b/docs/_posts/ahmedlone127/2023-09-12-flang_spanbert_en.md new file mode 100644 index 00000000000000..b79cb06b88f94a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-flang_spanbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English flang_spanbert BertEmbeddings from SALT-NLP +author: John Snow Labs +name: flang_spanbert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`flang_spanbert` is a English model originally trained by SALT-NLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/flang_spanbert_en_5.1.1_3.0_1694553636224.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/flang_spanbert_en_5.1.1_3.0_1694553636224.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("flang_spanbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("flang_spanbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|flang_spanbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/SALT-NLP/FLANG-SpanBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-gbert_large_en.md b/docs/_posts/ahmedlone127/2023-09-12-gbert_large_en.md new file mode 100644 index 00000000000000..ef130a63b743b0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-gbert_large_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English gbert_large BertEmbeddings from Anjoe +author: John Snow Labs +name: gbert_large +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`gbert_large` is a English model originally trained by Anjoe. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gbert_large_en_5.1.1_3.0_1694548898842.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gbert_large_en_5.1.1_3.0_1694548898842.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("gbert_large","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("gbert_large", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gbert_large| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/Anjoe/gbert-large \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-hinglish_bert_en.md b/docs/_posts/ahmedlone127/2023-09-12-hinglish_bert_en.md new file mode 100644 index 00000000000000..5701ea912237f8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-hinglish_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English hinglish_bert BertEmbeddings from meghanabhange +author: John Snow Labs +name: hinglish_bert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hinglish_bert` is a English model originally trained by meghanabhange. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hinglish_bert_en_5.1.1_3.0_1694552706521.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hinglish_bert_en_5.1.1_3.0_1694552706521.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hinglish_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hinglish_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hinglish_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/meghanabhange/Hinglish-Bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-hinglish_sbert_en.md b/docs/_posts/ahmedlone127/2023-09-12-hinglish_sbert_en.md new file mode 100644 index 00000000000000..7cf1830542349a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-hinglish_sbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English hinglish_sbert BertEmbeddings from meghanabhange +author: John Snow Labs +name: hinglish_sbert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hinglish_sbert` is a English model originally trained by meghanabhange. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hinglish_sbert_en_5.1.1_3.0_1694552855686.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hinglish_sbert_en_5.1.1_3.0_1694552855686.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hinglish_sbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hinglish_sbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hinglish_sbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.8 MB| + +## References + +https://huggingface.co/meghanabhange/hinglish-sbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-hinglish_sentence_bert_en.md b/docs/_posts/ahmedlone127/2023-09-12-hinglish_sentence_bert_en.md new file mode 100644 index 00000000000000..f4fd57510a2475 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-hinglish_sentence_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English hinglish_sentence_bert BertEmbeddings from meghanabhange +author: John Snow Labs +name: hinglish_sentence_bert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hinglish_sentence_bert` is a English model originally trained by meghanabhange. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hinglish_sentence_bert_en_5.1.1_3.0_1694553037722.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hinglish_sentence_bert_en_5.1.1_3.0_1694553037722.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hinglish_sentence_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hinglish_sentence_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hinglish_sentence_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.7 MB| + +## References + +https://huggingface.co/meghanabhange/hinglish-sentence-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-hm_model001_en.md b/docs/_posts/ahmedlone127/2023-09-12-hm_model001_en.md new file mode 100644 index 00000000000000..cb307dedd6ecb3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-hm_model001_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English hm_model001 BertEmbeddings from FAN-L +author: John Snow Labs +name: hm_model001 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hm_model001` is a English model originally trained by FAN-L. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hm_model001_en_5.1.1_3.0_1694547885453.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hm_model001_en_5.1.1_3.0_1694547885453.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hm_model001","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hm_model001", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hm_model001| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/FAN-L/HM_model001 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-hubertkl_en.md b/docs/_posts/ahmedlone127/2023-09-12-hubertkl_en.md new file mode 100644 index 00000000000000..19ba053bf5913d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-hubertkl_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English hubertkl BertEmbeddings from SzegedAI +author: John Snow Labs +name: hubertkl +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hubertkl` is a English model originally trained by SzegedAI. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hubertkl_en_5.1.1_3.0_1694550469948.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hubertkl_en_5.1.1_3.0_1694550469948.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hubertkl","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hubertkl", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hubertkl| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|421.1 MB| + +## References + +https://huggingface.co/SzegedAI/HuBERTkl \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-hubertmlm_en.md b/docs/_posts/ahmedlone127/2023-09-12-hubertmlm_en.md new file mode 100644 index 00000000000000..f6acb129c6dfad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-hubertmlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English hubertmlm BertEmbeddings from SzegedAI +author: John Snow Labs +name: hubertmlm +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hubertmlm` is a English model originally trained by SzegedAI. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hubertmlm_en_5.1.1_3.0_1694550296332.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hubertmlm_en_5.1.1_3.0_1694550296332.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hubertmlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hubertmlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hubertmlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.4 MB| + +## References + +https://huggingface.co/SzegedAI/HuBERTmlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-indojave_codemixed_indobert_base_id.md b/docs/_posts/ahmedlone127/2023-09-12-indojave_codemixed_indobert_base_id.md new file mode 100644 index 00000000000000..9ca84832e77dc6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-indojave_codemixed_indobert_base_id.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Indonesian indojave_codemixed_indobert_base BertEmbeddings from fathan +author: John Snow Labs +name: indojave_codemixed_indobert_base +date: 2023-09-12 +tags: [bert, id, open_source, fill_mask, onnx] +task: Embeddings +language: id +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indojave_codemixed_indobert_base` is a Indonesian model originally trained by fathan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indojave_codemixed_indobert_base_id_5.1.1_3.0_1694549511841.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indojave_codemixed_indobert_base_id_5.1.1_3.0_1694549511841.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("indojave_codemixed_indobert_base","id") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("indojave_codemixed_indobert_base", "id") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indojave_codemixed_indobert_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|id| +|Size:|411.5 MB| + +## References + +https://huggingface.co/fathan/indojave-codemixed-indobert-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-jobbert_german_de.md b/docs/_posts/ahmedlone127/2023-09-12-jobbert_german_de.md new file mode 100644 index 00000000000000..98ccda4cbfe005 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-jobbert_german_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German jobbert_german BertEmbeddings from agne +author: John Snow Labs +name: jobbert_german +date: 2023-09-12 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`jobbert_german` is a German model originally trained by agne. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/jobbert_german_de_5.1.1_3.0_1694508954522.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/jobbert_german_de_5.1.1_3.0_1694508954522.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("jobbert_german","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("jobbert_german", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|jobbert_german| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|406.9 MB| + +## References + +https://huggingface.co/agne/jobBERT-de \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-jobgbert_de.md b/docs/_posts/ahmedlone127/2023-09-12-jobgbert_de.md new file mode 100644 index 00000000000000..5bcbf8219d5633 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-jobgbert_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German jobgbert BertEmbeddings from agne +author: John Snow Labs +name: jobgbert +date: 2023-09-12 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`jobgbert` is a German model originally trained by agne. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/jobgbert_de_5.1.1_3.0_1694509133987.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/jobgbert_de_5.1.1_3.0_1694509133987.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("jobgbert","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("jobgbert", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|jobgbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|409.5 MB| + +## References + +https://huggingface.co/agne/jobGBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-jurisbert_base_portuguese_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-12-jurisbert_base_portuguese_uncased_en.md new file mode 100644 index 00000000000000..b30d0d72a3aeb8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-jurisbert_base_portuguese_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English jurisbert_base_portuguese_uncased BertEmbeddings from alfaneo +author: John Snow Labs +name: jurisbert_base_portuguese_uncased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`jurisbert_base_portuguese_uncased` is a English model originally trained by alfaneo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/jurisbert_base_portuguese_uncased_en_5.1.1_3.0_1694556436181.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/jurisbert_base_portuguese_uncased_en_5.1.1_3.0_1694556436181.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("jurisbert_base_portuguese_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("jurisbert_base_portuguese_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|jurisbert_base_portuguese_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.3 MB| + +## References + +https://huggingface.co/alfaneo/jurisbert-base-portuguese-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-k_12bert_en.md b/docs/_posts/ahmedlone127/2023-09-12-k_12bert_en.md new file mode 100644 index 00000000000000..f9372b7d4b0227 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-k_12bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English k_12bert BertEmbeddings from vasugoel +author: John Snow Labs +name: k_12bert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`k_12bert` is a English model originally trained by vasugoel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/k_12bert_en_5.1.1_3.0_1694557848064.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/k_12bert_en_5.1.1_3.0_1694557848064.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("k_12bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("k_12bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|k_12bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/vasugoel/K-12BERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-kbbert_distilled_cased_sv.md b/docs/_posts/ahmedlone127/2023-09-12-kbbert_distilled_cased_sv.md new file mode 100644 index 00000000000000..a9b5c5b7c321c1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-kbbert_distilled_cased_sv.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Swedish kbbert_distilled_cased BertEmbeddings from Addedk +author: John Snow Labs +name: kbbert_distilled_cased +date: 2023-09-12 +tags: [bert, sv, open_source, fill_mask, onnx] +task: Embeddings +language: sv +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kbbert_distilled_cased` is a Swedish model originally trained by Addedk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kbbert_distilled_cased_sv_5.1.1_3.0_1694550965718.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kbbert_distilled_cased_sv_5.1.1_3.0_1694550965718.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kbbert_distilled_cased","sv") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kbbert_distilled_cased", "sv") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kbbert_distilled_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|sv| +|Size:|305.8 MB| + +## References + +https://huggingface.co/Addedk/kbbert-distilled-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-kkmultbert_1e_en.md b/docs/_posts/ahmedlone127/2023-09-12-kkmultbert_1e_en.md new file mode 100644 index 00000000000000..66ed603ae401b9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-kkmultbert_1e_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kkmultbert_1e BertEmbeddings from TurkLangsTeamURFU +author: John Snow Labs +name: kkmultbert_1e +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kkmultbert_1e` is a English model originally trained by TurkLangsTeamURFU. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kkmultbert_1e_en_5.1.1_3.0_1694553093201.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kkmultbert_1e_en_5.1.1_3.0_1694553093201.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kkmultbert_1e","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kkmultbert_1e", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kkmultbert_1e| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|494.3 MB| + +## References + +https://huggingface.co/TurkLangsTeamURFU/KKMultBert_1e \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-kkmultbert_3e_en.md b/docs/_posts/ahmedlone127/2023-09-12-kkmultbert_3e_en.md new file mode 100644 index 00000000000000..b4eed81ec9c53b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-kkmultbert_3e_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kkmultbert_3e BertEmbeddings from TurkLangsTeamURFU +author: John Snow Labs +name: kkmultbert_3e +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kkmultbert_3e` is a English model originally trained by TurkLangsTeamURFU. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kkmultbert_3e_en_5.1.1_3.0_1694553624770.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kkmultbert_3e_en_5.1.1_3.0_1694553624770.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kkmultbert_3e","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kkmultbert_3e", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kkmultbert_3e| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|494.1 MB| + +## References + +https://huggingface.co/TurkLangsTeamURFU/KKMultBert_3e \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-kkturkbert_bynaumen_1e_en.md b/docs/_posts/ahmedlone127/2023-09-12-kkturkbert_bynaumen_1e_en.md new file mode 100644 index 00000000000000..5ef92c1e47d453 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-kkturkbert_bynaumen_1e_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kkturkbert_bynaumen_1e BertEmbeddings from TurkLangsTeamURFU +author: John Snow Labs +name: kkturkbert_bynaumen_1e +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kkturkbert_bynaumen_1e` is a English model originally trained by TurkLangsTeamURFU. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kkturkbert_bynaumen_1e_en_5.1.1_3.0_1694559139710.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kkturkbert_bynaumen_1e_en_5.1.1_3.0_1694559139710.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kkturkbert_bynaumen_1e","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kkturkbert_bynaumen_1e", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kkturkbert_bynaumen_1e| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|494.0 MB| + +## References + +https://huggingface.co/TurkLangsTeamURFU/KKTurkBert_byNAUMEN_1e \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-kkturkbert_bynaumen_2e_en.md b/docs/_posts/ahmedlone127/2023-09-12-kkturkbert_bynaumen_2e_en.md new file mode 100644 index 00000000000000..79fbc1e527c3e0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-kkturkbert_bynaumen_2e_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kkturkbert_bynaumen_2e BertEmbeddings from TurkLangsTeamURFU +author: John Snow Labs +name: kkturkbert_bynaumen_2e +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kkturkbert_bynaumen_2e` is a English model originally trained by TurkLangsTeamURFU. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kkturkbert_bynaumen_2e_en_5.1.1_3.0_1694558924867.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kkturkbert_bynaumen_2e_en_5.1.1_3.0_1694558924867.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kkturkbert_bynaumen_2e","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kkturkbert_bynaumen_2e", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kkturkbert_bynaumen_2e| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|493.6 MB| + +## References + +https://huggingface.co/TurkLangsTeamURFU/KKTurkBert_byNAUMEN_2e \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-kkturkbert_bynaumen_3e_en.md b/docs/_posts/ahmedlone127/2023-09-12-kkturkbert_bynaumen_3e_en.md new file mode 100644 index 00000000000000..b6c076196dbbe0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-kkturkbert_bynaumen_3e_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kkturkbert_bynaumen_3e BertEmbeddings from TurkLangsTeamURFU +author: John Snow Labs +name: kkturkbert_bynaumen_3e +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kkturkbert_bynaumen_3e` is a English model originally trained by TurkLangsTeamURFU. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kkturkbert_bynaumen_3e_en_5.1.1_3.0_1694558723631.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kkturkbert_bynaumen_3e_en_5.1.1_3.0_1694558723631.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kkturkbert_bynaumen_3e","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kkturkbert_bynaumen_3e", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kkturkbert_bynaumen_3e| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|493.0 MB| + +## References + +https://huggingface.co/TurkLangsTeamURFU/KKTurkBert_byNAUMEN_3e \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-korean_mathbert_en.md b/docs/_posts/ahmedlone127/2023-09-12-korean_mathbert_en.md new file mode 100644 index 00000000000000..9c7ef7b8dc8b8c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-korean_mathbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English korean_mathbert BertEmbeddings from jnsulee +author: John Snow Labs +name: korean_mathbert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`korean_mathbert` is a English model originally trained by jnsulee. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/korean_mathbert_en_5.1.1_3.0_1694553539059.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/korean_mathbert_en_5.1.1_3.0_1694553539059.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("korean_mathbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("korean_mathbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|korean_mathbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.4 MB| + +## References + +https://huggingface.co/jnsulee/ko-mathbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-kw_pubmed_keyword_sentence_10000_0.0003_en.md b/docs/_posts/ahmedlone127/2023-09-12-kw_pubmed_keyword_sentence_10000_0.0003_en.md new file mode 100644 index 00000000000000..eaf877612e1c35 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-kw_pubmed_keyword_sentence_10000_0.0003_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kw_pubmed_keyword_sentence_10000_0.0003 BertEmbeddings from enoriega +author: John Snow Labs +name: kw_pubmed_keyword_sentence_10000_0.0003 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kw_pubmed_keyword_sentence_10000_0.0003` is a English model originally trained by enoriega. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kw_pubmed_keyword_sentence_10000_0.0003_en_5.1.1_3.0_1694548488337.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kw_pubmed_keyword_sentence_10000_0.0003_en_5.1.1_3.0_1694548488337.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kw_pubmed_keyword_sentence_10000_0.0003","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kw_pubmed_keyword_sentence_10000_0.0003", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kw_pubmed_keyword_sentence_10000_0.0003| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/enoriega/kw_pubmed_keyword_sentence_10000_0.0003 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-kw_pubmed_vanilla_document_10000_0.0003_en.md b/docs/_posts/ahmedlone127/2023-09-12-kw_pubmed_vanilla_document_10000_0.0003_en.md new file mode 100644 index 00000000000000..9eb8a8f55f5076 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-kw_pubmed_vanilla_document_10000_0.0003_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kw_pubmed_vanilla_document_10000_0.0003 BertEmbeddings from enoriega +author: John Snow Labs +name: kw_pubmed_vanilla_document_10000_0.0003 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kw_pubmed_vanilla_document_10000_0.0003` is a English model originally trained by enoriega. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kw_pubmed_vanilla_document_10000_0.0003_en_5.1.1_3.0_1694548616966.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kw_pubmed_vanilla_document_10000_0.0003_en_5.1.1_3.0_1694548616966.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kw_pubmed_vanilla_document_10000_0.0003","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kw_pubmed_vanilla_document_10000_0.0003", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kw_pubmed_vanilla_document_10000_0.0003| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/enoriega/kw_pubmed_vanilla_document_10000_0.0003 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-kw_pubmed_vanilla_sentence_10000_0.0003_2_en.md b/docs/_posts/ahmedlone127/2023-09-12-kw_pubmed_vanilla_sentence_10000_0.0003_2_en.md new file mode 100644 index 00000000000000..3a43754a499d0a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-kw_pubmed_vanilla_sentence_10000_0.0003_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kw_pubmed_vanilla_sentence_10000_0.0003_2 BertEmbeddings from enoriega +author: John Snow Labs +name: kw_pubmed_vanilla_sentence_10000_0.0003_2 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kw_pubmed_vanilla_sentence_10000_0.0003_2` is a English model originally trained by enoriega. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kw_pubmed_vanilla_sentence_10000_0.0003_2_en_5.1.1_3.0_1694553337510.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kw_pubmed_vanilla_sentence_10000_0.0003_2_en_5.1.1_3.0_1694553337510.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kw_pubmed_vanilla_sentence_10000_0.0003_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kw_pubmed_vanilla_sentence_10000_0.0003_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kw_pubmed_vanilla_sentence_10000_0.0003_2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/enoriega/kw_pubmed_vanilla_sentence_10000_0.0003_2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-kw_pubmed_vanilla_sentence_10000_0.0003_en.md b/docs/_posts/ahmedlone127/2023-09-12-kw_pubmed_vanilla_sentence_10000_0.0003_en.md new file mode 100644 index 00000000000000..9406230adc3229 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-kw_pubmed_vanilla_sentence_10000_0.0003_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kw_pubmed_vanilla_sentence_10000_0.0003 BertEmbeddings from enoriega +author: John Snow Labs +name: kw_pubmed_vanilla_sentence_10000_0.0003 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kw_pubmed_vanilla_sentence_10000_0.0003` is a English model originally trained by enoriega. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kw_pubmed_vanilla_sentence_10000_0.0003_en_5.1.1_3.0_1694548333195.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kw_pubmed_vanilla_sentence_10000_0.0003_en_5.1.1_3.0_1694548333195.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kw_pubmed_vanilla_sentence_10000_0.0003","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kw_pubmed_vanilla_sentence_10000_0.0003", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kw_pubmed_vanilla_sentence_10000_0.0003| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/enoriega/kw_pubmed_vanilla_sentence_10000_0.0003 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-kykim_bert_kor_base_korean_en.md b/docs/_posts/ahmedlone127/2023-09-12-kykim_bert_kor_base_korean_en.md new file mode 100644 index 00000000000000..e391d45933b48a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-kykim_bert_kor_base_korean_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kykim_bert_kor_base_korean BertEmbeddings from koala +author: John Snow Labs +name: kykim_bert_kor_base_korean +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kykim_bert_kor_base_korean` is a English model originally trained by koala. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kykim_bert_kor_base_korean_en_5.1.1_3.0_1694510273861.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kykim_bert_kor_base_korean_en_5.1.1_3.0_1694510273861.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kykim_bert_kor_base_korean","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kykim_bert_kor_base_korean", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kykim_bert_kor_base_korean| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|441.2 MB| + +## References + +https://huggingface.co/koala/kykim-bert-kor-base-ko \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-legalbertpt_fp_en.md b/docs/_posts/ahmedlone127/2023-09-12-legalbertpt_fp_en.md new file mode 100644 index 00000000000000..9f8486bd7aaeb8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-legalbertpt_fp_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English legalbertpt_fp BertEmbeddings from raquelsilveira +author: John Snow Labs +name: legalbertpt_fp +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legalbertpt_fp` is a English model originally trained by raquelsilveira. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legalbertpt_fp_en_5.1.1_3.0_1694549902826.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legalbertpt_fp_en_5.1.1_3.0_1694549902826.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legalbertpt_fp","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legalbertpt_fp", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legalbertpt_fp| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.8 MB| + +## References + +https://huggingface.co/raquelsilveira/legalbertpt_fp \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-legalbertpt_sardinian_en.md b/docs/_posts/ahmedlone127/2023-09-12-legalbertpt_sardinian_en.md new file mode 100644 index 00000000000000..bfc82201ef1ea0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-legalbertpt_sardinian_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English legalbertpt_sardinian BertEmbeddings from raquelsilveira +author: John Snow Labs +name: legalbertpt_sardinian +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legalbertpt_sardinian` is a English model originally trained by raquelsilveira. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legalbertpt_sardinian_en_5.1.1_3.0_1694550099835.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legalbertpt_sardinian_en_5.1.1_3.0_1694550099835.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legalbertpt_sardinian","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legalbertpt_sardinian", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legalbertpt_sardinian| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|425.1 MB| + +## References + +https://huggingface.co/raquelsilveira/legalbertpt_sc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-lernnavi_bert_en.md b/docs/_posts/ahmedlone127/2023-09-12-lernnavi_bert_en.md new file mode 100644 index 00000000000000..65ebce65f7dec0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-lernnavi_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English lernnavi_bert BertEmbeddings from lucazed +author: John Snow Labs +name: lernnavi_bert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lernnavi_bert` is a English model originally trained by lucazed. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lernnavi_bert_en_5.1.1_3.0_1694561382262.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lernnavi_bert_en_5.1.1_3.0_1694561382262.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("lernnavi_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("lernnavi_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lernnavi_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/lucazed/lernnavi_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-lf_model_01_en.md b/docs/_posts/ahmedlone127/2023-09-12-lf_model_01_en.md new file mode 100644 index 00000000000000..93273c267fa6f8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-lf_model_01_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English lf_model_01 BertEmbeddings from lf +author: John Snow Labs +name: lf_model_01 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lf_model_01` is a English model originally trained by lf. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lf_model_01_en_5.1.1_3.0_1694548430541.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lf_model_01_en_5.1.1_3.0_1694548430541.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("lf_model_01","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("lf_model_01", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lf_model_01| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/lf/lf_model_01 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-lm_financial_v2_en.md b/docs/_posts/ahmedlone127/2023-09-12-lm_financial_v2_en.md new file mode 100644 index 00000000000000..06a7fec6d55cc3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-lm_financial_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English lm_financial_v2 BertEmbeddings from anablasi +author: John Snow Labs +name: lm_financial_v2 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lm_financial_v2` is a English model originally trained by anablasi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lm_financial_v2_en_5.1.1_3.0_1694551202277.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lm_financial_v2_en_5.1.1_3.0_1694551202277.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("lm_financial_v2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("lm_financial_v2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lm_financial_v2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/anablasi/lm_financial_v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-logion_base_en.md b/docs/_posts/ahmedlone127/2023-09-12-logion_base_en.md new file mode 100644 index 00000000000000..a492b8bb4662be --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-logion_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English logion_base BertEmbeddings from cabrooks +author: John Snow Labs +name: logion_base +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`logion_base` is a English model originally trained by cabrooks. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/logion_base_en_5.1.1_3.0_1694551525035.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/logion_base_en_5.1.1_3.0_1694551525035.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("logion_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("logion_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|logion_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|420.8 MB| + +## References + +https://huggingface.co/cabrooks/LOGION-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-m3_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-12-m3_mlm_en.md new file mode 100644 index 00000000000000..5eef13ec6a635a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-m3_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_mlm BertEmbeddings from S2312dal +author: John Snow Labs +name: m3_mlm +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_mlm` is a English model originally trained by S2312dal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_mlm_en_5.1.1_3.0_1694551397577.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_mlm_en_5.1.1_3.0_1694551397577.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("m3_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("m3_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.9 MB| + +## References + +https://huggingface.co/S2312dal/M3_MLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-m6_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-12-m6_mlm_en.md new file mode 100644 index 00000000000000..e853cd0fe38210 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-m6_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m6_mlm BertEmbeddings from S2312dal +author: John Snow Labs +name: m6_mlm +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m6_mlm` is a English model originally trained by S2312dal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m6_mlm_en_5.1.1_3.0_1694551618334.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m6_mlm_en_5.1.1_3.0_1694551618334.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("m6_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("m6_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m6_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/S2312dal/M6_MLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-marathi_bert_mr.md b/docs/_posts/ahmedlone127/2023-09-12-marathi_bert_mr.md new file mode 100644 index 00000000000000..feebdcd8cbd918 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-marathi_bert_mr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Marathi marathi_bert BertEmbeddings from l3cube-pune +author: John Snow Labs +name: marathi_bert +date: 2023-09-12 +tags: [bert, mr, open_source, fill_mask, onnx] +task: Embeddings +language: mr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`marathi_bert` is a Marathi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/marathi_bert_mr_5.1.1_3.0_1694547462490.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/marathi_bert_mr_5.1.1_3.0_1694547462490.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("marathi_bert","mr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("marathi_bert", "mr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|marathi_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|mr| +|Size:|665.1 MB| + +## References + +https://huggingface.co/l3cube-pune/marathi-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-masked_2ktweets_conflibert_cont_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-masked_2ktweets_conflibert_cont_cased_en.md new file mode 100644 index 00000000000000..c4c0a9c117f5a3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-masked_2ktweets_conflibert_cont_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English masked_2ktweets_conflibert_cont_cased BertEmbeddings from ipadmanaban +author: John Snow Labs +name: masked_2ktweets_conflibert_cont_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`masked_2ktweets_conflibert_cont_cased` is a English model originally trained by ipadmanaban. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/masked_2ktweets_conflibert_cont_cased_en_5.1.1_3.0_1694508985703.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/masked_2ktweets_conflibert_cont_cased_en_5.1.1_3.0_1694508985703.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("masked_2ktweets_conflibert_cont_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("masked_2ktweets_conflibert_cont_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|masked_2ktweets_conflibert_cont_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.9 MB| + +## References + +https://huggingface.co/ipadmanaban/Masked-2KTweets-ConfliBERT-cont-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-masked_2ktweets_conflibert_cont_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-12-masked_2ktweets_conflibert_cont_uncased_en.md new file mode 100644 index 00000000000000..9a37d284962cf0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-masked_2ktweets_conflibert_cont_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English masked_2ktweets_conflibert_cont_uncased BertEmbeddings from ipadmanaban +author: John Snow Labs +name: masked_2ktweets_conflibert_cont_uncased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`masked_2ktweets_conflibert_cont_uncased` is a English model originally trained by ipadmanaban. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/masked_2ktweets_conflibert_cont_uncased_en_5.1.1_3.0_1694509108326.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/masked_2ktweets_conflibert_cont_uncased_en_5.1.1_3.0_1694509108326.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("masked_2ktweets_conflibert_cont_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("masked_2ktweets_conflibert_cont_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|masked_2ktweets_conflibert_cont_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/ipadmanaban/Masked-2KTweets-ConfliBERT-cont-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-masked_2ktweets_conflibert_scr_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-masked_2ktweets_conflibert_scr_cased_en.md new file mode 100644 index 00000000000000..316c787c916fe5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-masked_2ktweets_conflibert_scr_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English masked_2ktweets_conflibert_scr_cased BertEmbeddings from ipadmanaban +author: John Snow Labs +name: masked_2ktweets_conflibert_scr_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`masked_2ktweets_conflibert_scr_cased` is a English model originally trained by ipadmanaban. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/masked_2ktweets_conflibert_scr_cased_en_5.1.1_3.0_1694508731836.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/masked_2ktweets_conflibert_scr_cased_en_5.1.1_3.0_1694508731836.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("masked_2ktweets_conflibert_scr_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("masked_2ktweets_conflibert_scr_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|masked_2ktweets_conflibert_scr_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.1 MB| + +## References + +https://huggingface.co/ipadmanaban/Masked-2KTweets-ConfliBERT-scr-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-masked_2ktweets_conflibert_scr_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-12-masked_2ktweets_conflibert_scr_uncased_en.md new file mode 100644 index 00000000000000..e007524b59d832 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-masked_2ktweets_conflibert_scr_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English masked_2ktweets_conflibert_scr_uncased BertEmbeddings from ipadmanaban +author: John Snow Labs +name: masked_2ktweets_conflibert_scr_uncased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`masked_2ktweets_conflibert_scr_uncased` is a English model originally trained by ipadmanaban. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/masked_2ktweets_conflibert_scr_uncased_en_5.1.1_3.0_1694508845965.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/masked_2ktweets_conflibert_scr_uncased_en_5.1.1_3.0_1694508845965.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("masked_2ktweets_conflibert_scr_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("masked_2ktweets_conflibert_scr_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|masked_2ktweets_conflibert_scr_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.0 MB| + +## References + +https://huggingface.co/ipadmanaban/Masked-2KTweets-ConfliBERT-scr-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-matscibert_en.md b/docs/_posts/ahmedlone127/2023-09-12-matscibert_en.md new file mode 100644 index 00000000000000..200e1f51c320b8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-matscibert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English matscibert BertEmbeddings from m3rg-iitd +author: John Snow Labs +name: matscibert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`matscibert` is a English model originally trained by m3rg-iitd. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/matscibert_en_5.1.1_3.0_1694550983161.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/matscibert_en_5.1.1_3.0_1694550983161.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("matscibert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("matscibert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|matscibert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/m3rg-iitd/matscibert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mbert_arabic_c19_ar.md b/docs/_posts/ahmedlone127/2023-09-12-mbert_arabic_c19_ar.md new file mode 100644 index 00000000000000..424a213070f24f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mbert_arabic_c19_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic mbert_arabic_c19 BertEmbeddings from moha +author: John Snow Labs +name: mbert_arabic_c19 +date: 2023-09-12 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbert_arabic_c19` is a Arabic model originally trained by moha. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbert_arabic_c19_ar_5.1.1_3.0_1694554545798.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbert_arabic_c19_ar_5.1.1_3.0_1694554545798.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbert_arabic_c19","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbert_arabic_c19", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbert_arabic_c19| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|624.7 MB| + +## References + +https://huggingface.co/moha/mbert_ar_c19 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mbert_resp_english_chinese_en.md b/docs/_posts/ahmedlone127/2023-09-12-mbert_resp_english_chinese_en.md new file mode 100644 index 00000000000000..d26011b5a91d9c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mbert_resp_english_chinese_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mbert_resp_english_chinese BertEmbeddings from nikitam +author: John Snow Labs +name: mbert_resp_english_chinese +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbert_resp_english_chinese` is a English model originally trained by nikitam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbert_resp_english_chinese_en_5.1.1_3.0_1694558912316.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbert_resp_english_chinese_en_5.1.1_3.0_1694558912316.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbert_resp_english_chinese","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbert_resp_english_chinese", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbert_resp_english_chinese| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|625.0 MB| + +## References + +https://huggingface.co/nikitam/mbert-resp-en-zh \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mbert_resp_english_german_en.md b/docs/_posts/ahmedlone127/2023-09-12-mbert_resp_english_german_en.md new file mode 100644 index 00000000000000..4e6a2f0393f96e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mbert_resp_english_german_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mbert_resp_english_german BertEmbeddings from nikitam +author: John Snow Labs +name: mbert_resp_english_german +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbert_resp_english_german` is a English model originally trained by nikitam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbert_resp_english_german_en_5.1.1_3.0_1694558476877.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbert_resp_english_german_en_5.1.1_3.0_1694558476877.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbert_resp_english_german","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbert_resp_english_german", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbert_resp_english_german| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|625.2 MB| + +## References + +https://huggingface.co/nikitam/mbert-resp-en-de \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mbert_resp_english_italian_en.md b/docs/_posts/ahmedlone127/2023-09-12-mbert_resp_english_italian_en.md new file mode 100644 index 00000000000000..3152cc6cee6769 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mbert_resp_english_italian_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mbert_resp_english_italian BertEmbeddings from nikitam +author: John Snow Labs +name: mbert_resp_english_italian +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbert_resp_english_italian` is a English model originally trained by nikitam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbert_resp_english_italian_en_5.1.1_3.0_1694558696793.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbert_resp_english_italian_en_5.1.1_3.0_1694558696793.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbert_resp_english_italian","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbert_resp_english_italian", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbert_resp_english_italian| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|625.1 MB| + +## References + +https://huggingface.co/nikitam/mbert-resp-en-it \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mbert_swedish_distilled_cased_sv.md b/docs/_posts/ahmedlone127/2023-09-12-mbert_swedish_distilled_cased_sv.md new file mode 100644 index 00000000000000..463773b8ada81e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mbert_swedish_distilled_cased_sv.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Swedish mbert_swedish_distilled_cased BertEmbeddings from Addedk +author: John Snow Labs +name: mbert_swedish_distilled_cased +date: 2023-09-12 +tags: [bert, sv, open_source, fill_mask, onnx] +task: Embeddings +language: sv +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbert_swedish_distilled_cased` is a Swedish model originally trained by Addedk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbert_swedish_distilled_cased_sv_5.1.1_3.0_1694549731872.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbert_swedish_distilled_cased_sv_5.1.1_3.0_1694549731872.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbert_swedish_distilled_cased","sv") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbert_swedish_distilled_cased", "sv") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbert_swedish_distilled_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|sv| +|Size:|505.7 MB| + +## References + +https://huggingface.co/Addedk/mbert-swedish-distilled-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mbert_tlm_chat_english_chinese_en.md b/docs/_posts/ahmedlone127/2023-09-12-mbert_tlm_chat_english_chinese_en.md new file mode 100644 index 00000000000000..9f9a6aaca92bb9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mbert_tlm_chat_english_chinese_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mbert_tlm_chat_english_chinese BertEmbeddings from nikitam +author: John Snow Labs +name: mbert_tlm_chat_english_chinese +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbert_tlm_chat_english_chinese` is a English model originally trained by nikitam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbert_tlm_chat_english_chinese_en_5.1.1_3.0_1694559504129.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbert_tlm_chat_english_chinese_en_5.1.1_3.0_1694559504129.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbert_tlm_chat_english_chinese","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbert_tlm_chat_english_chinese", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbert_tlm_chat_english_chinese| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|625.0 MB| + +## References + +https://huggingface.co/nikitam/mbert-tlm-chat-en-zh \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mbert_tlm_chat_english_german_en.md b/docs/_posts/ahmedlone127/2023-09-12-mbert_tlm_chat_english_german_en.md new file mode 100644 index 00000000000000..8e156a6e394c00 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mbert_tlm_chat_english_german_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mbert_tlm_chat_english_german BertEmbeddings from nikitam +author: John Snow Labs +name: mbert_tlm_chat_english_german +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbert_tlm_chat_english_german` is a English model originally trained by nikitam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbert_tlm_chat_english_german_en_5.1.1_3.0_1694559103177.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbert_tlm_chat_english_german_en_5.1.1_3.0_1694559103177.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbert_tlm_chat_english_german","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbert_tlm_chat_english_german", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbert_tlm_chat_english_german| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|625.2 MB| + +## References + +https://huggingface.co/nikitam/mbert-tlm-chat-en-de \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mbert_tlm_chat_english_italian_en.md b/docs/_posts/ahmedlone127/2023-09-12-mbert_tlm_chat_english_italian_en.md new file mode 100644 index 00000000000000..5525b45002cd0f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mbert_tlm_chat_english_italian_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mbert_tlm_chat_english_italian BertEmbeddings from nikitam +author: John Snow Labs +name: mbert_tlm_chat_english_italian +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbert_tlm_chat_english_italian` is a English model originally trained by nikitam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbert_tlm_chat_english_italian_en_5.1.1_3.0_1694559329950.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbert_tlm_chat_english_italian_en_5.1.1_3.0_1694559329950.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbert_tlm_chat_english_italian","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbert_tlm_chat_english_italian", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbert_tlm_chat_english_italian| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|625.1 MB| + +## References + +https://huggingface.co/nikitam/mbert-tlm-chat-en-it \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mbert_tlm_sent_english_chinese_en.md b/docs/_posts/ahmedlone127/2023-09-12-mbert_tlm_sent_english_chinese_en.md new file mode 100644 index 00000000000000..c7802343bfcfa9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mbert_tlm_sent_english_chinese_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mbert_tlm_sent_english_chinese BertEmbeddings from nikitam +author: John Snow Labs +name: mbert_tlm_sent_english_chinese +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbert_tlm_sent_english_chinese` is a English model originally trained by nikitam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbert_tlm_sent_english_chinese_en_5.1.1_3.0_1694560100127.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbert_tlm_sent_english_chinese_en_5.1.1_3.0_1694560100127.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbert_tlm_sent_english_chinese","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbert_tlm_sent_english_chinese", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbert_tlm_sent_english_chinese| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|622.3 MB| + +## References + +https://huggingface.co/nikitam/mbert-tlm-sent-en-zh \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mbert_tlm_sent_english_german_en.md b/docs/_posts/ahmedlone127/2023-09-12-mbert_tlm_sent_english_german_en.md new file mode 100644 index 00000000000000..ae91b6ff3b9dc0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mbert_tlm_sent_english_german_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mbert_tlm_sent_english_german BertEmbeddings from nikitam +author: John Snow Labs +name: mbert_tlm_sent_english_german +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbert_tlm_sent_english_german` is a English model originally trained by nikitam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbert_tlm_sent_english_german_en_5.1.1_3.0_1694559726822.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbert_tlm_sent_english_german_en_5.1.1_3.0_1694559726822.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbert_tlm_sent_english_german","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbert_tlm_sent_english_german", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbert_tlm_sent_english_german| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|624.8 MB| + +## References + +https://huggingface.co/nikitam/mbert-tlm-sent-en-de \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mbert_xdm_english_german_en.md b/docs/_posts/ahmedlone127/2023-09-12-mbert_xdm_english_german_en.md new file mode 100644 index 00000000000000..56975392b75a88 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mbert_xdm_english_german_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mbert_xdm_english_german BertEmbeddings from nikitam +author: John Snow Labs +name: mbert_xdm_english_german +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbert_xdm_english_german` is a English model originally trained by nikitam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbert_xdm_english_german_en_5.1.1_3.0_1694560316540.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbert_xdm_english_german_en_5.1.1_3.0_1694560316540.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbert_xdm_english_german","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbert_xdm_english_german", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbert_xdm_english_german| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|625.4 MB| + +## References + +https://huggingface.co/nikitam/mbert-xdm-en-de \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mbert_xdm_english_italian_en.md b/docs/_posts/ahmedlone127/2023-09-12-mbert_xdm_english_italian_en.md new file mode 100644 index 00000000000000..ee756f126a4d0e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mbert_xdm_english_italian_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mbert_xdm_english_italian BertEmbeddings from nikitam +author: John Snow Labs +name: mbert_xdm_english_italian +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbert_xdm_english_italian` is a English model originally trained by nikitam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbert_xdm_english_italian_en_5.1.1_3.0_1694560553745.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbert_xdm_english_italian_en_5.1.1_3.0_1694560553745.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbert_xdm_english_italian","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbert_xdm_english_italian", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbert_xdm_english_italian| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|625.4 MB| + +## References + +https://huggingface.co/nikitam/mbert-xdm-en-it \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-miem_scibert_linguistic_en.md b/docs/_posts/ahmedlone127/2023-09-12-miem_scibert_linguistic_en.md new file mode 100644 index 00000000000000..cef5decb871541 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-miem_scibert_linguistic_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English miem_scibert_linguistic BertEmbeddings from miemBertProject +author: John Snow Labs +name: miem_scibert_linguistic +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`miem_scibert_linguistic` is a English model originally trained by miemBertProject. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/miem_scibert_linguistic_en_5.1.1_3.0_1694549092975.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/miem_scibert_linguistic_en_5.1.1_3.0_1694549092975.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("miem_scibert_linguistic","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("miem_scibert_linguistic", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|miem_scibert_linguistic| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|657.4 MB| + +## References + +https://huggingface.co/miemBertProject/miem-scibert-linguistic \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-minilm_finetuned_imdb_accelerate_en.md b/docs/_posts/ahmedlone127/2023-09-12-minilm_finetuned_imdb_accelerate_en.md new file mode 100644 index 00000000000000..7fe241693778a8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-minilm_finetuned_imdb_accelerate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English minilm_finetuned_imdb_accelerate BertEmbeddings from lewtun +author: John Snow Labs +name: minilm_finetuned_imdb_accelerate +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`minilm_finetuned_imdb_accelerate` is a English model originally trained by lewtun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/minilm_finetuned_imdb_accelerate_en_5.1.1_3.0_1694548276227.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/minilm_finetuned_imdb_accelerate_en_5.1.1_3.0_1694548276227.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("minilm_finetuned_imdb_accelerate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("minilm_finetuned_imdb_accelerate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|minilm_finetuned_imdb_accelerate| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|124.5 MB| + +## References + +https://huggingface.co/lewtun/minilm-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-minilm_l12_h384_uncased_finetuned_imdb_en.md b/docs/_posts/ahmedlone127/2023-09-12-minilm_l12_h384_uncased_finetuned_imdb_en.md new file mode 100644 index 00000000000000..e339d027c3b24c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-minilm_l12_h384_uncased_finetuned_imdb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English minilm_l12_h384_uncased_finetuned_imdb BertEmbeddings from lewtun +author: John Snow Labs +name: minilm_l12_h384_uncased_finetuned_imdb +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`minilm_l12_h384_uncased_finetuned_imdb` is a English model originally trained by lewtun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/minilm_l12_h384_uncased_finetuned_imdb_en_5.1.1_3.0_1694548053126.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/minilm_l12_h384_uncased_finetuned_imdb_en_5.1.1_3.0_1694548053126.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("minilm_l12_h384_uncased_finetuned_imdb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("minilm_l12_h384_uncased_finetuned_imdb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|minilm_l12_h384_uncased_finetuned_imdb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|124.5 MB| + +## References + +https://huggingface.co/lewtun/MiniLM-L12-H384-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-minilm_l_12_stackoverflow_en.md b/docs/_posts/ahmedlone127/2023-09-12-minilm_l_12_stackoverflow_en.md new file mode 100644 index 00000000000000..14aec5f9c15ffb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-minilm_l_12_stackoverflow_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English minilm_l_12_stackoverflow BertEmbeddings from M-Chimiste +author: John Snow Labs +name: minilm_l_12_stackoverflow +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`minilm_l_12_stackoverflow` is a English model originally trained by M-Chimiste. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/minilm_l_12_stackoverflow_en_5.1.1_3.0_1694552531487.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/minilm_l_12_stackoverflow_en_5.1.1_3.0_1694552531487.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("minilm_l_12_stackoverflow","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("minilm_l_12_stackoverflow", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|minilm_l_12_stackoverflow| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|124.7 MB| + +## References + +https://huggingface.co/M-Chimiste/MiniLM-L-12-StackOverflow \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-minilmv2_l6_h384_distilled_from_bert_base_en.md b/docs/_posts/ahmedlone127/2023-09-12-minilmv2_l6_h384_distilled_from_bert_base_en.md new file mode 100644 index 00000000000000..48068504c90f0f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-minilmv2_l6_h384_distilled_from_bert_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English minilmv2_l6_h384_distilled_from_bert_base BertEmbeddings from nreimers +author: John Snow Labs +name: minilmv2_l6_h384_distilled_from_bert_base +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`minilmv2_l6_h384_distilled_from_bert_base` is a English model originally trained by nreimers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/minilmv2_l6_h384_distilled_from_bert_base_en_5.1.1_3.0_1694562156137.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/minilmv2_l6_h384_distilled_from_bert_base_en_5.1.1_3.0_1694562156137.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("minilmv2_l6_h384_distilled_from_bert_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("minilmv2_l6_h384_distilled_from_bert_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|minilmv2_l6_h384_distilled_from_bert_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|54.1 MB| + +## References + +https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-minilmv2_l6_h384_distilled_from_bert_large_en.md b/docs/_posts/ahmedlone127/2023-09-12-minilmv2_l6_h384_distilled_from_bert_large_en.md new file mode 100644 index 00000000000000..d7dbada6e7d0b9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-minilmv2_l6_h384_distilled_from_bert_large_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English minilmv2_l6_h384_distilled_from_bert_large BertEmbeddings from nreimers +author: John Snow Labs +name: minilmv2_l6_h384_distilled_from_bert_large +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`minilmv2_l6_h384_distilled_from_bert_large` is a English model originally trained by nreimers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/minilmv2_l6_h384_distilled_from_bert_large_en_5.1.1_3.0_1694562262970.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/minilmv2_l6_h384_distilled_from_bert_large_en_5.1.1_3.0_1694562262970.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("minilmv2_l6_h384_distilled_from_bert_large","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("minilmv2_l6_h384_distilled_from_bert_large", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|minilmv2_l6_h384_distilled_from_bert_large| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|54.2 MB| + +## References + +https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-minilmv2_l6_h768_distilled_from_bert_base_en.md b/docs/_posts/ahmedlone127/2023-09-12-minilmv2_l6_h768_distilled_from_bert_base_en.md new file mode 100644 index 00000000000000..47347c85f09f3f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-minilmv2_l6_h768_distilled_from_bert_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English minilmv2_l6_h768_distilled_from_bert_base BertEmbeddings from nreimers +author: John Snow Labs +name: minilmv2_l6_h768_distilled_from_bert_base +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`minilmv2_l6_h768_distilled_from_bert_base` is a English model originally trained by nreimers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/minilmv2_l6_h768_distilled_from_bert_base_en_5.1.1_3.0_1694562396390.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/minilmv2_l6_h768_distilled_from_bert_base_en_5.1.1_3.0_1694562396390.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("minilmv2_l6_h768_distilled_from_bert_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("minilmv2_l6_h768_distilled_from_bert_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|minilmv2_l6_h768_distilled_from_bert_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|158.7 MB| + +## References + +https://huggingface.co/nreimers/MiniLMv2-L6-H768-distilled-from-BERT-Base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mizbert_en.md b/docs/_posts/ahmedlone127/2023-09-12-mizbert_en.md new file mode 100644 index 00000000000000..6892e5120e8096 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mizbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mizbert BertEmbeddings from robzchhangte +author: John Snow Labs +name: mizbert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mizbert` is a English model originally trained by robzchhangte. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mizbert_en_5.1.1_3.0_1694556095325.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mizbert_en_5.1.1_3.0_1694556095325.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mizbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mizbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mizbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/robzchhangte/MizBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mlm_20230503_indobert_base_p1_combined_001_en.md b/docs/_posts/ahmedlone127/2023-09-12-mlm_20230503_indobert_base_p1_combined_001_en.md new file mode 100644 index 00000000000000..7696cc278c79aa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mlm_20230503_indobert_base_p1_combined_001_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230503_indobert_base_p1_combined_001 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230503_indobert_base_p1_combined_001 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230503_indobert_base_p1_combined_001` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230503_indobert_base_p1_combined_001_en_5.1.1_3.0_1694551387527.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230503_indobert_base_p1_combined_001_en_5.1.1_3.0_1694551387527.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230503_indobert_base_p1_combined_001","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230503_indobert_base_p1_combined_001", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230503_indobert_base_p1_combined_001| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.3 MB| + +## References + +https://huggingface.co/intanm/mlm-20230503-indobert-base-p1-combined-001 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mlm_20230503_indobert_large_p1_001_en.md b/docs/_posts/ahmedlone127/2023-09-12-mlm_20230503_indobert_large_p1_001_en.md new file mode 100644 index 00000000000000..b805b7b8824326 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mlm_20230503_indobert_large_p1_001_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230503_indobert_large_p1_001 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230503_indobert_large_p1_001 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230503_indobert_large_p1_001` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230503_indobert_large_p1_001_en_5.1.1_3.0_1694550821233.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230503_indobert_large_p1_001_en_5.1.1_3.0_1694550821233.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230503_indobert_large_p1_001","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230503_indobert_large_p1_001", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230503_indobert_large_p1_001| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/intanm/mlm-20230503-indobert-large-p1-001 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mlm_20230510_indobert_large_p1_001_pt2_en.md b/docs/_posts/ahmedlone127/2023-09-12-mlm_20230510_indobert_large_p1_001_pt2_en.md new file mode 100644 index 00000000000000..18a543b38a09f5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mlm_20230510_indobert_large_p1_001_pt2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230510_indobert_large_p1_001_pt2 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230510_indobert_large_p1_001_pt2 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230510_indobert_large_p1_001_pt2` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230510_indobert_large_p1_001_pt2_en_5.1.1_3.0_1694557161327.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230510_indobert_large_p1_001_pt2_en_5.1.1_3.0_1694557161327.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230510_indobert_large_p1_001_pt2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230510_indobert_large_p1_001_pt2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230510_indobert_large_p1_001_pt2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/intanm/mlm-20230510-indobert-large-p1-001-pt2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mlm_20230511_indobert_large_p1_combined_pt1_en.md b/docs/_posts/ahmedlone127/2023-09-12-mlm_20230511_indobert_large_p1_combined_pt1_en.md new file mode 100644 index 00000000000000..9dbe37041d55f9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mlm_20230511_indobert_large_p1_combined_pt1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230511_indobert_large_p1_combined_pt1 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230511_indobert_large_p1_combined_pt1 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230511_indobert_large_p1_combined_pt1` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230511_indobert_large_p1_combined_pt1_en_5.1.1_3.0_1694557729072.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230511_indobert_large_p1_combined_pt1_en_5.1.1_3.0_1694557729072.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230511_indobert_large_p1_combined_pt1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230511_indobert_large_p1_combined_pt1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230511_indobert_large_p1_combined_pt1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/intanm/mlm-20230511-indobert-large-p1-combined-pt1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mlm_20230513_indobert_large_p1_combined_pt2_en.md b/docs/_posts/ahmedlone127/2023-09-12-mlm_20230513_indobert_large_p1_combined_pt2_en.md new file mode 100644 index 00000000000000..831aff4aaf1d0d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mlm_20230513_indobert_large_p1_combined_pt2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230513_indobert_large_p1_combined_pt2 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230513_indobert_large_p1_combined_pt2 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230513_indobert_large_p1_combined_pt2` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230513_indobert_large_p1_combined_pt2_en_5.1.1_3.0_1694560283117.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230513_indobert_large_p1_combined_pt2_en_5.1.1_3.0_1694560283117.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230513_indobert_large_p1_combined_pt2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230513_indobert_large_p1_combined_pt2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230513_indobert_large_p1_combined_pt2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/intanm/mlm-20230513-indobert-large-p1-combined-pt2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-model202109_en.md b/docs/_posts/ahmedlone127/2023-09-12-model202109_en.md new file mode 100644 index 00000000000000..4cf88950d8266d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-model202109_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English model202109 BertEmbeddings from lyx10290516 +author: John Snow Labs +name: model202109 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`model202109` is a English model originally trained by lyx10290516. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/model202109_en_5.1.1_3.0_1694550495262.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/model202109_en_5.1.1_3.0_1694550495262.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("model202109","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("model202109", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|model202109| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/lyx10290516/model202109 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mybert_en.md b/docs/_posts/ahmedlone127/2023-09-12-mybert_en.md new file mode 100644 index 00000000000000..93e3eacbe46f8d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mybert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mybert BertEmbeddings from RavenK +author: John Snow Labs +name: mybert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mybert` is a English model originally trained by RavenK. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mybert_en_5.1.1_3.0_1694548245920.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mybert_en_5.1.1_3.0_1694548245920.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mybert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mybert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mybert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/RavenK/mybert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mymodel007_lbh020300_en.md b/docs/_posts/ahmedlone127/2023-09-12-mymodel007_lbh020300_en.md new file mode 100644 index 00000000000000..633fab71a48ce9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mymodel007_lbh020300_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mymodel007_lbh020300 BertEmbeddings from lbh020300 +author: John Snow Labs +name: mymodel007_lbh020300 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mymodel007_lbh020300` is a English model originally trained by lbh020300. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mymodel007_lbh020300_en_5.1.1_3.0_1694547914311.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mymodel007_lbh020300_en_5.1.1_3.0_1694547914311.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mymodel007_lbh020300","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mymodel007_lbh020300", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mymodel007_lbh020300| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/lbh020300/mymodel007 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-myrubert_tiny2_en.md b/docs/_posts/ahmedlone127/2023-09-12-myrubert_tiny2_en.md new file mode 100644 index 00000000000000..a4f1b599f7653c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-myrubert_tiny2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English myrubert_tiny2 BertEmbeddings from nlp-testing +author: John Snow Labs +name: myrubert_tiny2 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`myrubert_tiny2` is a English model originally trained by nlp-testing. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/myrubert_tiny2_en_5.1.1_3.0_1694555345139.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/myrubert_tiny2_en_5.1.1_3.0_1694555345139.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("myrubert_tiny2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("myrubert_tiny2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|myrubert_tiny2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|109.0 MB| + +## References + +https://huggingface.co/nlp-testing/myrubert-tiny2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-nepal_bhasa_bert_en.md b/docs/_posts/ahmedlone127/2023-09-12-nepal_bhasa_bert_en.md new file mode 100644 index 00000000000000..3595373bc137f1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-nepal_bhasa_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English nepal_bhasa_bert BertEmbeddings from onlydj96 +author: John Snow Labs +name: nepal_bhasa_bert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nepal_bhasa_bert` is a English model originally trained by onlydj96. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nepal_bhasa_bert_en_5.1.1_3.0_1694549877596.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nepal_bhasa_bert_en_5.1.1_3.0_1694549877596.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("nepal_bhasa_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("nepal_bhasa_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nepal_bhasa_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|411.1 MB| + +## References + +https://huggingface.co/onlydj96/new_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-nontoxiccivilbert_en.md b/docs/_posts/ahmedlone127/2023-09-12-nontoxiccivilbert_en.md new file mode 100644 index 00000000000000..1b6223c22ac5e3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-nontoxiccivilbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English nontoxiccivilbert BertEmbeddings from Ashokajou51 +author: John Snow Labs +name: nontoxiccivilbert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nontoxiccivilbert` is a English model originally trained by Ashokajou51. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nontoxiccivilbert_en_5.1.1_3.0_1694558265623.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nontoxiccivilbert_en_5.1.1_3.0_1694558265623.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("nontoxiccivilbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("nontoxiccivilbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nontoxiccivilbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/Ashokajou51/NonToxicCivilBert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-norbert2_no.md b/docs/_posts/ahmedlone127/2023-09-12-norbert2_no.md new file mode 100644 index 00000000000000..213b1d443ca767 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-norbert2_no.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Norwegian norbert2 BertEmbeddings from ltg +author: John Snow Labs +name: norbert2 +date: 2023-09-12 +tags: [bert, "no", open_source, fill_mask, onnx] +task: Embeddings +language: "no" +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`norbert2` is a Norwegian model originally trained by ltg. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/norbert2_no_5.1.1_3.0_1694549723258.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/norbert2_no_5.1.1_3.0_1694549723258.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("norbert2","no") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("norbert2", "no") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|norbert2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|no| +|Size:|465.2 MB| + +## References + +https://huggingface.co/ltg/norbert2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-norbert_no.md b/docs/_posts/ahmedlone127/2023-09-12-norbert_no.md new file mode 100644 index 00000000000000..a4df72e7efb168 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-norbert_no.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Norwegian norbert BertEmbeddings from ltg +author: John Snow Labs +name: norbert +date: 2023-09-12 +tags: [bert, "no", open_source, fill_mask, onnx] +task: Embeddings +language: "no" +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`norbert` is a Norwegian model originally trained by ltg. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/norbert_no_5.1.1_3.0_1694549544686.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/norbert_no_5.1.1_3.0_1694549544686.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("norbert","no") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("norbert", "no") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|norbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|no| +|Size:|415.1 MB| + +## References + +https://huggingface.co/ltg/norbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-original_topic_sports_bert_en.md b/docs/_posts/ahmedlone127/2023-09-12-original_topic_sports_bert_en.md new file mode 100644 index 00000000000000..3232ea9752d74a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-original_topic_sports_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English original_topic_sports_bert BertEmbeddings from Kdogs +author: John Snow Labs +name: original_topic_sports_bert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`original_topic_sports_bert` is a English model originally trained by Kdogs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/original_topic_sports_bert_en_5.1.1_3.0_1694560482579.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/original_topic_sports_bert_en_5.1.1_3.0_1694560482579.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("original_topic_sports_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("original_topic_sports_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|original_topic_sports_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.3 MB| + +## References + +https://huggingface.co/Kdogs/original_topic-sports_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-pharmbert_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-pharmbert_cased_en.md new file mode 100644 index 00000000000000..ed24d3598bf5d2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-pharmbert_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pharmbert_cased BertEmbeddings from Lianglab +author: John Snow Labs +name: pharmbert_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pharmbert_cased` is a English model originally trained by Lianglab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pharmbert_cased_en_5.1.1_3.0_1694557329356.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pharmbert_cased_en_5.1.1_3.0_1694557329356.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("pharmbert_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("pharmbert_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pharmbert_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.5 MB| + +## References + +https://huggingface.co/Lianglab/PharmBERT-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-pharmbert_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-12-pharmbert_uncased_en.md new file mode 100644 index 00000000000000..5f2cb33bf1ba7e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-pharmbert_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pharmbert_uncased BertEmbeddings from Lianglab +author: John Snow Labs +name: pharmbert_uncased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pharmbert_uncased` is a English model originally trained by Lianglab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pharmbert_uncased_en_5.1.1_3.0_1694557457237.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pharmbert_uncased_en_5.1.1_3.0_1694557457237.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("pharmbert_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("pharmbert_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pharmbert_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/Lianglab/PharmBERT-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-politbert_en.md b/docs/_posts/ahmedlone127/2023-09-12-politbert_en.md new file mode 100644 index 00000000000000..195e729b9ccd71 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-politbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English politbert BertEmbeddings from maurice +author: John Snow Labs +name: politbert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`politbert` is a English model originally trained by maurice. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/politbert_en_5.1.1_3.0_1694552201401.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/politbert_en_5.1.1_3.0_1694552201401.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("politbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("politbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|politbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.7 MB| + +## References + +https://huggingface.co/maurice/PolitBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-politibeto_es.md b/docs/_posts/ahmedlone127/2023-09-12-politibeto_es.md new file mode 100644 index 00000000000000..bc5c7353660b4f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-politibeto_es.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Castilian, Spanish politibeto BertEmbeddings from nlp-cimat +author: John Snow Labs +name: politibeto +date: 2023-09-12 +tags: [bert, es, open_source, fill_mask, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`politibeto` is a Castilian, Spanish model originally trained by nlp-cimat. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/politibeto_es_5.1.1_3.0_1694552289989.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/politibeto_es_5.1.1_3.0_1694552289989.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("politibeto","es") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("politibeto", "es") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|politibeto| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|es| +|Size:|409.6 MB| + +## References + +https://huggingface.co/nlp-cimat/politibeto \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-practice00_en.md b/docs/_posts/ahmedlone127/2023-09-12-practice00_en.md new file mode 100644 index 00000000000000..f9d0a6824d6643 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-practice00_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English practice00 BertEmbeddings from maroo93 +author: John Snow Labs +name: practice00 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`practice00` is a English model originally trained by maroo93. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/practice00_en_5.1.1_3.0_1694551890248.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/practice00_en_5.1.1_3.0_1694551890248.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("practice00","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("practice00", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|practice00| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/maroo93/practice00 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-practice01_en.md b/docs/_posts/ahmedlone127/2023-09-12-practice01_en.md new file mode 100644 index 00000000000000..5f9e32a79436c9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-practice01_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English practice01 BertEmbeddings from maroo93 +author: John Snow Labs +name: practice01 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`practice01` is a English model originally trained by maroo93. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/practice01_en_5.1.1_3.0_1694552050711.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/practice01_en_5.1.1_3.0_1694552050711.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("practice01","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("practice01", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|practice01| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/maroo93/practice01 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-prop_wiki_en.md b/docs/_posts/ahmedlone127/2023-09-12-prop_wiki_en.md new file mode 100644 index 00000000000000..aeb8ae5ad638cc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-prop_wiki_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English prop_wiki BertEmbeddings from xyma +author: John Snow Labs +name: prop_wiki +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`prop_wiki` is a English model originally trained by xyma. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/prop_wiki_en_5.1.1_3.0_1694559423925.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/prop_wiki_en_5.1.1_3.0_1694559423925.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("prop_wiki","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("prop_wiki", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|prop_wiki| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/xyma/PROP-wiki \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-psych_search_en.md b/docs/_posts/ahmedlone127/2023-09-12-psych_search_en.md new file mode 100644 index 00000000000000..48d5a48e6daeaa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-psych_search_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English psych_search BertEmbeddings from nlp4good +author: John Snow Labs +name: psych_search +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`psych_search` is a English model originally trained by nlp4good. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/psych_search_en_5.1.1_3.0_1694560934620.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/psych_search_en_5.1.1_3.0_1694560934620.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("psych_search","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("psych_search", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|psych_search| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/nlp4good/psych-search \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-push_hub_test_en.md b/docs/_posts/ahmedlone127/2023-09-12-push_hub_test_en.md new file mode 100644 index 00000000000000..b95fccd27651ae --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-push_hub_test_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English push_hub_test BertEmbeddings from ksmcg +author: John Snow Labs +name: push_hub_test +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`push_hub_test` is a English model originally trained by ksmcg. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/push_hub_test_en_5.1.1_3.0_1694510535660.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/push_hub_test_en_5.1.1_3.0_1694510535660.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("push_hub_test","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("push_hub_test", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|push_hub_test| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/ksmcg/push_hub_test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-quranexe_bert_en.md b/docs/_posts/ahmedlone127/2023-09-12-quranexe_bert_en.md new file mode 100644 index 00000000000000..4bbdd21ce194d0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-quranexe_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English quranexe_bert BertEmbeddings from mustapha +author: John Snow Labs +name: quranexe_bert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`quranexe_bert` is a English model originally trained by mustapha. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/quranexe_bert_en_5.1.1_3.0_1694558398605.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/quranexe_bert_en_5.1.1_3.0_1694558398605.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("quranexe_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("quranexe_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|quranexe_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|504.6 MB| + +## References + +https://huggingface.co/mustapha/QuranExe-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-recipe_clean_steps_en.md b/docs/_posts/ahmedlone127/2023-09-12-recipe_clean_steps_en.md new file mode 100644 index 00000000000000..417c168c798e0d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-recipe_clean_steps_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English recipe_clean_steps BertEmbeddings from paola-md +author: John Snow Labs +name: recipe_clean_steps +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`recipe_clean_steps` is a English model originally trained by paola-md. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recipe_clean_steps_en_5.1.1_3.0_1694553199740.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recipe_clean_steps_en_5.1.1_3.0_1694553199740.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("recipe_clean_steps","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("recipe_clean_steps", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recipe_clean_steps| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.7 MB| + +## References + +https://huggingface.co/paola-md/recipe-clean_steps \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-recipe_icelandic_en.md b/docs/_posts/ahmedlone127/2023-09-12-recipe_icelandic_en.md new file mode 100644 index 00000000000000..1cb03fb36da554 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-recipe_icelandic_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English recipe_icelandic BertEmbeddings from paola-md +author: John Snow Labs +name: recipe_icelandic +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`recipe_icelandic` is a English model originally trained by paola-md. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recipe_icelandic_en_5.1.1_3.0_1694552871118.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recipe_icelandic_en_5.1.1_3.0_1694552871118.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("recipe_icelandic","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("recipe_icelandic", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recipe_icelandic| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.7 MB| + +## References + +https://huggingface.co/paola-md/recipe-is \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-recipe_tis_en.md b/docs/_posts/ahmedlone127/2023-09-12-recipe_tis_en.md new file mode 100644 index 00000000000000..729cacd4bd5546 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-recipe_tis_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English recipe_tis BertEmbeddings from paola-md +author: John Snow Labs +name: recipe_tis +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`recipe_tis` is a English model originally trained by paola-md. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recipe_tis_en_5.1.1_3.0_1694552674837.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recipe_tis_en_5.1.1_3.0_1694552674837.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("recipe_tis","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("recipe_tis", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recipe_tis| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.5 MB| + +## References + +https://huggingface.co/paola-md/recipe-tis \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-recipe_tsonga_en.md b/docs/_posts/ahmedlone127/2023-09-12-recipe_tsonga_en.md new file mode 100644 index 00000000000000..98ebf54472d533 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-recipe_tsonga_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English recipe_tsonga BertEmbeddings from paola-md +author: John Snow Labs +name: recipe_tsonga +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`recipe_tsonga` is a English model originally trained by paola-md. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recipe_tsonga_en_5.1.1_3.0_1694553036897.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recipe_tsonga_en_5.1.1_3.0_1694553036897.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("recipe_tsonga","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("recipe_tsonga", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recipe_tsonga| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/paola-md/recipe-ts \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-regex_gb_2021_en.md b/docs/_posts/ahmedlone127/2023-09-12-regex_gb_2021_en.md new file mode 100644 index 00000000000000..f2188a7025182c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-regex_gb_2021_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English regex_gb_2021 BertEmbeddings from mossaic-candle +author: John Snow Labs +name: regex_gb_2021 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`regex_gb_2021` is a English model originally trained by mossaic-candle. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/regex_gb_2021_en_5.1.1_3.0_1694508052972.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/regex_gb_2021_en_5.1.1_3.0_1694508052972.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("regex_gb_2021","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("regex_gb_2021", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|regex_gb_2021| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|258.7 MB| + +## References + +https://huggingface.co/mossaic-candle/regex-gb-2021 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-relu_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-12-relu_bert_base_uncased_en.md new file mode 100644 index 00000000000000..6d7f6bb1a6785b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-relu_bert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English relu_bert_base_uncased BertEmbeddings from mpiorczynski +author: John Snow Labs +name: relu_bert_base_uncased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`relu_bert_base_uncased` is a English model originally trained by mpiorczynski. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/relu_bert_base_uncased_en_5.1.1_3.0_1694552781766.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/relu_bert_base_uncased_en_5.1.1_3.0_1694552781766.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("relu_bert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("relu_bert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|relu_bert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/mpiorczynski/relu-bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-review_en.md b/docs/_posts/ahmedlone127/2023-09-12-review_en.md new file mode 100644 index 00000000000000..197f37101b5c9a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-review_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English review BertEmbeddings from Hikam22 +author: John Snow Labs +name: review +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`review` is a English model originally trained by Hikam22. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/review_en_5.1.1_3.0_1694548928369.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/review_en_5.1.1_3.0_1694548928369.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("review","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("review", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|review| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|304.8 MB| + +## References + +https://huggingface.co/Hikam22/Review \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-sample_model_en.md b/docs/_posts/ahmedlone127/2023-09-12-sample_model_en.md new file mode 100644 index 00000000000000..912b4592909e62 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-sample_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sample_model BertEmbeddings from paopao0226 +author: John Snow Labs +name: sample_model +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sample_model` is a English model originally trained by paopao0226. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sample_model_en_5.1.1_3.0_1694509238918.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sample_model_en_5.1.1_3.0_1694509238918.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sample_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sample_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sample_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/paopao0226/sample-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-satd_identify_en.md b/docs/_posts/ahmedlone127/2023-09-12-satd_identify_en.md new file mode 100644 index 00000000000000..e993ed0a4e0b9b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-satd_identify_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English satd_identify BertEmbeddings from aavvvv +author: John Snow Labs +name: satd_identify +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`satd_identify` is a English model originally trained by aavvvv. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/satd_identify_en_5.1.1_3.0_1694557773164.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/satd_identify_en_5.1.1_3.0_1694557773164.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("satd_identify","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("satd_identify", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|satd_identify| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.0 MB| + +## References + +https://huggingface.co/aavvvv/satd-identify \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-scibert_lm_const_finetuned_20_en.md b/docs/_posts/ahmedlone127/2023-09-12-scibert_lm_const_finetuned_20_en.md new file mode 100644 index 00000000000000..95bdfad4001cd1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-scibert_lm_const_finetuned_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English scibert_lm_const_finetuned_20 BertEmbeddings from ariesutiono +author: John Snow Labs +name: scibert_lm_const_finetuned_20 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scibert_lm_const_finetuned_20` is a English model originally trained by ariesutiono. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scibert_lm_const_finetuned_20_en_5.1.1_3.0_1694559809758.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scibert_lm_const_finetuned_20_en_5.1.1_3.0_1694559809758.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("scibert_lm_const_finetuned_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("scibert_lm_const_finetuned_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scibert_lm_const_finetuned_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|499.5 MB| + +## References + +https://huggingface.co/ariesutiono/scibert-lm-const-finetuned-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-scibert_scivocab_finetuned_cord19_en.md b/docs/_posts/ahmedlone127/2023-09-12-scibert_scivocab_finetuned_cord19_en.md new file mode 100644 index 00000000000000..07418accd72d53 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-scibert_scivocab_finetuned_cord19_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English scibert_scivocab_finetuned_cord19 BertEmbeddings from mrm8488 +author: John Snow Labs +name: scibert_scivocab_finetuned_cord19 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scibert_scivocab_finetuned_cord19` is a English model originally trained by mrm8488. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scibert_scivocab_finetuned_cord19_en_5.1.1_3.0_1694556414909.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scibert_scivocab_finetuned_cord19_en_5.1.1_3.0_1694556414909.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("scibert_scivocab_finetuned_cord19","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("scibert_scivocab_finetuned_cord19", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scibert_scivocab_finetuned_cord19| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/mrm8488/scibert_scivocab-finetuned-CORD19 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-scibert_scivocab_uncased_ft_mlm_sdu21_ai_en.md b/docs/_posts/ahmedlone127/2023-09-12-scibert_scivocab_uncased_ft_mlm_sdu21_ai_en.md new file mode 100644 index 00000000000000..9565c044e1500c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-scibert_scivocab_uncased_ft_mlm_sdu21_ai_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English scibert_scivocab_uncased_ft_mlm_sdu21_ai BertEmbeddings from napsternxg +author: John Snow Labs +name: scibert_scivocab_uncased_ft_mlm_sdu21_ai +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scibert_scivocab_uncased_ft_mlm_sdu21_ai` is a English model originally trained by napsternxg. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scibert_scivocab_uncased_ft_mlm_sdu21_ai_en_5.1.1_3.0_1694557218055.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scibert_scivocab_uncased_ft_mlm_sdu21_ai_en_5.1.1_3.0_1694557218055.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("scibert_scivocab_uncased_ft_mlm_sdu21_ai","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("scibert_scivocab_uncased_ft_mlm_sdu21_ai", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scibert_scivocab_uncased_ft_mlm_sdu21_ai| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.0 MB| + +## References + +https://huggingface.co/napsternxg/scibert_scivocab_uncased_ft_mlm_SDU21_AI \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-sec_bert_num_en.md b/docs/_posts/ahmedlone127/2023-09-12-sec_bert_num_en.md new file mode 100644 index 00000000000000..469ff415dafe13 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-sec_bert_num_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sec_bert_num BertEmbeddings from nlpaueb +author: John Snow Labs +name: sec_bert_num +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sec_bert_num` is a English model originally trained by nlpaueb. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sec_bert_num_en_5.1.1_3.0_1694561918110.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sec_bert_num_en_5.1.1_3.0_1694561918110.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sec_bert_num","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sec_bert_num", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sec_bert_num| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/nlpaueb/sec-bert-num \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-sec_bert_shape_en.md b/docs/_posts/ahmedlone127/2023-09-12-sec_bert_shape_en.md new file mode 100644 index 00000000000000..8a2d6149f908f4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-sec_bert_shape_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sec_bert_shape BertEmbeddings from nlpaueb +author: John Snow Labs +name: sec_bert_shape +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sec_bert_shape` is a English model originally trained by nlpaueb. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sec_bert_shape_en_5.1.1_3.0_1694562038680.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sec_bert_shape_en_5.1.1_3.0_1694562038680.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sec_bert_shape","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sec_bert_shape", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sec_bert_shape| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/nlpaueb/sec-bert-shape \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-set_date_1_bert_base_uncased_finetuned_with_haystack_en.md b/docs/_posts/ahmedlone127/2023-09-12-set_date_1_bert_base_uncased_finetuned_with_haystack_en.md new file mode 100644 index 00000000000000..6edab9d5cd7a1c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-set_date_1_bert_base_uncased_finetuned_with_haystack_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English set_date_1_bert_base_uncased_finetuned_with_haystack BertEmbeddings from motiondew +author: John Snow Labs +name: set_date_1_bert_base_uncased_finetuned_with_haystack +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`set_date_1_bert_base_uncased_finetuned_with_haystack` is a English model originally trained by motiondew. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/set_date_1_bert_base_uncased_finetuned_with_haystack_en_5.1.1_3.0_1694556116394.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/set_date_1_bert_base_uncased_finetuned_with_haystack_en_5.1.1_3.0_1694556116394.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("set_date_1_bert_base_uncased_finetuned_with_haystack","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("set_date_1_bert_base_uncased_finetuned_with_haystack", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|set_date_1_bert_base_uncased_finetuned_with_haystack| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/motiondew/set_date_1_bert-base-uncased_finetuned_with_haystack \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-set_date_1_bert_en.md b/docs/_posts/ahmedlone127/2023-09-12-set_date_1_bert_en.md new file mode 100644 index 00000000000000..bf626cb2fa66c2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-set_date_1_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English set_date_1_bert BertEmbeddings from motiondew +author: John Snow Labs +name: set_date_1_bert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`set_date_1_bert` is a English model originally trained by motiondew. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/set_date_1_bert_en_5.1.1_3.0_1694555975958.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/set_date_1_bert_en_5.1.1_3.0_1694555975958.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("set_date_1_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("set_date_1_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|set_date_1_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/motiondew/set_date_1-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_conll2003_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_conll2003_custom_tokenizer_en.md new file mode 100644 index 00000000000000..b8ea3e2415a253 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_conll2003_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_conll2003_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_conll2003_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_conll2003_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_conll2003_custom_tokenizer_en_5.1.1_3.0_1694549308806.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_conll2003_custom_tokenizer_en_5.1.1_3.0_1694549308806.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_conll2003_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_conll2003_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_conll2003_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-conll2003-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_conll2003_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_conll2003_en.md new file mode 100644 index 00000000000000..c76e180e686d07 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_conll2003_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_conll2003 BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_conll2003 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_conll2003` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_conll2003_en_5.1.1_3.0_1694549226176.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_conll2003_en_5.1.1_3.0_1694549226176.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_conll2003","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_conll2003", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_conll2003| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-conll2003 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_conll2003_from_scratch_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_conll2003_from_scratch_en.md new file mode 100644 index 00000000000000..07fbaa12c1f3cf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_conll2003_from_scratch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_conll2003_from_scratch BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_conll2003_from_scratch +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_conll2003_from_scratch` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_conll2003_from_scratch_en_5.1.1_3.0_1694555844684.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_conll2003_from_scratch_en_5.1.1_3.0_1694555844684.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_conll2003_from_scratch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_conll2003_from_scratch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_conll2003_from_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-conll2003-from-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_cola_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_cola_custom_tokenizer_en.md new file mode 100644 index 00000000000000..6d9d9821b8bb12 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_cola_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_cola_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_cola_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_cola_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_cola_custom_tokenizer_en_5.1.1_3.0_1694509168848.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_cola_custom_tokenizer_en_5.1.1_3.0_1694509168848.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_cola_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_cola_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_cola_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.7 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-cola-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_cola_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_cola_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..4174b6cd63d5ad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_cola_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_cola_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_cola_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_cola_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_cola_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561193785.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_cola_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561193785.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_cola_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_cola_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_cola_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|112.1 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-cola-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_cola_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_cola_en.md new file mode 100644 index 00000000000000..8f757685b7a828 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_cola_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_cola BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_cola +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_cola` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_cola_en_5.1.1_3.0_1694508239344.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_cola_en_5.1.1_3.0_1694508239344.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_cola","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_cola", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_cola| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-cola \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..24e218dc859816 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561372145.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561372145.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|112.1 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-cola-from-scratch-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mnli_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mnli_custom_tokenizer_en.md new file mode 100644 index 00000000000000..c31269ced1c35b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mnli_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_mnli_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_mnli_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_mnli_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_mnli_custom_tokenizer_en_5.1.1_3.0_1694509261561.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_mnli_custom_tokenizer_en_5.1.1_3.0_1694509261561.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_mnli_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_mnli_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_mnli_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-mnli-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mnli_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mnli_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..043b61fafdca4f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mnli_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_mnli_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_mnli_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_mnli_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_mnli_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561610937.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_mnli_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561610937.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_mnli_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_mnli_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_mnli_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|132.3 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-mnli-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mnli_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mnli_en.md new file mode 100644 index 00000000000000..2332ba65e00450 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mnli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_mnli BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_mnli +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_mnli` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_mnli_en_5.1.1_3.0_1694508322641.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_mnli_en_5.1.1_3.0_1694508322641.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_mnli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_mnli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_mnli| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..4e699bc0e5e97b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561907695.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561907695.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|132.3 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-mnli-from-scratch-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mrpc_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mrpc_custom_tokenizer_en.md new file mode 100644 index 00000000000000..bd525709fd0d84 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mrpc_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_mrpc_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_mrpc_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_mrpc_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_mrpc_custom_tokenizer_en_5.1.1_3.0_1694509478690.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_mrpc_custom_tokenizer_en_5.1.1_3.0_1694509478690.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_mrpc_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_mrpc_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_mrpc_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.8 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-mrpc-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mrpc_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mrpc_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..38d99dfd891555 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mrpc_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_mrpc_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_mrpc_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_mrpc_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_mrpc_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562480371.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_mrpc_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562480371.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_mrpc_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_mrpc_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_mrpc_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|120.1 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-mrpc-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mrpc_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mrpc_en.md new file mode 100644 index 00000000000000..bbb332065ce655 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mrpc_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_mrpc BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_mrpc +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_mrpc` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_mrpc_en_5.1.1_3.0_1694508535779.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_mrpc_en_5.1.1_3.0_1694508535779.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_mrpc","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_mrpc", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_mrpc| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-mrpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..52fbd586d3f702 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562732313.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562732313.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|120.1 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-mrpc-from-scratch-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_qnli_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_qnli_custom_tokenizer_en.md new file mode 100644 index 00000000000000..97470becbbd9c1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_qnli_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_qnli_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_qnli_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_qnli_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_qnli_custom_tokenizer_en_5.1.1_3.0_1694509564873.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_qnli_custom_tokenizer_en_5.1.1_3.0_1694509564873.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_qnli_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_qnli_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_qnli_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-qnli-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_qnli_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_qnli_en.md new file mode 100644 index 00000000000000..71498ecaadd4d8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_qnli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_qnli BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_qnli +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_qnli` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_qnli_en_5.1.1_3.0_1694508618886.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_qnli_en_5.1.1_3.0_1694508618886.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_qnli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_qnli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_qnli| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-qnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_qnli_from_scratch_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_qnli_from_scratch_custom_tokenizer_en.md new file mode 100644 index 00000000000000..045f2965f76d2e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_qnli_from_scratch_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_qnli_from_scratch_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_qnli_from_scratch_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_qnli_from_scratch_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_qnli_from_scratch_custom_tokenizer_en_5.1.1_3.0_1694552340480.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_qnli_from_scratch_custom_tokenizer_en_5.1.1_3.0_1694552340480.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_qnli_from_scratch_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_qnli_from_scratch_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_qnli_from_scratch_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-qnli-from-scratch-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_qqp_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_qqp_custom_tokenizer_en.md new file mode 100644 index 00000000000000..bd5955a1f6e764 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_qqp_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_qqp_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_qqp_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_qqp_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_qqp_custom_tokenizer_en_5.1.1_3.0_1694509649898.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_qqp_custom_tokenizer_en_5.1.1_3.0_1694509649898.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_qqp_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_qqp_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_qqp_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.8 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-qqp-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_qqp_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_qqp_en.md new file mode 100644 index 00000000000000..6e4f20b08ad113 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_qqp_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_qqp BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_qqp +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_qqp` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_qqp_en_5.1.1_3.0_1694508712348.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_qqp_en_5.1.1_3.0_1694508712348.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_qqp","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_qqp", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_qqp| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-qqp \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_rte_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_rte_custom_tokenizer_en.md new file mode 100644 index 00000000000000..4d21a83f168089 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_rte_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_rte_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_rte_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_rte_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_rte_custom_tokenizer_en_5.1.1_3.0_1694509735612.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_rte_custom_tokenizer_en_5.1.1_3.0_1694509735612.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_rte_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_rte_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_rte_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-rte-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_rte_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_rte_en.md new file mode 100644 index 00000000000000..e259afbe76f735 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_rte_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_rte BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_rte +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_rte` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_rte_en_5.1.1_3.0_1694508793653.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_rte_en_5.1.1_3.0_1694508793653.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_rte","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_rte", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_rte| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-rte \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_sst2_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_sst2_custom_tokenizer_en.md new file mode 100644 index 00000000000000..39f5f36b12d5e0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_sst2_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_sst2_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_sst2_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_sst2_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_sst2_custom_tokenizer_en_5.1.1_3.0_1694509835393.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_sst2_custom_tokenizer_en_5.1.1_3.0_1694509835393.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_sst2_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_sst2_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_sst2_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.8 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-sst2-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_sst2_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_sst2_en.md new file mode 100644 index 00000000000000..0ccb73aaf94f88 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_sst2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_sst2 BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_sst2 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_sst2` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_sst2_en_5.1.1_3.0_1694508920238.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_sst2_en_5.1.1_3.0_1694508920238.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_sst2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_sst2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_sst2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-sst2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_stsb_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_stsb_custom_tokenizer_en.md new file mode 100644 index 00000000000000..99565eded80454 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_stsb_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_stsb_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_stsb_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_stsb_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_stsb_custom_tokenizer_en_5.1.1_3.0_1694509912489.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_stsb_custom_tokenizer_en_5.1.1_3.0_1694509912489.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_stsb_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_stsb_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_stsb_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.8 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-stsb-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_stsb_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_stsb_en.md new file mode 100644 index 00000000000000..bfa92c4f11fa24 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_stsb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_stsb BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_stsb +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_stsb` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_stsb_en_5.1.1_3.0_1694509008527.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_stsb_en_5.1.1_3.0_1694509008527.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_stsb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_stsb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_stsb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-stsb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_wnli_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_wnli_custom_tokenizer_en.md new file mode 100644 index 00000000000000..5a24f818bbf09a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_wnli_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_wnli_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_wnli_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_wnli_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_wnli_custom_tokenizer_en_5.1.1_3.0_1694510024053.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_wnli_custom_tokenizer_en_5.1.1_3.0_1694510024053.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_wnli_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_wnli_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_wnli_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.7 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-wnli-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_wnli_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_wnli_en.md new file mode 100644 index 00000000000000..e7ad7199f2b60b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_wnli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_wnli BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_wnli +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_wnli` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_wnli_en_5.1.1_3.0_1694509092224.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_wnli_en_5.1.1_3.0_1694509092224.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_wnli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_wnli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_wnli| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-wnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_imdb_from_scratch_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_imdb_from_scratch_en.md new file mode 100644 index 00000000000000..435fc8604cbd35 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_imdb_from_scratch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_imdb_from_scratch BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_imdb_from_scratch +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_imdb_from_scratch` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_imdb_from_scratch_en_5.1.1_3.0_1694556494575.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_imdb_from_scratch_en_5.1.1_3.0_1694556494575.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_imdb_from_scratch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_imdb_from_scratch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_imdb_from_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-imdb-from-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_rotten_tomatoes_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_rotten_tomatoes_custom_tokenizer_en.md new file mode 100644 index 00000000000000..d6072ae617d93e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_rotten_tomatoes_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_rotten_tomatoes_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_rotten_tomatoes_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_rotten_tomatoes_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_rotten_tomatoes_custom_tokenizer_en_5.1.1_3.0_1694549132801.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_rotten_tomatoes_custom_tokenizer_en_5.1.1_3.0_1694549132801.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_rotten_tomatoes_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_rotten_tomatoes_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_rotten_tomatoes_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-rotten_tomatoes-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_rotten_tomatoes_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_rotten_tomatoes_en.md new file mode 100644 index 00000000000000..e6d871875cbae0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_rotten_tomatoes_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_rotten_tomatoes BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_rotten_tomatoes +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_rotten_tomatoes` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_rotten_tomatoes_en_5.1.1_3.0_1694549024792.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_rotten_tomatoes_en_5.1.1_3.0_1694549024792.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_rotten_tomatoes","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_rotten_tomatoes", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_rotten_tomatoes| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-rotten_tomatoes \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_rotten_tomatoes_from_scratch_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_rotten_tomatoes_from_scratch_en.md new file mode 100644 index 00000000000000..f02bcd6dc7d0e2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_rotten_tomatoes_from_scratch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_rotten_tomatoes_from_scratch BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_rotten_tomatoes_from_scratch +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_rotten_tomatoes_from_scratch` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_rotten_tomatoes_from_scratch_en_5.1.1_3.0_1694556082715.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_rotten_tomatoes_from_scratch_en_5.1.1_3.0_1694556082715.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_rotten_tomatoes_from_scratch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_rotten_tomatoes_from_scratch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_rotten_tomatoes_from_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-rotten_tomatoes-from-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_snli_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_snli_custom_tokenizer_en.md new file mode 100644 index 00000000000000..b7baa5d9ae69d7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_snli_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_snli_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_snli_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_snli_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_snli_custom_tokenizer_en_5.1.1_3.0_1694549568395.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_snli_custom_tokenizer_en_5.1.1_3.0_1694549568395.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_snli_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_snli_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_snli_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.7 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-snli-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_snli_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_snli_en.md new file mode 100644 index 00000000000000..7184e8b0f81254 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_snli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_snli BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_snli +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_snli` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_snli_en_5.1.1_3.0_1694549486115.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_snli_en_5.1.1_3.0_1694549486115.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_snli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_snli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_snli| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-snli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_snli_from_scratch_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_snli_from_scratch_en.md new file mode 100644 index 00000000000000..af74e062bd8cd7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_snli_from_scratch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_snli_from_scratch BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_snli_from_scratch +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_snli_from_scratch` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_snli_from_scratch_en_5.1.1_3.0_1694556408270.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_snli_from_scratch_en_5.1.1_3.0_1694556408270.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_snli_from_scratch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_snli_from_scratch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_snli_from_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-snli-from-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_squad_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_squad_custom_tokenizer_en.md new file mode 100644 index 00000000000000..71c53adcb7862f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_squad_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_squad_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_squad_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_squad_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_squad_custom_tokenizer_en_5.1.1_3.0_1694547690830.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_squad_custom_tokenizer_en_5.1.1_3.0_1694547690830.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_squad_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_squad_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_squad_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.8 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-squad-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_squad_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_squad_en.md new file mode 100644 index 00000000000000..88957ff7bd7aa9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_squad_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_squad BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_squad +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_squad` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_squad_en_5.1.1_3.0_1694547514364.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_squad_en_5.1.1_3.0_1694547514364.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_squad","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_squad", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_squad| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-squad \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_tweet_eval_from_scratch_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_tweet_eval_from_scratch_en.md new file mode 100644 index 00000000000000..ffba4d5978e137 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_tweet_eval_from_scratch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_tweet_eval_from_scratch BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_tweet_eval_from_scratch +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_tweet_eval_from_scratch` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_tweet_eval_from_scratch_en_5.1.1_3.0_1694555281014.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_tweet_eval_from_scratch_en_5.1.1_3.0_1694555281014.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_tweet_eval_from_scratch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_tweet_eval_from_scratch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_tweet_eval_from_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-tweet_eval-from-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_wikitext_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_wikitext_custom_tokenizer_en.md new file mode 100644 index 00000000000000..a678a8c8a89aec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_wikitext_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_wikitext_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_wikitext_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_wikitext_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_wikitext_custom_tokenizer_en_5.1.1_3.0_1694548914392.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_wikitext_custom_tokenizer_en_5.1.1_3.0_1694548914392.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_wikitext_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_wikitext_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_wikitext_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-wikitext-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_wikitext_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_wikitext_en.md new file mode 100644 index 00000000000000..5addb769947b43 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_wikitext_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_wikitext BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_wikitext +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_wikitext` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_wikitext_en_5.1.1_3.0_1694548827535.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_wikitext_en_5.1.1_3.0_1694548827535.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_wikitext","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_wikitext", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_wikitext| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-wikitext \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_wikitext_from_scratch_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_wikitext_from_scratch_custom_tokenizer_en.md new file mode 100644 index 00000000000000..7b18c3d6f73109 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_wikitext_from_scratch_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_wikitext_from_scratch_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_wikitext_from_scratch_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_wikitext_from_scratch_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_wikitext_from_scratch_custom_tokenizer_en_5.1.1_3.0_1694554676967.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_wikitext_from_scratch_custom_tokenizer_en_5.1.1_3.0_1694554676967.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_wikitext_from_scratch_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_wikitext_from_scratch_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_wikitext_from_scratch_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-wikitext-from-scratch-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_wikitext_from_scratch_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_wikitext_from_scratch_en.md new file mode 100644 index 00000000000000..3effc5c3c26392 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_wikitext_from_scratch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_wikitext_from_scratch BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_wikitext_from_scratch +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_wikitext_from_scratch` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_wikitext_from_scratch_en_5.1.1_3.0_1694556248548.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_wikitext_from_scratch_en_5.1.1_3.0_1694556248548.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_wikitext_from_scratch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_wikitext_from_scratch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_wikitext_from_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-wikitext-from-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-sp_bert_en.md b/docs/_posts/ahmedlone127/2023-09-12-sp_bert_en.md new file mode 100644 index 00000000000000..63763e7dbc4f14 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-sp_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sp_bert BertEmbeddings from tumd +author: John Snow Labs +name: sp_bert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sp_bert` is a English model originally trained by tumd. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sp_bert_en_5.1.1_3.0_1694549714006.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sp_bert_en_5.1.1_3.0_1694549714006.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sp_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sp_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sp_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|664.7 MB| + +## References + +https://huggingface.co/tumd/sp-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-ssci_bert_e2_en.md b/docs/_posts/ahmedlone127/2023-09-12-ssci_bert_e2_en.md new file mode 100644 index 00000000000000..ba92b1d931e349 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-ssci_bert_e2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ssci_bert_e2 BertEmbeddings from KM4STfulltext +author: John Snow Labs +name: ssci_bert_e2 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ssci_bert_e2` is a English model originally trained by KM4STfulltext. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ssci_bert_e2_en_5.1.1_3.0_1694508246464.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ssci_bert_e2_en_5.1.1_3.0_1694508246464.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ssci_bert_e2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ssci_bert_e2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ssci_bert_e2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/KM4STfulltext/SSCI-BERT-e2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-ssci_bert_e4_en.md b/docs/_posts/ahmedlone127/2023-09-12-ssci_bert_e4_en.md new file mode 100644 index 00000000000000..b64ed6bbf787a0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-ssci_bert_e4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ssci_bert_e4 BertEmbeddings from KM4STfulltext +author: John Snow Labs +name: ssci_bert_e4 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ssci_bert_e4` is a English model originally trained by KM4STfulltext. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ssci_bert_e4_en_5.1.1_3.0_1694508373404.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ssci_bert_e4_en_5.1.1_3.0_1694508373404.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ssci_bert_e4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ssci_bert_e4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ssci_bert_e4| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/KM4STfulltext/SSCI-BERT-e4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-ssci_scibert_e2_en.md b/docs/_posts/ahmedlone127/2023-09-12-ssci_scibert_e2_en.md new file mode 100644 index 00000000000000..1e99d99f79b07a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-ssci_scibert_e2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ssci_scibert_e2 BertEmbeddings from KM4STfulltext +author: John Snow Labs +name: ssci_scibert_e2 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ssci_scibert_e2` is a English model originally trained by KM4STfulltext. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ssci_scibert_e2_en_5.1.1_3.0_1694508512306.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ssci_scibert_e2_en_5.1.1_3.0_1694508512306.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ssci_scibert_e2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ssci_scibert_e2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ssci_scibert_e2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/KM4STfulltext/SSCI-SciBERT-e2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-ssci_scibert_e4_en.md b/docs/_posts/ahmedlone127/2023-09-12-ssci_scibert_e4_en.md new file mode 100644 index 00000000000000..092e2a997f643a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-ssci_scibert_e4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ssci_scibert_e4 BertEmbeddings from KM4STfulltext +author: John Snow Labs +name: ssci_scibert_e4 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ssci_scibert_e4` is a English model originally trained by KM4STfulltext. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ssci_scibert_e4_en_5.1.1_3.0_1694508639778.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ssci_scibert_e4_en_5.1.1_3.0_1694508639778.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ssci_scibert_e4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ssci_scibert_e4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ssci_scibert_e4| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/KM4STfulltext/SSCI-SciBERT-e4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-telugu_bertu_te.md b/docs/_posts/ahmedlone127/2023-09-12-telugu_bertu_te.md new file mode 100644 index 00000000000000..feb4cc314f2447 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-telugu_bertu_te.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Telugu telugu_bertu BertEmbeddings from kuppuluri +author: John Snow Labs +name: telugu_bertu +date: 2023-09-12 +tags: [bert, te, open_source, fill_mask, onnx] +task: Embeddings +language: te +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`telugu_bertu` is a Telugu model originally trained by kuppuluri. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/telugu_bertu_te_5.1.1_3.0_1694510680655.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/telugu_bertu_te_5.1.1_3.0_1694510680655.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("telugu_bertu","te") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("telugu_bertu", "te") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|telugu_bertu| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|te| +|Size:|412.5 MB| + +## References + +https://huggingface.co/kuppuluri/telugu_bertu \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-test_model_nws_en.md b/docs/_posts/ahmedlone127/2023-09-12-test_model_nws_en.md new file mode 100644 index 00000000000000..9c8ca16c8f2d54 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-test_model_nws_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English test_model_nws BertEmbeddings from nws +author: John Snow Labs +name: test_model_nws +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`test_model_nws` is a English model originally trained by nws. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/test_model_nws_en_5.1.1_3.0_1694562663160.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/test_model_nws_en_5.1.1_3.0_1694562663160.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("test_model_nws","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("test_model_nws", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|test_model_nws| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/nws/test_model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_bert_turkish_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_bert_turkish_cased_en.md new file mode 100644 index 00000000000000..98cdccd4f0dfac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_bert_turkish_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_bert_turkish_cased BertEmbeddings from uygarkurt +author: John Snow Labs +name: tiny_bert_turkish_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_turkish_cased` is a English model originally trained by uygarkurt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_turkish_cased_en_5.1.1_3.0_1694509025179.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_turkish_cased_en_5.1.1_3.0_1694509025179.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_bert_turkish_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_bert_turkish_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_turkish_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|17.4 MB| + +## References + +https://huggingface.co/uygarkurt/tiny-bert-turkish-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_conll2003_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_conll2003_custom_tokenizer_en.md new file mode 100644 index 00000000000000..3ad78833fd3a6b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_conll2003_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_conll2003_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_conll2003_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_conll2003_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_conll2003_custom_tokenizer_en_5.1.1_3.0_1694548724810.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_conll2003_custom_tokenizer_en_5.1.1_3.0_1694548724810.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_conll2003_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_conll2003_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_conll2003_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.6 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-conll2003-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_conll2003_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_conll2003_en.md new file mode 100644 index 00000000000000..c6797bc36f72fa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_conll2003_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_conll2003 BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_conll2003 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_conll2003` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_conll2003_en_5.1.1_3.0_1694548620549.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_conll2003_en_5.1.1_3.0_1694548620549.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_conll2003","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_conll2003", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_conll2003| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-conll2003 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_conll2003_from_scratch_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_conll2003_from_scratch_en.md new file mode 100644 index 00000000000000..7467ed42fd81a4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_conll2003_from_scratch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_conll2003_from_scratch BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_conll2003_from_scratch +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_conll2003_from_scratch` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_conll2003_from_scratch_en_5.1.1_3.0_1694555420238.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_conll2003_from_scratch_en_5.1.1_3.0_1694555420238.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_conll2003_from_scratch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_conll2003_from_scratch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_conll2003_from_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.4 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-conll2003-from-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_cola_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_cola_custom_tokenizer_en.md new file mode 100644 index 00000000000000..3c2eeb688abfad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_cola_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_cola_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_cola_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_cola_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_cola_custom_tokenizer_en_5.1.1_3.0_1694510104742.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_cola_custom_tokenizer_en_5.1.1_3.0_1694510104742.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_cola_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_cola_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_cola_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.4 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-cola-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_cola_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_cola_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..acaa7b9bf20156 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_cola_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_cola_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_cola_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_cola_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_cola_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561081439.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_cola_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561081439.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_cola_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_cola_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_cola_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|17.9 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-cola-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..f74c75c1c82133 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561266748.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561266748.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_cola_from_scratch_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|17.6 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-cola-from-scratch-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_mnli_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_mnli_custom_tokenizer_en.md new file mode 100644 index 00000000000000..5b3b531154c3ac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_mnli_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_mnli_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_mnli_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_mnli_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_mnli_custom_tokenizer_en_5.1.1_3.0_1694510167051.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_mnli_custom_tokenizer_en_5.1.1_3.0_1694510167051.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_mnli_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_mnli_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_mnli_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.6 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-mnli-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_mnli_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_mnli_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..7de7e773837c40 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_mnli_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_mnli_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_mnli_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_mnli_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_mnli_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561453097.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_mnli_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561453097.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_mnli_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_mnli_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_mnli_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|23.0 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-mnli-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..963f638a4de5a8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561530936.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561530936.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_mnli_from_scratch_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|22.6 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-mnli-from-scratch-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_mrpc_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_mrpc_custom_tokenizer_en.md new file mode 100644 index 00000000000000..5fbfae07caa856 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_mrpc_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_mrpc_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_mrpc_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_mrpc_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_mrpc_custom_tokenizer_en_5.1.1_3.0_1694510364675.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_mrpc_custom_tokenizer_en_5.1.1_3.0_1694510364675.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_mrpc_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_mrpc_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_mrpc_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.5 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-mrpc-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..a997aa904cf11f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561983732.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561983732.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_mrpc_from_scratch_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|19.7 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-mrpc-from-scratch-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qnli_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qnli_custom_tokenizer_en.md new file mode 100644 index 00000000000000..270d4f0f4faaad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qnli_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_qnli_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_qnli_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_qnli_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_qnli_custom_tokenizer_en_5.1.1_3.0_1694510431505.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_qnli_custom_tokenizer_en_5.1.1_3.0_1694510431505.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_qnli_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_qnli_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_qnli_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.6 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-qnli-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qnli_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qnli_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..156a579db32ff1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qnli_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_qnli_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_qnli_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_qnli_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_qnli_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562076733.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_qnli_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562076733.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_qnli_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_qnli_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_qnli_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|23.7 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-qnli-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qnli_from_scratch_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qnli_from_scratch_custom_tokenizer_en.md new file mode 100644 index 00000000000000..0e3fca53ddbf77 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qnli_from_scratch_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_qnli_from_scratch_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_qnli_from_scratch_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_qnli_from_scratch_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_qnli_from_scratch_custom_tokenizer_en_5.1.1_3.0_1694551714525.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_qnli_from_scratch_custom_tokenizer_en_5.1.1_3.0_1694551714525.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_qnli_from_scratch_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_qnli_from_scratch_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_qnli_from_scratch_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.6 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-qnli-from-scratch-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..478509e8a9a56e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562151869.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562151869.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|23.4 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-qnli-from-scratch-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qqp_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qqp_custom_tokenizer_en.md new file mode 100644 index 00000000000000..6909097034f0a1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qqp_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_qqp_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_qqp_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_qqp_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_qqp_custom_tokenizer_en_5.1.1_3.0_1694510502065.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_qqp_custom_tokenizer_en_5.1.1_3.0_1694510502065.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_qqp_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_qqp_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_qqp_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.5 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-qqp-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qqp_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qqp_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..3cf412c1eaba1d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qqp_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_qqp_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_qqp_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_qqp_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_qqp_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562240997.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_qqp_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562240997.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_qqp_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_qqp_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_qqp_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|20.6 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-qqp-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_rte_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_rte_custom_tokenizer_en.md new file mode 100644 index 00000000000000..7246fe54c0f393 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_rte_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_rte_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_rte_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_rte_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_rte_custom_tokenizer_en_5.1.1_3.0_1694510577450.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_rte_custom_tokenizer_en_5.1.1_3.0_1694510577450.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_rte_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_rte_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_rte_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.6 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-rte-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_rte_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_rte_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..09ab6e1ca7c962 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_rte_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_rte_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_rte_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_rte_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_rte_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562388375.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_rte_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562388375.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_rte_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_rte_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_rte_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|21.5 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-rte-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_rte_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_rte_en.md new file mode 100644 index 00000000000000..05284e64932d0a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_rte_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_rte BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_rte +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_rte` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_rte_en_5.1.1_3.0_1694507941819.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_rte_en_5.1.1_3.0_1694507941819.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_rte","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_rte", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_rte| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-rte \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..e19640033c6f5f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562556409.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562556409.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|21.2 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-rte-from-scratch-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_sst2_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_sst2_custom_tokenizer_en.md new file mode 100644 index 00000000000000..ba54ab1e43eaac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_sst2_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_sst2_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_sst2_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_sst2_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_sst2_custom_tokenizer_en_5.1.1_3.0_1694510651635.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_sst2_custom_tokenizer_en_5.1.1_3.0_1694510651635.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_sst2_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_sst2_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_sst2_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.5 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-sst2-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_sst2_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_sst2_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..f9dad70b89ea83 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_sst2_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_sst2_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_sst2_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_sst2_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_sst2_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562631751.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_sst2_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562631751.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_sst2_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_sst2_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_sst2_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|20.1 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-sst2-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_sst2_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_sst2_en.md new file mode 100644 index 00000000000000..6933cb5f41fb70 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_sst2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_sst2 BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_sst2 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_sst2` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_sst2_en_5.1.1_3.0_1694508015211.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_sst2_en_5.1.1_3.0_1694508015211.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_sst2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_sst2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_sst2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-sst2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..61d3b9de6cc152 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562828413.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562828413.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|19.9 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-sst2-from-scratch-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_stsb_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_stsb_custom_tokenizer_en.md new file mode 100644 index 00000000000000..262b9d228c901d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_stsb_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_stsb_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_stsb_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_stsb_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_stsb_custom_tokenizer_en_5.1.1_3.0_1694510712289.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_stsb_custom_tokenizer_en_5.1.1_3.0_1694510712289.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_stsb_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_stsb_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_stsb_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.5 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-stsb-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_stsb_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_stsb_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..33f2db9d861ce5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_stsb_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_stsb_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_stsb_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_stsb_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_stsb_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562907425.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_stsb_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562907425.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_stsb_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_stsb_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_stsb_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|19.3 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-stsb-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_stsb_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_stsb_en.md new file mode 100644 index 00000000000000..f328d8d1a3cfcb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_stsb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_stsb BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_stsb +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_stsb` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_stsb_en_5.1.1_3.0_1694508086728.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_stsb_en_5.1.1_3.0_1694508086728.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_stsb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_stsb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_stsb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-stsb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..0bfa6281e1b19d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563003418.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563003418.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|19.1 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-stsb-from-scratch-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_wnli_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_wnli_custom_tokenizer_en.md new file mode 100644 index 00000000000000..88db108e7cdc39 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_wnli_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_wnli_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_wnli_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_wnli_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_wnli_custom_tokenizer_en_5.1.1_3.0_1694510789128.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_wnli_custom_tokenizer_en_5.1.1_3.0_1694510789128.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_wnli_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_wnli_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_wnli_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.4 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-wnli-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_wnli_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_wnli_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..da84a639e3f14d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_wnli_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_wnli_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_wnli_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_wnli_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_wnli_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563181376.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_wnli_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563181376.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_wnli_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_wnli_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_wnli_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.9 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-wnli-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_wnli_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_wnli_en.md new file mode 100644 index 00000000000000..94cd0e4bf27200 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_wnli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_wnli BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_wnli +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_wnli` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_wnli_en_5.1.1_3.0_1694508154889.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_wnli_en_5.1.1_3.0_1694508154889.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_wnli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_wnli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_wnli| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-wnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_imdb_from_scratch_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_imdb_from_scratch_en.md new file mode 100644 index 00000000000000..369e2feb274541 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_imdb_from_scratch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_imdb_from_scratch BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_imdb_from_scratch +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_imdb_from_scratch` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_imdb_from_scratch_en_5.1.1_3.0_1694556163748.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_imdb_from_scratch_en_5.1.1_3.0_1694556163748.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_imdb_from_scratch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_imdb_from_scratch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_imdb_from_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.6 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-imdb-from-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_rotten_tomatoes_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_rotten_tomatoes_custom_tokenizer_en.md new file mode 100644 index 00000000000000..d39c2419c31401 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_rotten_tomatoes_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_rotten_tomatoes_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_rotten_tomatoes_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_rotten_tomatoes_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_rotten_tomatoes_custom_tokenizer_en_5.1.1_3.0_1694548541463.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_rotten_tomatoes_custom_tokenizer_en_5.1.1_3.0_1694548541463.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_rotten_tomatoes_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_rotten_tomatoes_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_rotten_tomatoes_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.6 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-rotten_tomatoes-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_rotten_tomatoes_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_rotten_tomatoes_en.md new file mode 100644 index 00000000000000..12fa5aab55c4b4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_rotten_tomatoes_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_rotten_tomatoes BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_rotten_tomatoes +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_rotten_tomatoes` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_rotten_tomatoes_en_5.1.1_3.0_1694548478733.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_rotten_tomatoes_en_5.1.1_3.0_1694548478733.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_rotten_tomatoes","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_rotten_tomatoes", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_rotten_tomatoes| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-rotten_tomatoes \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_rotten_tomatoes_from_scratch_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_rotten_tomatoes_from_scratch_en.md new file mode 100644 index 00000000000000..6daac517dfb2b6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_rotten_tomatoes_from_scratch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_rotten_tomatoes_from_scratch BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_rotten_tomatoes_from_scratch +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_rotten_tomatoes_from_scratch` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_rotten_tomatoes_from_scratch_en_5.1.1_3.0_1694555692732.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_rotten_tomatoes_from_scratch_en_5.1.1_3.0_1694555692732.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_rotten_tomatoes_from_scratch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_rotten_tomatoes_from_scratch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_rotten_tomatoes_from_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.5 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-rotten_tomatoes-from-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_snli_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_snli_custom_tokenizer_en.md new file mode 100644 index 00000000000000..4ca9d57aca3214 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_snli_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_snli_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_snli_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_snli_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_snli_custom_tokenizer_en_5.1.1_3.0_1694549373032.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_snli_custom_tokenizer_en_5.1.1_3.0_1694549373032.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_snli_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_snli_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_snli_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.4 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-snli-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_snli_from_scratch_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_snli_from_scratch_en.md new file mode 100644 index 00000000000000..c9ceda951d0c43 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_snli_from_scratch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_snli_from_scratch BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_snli_from_scratch +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_snli_from_scratch` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_snli_from_scratch_en_5.1.1_3.0_1694555995371.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_snli_from_scratch_en_5.1.1_3.0_1694555995371.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_snli_from_scratch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_snli_from_scratch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_snli_from_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.4 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-snli-from-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_squad_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_squad_custom_tokenizer_en.md new file mode 100644 index 00000000000000..e0dcd2d50a5ab6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_squad_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_squad_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_squad_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_squad_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_squad_custom_tokenizer_en_5.1.1_3.0_1694547599498.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_squad_custom_tokenizer_en_5.1.1_3.0_1694547599498.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_squad_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_squad_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_squad_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.5 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-squad-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_squad_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_squad_en.md new file mode 100644 index 00000000000000..44e83102346a03 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_squad_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_squad BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_squad +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_squad` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_squad_en_5.1.1_3.0_1694547423091.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_squad_en_5.1.1_3.0_1694547423091.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_squad","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_squad", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_squad| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-squad \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_tweet_eval_from_scratch_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_tweet_eval_from_scratch_en.md new file mode 100644 index 00000000000000..3105b80e311ead --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_tweet_eval_from_scratch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_tweet_eval_from_scratch BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_tweet_eval_from_scratch +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_tweet_eval_from_scratch` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_tweet_eval_from_scratch_en_5.1.1_3.0_1694555163619.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_tweet_eval_from_scratch_en_5.1.1_3.0_1694555163619.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_tweet_eval_from_scratch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_tweet_eval_from_scratch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_tweet_eval_from_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.6 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-tweet_eval-from-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_wikitext_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_wikitext_custom_tokenizer_en.md new file mode 100644 index 00000000000000..23a14f4c6ed1a1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_wikitext_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_wikitext_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_wikitext_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_wikitext_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_wikitext_custom_tokenizer_en_5.1.1_3.0_1694548410883.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_wikitext_custom_tokenizer_en_5.1.1_3.0_1694548410883.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_wikitext_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_wikitext_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_wikitext_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.6 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-wikitext-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_wikitext_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_wikitext_en.md new file mode 100644 index 00000000000000..718a20122eed20 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_wikitext_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_wikitext BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_wikitext +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_wikitext` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_wikitext_en_5.1.1_3.0_1694548328415.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_wikitext_en_5.1.1_3.0_1694548328415.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_wikitext","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_wikitext", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_wikitext| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-wikitext \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_wikitext_from_scratch_custom_tokenizer_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_wikitext_from_scratch_custom_tokenizer_en.md new file mode 100644 index 00000000000000..1afc42653b28cb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_wikitext_from_scratch_custom_tokenizer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_wikitext_from_scratch_custom_tokenizer BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_wikitext_from_scratch_custom_tokenizer +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_wikitext_from_scratch_custom_tokenizer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_wikitext_from_scratch_custom_tokenizer_en_5.1.1_3.0_1694554282672.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_wikitext_from_scratch_custom_tokenizer_en_5.1.1_3.0_1694554282672.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_wikitext_from_scratch_custom_tokenizer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_wikitext_from_scratch_custom_tokenizer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_wikitext_from_scratch_custom_tokenizer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.6 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-wikitext-from-scratch-custom-tokenizer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_wikitext_from_scratch_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_wikitext_from_scratch_en.md new file mode 100644 index 00000000000000..7e8d46e34f0bee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_wikitext_from_scratch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_wikitext_from_scratch BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_wikitext_from_scratch +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_wikitext_from_scratch` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_wikitext_from_scratch_en_5.1.1_3.0_1694555916739.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_wikitext_from_scratch_en_5.1.1_3.0_1694555916739.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_wikitext_from_scratch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_wikitext_from_scratch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_wikitext_from_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.6 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-wikitext-from-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-twitch_bert_base_cased_pytorch_en.md b/docs/_posts/ahmedlone127/2023-09-12-twitch_bert_base_cased_pytorch_en.md new file mode 100644 index 00000000000000..da34a0b57d8d48 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-twitch_bert_base_cased_pytorch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English twitch_bert_base_cased_pytorch BertEmbeddings from veb +author: John Snow Labs +name: twitch_bert_base_cased_pytorch +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`twitch_bert_base_cased_pytorch` is a English model originally trained by veb. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/twitch_bert_base_cased_pytorch_en_5.1.1_3.0_1694554479279.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/twitch_bert_base_cased_pytorch_en_5.1.1_3.0_1694554479279.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("twitch_bert_base_cased_pytorch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("twitch_bert_base_cased_pytorch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|twitch_bert_base_cased_pytorch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/veb/twitch-bert-base-cased-pytorch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-ugmultbert_e1_en.md b/docs/_posts/ahmedlone127/2023-09-12-ugmultbert_e1_en.md new file mode 100644 index 00000000000000..dad45e9deeb701 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-ugmultbert_e1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ugmultbert_e1 BertEmbeddings from TurkLangsTeamURFU +author: John Snow Labs +name: ugmultbert_e1 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ugmultbert_e1` is a English model originally trained by TurkLangsTeamURFU. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ugmultbert_e1_en_5.1.1_3.0_1694550516279.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ugmultbert_e1_en_5.1.1_3.0_1694550516279.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ugmultbert_e1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ugmultbert_e1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ugmultbert_e1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|494.4 MB| + +## References + +https://huggingface.co/TurkLangsTeamURFU/UgMultBERT_e1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-ugmultbert_e2_en.md b/docs/_posts/ahmedlone127/2023-09-12-ugmultbert_e2_en.md new file mode 100644 index 00000000000000..e4235ef45efb29 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-ugmultbert_e2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ugmultbert_e2 BertEmbeddings from TurkLangsTeamURFU +author: John Snow Labs +name: ugmultbert_e2 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ugmultbert_e2` is a English model originally trained by TurkLangsTeamURFU. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ugmultbert_e2_en_5.1.1_3.0_1694551678403.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ugmultbert_e2_en_5.1.1_3.0_1694551678403.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ugmultbert_e2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ugmultbert_e2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ugmultbert_e2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|494.2 MB| + +## References + +https://huggingface.co/TurkLangsTeamURFU/UgMultBERT_e2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-ugmultbert_e3_en.md b/docs/_posts/ahmedlone127/2023-09-12-ugmultbert_e3_en.md new file mode 100644 index 00000000000000..63facb54a8d3a4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-ugmultbert_e3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ugmultbert_e3 BertEmbeddings from TurkLangsTeamURFU +author: John Snow Labs +name: ugmultbert_e3 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ugmultbert_e3` is a English model originally trained by TurkLangsTeamURFU. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ugmultbert_e3_en_5.1.1_3.0_1694551842232.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ugmultbert_e3_en_5.1.1_3.0_1694551842232.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ugmultbert_e3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ugmultbert_e3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ugmultbert_e3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|494.0 MB| + +## References + +https://huggingface.co/TurkLangsTeamURFU/UgMultBERT_e3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-ugmultmtokbert_3e_en.md b/docs/_posts/ahmedlone127/2023-09-12-ugmultmtokbert_3e_en.md new file mode 100644 index 00000000000000..0c2e7e90013903 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-ugmultmtokbert_3e_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ugmultmtokbert_3e BertEmbeddings from TurkLangsTeamURFU +author: John Snow Labs +name: ugmultmtokbert_3e +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ugmultmtokbert_3e` is a English model originally trained by TurkLangsTeamURFU. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ugmultmtokbert_3e_en_5.1.1_3.0_1694551069053.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ugmultmtokbert_3e_en_5.1.1_3.0_1694551069053.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ugmultmtokbert_3e","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ugmultmtokbert_3e", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ugmultmtokbert_3e| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|742.6 MB| + +## References + +https://huggingface.co/TurkLangsTeamURFU/UgMultMTokBERT_3e \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-ugturkbert_e1_en.md b/docs/_posts/ahmedlone127/2023-09-12-ugturkbert_e1_en.md new file mode 100644 index 00000000000000..841bbafc098785 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-ugturkbert_e1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ugturkbert_e1 BertEmbeddings from TurkLangsTeamURFU +author: John Snow Labs +name: ugturkbert_e1 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ugturkbert_e1` is a English model originally trained by TurkLangsTeamURFU. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ugturkbert_e1_en_5.1.1_3.0_1694551986988.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ugturkbert_e1_en_5.1.1_3.0_1694551986988.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ugturkbert_e1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ugturkbert_e1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ugturkbert_e1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|494.3 MB| + +## References + +https://huggingface.co/TurkLangsTeamURFU/UgTurkBERT_e1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-ugturkbert_e2_en.md b/docs/_posts/ahmedlone127/2023-09-12-ugturkbert_e2_en.md new file mode 100644 index 00000000000000..1cb2829bc16a32 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-ugturkbert_e2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ugturkbert_e2 BertEmbeddings from TurkLangsTeamURFU +author: John Snow Labs +name: ugturkbert_e2 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ugturkbert_e2` is a English model originally trained by TurkLangsTeamURFU. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ugturkbert_e2_en_5.1.1_3.0_1694552141789.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ugturkbert_e2_en_5.1.1_3.0_1694552141789.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ugturkbert_e2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ugturkbert_e2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ugturkbert_e2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|494.2 MB| + +## References + +https://huggingface.co/TurkLangsTeamURFU/UgTurkBERT_e2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-ugturkbert_e3_en.md b/docs/_posts/ahmedlone127/2023-09-12-ugturkbert_e3_en.md new file mode 100644 index 00000000000000..5d30aadeabb4c4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-ugturkbert_e3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ugturkbert_e3 BertEmbeddings from TurkLangsTeamURFU +author: John Snow Labs +name: ugturkbert_e3 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ugturkbert_e3` is a English model originally trained by TurkLangsTeamURFU. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ugturkbert_e3_en_5.1.1_3.0_1694552327758.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ugturkbert_e3_en_5.1.1_3.0_1694552327758.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ugturkbert_e3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ugturkbert_e3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ugturkbert_e3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|494.1 MB| + +## References + +https://huggingface.co/TurkLangsTeamURFU/UgTurkBERT_e3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-xbert2_en.md b/docs/_posts/ahmedlone127/2023-09-12-xbert2_en.md new file mode 100644 index 00000000000000..4f76afc566c76a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-xbert2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English xbert2 BertEmbeddings from tmc +author: John Snow Labs +name: xbert2 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`xbert2` is a English model originally trained by tmc. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xbert2_en_5.1.1_3.0_1694551885455.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xbert2_en_5.1.1_3.0_1694551885455.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("xbert2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("xbert2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xbert2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/tmc/xbert2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-absa_mlm_1_en.md b/docs/_posts/ahmedlone127/2023-09-13-absa_mlm_1_en.md new file mode 100644 index 00000000000000..d0577d6f17857a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-absa_mlm_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English absa_mlm_1 BertEmbeddings from UchihaMadara +author: John Snow Labs +name: absa_mlm_1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`absa_mlm_1` is a English model originally trained by UchihaMadara. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/absa_mlm_1_en_5.1.1_3.0_1694579162814.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/absa_mlm_1_en_5.1.1_3.0_1694579162814.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("absa_mlm_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("absa_mlm_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|absa_mlm_1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/UchihaMadara/absa-mlm-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-adrbert_base_p1_en.md b/docs/_posts/ahmedlone127/2023-09-13-adrbert_base_p1_en.md new file mode 100644 index 00000000000000..0caa21b7ce0587 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-adrbert_base_p1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English adrbert_base_p1 BertEmbeddings from adriansyahdr +author: John Snow Labs +name: adrbert_base_p1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`adrbert_base_p1` is a English model originally trained by adriansyahdr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/adrbert_base_p1_en_5.1.1_3.0_1694576788237.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/adrbert_base_p1_en_5.1.1_3.0_1694576788237.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("adrbert_base_p1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("adrbert_base_p1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|adrbert_base_p1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|470.1 MB| + +## References + +https://huggingface.co/adriansyahdr/adrBert-base-p1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-alberti_bert_base_multilingual_cased_linhd_postdata_xx.md b/docs/_posts/ahmedlone127/2023-09-13-alberti_bert_base_multilingual_cased_linhd_postdata_xx.md new file mode 100644 index 00000000000000..b1f4da96e304e4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-alberti_bert_base_multilingual_cased_linhd_postdata_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual alberti_bert_base_multilingual_cased_linhd_postdata BertEmbeddings from linhd-postdata +author: John Snow Labs +name: alberti_bert_base_multilingual_cased_linhd_postdata +date: 2023-09-13 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`alberti_bert_base_multilingual_cased_linhd_postdata` is a Multilingual model originally trained by linhd-postdata. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/alberti_bert_base_multilingual_cased_linhd_postdata_xx_5.1.1_3.0_1694577289966.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/alberti_bert_base_multilingual_cased_linhd_postdata_xx_5.1.1_3.0_1694577289966.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("alberti_bert_base_multilingual_cased_linhd_postdata","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("alberti_bert_base_multilingual_cased_linhd_postdata", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|alberti_bert_base_multilingual_cased_linhd_postdata| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|664.4 MB| + +## References + +https://huggingface.co/linhd-postdata/alberti-bert-base-multilingual-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-algarlegal_base_arabertv2_en.md b/docs/_posts/ahmedlone127/2023-09-13-algarlegal_base_arabertv2_en.md new file mode 100644 index 00000000000000..46c5273fbbb754 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-algarlegal_base_arabertv2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English algarlegal_base_arabertv2 BertEmbeddings from hatemestinbejaia +author: John Snow Labs +name: algarlegal_base_arabertv2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`algarlegal_base_arabertv2` is a English model originally trained by hatemestinbejaia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/algarlegal_base_arabertv2_en_5.1.1_3.0_1694566807785.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/algarlegal_base_arabertv2_en_5.1.1_3.0_1694566807785.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("algarlegal_base_arabertv2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("algarlegal_base_arabertv2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|algarlegal_base_arabertv2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|504.8 MB| + +## References + +https://huggingface.co/hatemestinbejaia/AlgArLegal-base-arabertv2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-algarlegal_large_arabertv2_en.md b/docs/_posts/ahmedlone127/2023-09-13-algarlegal_large_arabertv2_en.md new file mode 100644 index 00000000000000..470ed97e8f00d2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-algarlegal_large_arabertv2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English algarlegal_large_arabertv2 BertEmbeddings from hatemestinbejaia +author: John Snow Labs +name: algarlegal_large_arabertv2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`algarlegal_large_arabertv2` is a English model originally trained by hatemestinbejaia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/algarlegal_large_arabertv2_en_5.1.1_3.0_1694566592236.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/algarlegal_large_arabertv2_en_5.1.1_3.0_1694566592236.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("algarlegal_large_arabertv2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("algarlegal_large_arabertv2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|algarlegal_large_arabertv2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.4 GB| + +## References + +https://huggingface.co/hatemestinbejaia/AlgArLegal-large-arabertv2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-algarlegal_nonumber_base_arabertv2_en.md b/docs/_posts/ahmedlone127/2023-09-13-algarlegal_nonumber_base_arabertv2_en.md new file mode 100644 index 00000000000000..0ed7d52a89de75 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-algarlegal_nonumber_base_arabertv2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English algarlegal_nonumber_base_arabertv2 BertEmbeddings from hatemestinbejaia +author: John Snow Labs +name: algarlegal_nonumber_base_arabertv2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`algarlegal_nonumber_base_arabertv2` is a English model originally trained by hatemestinbejaia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/algarlegal_nonumber_base_arabertv2_en_5.1.1_3.0_1694567513179.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/algarlegal_nonumber_base_arabertv2_en_5.1.1_3.0_1694567513179.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("algarlegal_nonumber_base_arabertv2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("algarlegal_nonumber_base_arabertv2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|algarlegal_nonumber_base_arabertv2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|504.8 MB| + +## References + +https://huggingface.co/hatemestinbejaia/AlgArLegal-Nonumber-base-arabertv2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-algarlegal_nonumber_large_arabertv2_en.md b/docs/_posts/ahmedlone127/2023-09-13-algarlegal_nonumber_large_arabertv2_en.md new file mode 100644 index 00000000000000..1f5104e2a5d6ea --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-algarlegal_nonumber_large_arabertv2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English algarlegal_nonumber_large_arabertv2 BertEmbeddings from hatemestinbejaia +author: John Snow Labs +name: algarlegal_nonumber_large_arabertv2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`algarlegal_nonumber_large_arabertv2` is a English model originally trained by hatemestinbejaia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/algarlegal_nonumber_large_arabertv2_en_5.1.1_3.0_1694567316136.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/algarlegal_nonumber_large_arabertv2_en_5.1.1_3.0_1694567316136.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("algarlegal_nonumber_large_arabertv2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("algarlegal_nonumber_large_arabertv2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|algarlegal_nonumber_large_arabertv2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.4 GB| + +## References + +https://huggingface.co/hatemestinbejaia/AlgArLegal-Nonumber-large-arabertv2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-applicanttrackingsystembert_en.md b/docs/_posts/ahmedlone127/2023-09-13-applicanttrackingsystembert_en.md new file mode 100644 index 00000000000000..7eb98f7748696d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-applicanttrackingsystembert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English applicanttrackingsystembert BertEmbeddings from Shushant +author: John Snow Labs +name: applicanttrackingsystembert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`applicanttrackingsystembert` is a English model originally trained by Shushant. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/applicanttrackingsystembert_en_5.1.1_3.0_1694574178617.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/applicanttrackingsystembert_en_5.1.1_3.0_1694574178617.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("applicanttrackingsystembert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("applicanttrackingsystembert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|applicanttrackingsystembert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.0 MB| + +## References + +https://huggingface.co/Shushant/ApplicantTrackingSystemBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-ara_dialectbert_ar.md b/docs/_posts/ahmedlone127/2023-09-13-ara_dialectbert_ar.md new file mode 100644 index 00000000000000..4826748be7afc1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-ara_dialectbert_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic ara_dialectbert BertEmbeddings from MutazYoune +author: John Snow Labs +name: ara_dialectbert +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ara_dialectbert` is a Arabic model originally trained by MutazYoune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ara_dialectbert_ar_5.1.1_3.0_1694567913022.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ara_dialectbert_ar_5.1.1_3.0_1694567913022.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ara_dialectbert","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ara_dialectbert", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ara_dialectbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|406.3 MB| + +## References + +https://huggingface.co/MutazYoune/Ara_DialectBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-arbert_ar.md b/docs/_posts/ahmedlone127/2023-09-13-arbert_ar.md new file mode 100644 index 00000000000000..e3174cd469c425 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-arbert_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic arbert BertEmbeddings from UBC-NLP +author: John Snow Labs +name: arbert +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arbert` is a Arabic model originally trained by UBC-NLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arbert_ar_5.1.1_3.0_1694573787008.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arbert_ar_5.1.1_3.0_1694573787008.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arbert","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arbert", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|605.3 MB| + +## References + +https://huggingface.co/UBC-NLP/ARBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-archeobertje_en.md b/docs/_posts/ahmedlone127/2023-09-13-archeobertje_en.md new file mode 100644 index 00000000000000..95c2c34d71ee75 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-archeobertje_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English archeobertje BertEmbeddings from alexbrandsen +author: John Snow Labs +name: archeobertje +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`archeobertje` is a English model originally trained by alexbrandsen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/archeobertje_en_5.1.1_3.0_1694578129084.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/archeobertje_en_5.1.1_3.0_1694578129084.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("archeobertje","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("archeobertje", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|archeobertje| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.5 MB| + +## References + +https://huggingface.co/alexbrandsen/ArcheoBERTje \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-author_identification_en.md b/docs/_posts/ahmedlone127/2023-09-13-author_identification_en.md new file mode 100644 index 00000000000000..36ba5b6015a9f5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-author_identification_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English author_identification BertEmbeddings from Omar2027 +author: John Snow Labs +name: author_identification +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`author_identification` is a English model originally trained by Omar2027. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/author_identification_en_5.1.1_3.0_1694569409752.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/author_identification_en_5.1.1_3.0_1694569409752.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("author_identification","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("author_identification", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|author_identification| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|505.1 MB| + +## References + +https://huggingface.co/Omar2027/Author_identification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bangla_bert_base_bn.md b/docs/_posts/ahmedlone127/2023-09-13-bangla_bert_base_bn.md new file mode 100644 index 00000000000000..fa3ca966eae826 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bangla_bert_base_bn.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Bengali bangla_bert_base BertEmbeddings from sagorsarker +author: John Snow Labs +name: bangla_bert_base +date: 2023-09-13 +tags: [bert, bn, open_source, fill_mask, onnx] +task: Embeddings +language: bn +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bangla_bert_base` is a Bengali model originally trained by sagorsarker. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bangla_bert_base_bn_5.1.1_3.0_1694569397272.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bangla_bert_base_bn_5.1.1_3.0_1694569397272.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bangla_bert_base","bn") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bangla_bert_base", "bn") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bangla_bert_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|bn| +|Size:|614.7 MB| + +## References + +https://huggingface.co/sagorsarker/bangla-bert-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bangla_bert_bn.md b/docs/_posts/ahmedlone127/2023-09-13-bangla_bert_bn.md new file mode 100644 index 00000000000000..33c51b5cdcf1ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bangla_bert_bn.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Bengali bangla_bert BertEmbeddings from Kowsher +author: John Snow Labs +name: bangla_bert +date: 2023-09-13 +tags: [bert, bn, open_source, fill_mask, onnx] +task: Embeddings +language: bn +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bangla_bert` is a Bengali model originally trained by Kowsher. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bangla_bert_bn_5.1.1_3.0_1694564809999.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bangla_bert_bn_5.1.1_3.0_1694564809999.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bangla_bert","bn") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bangla_bert", "bn") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bangla_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|bn| +|Size:|612.1 MB| + +## References + +https://huggingface.co/Kowsher/bangla-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-batterybert_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-batterybert_cased_en.md new file mode 100644 index 00000000000000..5ffeec3a3bcb64 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-batterybert_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English batterybert_cased BertEmbeddings from batterydata +author: John Snow Labs +name: batterybert_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`batterybert_cased` is a English model originally trained by batterydata. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/batterybert_cased_en_5.1.1_3.0_1694585277909.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/batterybert_cased_en_5.1.1_3.0_1694585277909.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("batterybert_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("batterybert_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|batterybert_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/batterydata/batterybert-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-batterybert_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-batterybert_uncased_en.md new file mode 100644 index 00000000000000..17256679f12a65 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-batterybert_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English batterybert_uncased BertEmbeddings from batterydata +author: John Snow Labs +name: batterybert_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`batterybert_uncased` is a English model originally trained by batterydata. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/batterybert_uncased_en_5.1.1_3.0_1694585447411.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/batterybert_uncased_en_5.1.1_3.0_1694585447411.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("batterybert_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("batterybert_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|batterybert_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/batterydata/batterybert-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bbc_gqa_eval_en.md b/docs/_posts/ahmedlone127/2023-09-13-bbc_gqa_eval_en.md new file mode 100644 index 00000000000000..8be4e06e21cb70 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bbc_gqa_eval_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bbc_gqa_eval BertEmbeddings from rjbownes +author: John Snow Labs +name: bbc_gqa_eval +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bbc_gqa_eval` is a English model originally trained by rjbownes. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bbc_gqa_eval_en_5.1.1_3.0_1694568579505.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bbc_gqa_eval_en_5.1.1_3.0_1694568579505.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bbc_gqa_eval","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bbc_gqa_eval", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bbc_gqa_eval| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/rjbownes/BBC-GQA-eval \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-beauty_base_klcp2_en.md b/docs/_posts/ahmedlone127/2023-09-13-beauty_base_klcp2_en.md new file mode 100644 index 00000000000000..c9e768c152c595 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-beauty_base_klcp2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English beauty_base_klcp2 BertEmbeddings from Kyoungmin +author: John Snow Labs +name: beauty_base_klcp2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`beauty_base_klcp2` is a English model originally trained by Kyoungmin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/beauty_base_klcp2_en_5.1.1_3.0_1694565176499.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/beauty_base_klcp2_en_5.1.1_3.0_1694565176499.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("beauty_base_klcp2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("beauty_base_klcp2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|beauty_base_klcp2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.2 MB| + +## References + +https://huggingface.co/Kyoungmin/beauty-base-KLCP2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-berel_sivan22_he.md b/docs/_posts/ahmedlone127/2023-09-13-berel_sivan22_he.md new file mode 100644 index 00000000000000..5f965cde2c7c69 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-berel_sivan22_he.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hebrew berel_sivan22 BertEmbeddings from sivan22 +author: John Snow Labs +name: berel_sivan22 +date: 2023-09-13 +tags: [bert, he, open_source, fill_mask, onnx] +task: Embeddings +language: he +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`berel_sivan22` is a Hebrew model originally trained by sivan22. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/berel_sivan22_he_5.1.1_3.0_1694564843869.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/berel_sivan22_he_5.1.1_3.0_1694564843869.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("berel_sivan22","he") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("berel_sivan22", "he") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|berel_sivan22| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|he| +|Size:|690.1 MB| + +## References + +https://huggingface.co/sivan22/BEREL \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_120_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_120_en.md new file mode 100644 index 00000000000000..3eef4b14c03791 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_120_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_120 BertEmbeddings from gokuls +author: John Snow Labs +name: bert_base_120 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_120` is a English model originally trained by gokuls. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_120_en_5.1.1_3.0_1694585728673.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_120_en_5.1.1_3.0_1694585728673.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_120","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_120", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_120| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/gokuls/bert_base_120 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_24_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_24_en.md new file mode 100644 index 00000000000000..2c9ff0193ede01 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_24_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_24 BertEmbeddings from gokuls +author: John Snow Labs +name: bert_base_24 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_24` is a English model originally trained by gokuls. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_24_en_5.1.1_3.0_1694580140710.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_24_en_5.1.1_3.0_1694580140710.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_24","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_24", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_24| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.9 MB| + +## References + +https://huggingface.co/gokuls/bert_base_24 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_48_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_48_en.md new file mode 100644 index 00000000000000..90b6f1e13f7ff3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_48_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_48 BertEmbeddings from gokuls +author: John Snow Labs +name: bert_base_48 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_48` is a English model originally trained by gokuls. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_48_en_5.1.1_3.0_1694580489996.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_48_en_5.1.1_3.0_1694580489996.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_48","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_48", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_48| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.0 MB| + +## References + +https://huggingface.co/gokuls/bert_base_48 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_5lang_cased_xx.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_5lang_cased_xx.md new file mode 100644 index 00000000000000..61728a0b5a0df5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_5lang_cased_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_5lang_cased BertEmbeddings from amine +author: John Snow Labs +name: bert_base_5lang_cased +date: 2023-09-13 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_5lang_cased` is a Multilingual model originally trained by amine. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_5lang_cased_xx_5.1.1_3.0_1694579745976.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_5lang_cased_xx_5.1.1_3.0_1694579745976.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_5lang_cased","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_5lang_cased", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_5lang_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|461.1 MB| + +## References + +https://huggingface.co/amine/bert-base-5lang-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_72_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_72_en.md new file mode 100644 index 00000000000000..05ade8a445f4af --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_72_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_72 BertEmbeddings from gokuls +author: John Snow Labs +name: bert_base_72 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_72` is a English model originally trained by gokuls. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_72_en_5.1.1_3.0_1694581572483.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_72_en_5.1.1_3.0_1694581572483.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_72","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_72", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_72| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.0 MB| + +## References + +https://huggingface.co/gokuls/bert_base_72 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_96_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_96_en.md new file mode 100644 index 00000000000000..866e65f7f7d285 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_96_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_96 BertEmbeddings from gokuls +author: John Snow Labs +name: bert_base_96 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_96` is a English model originally trained by gokuls. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_96_en_5.1.1_3.0_1694581741068.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_96_en_5.1.1_3.0_1694581741068.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_96","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_96", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_96| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.0 MB| + +## References + +https://huggingface.co/gokuls/bert_base_96 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabertv02_ar.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabertv02_ar.md new file mode 100644 index 00000000000000..67d9bda6177435 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabertv02_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_base_arabertv02 BertEmbeddings from aubmindlab +author: John Snow Labs +name: bert_base_arabertv02 +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabertv02` is a Arabic model originally trained by aubmindlab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabertv02_ar_5.1.1_3.0_1694583153678.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabertv02_ar_5.1.1_3.0_1694583153678.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_arabertv02","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_arabertv02", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabertv02| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|505.1 MB| + +## References + +https://huggingface.co/aubmindlab/bert-base-arabertv02 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabertv02_twitter_ar.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabertv02_twitter_ar.md new file mode 100644 index 00000000000000..7b2ce7d378f205 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabertv02_twitter_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_base_arabertv02_twitter BertEmbeddings from aubmindlab +author: John Snow Labs +name: bert_base_arabertv02_twitter +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabertv02_twitter` is a Arabic model originally trained by aubmindlab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabertv02_twitter_ar_5.1.1_3.0_1694583024700.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabertv02_twitter_ar_5.1.1_3.0_1694583024700.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_arabertv02_twitter","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_arabertv02_twitter", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabertv02_twitter| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|505.0 MB| + +## References + +https://huggingface.co/aubmindlab/bert-base-arabertv02-twitter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_bert_yoga_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_bert_yoga_finetuned_en.md new file mode 100644 index 00000000000000..e9283fb3e215af --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_bert_yoga_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_bert_yoga_finetuned BertEmbeddings from dsantistevan +author: John Snow Labs +name: bert_base_cased_bert_yoga_finetuned +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_bert_yoga_finetuned` is a English model originally trained by dsantistevan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_bert_yoga_finetuned_en_5.1.1_3.0_1694566346146.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_bert_yoga_finetuned_en_5.1.1_3.0_1694566346146.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_bert_yoga_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_bert_yoga_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_bert_yoga_finetuned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/dsantistevan/bert-base-cased-bert-yoga-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_finetuned_imdb_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_finetuned_imdb_en.md new file mode 100644 index 00000000000000..fbe7bb23bb5b01 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_finetuned_imdb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_imdb BertEmbeddings from TimShieh +author: John Snow Labs +name: bert_base_cased_finetuned_imdb +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_imdb` is a English model originally trained by TimShieh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_imdb_en_5.1.1_3.0_1694580692039.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_imdb_en_5.1.1_3.0_1694580692039.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_imdb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_imdb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_imdb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/TimShieh/bert-base-cased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_finetuned_semeval2017_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_finetuned_semeval2017_mlm_en.md new file mode 100644 index 00000000000000..14698d2b675138 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_finetuned_semeval2017_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_semeval2017_mlm BertEmbeddings from TimShieh +author: John Snow Labs +name: bert_base_cased_finetuned_semeval2017_mlm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_semeval2017_mlm` is a English model originally trained by TimShieh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_semeval2017_mlm_en_5.1.1_3.0_1694580947575.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_semeval2017_mlm_en_5.1.1_3.0_1694580947575.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_semeval2017_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_semeval2017_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_semeval2017_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/TimShieh/bert-base-cased-finetuned-semeval2017-MLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_portuguese_lenerbr_pt.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_portuguese_lenerbr_pt.md new file mode 100644 index 00000000000000..8335847887a8d6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_portuguese_lenerbr_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese bert_base_cased_portuguese_lenerbr BertEmbeddings from pierreguillou +author: John Snow Labs +name: bert_base_cased_portuguese_lenerbr +date: 2023-09-13 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_portuguese_lenerbr` is a Portuguese model originally trained by pierreguillou. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_portuguese_lenerbr_pt_5.1.1_3.0_1694563345437.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_portuguese_lenerbr_pt_5.1.1_3.0_1694563345437.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_portuguese_lenerbr","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_portuguese_lenerbr", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_portuguese_lenerbr| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|405.9 MB| + +## References + +https://huggingface.co/pierreguillou/bert-base-cased-pt-lenerbr \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_dutch_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_dutch_cased_en.md new file mode 100644 index 00000000000000..88af2281474189 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_dutch_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_dutch_cased BertEmbeddings from wietsedv +author: John Snow Labs +name: bert_base_dutch_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_dutch_cased` is a English model originally trained by wietsedv. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_dutch_cased_en_5.1.1_3.0_1694583992022.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_dutch_cased_en_5.1.1_3.0_1694583992022.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_dutch_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_dutch_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_dutch_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/wietsedv/bert-base-dutch-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_dutch_cased_finetuned_manx_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_dutch_cased_finetuned_manx_en.md new file mode 100644 index 00000000000000..c1fd54a709286a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_dutch_cased_finetuned_manx_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_dutch_cased_finetuned_manx BertEmbeddings from Pyjay +author: John Snow Labs +name: bert_base_dutch_cased_finetuned_manx +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_dutch_cased_finetuned_manx` is a English model originally trained by Pyjay. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_dutch_cased_finetuned_manx_en_5.1.1_3.0_1694569643622.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_dutch_cased_finetuned_manx_en_5.1.1_3.0_1694569643622.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_dutch_cased_finetuned_manx","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_dutch_cased_finetuned_manx", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_dutch_cased_finetuned_manx| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/Pyjay/bert-base-dutch-cased-finetuned-gv \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_finetuned_wellness_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_finetuned_wellness_en.md new file mode 100644 index 00000000000000..c57a02843b5909 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_finetuned_wellness_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_finetuned_wellness BertEmbeddings from gaeunseo +author: John Snow Labs +name: bert_base_finetuned_wellness +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_finetuned_wellness` is a English model originally trained by gaeunseo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_finetuned_wellness_en_5.1.1_3.0_1694583681541.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_finetuned_wellness_en_5.1.1_3.0_1694583681541.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_finetuned_wellness","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_finetuned_wellness", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_finetuned_wellness| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.4 MB| + +## References + +https://huggingface.co/gaeunseo/bert-base-finetuned-wellness \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_finnish_cased_v1_fi.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_finnish_cased_v1_fi.md new file mode 100644 index 00000000000000..c3de96cbd33ceb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_finnish_cased_v1_fi.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Finnish bert_base_finnish_cased_v1 BertEmbeddings from TurkuNLP +author: John Snow Labs +name: bert_base_finnish_cased_v1 +date: 2023-09-13 +tags: [bert, fi, open_source, fill_mask, onnx] +task: Embeddings +language: fi +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_finnish_cased_v1` is a Finnish model originally trained by TurkuNLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_finnish_cased_v1_fi_5.1.1_3.0_1694573404619.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_finnish_cased_v1_fi_5.1.1_3.0_1694573404619.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_finnish_cased_v1","fi") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_finnish_cased_v1", "fi") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_finnish_cased_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|fi| +|Size:|464.7 MB| + +## References + +https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_finnish_uncased_v1_fi.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_finnish_uncased_v1_fi.md new file mode 100644 index 00000000000000..902c15dd577caa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_finnish_uncased_v1_fi.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Finnish bert_base_finnish_uncased_v1 BertEmbeddings from TurkuNLP +author: John Snow Labs +name: bert_base_finnish_uncased_v1 +date: 2023-09-13 +tags: [bert, fi, open_source, fill_mask, onnx] +task: Embeddings +language: fi +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_finnish_uncased_v1` is a Finnish model originally trained by TurkuNLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_finnish_uncased_v1_fi_5.1.1_3.0_1694573578470.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_finnish_uncased_v1_fi_5.1.1_3.0_1694573578470.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_finnish_uncased_v1","fi") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_finnish_uncased_v1", "fi") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_finnish_uncased_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|fi| +|Size:|464.7 MB| + +## References + +https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_frozen_generics_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_frozen_generics_mlm_en.md new file mode 100644 index 00000000000000..a619f0ecb1195a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_frozen_generics_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_frozen_generics_mlm BertEmbeddings from sello-ralethe +author: John Snow Labs +name: bert_base_frozen_generics_mlm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_frozen_generics_mlm` is a English model originally trained by sello-ralethe. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_frozen_generics_mlm_en_5.1.1_3.0_1694572672553.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_frozen_generics_mlm_en_5.1.1_3.0_1694572672553.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_frozen_generics_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_frozen_generics_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_frozen_generics_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/sello-ralethe/bert-base-frozen-generics-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_historical_german_kinyarwanda_cased_de.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_historical_german_kinyarwanda_cased_de.md new file mode 100644 index 00000000000000..54c81fefde4399 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_historical_german_kinyarwanda_cased_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German bert_base_historical_german_kinyarwanda_cased BertEmbeddings from redewiedergabe +author: John Snow Labs +name: bert_base_historical_german_kinyarwanda_cased +date: 2023-09-13 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_historical_german_kinyarwanda_cased` is a German model originally trained by redewiedergabe. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_historical_german_kinyarwanda_cased_de_5.1.1_3.0_1694567053989.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_historical_german_kinyarwanda_cased_de_5.1.1_3.0_1694567053989.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_historical_german_kinyarwanda_cased","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_historical_german_kinyarwanda_cased", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_historical_german_kinyarwanda_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|406.9 MB| + +## References + +https://huggingface.co/redewiedergabe/bert-base-historical-german-rw-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_italian_cased_osiria_it.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_italian_cased_osiria_it.md new file mode 100644 index 00000000000000..d08ae6ea1fc640 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_italian_cased_osiria_it.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Italian bert_base_italian_cased_osiria BertEmbeddings from osiria +author: John Snow Labs +name: bert_base_italian_cased_osiria +date: 2023-09-13 +tags: [bert, it, open_source, fill_mask, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_italian_cased_osiria` is a Italian model originally trained by osiria. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_italian_cased_osiria_it_5.1.1_3.0_1694568086773.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_italian_cased_osiria_it_5.1.1_3.0_1694568086773.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_italian_cased_osiria","it") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_italian_cased_osiria", "it") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_italian_cased_osiria| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|it| +|Size:|409.0 MB| + +## References + +https://huggingface.co/osiria/bert-base-italian-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_macedonian_bulgarian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_macedonian_bulgarian_cased_en.md new file mode 100644 index 00000000000000..87202748969a34 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_macedonian_bulgarian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_macedonian_bulgarian_cased BertEmbeddings from anon-submission-mk +author: John Snow Labs +name: bert_base_macedonian_bulgarian_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_macedonian_bulgarian_cased` is a English model originally trained by anon-submission-mk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_macedonian_bulgarian_cased_en_5.1.1_3.0_1694580092524.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_macedonian_bulgarian_cased_en_5.1.1_3.0_1694580092524.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_macedonian_bulgarian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_macedonian_bulgarian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_macedonian_bulgarian_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.1 MB| + +## References + +https://huggingface.co/anon-submission-mk/bert-base-macedonian-bulgarian-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_macedonian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_macedonian_cased_en.md new file mode 100644 index 00000000000000..915665f614518f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_macedonian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_macedonian_cased BertEmbeddings from anon-submission-mk +author: John Snow Labs +name: bert_base_macedonian_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_macedonian_cased` is a English model originally trained by anon-submission-mk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_macedonian_cased_en_5.1.1_3.0_1694580230869.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_macedonian_cased_en_5.1.1_3.0_1694580230869.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_macedonian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_macedonian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_macedonian_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.2 MB| + +## References + +https://huggingface.co/anon-submission-mk/bert-base-macedonian-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_nli_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_nli_en.md new file mode 100644 index 00000000000000..ee2fc4b398a3b1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_nli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_nli BertEmbeddings from binwang +author: John Snow Labs +name: bert_base_nli +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_nli` is a English model originally trained by binwang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_nli_en_5.1.1_3.0_1694587924757.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_nli_en_5.1.1_3.0_1694587924757.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_nli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_nli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_nli| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/binwang/bert-base-nli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_nli_stsb_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_nli_stsb_en.md new file mode 100644 index 00000000000000..10976f99b7af41 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_nli_stsb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_nli_stsb BertEmbeddings from binwang +author: John Snow Labs +name: bert_base_nli_stsb +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_nli_stsb` is a English model originally trained by binwang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_nli_stsb_en_5.1.1_3.0_1694587761118.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_nli_stsb_en_5.1.1_3.0_1694587761118.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_nli_stsb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_nli_stsb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_nli_stsb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/binwang/bert-base-nli-stsb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_persian_sport_bert_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_persian_sport_bert_uncased_en.md new file mode 100644 index 00000000000000..25f4faa459ab63 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_persian_sport_bert_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_persian_sport_bert_uncased BertEmbeddings from montazeri +author: John Snow Labs +name: bert_base_persian_sport_bert_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_persian_sport_bert_uncased` is a English model originally trained by montazeri. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_persian_sport_bert_uncased_en_5.1.1_3.0_1694564033173.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_persian_sport_bert_uncased_en_5.1.1_3.0_1694564033173.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_persian_sport_bert_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_persian_sport_bert_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_persian_sport_bert_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|606.0 MB| + +## References + +https://huggingface.co/montazeri/bert-base-persian-sport-bert-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_portuguese_cased_finetuned_peticoes_pt.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_portuguese_cased_finetuned_peticoes_pt.md new file mode 100644 index 00000000000000..d641307dc0233e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_portuguese_cased_finetuned_peticoes_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese bert_base_portuguese_cased_finetuned_peticoes BertEmbeddings from Luciano +author: John Snow Labs +name: bert_base_portuguese_cased_finetuned_peticoes +date: 2023-09-13 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_portuguese_cased_finetuned_peticoes` is a Portuguese model originally trained by Luciano. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_portuguese_cased_finetuned_peticoes_pt_5.1.1_3.0_1694566319648.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_portuguese_cased_finetuned_peticoes_pt_5.1.1_3.0_1694566319648.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_portuguese_cased_finetuned_peticoes","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_portuguese_cased_finetuned_peticoes", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_portuguese_cased_finetuned_peticoes| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|405.9 MB| + +## References + +https://huggingface.co/Luciano/bert-base-portuguese-cased-finetuned-peticoes \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_qarib60_1790k_ar.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_qarib60_1790k_ar.md new file mode 100644 index 00000000000000..12aa435d15dd47 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_qarib60_1790k_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_base_qarib60_1790k BertEmbeddings from qarib +author: John Snow Labs +name: bert_base_qarib60_1790k +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_qarib60_1790k` is a Arabic model originally trained by qarib. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_qarib60_1790k_ar_5.1.1_3.0_1694565184223.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_qarib60_1790k_ar_5.1.1_3.0_1694565184223.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_qarib60_1790k","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_qarib60_1790k", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_qarib60_1790k| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|504.9 MB| + +## References + +https://huggingface.co/qarib/bert-base-qarib60_1790k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_qarib60_1970k_ar.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_qarib60_1970k_ar.md new file mode 100644 index 00000000000000..c10604c566e8e1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_qarib60_1970k_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_base_qarib60_1970k BertEmbeddings from qarib +author: John Snow Labs +name: bert_base_qarib60_1970k +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_qarib60_1970k` is a Arabic model originally trained by qarib. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_qarib60_1970k_ar_5.1.1_3.0_1694565365666.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_qarib60_1970k_ar_5.1.1_3.0_1694565365666.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_qarib60_1970k","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_qarib60_1970k", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_qarib60_1970k| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|504.9 MB| + +## References + +https://huggingface.co/qarib/bert-base-qarib60_1970k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_qarib60_860k_ar.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_qarib60_860k_ar.md new file mode 100644 index 00000000000000..d2ee1a6aae964d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_qarib60_860k_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_base_qarib60_860k BertEmbeddings from qarib +author: John Snow Labs +name: bert_base_qarib60_860k +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_qarib60_860k` is a Arabic model originally trained by qarib. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_qarib60_860k_ar_5.1.1_3.0_1694565528171.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_qarib60_860k_ar_5.1.1_3.0_1694565528171.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_qarib60_860k","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_qarib60_860k", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_qarib60_860k| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|504.9 MB| + +## References + +https://huggingface.co/qarib/bert-base-qarib60_860k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_qarib_ar.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_qarib_ar.md new file mode 100644 index 00000000000000..0692ef43688be0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_qarib_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_base_qarib BertEmbeddings from qarib +author: John Snow Labs +name: bert_base_qarib +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_qarib` is a Arabic model originally trained by qarib. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_qarib_ar_5.1.1_3.0_1694565010110.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_qarib_ar_5.1.1_3.0_1694565010110.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_qarib","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_qarib", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_qarib| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|504.0 MB| + +## References + +https://huggingface.co/qarib/bert-base-qarib \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_cased_finetuned_literature_pro_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_cased_finetuned_literature_pro_en.md new file mode 100644 index 00000000000000..0cc9b509d799fc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_cased_finetuned_literature_pro_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_spanish_wwm_cased_finetuned_literature_pro BertEmbeddings from a-v-bely +author: John Snow Labs +name: bert_base_spanish_wwm_cased_finetuned_literature_pro +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_spanish_wwm_cased_finetuned_literature_pro` is a English model originally trained by a-v-bely. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_literature_pro_en_5.1.1_3.0_1694586382623.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_literature_pro_en_5.1.1_3.0_1694586382623.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_spanish_wwm_cased_finetuned_literature_pro","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_spanish_wwm_cased_finetuned_literature_pro", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_spanish_wwm_cased_finetuned_literature_pro| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/a-v-bely/bert-base-spanish-wwm-cased-finetuned-literature-pro \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_standard_bahasa_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_standard_bahasa_cased_en.md new file mode 100644 index 00000000000000..9429486248ee7d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_standard_bahasa_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_standard_bahasa_cased BertEmbeddings from mesolitica +author: John Snow Labs +name: bert_base_standard_bahasa_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_standard_bahasa_cased` is a English model originally trained by mesolitica. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_standard_bahasa_cased_en_5.1.1_3.0_1694586245028.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_standard_bahasa_cased_en_5.1.1_3.0_1694586245028.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_standard_bahasa_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_standard_bahasa_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_standard_bahasa_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.6 MB| + +## References + +https://huggingface.co/mesolitica/bert-base-standard-bahasa-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_swedish_cased_kblab_sv.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_swedish_cased_kblab_sv.md new file mode 100644 index 00000000000000..b4368b621b30b8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_swedish_cased_kblab_sv.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Swedish bert_base_swedish_cased_kblab BertEmbeddings from KBLab +author: John Snow Labs +name: bert_base_swedish_cased_kblab +date: 2023-09-13 +tags: [bert, sv, open_source, fill_mask, onnx] +task: Embeddings +language: sv +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_swedish_cased_kblab` is a Swedish model originally trained by KBLab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_swedish_cased_kblab_sv_5.1.1_3.0_1694563899305.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_swedish_cased_kblab_sv_5.1.1_3.0_1694563899305.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_swedish_cased_kblab","sv") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_swedish_cased_kblab", "sv") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_swedish_cased_kblab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|sv| +|Size:|465.2 MB| + +## References + +https://huggingface.co/KBLab/bert-base-swedish-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_habana_test_1_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_habana_test_1_en.md new file mode 100644 index 00000000000000..5c6c6bdf288220 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_habana_test_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_2022_habana_test_1 BertEmbeddings from philschmid +author: John Snow Labs +name: bert_base_uncased_2022_habana_test_1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_2022_habana_test_1` is a English model originally trained by philschmid. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_2022_habana_test_1_en_5.1.1_3.0_1694566037618.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_2022_habana_test_1_en_5.1.1_3.0_1694566037618.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_2022_habana_test_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_2022_habana_test_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_2022_habana_test_1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.7 MB| + +## References + +https://huggingface.co/philschmid/bert-base-uncased-2022-habana-test-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_habana_test_2_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_habana_test_2_en.md new file mode 100644 index 00000000000000..3cd591e38fdee4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_habana_test_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_2022_habana_test_2 BertEmbeddings from philschmid +author: John Snow Labs +name: bert_base_uncased_2022_habana_test_2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_2022_habana_test_2` is a English model originally trained by philschmid. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_2022_habana_test_2_en_5.1.1_3.0_1694569462383.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_2022_habana_test_2_en_5.1.1_3.0_1694569462383.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_2022_habana_test_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_2022_habana_test_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_2022_habana_test_2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.7 MB| + +## References + +https://huggingface.co/philschmid/bert-base-uncased-2022-habana-test-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_habana_test_3_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_habana_test_3_en.md new file mode 100644 index 00000000000000..218d2f78c5da77 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_habana_test_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_2022_habana_test_3 BertEmbeddings from philschmid +author: John Snow Labs +name: bert_base_uncased_2022_habana_test_3 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_2022_habana_test_3` is a English model originally trained by philschmid. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_2022_habana_test_3_en_5.1.1_3.0_1694569751143.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_2022_habana_test_3_en_5.1.1_3.0_1694569751143.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_2022_habana_test_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_2022_habana_test_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_2022_habana_test_3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.0 MB| + +## References + +https://huggingface.co/philschmid/bert-base-uncased-2022-habana-test-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_habana_test_4_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_habana_test_4_en.md new file mode 100644 index 00000000000000..e5f8c8ef0bcff7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_habana_test_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_2022_habana_test_4 BertEmbeddings from philschmid +author: John Snow Labs +name: bert_base_uncased_2022_habana_test_4 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_2022_habana_test_4` is a English model originally trained by philschmid. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_2022_habana_test_4_en_5.1.1_3.0_1694572974212.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_2022_habana_test_4_en_5.1.1_3.0_1694572974212.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_2022_habana_test_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_2022_habana_test_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_2022_habana_test_4| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.0 MB| + +## References + +https://huggingface.co/philschmid/bert-base-uncased-2022-habana-test-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_habana_test_5_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_habana_test_5_en.md new file mode 100644 index 00000000000000..4a619767e6925b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_habana_test_5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_2022_habana_test_5 BertEmbeddings from philschmid +author: John Snow Labs +name: bert_base_uncased_2022_habana_test_5 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_2022_habana_test_5` is a English model originally trained by philschmid. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_2022_habana_test_5_en_5.1.1_3.0_1694574203077.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_2022_habana_test_5_en_5.1.1_3.0_1694574203077.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_2022_habana_test_5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_2022_habana_test_5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_2022_habana_test_5| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.0 MB| + +## References + +https://huggingface.co/philschmid/bert-base-uncased-2022-habana-test-5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_habana_test_6_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_habana_test_6_en.md new file mode 100644 index 00000000000000..1e515cfb8302f3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_habana_test_6_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_2022_habana_test_6 BertEmbeddings from philschmid +author: John Snow Labs +name: bert_base_uncased_2022_habana_test_6 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_2022_habana_test_6` is a English model originally trained by philschmid. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_2022_habana_test_6_en_5.1.1_3.0_1694574566491.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_2022_habana_test_6_en_5.1.1_3.0_1694574566491.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_2022_habana_test_6","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_2022_habana_test_6", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_2022_habana_test_6| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.2 MB| + +## References + +https://huggingface.co/philschmid/bert-base-uncased-2022-habana-test-6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_bert_mask_complete_word_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_bert_mask_complete_word_en.md new file mode 100644 index 00000000000000..02279fa9c971ce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_bert_mask_complete_word_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_bert_mask_complete_word BertEmbeddings from keshavG +author: John Snow Labs +name: bert_base_uncased_bert_mask_complete_word +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_bert_mask_complete_word` is a English model originally trained by keshavG. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_bert_mask_complete_word_en_5.1.1_3.0_1694578511284.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_bert_mask_complete_word_en_5.1.1_3.0_1694578511284.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_bert_mask_complete_word","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_bert_mask_complete_word", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_bert_mask_complete_word| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/keshavG/bert-base-uncased-bert_mask_complete_word \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_bert_mask_complete_word_updated_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_bert_mask_complete_word_updated_vocab_en.md new file mode 100644 index 00000000000000..c5125a4ea23f0c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_bert_mask_complete_word_updated_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_bert_mask_complete_word_updated_vocab BertEmbeddings from keshavG +author: John Snow Labs +name: bert_base_uncased_bert_mask_complete_word_updated_vocab +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_bert_mask_complete_word_updated_vocab` is a English model originally trained by keshavG. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_bert_mask_complete_word_updated_vocab_en_5.1.1_3.0_1694585644846.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_bert_mask_complete_word_updated_vocab_en_5.1.1_3.0_1694585644846.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_bert_mask_complete_word_updated_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_bert_mask_complete_word_updated_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_bert_mask_complete_word_updated_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|413.5 MB| + +## References + +https://huggingface.co/keshavG/bert-base-uncased-bert_mask_complete_word_updated_vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_binwang_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_binwang_en.md new file mode 100644 index 00000000000000..4a2802f534a14f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_binwang_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_binwang BertEmbeddings from binwang +author: John Snow Labs +name: bert_base_uncased_binwang +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_binwang` is a English model originally trained by binwang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_binwang_en_5.1.1_3.0_1694588138775.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_binwang_en_5.1.1_3.0_1694588138775.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_binwang","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_binwang", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_binwang| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/binwang/bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_contents_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_contents_en.md new file mode 100644 index 00000000000000..134c377b26fe41 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_contents_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_contents BertEmbeddings from Contents +author: John Snow Labs +name: bert_base_uncased_contents +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_contents` is a English model originally trained by Contents. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_contents_en_5.1.1_3.0_1694579787819.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_contents_en_5.1.1_3.0_1694579787819.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_contents","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_contents", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_contents| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Contents/bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_dstc9_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_dstc9_en.md new file mode 100644 index 00000000000000..5aa5e23b4e62f1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_dstc9_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_dstc9 BertEmbeddings from wilsontam +author: John Snow Labs +name: bert_base_uncased_dstc9 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_dstc9` is a English model originally trained by wilsontam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_dstc9_en_5.1.1_3.0_1694585476644.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_dstc9_en_5.1.1_3.0_1694585476644.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_dstc9","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_dstc9", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_dstc9| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/wilsontam/bert-base-uncased-dstc9 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bertbero_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bertbero_en.md new file mode 100644 index 00000000000000..a51fd851d73000 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bertbero_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_bertbero BertEmbeddings from Transabrar +author: John Snow Labs +name: bert_base_uncased_finetuned_bertbero +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_bertbero` is a English model originally trained by Transabrar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_bertbero_en_5.1.1_3.0_1694572476667.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_bertbero_en_5.1.1_3.0_1694572476667.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_bertbero","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_bertbero", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_bertbero| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Transabrar/bert-base-uncased-finetuned-bertbero \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_gap_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_gap_en.md new file mode 100644 index 00000000000000..ba346fe26b01d8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_gap_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_gap BertEmbeddings from AriyanH +author: John Snow Labs +name: bert_base_uncased_finetuned_gap +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_gap` is a English model originally trained by AriyanH. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_gap_en_5.1.1_3.0_1694571134435.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_gap_en_5.1.1_3.0_1694571134435.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_gap","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_gap", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_gap| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/AriyanH/bert-base-uncased-finetuned-gap \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_rramicus_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_rramicus_en.md new file mode 100644 index 00000000000000..55876135fdce1b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_rramicus_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_rramicus BertEmbeddings from repro-rights-amicus-briefs +author: John Snow Labs +name: bert_base_uncased_finetuned_rramicus +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_rramicus` is a English model originally trained by repro-rights-amicus-briefs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_rramicus_en_5.1.1_3.0_1694567579152.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_rramicus_en_5.1.1_3.0_1694567579152.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_rramicus","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_rramicus", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_rramicus| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/repro-rights-amicus-briefs/bert-base-uncased-finetuned-RRamicus \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuning_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuning_en.md new file mode 100644 index 00000000000000..cedd2b66153091 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuning_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuning BertEmbeddings from shimu +author: John Snow Labs +name: bert_base_uncased_finetuning +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuning` is a English model originally trained by shimu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuning_en_5.1.1_3.0_1694573972903.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuning_en_5.1.1_3.0_1694573972903.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuning","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuning", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuning| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/shimu/bert_base_uncased_finetuning \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_chrispfield_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_chrispfield_en.md new file mode 100644 index 00000000000000..31f2b3f3ae9c6e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_chrispfield_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_chrispfield BertEmbeddings from Chrispfield +author: John Snow Labs +name: bert_base_uncased_issues_128_chrispfield +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_chrispfield` is a English model originally trained by Chrispfield. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_chrispfield_en_5.1.1_3.0_1694567399706.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_chrispfield_en_5.1.1_3.0_1694567399706.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_chrispfield","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_chrispfield", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_chrispfield| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Chrispfield/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_issues_128_kiri1701_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_issues_128_kiri1701_en.md new file mode 100644 index 00000000000000..07ee9e81042a40 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_issues_128_kiri1701_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_issues_128_kiri1701 BertEmbeddings from kiri1701 +author: John Snow Labs +name: bert_base_uncased_issues_128_issues_128_kiri1701 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_issues_128_kiri1701` is a English model originally trained by kiri1701. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_issues_128_kiri1701_en_5.1.1_3.0_1694564477310.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_issues_128_kiri1701_en_5.1.1_3.0_1694564477310.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_issues_128_kiri1701","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_issues_128_kiri1701", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_issues_128_kiri1701| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/kiri1701/bert-base-uncased-issues-128-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_munsu_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_munsu_en.md new file mode 100644 index 00000000000000..e1ad51b8371472 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_munsu_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_munsu BertEmbeddings from MunSu +author: John Snow Labs +name: bert_base_uncased_issues_128_munsu +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_munsu` is a English model originally trained by MunSu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_munsu_en_5.1.1_3.0_1694586892919.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_munsu_en_5.1.1_3.0_1694586892919.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_munsu","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_munsu", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_munsu| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/MunSu/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_twidfeel_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_twidfeel_en.md new file mode 100644 index 00000000000000..82bd79398682ee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_twidfeel_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_twidfeel BertEmbeddings from twidfeel +author: John Snow Labs +name: bert_base_uncased_issues_128_twidfeel +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_twidfeel` is a English model originally trained by twidfeel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_twidfeel_en_5.1.1_3.0_1694586346305.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_twidfeel_en_5.1.1_3.0_1694586346305.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_twidfeel","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_twidfeel", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_twidfeel| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/twidfeel/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_lm_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_lm_en.md new file mode 100644 index 00000000000000..5a7ec6224cf0d2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_lm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_lm BertEmbeddings from iewaij +author: John Snow Labs +name: bert_base_uncased_lm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_lm` is a English model originally trained by iewaij. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_lm_en_5.1.1_3.0_1694578212936.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_lm_en_5.1.1_3.0_1694578212936.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_lm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_lm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_lm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/iewaij/bert-base-uncased-lm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_noisy_orcas_1.0positive_0.5_negative_margin1.0_cosine_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_noisy_orcas_1.0positive_0.5_negative_margin1.0_cosine_en.md new file mode 100644 index 00000000000000..ab77562c3516e5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_noisy_orcas_1.0positive_0.5_negative_margin1.0_cosine_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_noisy_orcas_1.0positive_0.5_negative_margin1.0_cosine BertEmbeddings from spacemanidol +author: John Snow Labs +name: bert_base_uncased_noisy_orcas_1.0positive_0.5_negative_margin1.0_cosine +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_noisy_orcas_1.0positive_0.5_negative_margin1.0_cosine` is a English model originally trained by spacemanidol. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_noisy_orcas_1.0positive_0.5_negative_margin1.0_cosine_en_5.1.1_3.0_1694578896728.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_noisy_orcas_1.0positive_0.5_negative_margin1.0_cosine_en_5.1.1_3.0_1694578896728.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_noisy_orcas_1.0positive_0.5_negative_margin1.0_cosine","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_noisy_orcas_1.0positive_0.5_negative_margin1.0_cosine", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_noisy_orcas_1.0positive_0.5_negative_margin1.0_cosine| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/spacemanidol/bert-base-uncased-noisy-orcas-1.0positive-0.5-negative-margin1.0-cosine \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_reviews_2_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_reviews_2_en.md new file mode 100644 index 00000000000000..080d928ea56603 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_reviews_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_reviews_2 BertEmbeddings from insaf +author: John Snow Labs +name: bert_base_uncased_reviews_2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_reviews_2` is a English model originally trained by insaf. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_reviews_2_en_5.1.1_3.0_1694580821822.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_reviews_2_en_5.1.1_3.0_1694580821822.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_reviews_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_reviews_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_reviews_2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/insaf/bert-base-uncased-reviews-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_reviews_3_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_reviews_3_en.md new file mode 100644 index 00000000000000..bdd2578e56df05 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_reviews_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_reviews_3 BertEmbeddings from insaf +author: John Snow Labs +name: bert_base_uncased_reviews_3 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_reviews_3` is a English model originally trained by insaf. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_reviews_3_en_5.1.1_3.0_1694581488679.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_reviews_3_en_5.1.1_3.0_1694581488679.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_reviews_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_reviews_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_reviews_3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/insaf/bert-base-uncased-reviews-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_rotten_tomatoes_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_rotten_tomatoes_en.md new file mode 100644 index 00000000000000..388ba75ecb9555 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_rotten_tomatoes_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_rotten_tomatoes BertEmbeddings from textattack +author: John Snow Labs +name: bert_base_uncased_rotten_tomatoes +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_rotten_tomatoes` is a English model originally trained by textattack. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_rotten_tomatoes_en_5.1.1_3.0_1694577597481.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_rotten_tomatoes_en_5.1.1_3.0_1694577597481.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_rotten_tomatoes","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_rotten_tomatoes", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_rotten_tomatoes| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/textattack/bert-base-uncased-rotten_tomatoes \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_test_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_test_en.md new file mode 100644 index 00000000000000..a8382838dcce78 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_test_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_test BertEmbeddings from Contents +author: John Snow Labs +name: bert_base_uncased_test +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_test` is a English model originally trained by Contents. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_test_en_5.1.1_3.0_1694584225340.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_test_en_5.1.1_3.0_1694584225340.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_test","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_test", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_test| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Contents/bert-base-uncased-test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_vn_finetuned_portuguese_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_vn_finetuned_portuguese_en.md new file mode 100644 index 00000000000000..f672f2eb5369a0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_vn_finetuned_portuguese_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_vn_finetuned_portuguese BertEmbeddings from dotansang +author: John Snow Labs +name: bert_base_vn_finetuned_portuguese +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_vn_finetuned_portuguese` is a English model originally trained by dotansang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_vn_finetuned_portuguese_en_5.1.1_3.0_1694582275769.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_vn_finetuned_portuguese_en_5.1.1_3.0_1694582275769.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_vn_finetuned_portuguese","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_vn_finetuned_portuguese", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_vn_finetuned_portuguese| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|498.8 MB| + +## References + +https://huggingface.co/dotansang/bert-base-vn-finetuned-pt \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_yc_recipe_30_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_yc_recipe_30_en.md new file mode 100644 index 00000000000000..214621443f1dfb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_yc_recipe_30_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_yc_recipe_30 BertEmbeddings from CennetOguz +author: John Snow Labs +name: bert_base_yc_recipe_30 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_yc_recipe_30` is a English model originally trained by CennetOguz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_yc_recipe_30_en_5.1.1_3.0_1694568535972.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_yc_recipe_30_en_5.1.1_3.0_1694568535972.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_yc_recipe_30","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_yc_recipe_30", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_yc_recipe_30| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/CennetOguz/bert_base_yc_recipe_30 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_cn_wudi7758521521_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_cn_wudi7758521521_en.md new file mode 100644 index 00000000000000..5761861a3274d7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_cn_wudi7758521521_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_cn_wudi7758521521 BertEmbeddings from wudi7758521521 +author: John Snow Labs +name: bert_cn_wudi7758521521 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_cn_wudi7758521521` is a English model originally trained by wudi7758521521. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_cn_wudi7758521521_en_5.1.1_3.0_1694585808653.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_cn_wudi7758521521_en_5.1.1_3.0_1694585808653.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_cn_wudi7758521521","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_cn_wudi7758521521", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_cn_wudi7758521521| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/wudi7758521521/bert_cn \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_concat_2_finetune_simcse_truncate_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_concat_2_finetune_simcse_truncate_en.md new file mode 100644 index 00000000000000..7dfa950dce2240 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_concat_2_finetune_simcse_truncate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_concat_2_finetune_simcse_truncate BertEmbeddings from NasimB +author: John Snow Labs +name: bert_concat_2_finetune_simcse_truncate +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_concat_2_finetune_simcse_truncate` is a English model originally trained by NasimB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_concat_2_finetune_simcse_truncate_en_5.1.1_3.0_1694588304153.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_concat_2_finetune_simcse_truncate_en_5.1.1_3.0_1694588304153.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_concat_2_finetune_simcse_truncate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_concat_2_finetune_simcse_truncate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_concat_2_finetune_simcse_truncate| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|399.9 MB| + +## References + +https://huggingface.co/NasimB/bert-concat-2-finetune-simcse-truncate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_concat_3_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_concat_3_en.md new file mode 100644 index 00000000000000..84ec7e94261b5b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_concat_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_concat_3 BertEmbeddings from NasimB +author: John Snow Labs +name: bert_concat_3 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_concat_3` is a English model originally trained by NasimB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_concat_3_en_5.1.1_3.0_1694587599955.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_concat_3_en_5.1.1_3.0_1694587599955.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_concat_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_concat_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_concat_3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.2 MB| + +## References + +https://huggingface.co/NasimB/bert-concat-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_concat_3_finetune_simcse_truncate_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_concat_3_finetune_simcse_truncate_en.md new file mode 100644 index 00000000000000..40ade906ae5fb9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_concat_3_finetune_simcse_truncate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_concat_3_finetune_simcse_truncate BertEmbeddings from NasimB +author: John Snow Labs +name: bert_concat_3_finetune_simcse_truncate +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_concat_3_finetune_simcse_truncate` is a English model originally trained by NasimB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_concat_3_finetune_simcse_truncate_en_5.1.1_3.0_1694588145224.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_concat_3_finetune_simcse_truncate_en_5.1.1_3.0_1694588145224.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_concat_3_finetune_simcse_truncate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_concat_3_finetune_simcse_truncate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_concat_3_finetune_simcse_truncate| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|401.0 MB| + +## References + +https://huggingface.co/NasimB/bert-concat-3-finetune-simcse-truncate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_csl_gold8k_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_csl_gold8k_en.md new file mode 100644 index 00000000000000..453130fb2c2cb8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_csl_gold8k_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_csl_gold8k BertEmbeddings from subbareddyiiit +author: John Snow Labs +name: bert_csl_gold8k +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_csl_gold8k` is a English model originally trained by subbareddyiiit. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_csl_gold8k_en_5.1.1_3.0_1694575569274.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_csl_gold8k_en_5.1.1_3.0_1694575569274.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_csl_gold8k","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_csl_gold8k", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_csl_gold8k| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/subbareddyiiit/bert_csl_gold8k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_dk_laptop_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_dk_laptop_en.md new file mode 100644 index 00000000000000..184b207184d8e4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_dk_laptop_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_dk_laptop BertEmbeddings from activebus +author: John Snow Labs +name: bert_dk_laptop +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_dk_laptop` is a English model originally trained by activebus. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_dk_laptop_en_5.1.1_3.0_1694575473487.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_dk_laptop_en_5.1.1_3.0_1694575473487.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_dk_laptop","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_dk_laptop", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_dk_laptop| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|258.4 MB| + +## References + +https://huggingface.co/activebus/BERT-DK_laptop \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_embding_finetuned_spmlm_02_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_embding_finetuned_spmlm_02_en.md new file mode 100644 index 00000000000000..6b2382812cd515 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_embding_finetuned_spmlm_02_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_embding_finetuned_spmlm_02 BertEmbeddings from ashwathjadhav23 +author: John Snow Labs +name: bert_embding_finetuned_spmlm_02 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_embding_finetuned_spmlm_02` is a English model originally trained by ashwathjadhav23. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embding_finetuned_spmlm_02_en_5.1.1_3.0_1694585695765.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embding_finetuned_spmlm_02_en_5.1.1_3.0_1694585695765.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_embding_finetuned_spmlm_02","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_embding_finetuned_spmlm_02", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embding_finetuned_spmlm_02| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/ashwathjadhav23/Bert_Embding_Finetuned_SpMLM_02 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_finetune_simcse_truncate_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_finetune_simcse_truncate_en.md new file mode 100644 index 00000000000000..f911da42bcbdf5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_finetune_simcse_truncate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_finetune_simcse_truncate BertEmbeddings from NasimB +author: John Snow Labs +name: bert_finetune_simcse_truncate +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetune_simcse_truncate` is a English model originally trained by NasimB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetune_simcse_truncate_en_5.1.1_3.0_1694587925442.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetune_simcse_truncate_en_5.1.1_3.0_1694587925442.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_finetune_simcse_truncate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_finetune_simcse_truncate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetune_simcse_truncate| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.3 MB| + +## References + +https://huggingface.co/NasimB/bert-finetune-simcse-truncate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_finetuning_test1_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_finetuning_test1_en.md new file mode 100644 index 00000000000000..22ee728365f6b5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_finetuning_test1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_finetuning_test1 BertEmbeddings from bill +author: John Snow Labs +name: bert_finetuning_test1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuning_test1` is a English model originally trained by bill. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuning_test1_en_5.1.1_3.0_1694587604269.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuning_test1_en_5.1.1_3.0_1694587604269.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_finetuning_test1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_finetuning_test1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuning_test1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/bill/bert_finetuning_test1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_finetuning_test_mine_result_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_finetuning_test_mine_result_en.md new file mode 100644 index 00000000000000..5a4c08c126c555 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_finetuning_test_mine_result_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_finetuning_test_mine_result BertEmbeddings from Martinlabla +author: John Snow Labs +name: bert_finetuning_test_mine_result +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuning_test_mine_result` is a English model originally trained by Martinlabla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuning_test_mine_result_en_5.1.1_3.0_1694567497197.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuning_test_mine_result_en_5.1.1_3.0_1694567497197.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_finetuning_test_mine_result","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_finetuning_test_mine_result", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuning_test_mine_result| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Martinlabla/bert_finetuning_test_mine_result \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_finetuning_test_xiejiafang_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_finetuning_test_xiejiafang_en.md new file mode 100644 index 00000000000000..1def6ee443a447 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_finetuning_test_xiejiafang_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_finetuning_test_xiejiafang BertEmbeddings from xiejiafang +author: John Snow Labs +name: bert_finetuning_test_xiejiafang +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuning_test_xiejiafang` is a English model originally trained by xiejiafang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuning_test_xiejiafang_en_5.1.1_3.0_1694586563453.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuning_test_xiejiafang_en_5.1.1_3.0_1694586563453.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_finetuning_test_xiejiafang","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_finetuning_test_xiejiafang", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuning_test_xiejiafang| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/xiejiafang/bert_finetuning_test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_hinglish_big_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_hinglish_big_en.md new file mode 100644 index 00000000000000..9076731484a34a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_hinglish_big_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_hinglish_big BertEmbeddings from aditeyabaral +author: John Snow Labs +name: bert_hinglish_big +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_hinglish_big` is a English model originally trained by aditeyabaral. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_hinglish_big_en_5.1.1_3.0_1694576522327.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_hinglish_big_en_5.1.1_3.0_1694576522327.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_hinglish_big","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_hinglish_big", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_hinglish_big| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.9 MB| + +## References + +https://huggingface.co/aditeyabaral/bert-hinglish-big \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_hinglish_small_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_hinglish_small_en.md new file mode 100644 index 00000000000000..b6ca09020bdff6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_hinglish_small_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_hinglish_small BertEmbeddings from aditeyabaral +author: John Snow Labs +name: bert_hinglish_small +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_hinglish_small` is a English model originally trained by aditeyabaral. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_hinglish_small_en_5.1.1_3.0_1694576634952.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_hinglish_small_en_5.1.1_3.0_1694576634952.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_hinglish_small","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_hinglish_small", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_hinglish_small| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.0 MB| + +## References + +https://huggingface.co/aditeyabaral/bert-hinglish-small \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_java_bfp_single_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_java_bfp_single_en.md new file mode 100644 index 00000000000000..b4bb0eeb54081b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_java_bfp_single_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_java_bfp_single BertEmbeddings from up201806461 +author: John Snow Labs +name: bert_java_bfp_single +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_java_bfp_single` is a English model originally trained by up201806461. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_java_bfp_single_en_5.1.1_3.0_1694578962374.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_java_bfp_single_en_5.1.1_3.0_1694578962374.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_java_bfp_single","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_java_bfp_single", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_java_bfp_single| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/up201806461/bert-java-bfp_single \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_arabertv2_ar.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_arabertv2_ar.md new file mode 100644 index 00000000000000..f11181a70680fd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_arabertv2_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_large_arabertv2 BertEmbeddings from aubmindlab +author: John Snow Labs +name: bert_large_arabertv2 +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_arabertv2` is a Arabic model originally trained by aubmindlab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_arabertv2_ar_5.1.1_3.0_1694584366192.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_arabertv2_ar_5.1.1_3.0_1694584366192.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_arabertv2","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_arabertv2", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_arabertv2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|1.4 GB| + +## References + +https://huggingface.co/aubmindlab/bert-large-arabertv2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_cased_da_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_cased_da_20_en.md new file mode 100644 index 00000000000000..cf89ae83f44e9e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_cased_da_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_finetuned_cased_da_20 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_finetuned_cased_da_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_finetuned_cased_da_20` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_cased_da_20_en_5.1.1_3.0_1694564770508.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_cased_da_20_en_5.1.1_3.0_1694564770508.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_finetuned_cased_da_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_finetuned_cased_da_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_finetuned_cased_da_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-finetuned-cased-DA-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_low20_1_cased_da_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_low20_1_cased_da_20_en.md new file mode 100644 index 00000000000000..742afa69b26391 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_low20_1_cased_da_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_finetuned_low20_1_cased_da_20 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_finetuned_low20_1_cased_da_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_finetuned_low20_1_cased_da_20` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_low20_1_cased_da_20_en_5.1.1_3.0_1694565382077.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_low20_1_cased_da_20_en_5.1.1_3.0_1694565382077.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_finetuned_low20_1_cased_da_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_finetuned_low20_1_cased_da_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_finetuned_low20_1_cased_da_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-finetuned-low20-1-cased-DA-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_low20_cased_da_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_low20_cased_da_20_en.md new file mode 100644 index 00000000000000..5628db32798146 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_low20_cased_da_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_finetuned_low20_cased_da_20 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_finetuned_low20_cased_da_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_finetuned_low20_cased_da_20` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_low20_cased_da_20_en_5.1.1_3.0_1694565075875.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_low20_cased_da_20_en_5.1.1_3.0_1694565075875.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_finetuned_low20_cased_da_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_finetuned_low20_cased_da_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_finetuned_low20_cased_da_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-finetuned-low20-cased-DA-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_lowr100_0_cased_da_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_lowr100_0_cased_da_20_en.md new file mode 100644 index 00000000000000..bf2d0cdadfa73c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_lowr100_0_cased_da_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_finetuned_lowr100_0_cased_da_20 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_finetuned_lowr100_0_cased_da_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_finetuned_lowr100_0_cased_da_20` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_lowr100_0_cased_da_20_en_5.1.1_3.0_1694566867523.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_lowr100_0_cased_da_20_en_5.1.1_3.0_1694566867523.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_finetuned_lowr100_0_cased_da_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_finetuned_lowr100_0_cased_da_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_finetuned_lowr100_0_cased_da_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-finetuned-lowR100-0-cased-DA-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_lowr100_2_cased_da_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_lowr100_2_cased_da_20_en.md new file mode 100644 index 00000000000000..9dc2a676cb3740 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_lowr100_2_cased_da_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_finetuned_lowr100_2_cased_da_20 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_finetuned_lowr100_2_cased_da_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_finetuned_lowr100_2_cased_da_20` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_lowr100_2_cased_da_20_en_5.1.1_3.0_1694567487345.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_lowr100_2_cased_da_20_en_5.1.1_3.0_1694567487345.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_finetuned_lowr100_2_cased_da_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_finetuned_lowr100_2_cased_da_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_finetuned_lowr100_2_cased_da_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-finetuned-lowR100-2-cased-DA-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_lowr100_3_cased_da_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_lowr100_3_cased_da_20_en.md new file mode 100644 index 00000000000000..c5885ec396d33b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_lowr100_3_cased_da_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_finetuned_lowr100_3_cased_da_20 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_finetuned_lowr100_3_cased_da_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_finetuned_lowr100_3_cased_da_20` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_lowr100_3_cased_da_20_en_5.1.1_3.0_1694568354875.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_lowr100_3_cased_da_20_en_5.1.1_3.0_1694568354875.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_finetuned_lowr100_3_cased_da_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_finetuned_lowr100_3_cased_da_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_finetuned_lowr100_3_cased_da_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-finetuned-lowR100-3-cased-DA-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_lowr10_0_cased_da_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_lowr10_0_cased_da_20_en.md new file mode 100644 index 00000000000000..6b434478dd0b6c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_lowr10_0_cased_da_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_finetuned_lowr10_0_cased_da_20 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_finetuned_lowr10_0_cased_da_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_finetuned_lowr10_0_cased_da_20` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_lowr10_0_cased_da_20_en_5.1.1_3.0_1694566558054.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_lowr10_0_cased_da_20_en_5.1.1_3.0_1694566558054.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_finetuned_lowr10_0_cased_da_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_finetuned_lowr10_0_cased_da_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_finetuned_lowr10_0_cased_da_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-finetuned-lowR10-0-cased-DA-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_portuguese_lenerbr_pt.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_portuguese_lenerbr_pt.md new file mode 100644 index 00000000000000..c79af0651c7296 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_portuguese_lenerbr_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese bert_large_cased_portuguese_lenerbr BertEmbeddings from pierreguillou +author: John Snow Labs +name: bert_large_cased_portuguese_lenerbr +date: 2023-09-13 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_portuguese_lenerbr` is a Portuguese model originally trained by pierreguillou. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_portuguese_lenerbr_pt_5.1.1_3.0_1694563642184.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_portuguese_lenerbr_pt_5.1.1_3.0_1694563642184.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_portuguese_lenerbr","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_portuguese_lenerbr", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_portuguese_lenerbr| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|1.2 GB| + +## References + +https://huggingface.co/pierreguillou/bert-large-cased-pt-lenerbr \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_full_norwegian_label_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_full_norwegian_label_20_en.md new file mode 100644 index 00000000000000..fc968c1493760b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_full_norwegian_label_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_full_norwegian_label_20 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_full_norwegian_label_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_full_norwegian_label_20` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_full_norwegian_label_20_en_5.1.1_3.0_1694571475257.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_full_norwegian_label_20_en_5.1.1_3.0_1694571475257.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_full_norwegian_label_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_full_norwegian_label_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_full_norwegian_label_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-full-no-label-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_lr100_0_cased_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_lr100_0_cased_20_en.md new file mode 100644 index 00000000000000..2a6aeb41314249 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_lr100_0_cased_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_lr100_0_cased_20 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_lr100_0_cased_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_lr100_0_cased_20` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_lr100_0_cased_20_en_5.1.1_3.0_1694569625563.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_lr100_0_cased_20_en_5.1.1_3.0_1694569625563.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_lr100_0_cased_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_lr100_0_cased_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_lr100_0_cased_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-LR100-0-cased-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_lr100_1_cased_150_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_lr100_1_cased_150_en.md new file mode 100644 index 00000000000000..6d946f77f19d95 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_lr100_1_cased_150_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_lr100_1_cased_150 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_lr100_1_cased_150 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_lr100_1_cased_150` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_lr100_1_cased_150_en_5.1.1_3.0_1694569943900.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_lr100_1_cased_150_en_5.1.1_3.0_1694569943900.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_lr100_1_cased_150","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_lr100_1_cased_150", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_lr100_1_cased_150| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-LR100-1-cased-150 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_norwegian_label_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_norwegian_label_20_en.md new file mode 100644 index 00000000000000..32011553115ddb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_norwegian_label_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_norwegian_label_20 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_norwegian_label_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_norwegian_label_20` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_norwegian_label_20_en_5.1.1_3.0_1694571918630.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_norwegian_label_20_en_5.1.1_3.0_1694571918630.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_norwegian_label_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_norwegian_label_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_norwegian_label_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-no-label-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_norwegian_label_20_sigir_tune2nd_lr10_labelled_30_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_norwegian_label_20_sigir_tune2nd_lr10_labelled_30_en.md new file mode 100644 index 00000000000000..ff2b3585327705 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_norwegian_label_20_sigir_tune2nd_lr10_labelled_30_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_norwegian_label_20_sigir_tune2nd_lr10_labelled_30 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_norwegian_label_20_sigir_tune2nd_lr10_labelled_30 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_norwegian_label_20_sigir_tune2nd_lr10_labelled_30` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_norwegian_label_20_sigir_tune2nd_lr10_labelled_30_en_5.1.1_3.0_1694572892853.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_norwegian_label_20_sigir_tune2nd_lr10_labelled_30_en_5.1.1_3.0_1694572892853.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_norwegian_label_20_sigir_tune2nd_lr10_labelled_30","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_norwegian_label_20_sigir_tune2nd_lr10_labelled_30", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_norwegian_label_20_sigir_tune2nd_lr10_labelled_30| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-no-label-20-sigir-tune2nd-LR10-labelled-30 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr100_labelled_30_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr100_labelled_30_en.md new file mode 100644 index 00000000000000..c8a51f528afb3e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr100_labelled_30_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr100_labelled_30 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr100_labelled_30 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr100_labelled_30` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr100_labelled_30_en_5.1.1_3.0_1694573387926.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr100_labelled_30_en_5.1.1_3.0_1694573387926.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr100_labelled_30","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr100_labelled_30", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr100_labelled_30| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-no-label-40-sigir-tune2nd-LR100-labelled-30 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr100_40_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr100_40_en.md new file mode 100644 index 00000000000000..42baa44b0f5b1a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr100_40_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr100_40 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr100_40 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr100_40` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr100_40_en_5.1.1_3.0_1694575697199.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr100_40_en_5.1.1_3.0_1694575697199.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr100_40","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr100_40", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr100_40| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR100-40 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_0_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_0_en.md new file mode 100644 index 00000000000000..8d51641eafa082 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_0 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_0 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_0` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_0_en_5.1.1_3.0_1694587179778.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_0_en_5.1.1_3.0_1694587179778.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_0| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-20-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_1_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_1_en.md new file mode 100644 index 00000000000000..63c70aa648c253 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_1 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_1` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_1_en_5.1.1_3.0_1694587498641.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_1_en_5.1.1_3.0_1694587498641.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_20_1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-20-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_2_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_2_en.md new file mode 100644 index 00000000000000..aa4440ab6b4e39 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_2 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_2` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_2_en_5.1.1_3.0_1694583363515.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_2_en_5.1.1_3.0_1694583363515.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-40-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_3_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_3_en.md new file mode 100644 index 00000000000000..1f3705914e7257 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_3 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_3 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_3` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_3_en_5.1.1_3.0_1694584252445.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_3_en_5.1.1_3.0_1694584252445.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-40-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_20_en.md new file mode 100644 index 00000000000000..10d26077bb674b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_20 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_20` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_20_en_5.1.1_3.0_1694569334144.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_20_en_5.1.1_3.0_1694569334144.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_23_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_23_en.md new file mode 100644 index 00000000000000..d6f9c2bf77517f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_23_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_23 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_23 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_23` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_23_en_5.1.1_3.0_1694570314564.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_23_en_5.1.1_3.0_1694570314564.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_23","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_23", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_23| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-23 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_25_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_25_en.md new file mode 100644 index 00000000000000..aaedbbe999655e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_25_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_25 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_25 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_25` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_25_en_5.1.1_3.0_1694570981130.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_25_en_5.1.1_3.0_1694570981130.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_25","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_25", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_25| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-25 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_12_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_12_en.md new file mode 100644 index 00000000000000..49f1ac33dd7388 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_12_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_12 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_12 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_12` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_12_en_5.1.1_3.0_1694574046482.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_12_en_5.1.1_3.0_1694574046482.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_12","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_12", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_12| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR50-8-fast-12 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_13_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_13_en.md new file mode 100644 index 00000000000000..56968d95fd37a9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_13_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_13 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_13 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_13` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_13_en_5.1.1_3.0_1694574358005.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_13_en_5.1.1_3.0_1694574358005.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_13","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_13", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_13| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR50-8-fast-13 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_14_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_14_en.md new file mode 100644 index 00000000000000..85029202afe2be --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_14_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_14 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_14 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_14` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_14_en_5.1.1_3.0_1694574703123.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_14_en_5.1.1_3.0_1694574703123.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_14","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_14", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_14| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR50-8-fast-14 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_16_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_16_en.md new file mode 100644 index 00000000000000..3c74ff9fe82398 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_16_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_16 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_16 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_16` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_16_en_5.1.1_3.0_1694575328626.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_16_en_5.1.1_3.0_1694575328626.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_16","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_16", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_16| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR50-8-fast-16 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_17_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_17_en.md new file mode 100644 index 00000000000000..fbfc1cf4982f05 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_17_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_17 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_17 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_17` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_17_en_5.1.1_3.0_1694575626182.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_17_en_5.1.1_3.0_1694575626182.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_17","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_17", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_17| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR50-8-fast-17 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_6_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_6_en.md new file mode 100644 index 00000000000000..78a13e0d225837 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_6_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_6 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_6 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_6` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_6_en_5.1.1_3.0_1694572150639.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_6_en_5.1.1_3.0_1694572150639.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_6","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_6", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_6| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR50-8-fast-6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_9_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_9_en.md new file mode 100644 index 00000000000000..86b598b7a2c880 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_9_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_9 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_9 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_9` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_9_en_5.1.1_3.0_1694573104931.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_9_en_5.1.1_3.0_1694573104931.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_9","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_9", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_9| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR50-8-fast-9 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_en.md new file mode 100644 index 00000000000000..11a6c25a169b43 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_en_5.1.1_3.0_1694575070771.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_en_5.1.1_3.0_1694575070771.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_contrastive_self_supervised_acl2020_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_contrastive_self_supervised_acl2020_en.md new file mode 100644 index 00000000000000..09d9d0127b41ee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_contrastive_self_supervised_acl2020_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_contrastive_self_supervised_acl2020 BertEmbeddings from sap-ai-research +author: John Snow Labs +name: bert_large_contrastive_self_supervised_acl2020 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_contrastive_self_supervised_acl2020` is a English model originally trained by sap-ai-research. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_contrastive_self_supervised_acl2020_en_5.1.1_3.0_1694571008191.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_contrastive_self_supervised_acl2020_en_5.1.1_3.0_1694571008191.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_contrastive_self_supervised_acl2020","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_contrastive_self_supervised_acl2020", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_contrastive_self_supervised_acl2020| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/sap-ai-research/BERT-Large-Contrastive-Self-Supervised-ACL2020 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_swedish_nordic_pile_150_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_swedish_nordic_pile_150_en.md new file mode 100644 index 00000000000000..ea5af26fb79536 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_swedish_nordic_pile_150_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_swedish_nordic_pile_150 BertEmbeddings from timpal0l +author: John Snow Labs +name: bert_large_swedish_nordic_pile_150 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_swedish_nordic_pile_150` is a English model originally trained by timpal0l. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_swedish_nordic_pile_150_en_5.1.1_3.0_1694579612852.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_swedish_nordic_pile_150_en_5.1.1_3.0_1694579612852.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_swedish_nordic_pile_150","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_swedish_nordic_pile_150", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_swedish_nordic_pile_150| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.4 GB| + +## References + +https://huggingface.co/timpal0l/bert_large_sv_nordic_pile_150 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_swedish_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_swedish_uncased_en.md new file mode 100644 index 00000000000000..234d4adf88b1ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_swedish_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_swedish_uncased BertEmbeddings from af-ai-center +author: John Snow Labs +name: bert_large_swedish_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_swedish_uncased` is a English model originally trained by af-ai-center. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_swedish_uncased_en_5.1.1_3.0_1694577225450.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_swedish_uncased_en_5.1.1_3.0_1694577225450.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_swedish_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_swedish_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_swedish_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/af-ai-center/bert-large-swedish-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_uncased_finetuned_lowr100_4_uncased_da_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_uncased_finetuned_lowr100_4_uncased_da_20_en.md new file mode 100644 index 00000000000000..94d90204495b8d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_uncased_finetuned_lowr100_4_uncased_da_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_finetuned_lowr100_4_uncased_da_20 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_uncased_finetuned_lowr100_4_uncased_da_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_finetuned_lowr100_4_uncased_da_20` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_finetuned_lowr100_4_uncased_da_20_en_5.1.1_3.0_1694568669304.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_finetuned_lowr100_4_uncased_da_20_en_5.1.1_3.0_1694568669304.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_finetuned_lowr100_4_uncased_da_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_finetuned_lowr100_4_uncased_da_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_finetuned_lowr100_4_uncased_da_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-uncased-finetuned-lowR100-4-uncased-DA-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_uncased_finetuned_lowr100_5_uncased_da_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_uncased_finetuned_lowr100_5_uncased_da_20_en.md new file mode 100644 index 00000000000000..290ff49ccce50e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_uncased_finetuned_lowr100_5_uncased_da_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_finetuned_lowr100_5_uncased_da_20 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_uncased_finetuned_lowr100_5_uncased_da_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_finetuned_lowr100_5_uncased_da_20` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_finetuned_lowr100_5_uncased_da_20_en_5.1.1_3.0_1694568976431.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_finetuned_lowr100_5_uncased_da_20_en_5.1.1_3.0_1694568976431.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_finetuned_lowr100_5_uncased_da_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_finetuned_lowr100_5_uncased_da_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_finetuned_lowr100_5_uncased_da_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-uncased-finetuned-lowR100-5-uncased-DA-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_yc_recipe_30_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_yc_recipe_30_en.md new file mode 100644 index 00000000000000..4a7e7a1eef328a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_yc_recipe_30_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_yc_recipe_30 BertEmbeddings from CennetOguz +author: John Snow Labs +name: bert_large_yc_recipe_30 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_yc_recipe_30` is a English model originally trained by CennetOguz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_yc_recipe_30_en_5.1.1_3.0_1694568821432.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_yc_recipe_30_en_5.1.1_3.0_1694568821432.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_yc_recipe_30","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_yc_recipe_30", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_yc_recipe_30| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/CennetOguz/bert_large_yc_recipe_30 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_mini_arabic_ar.md b/docs/_posts/ahmedlone127/2023-09-13-bert_mini_arabic_ar.md new file mode 100644 index 00000000000000..0754572efb6391 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_mini_arabic_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_mini_arabic BertEmbeddings from asafaya +author: John Snow Labs +name: bert_mini_arabic +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_mini_arabic` is a Arabic model originally trained by asafaya. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_mini_arabic_ar_5.1.1_3.0_1694582500015.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_mini_arabic_ar_5.1.1_3.0_1694582500015.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_mini_arabic","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_mini_arabic", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_mini_arabic| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|43.3 MB| + +## References + +https://huggingface.co/asafaya/bert-mini-arabic \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_model_nyashavision22_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_model_nyashavision22_en.md new file mode 100644 index 00000000000000..3c902faad2fe87 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_model_nyashavision22_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_model_nyashavision22 BertEmbeddings from NyashaVision22 +author: John Snow Labs +name: bert_model_nyashavision22 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_model_nyashavision22` is a English model originally trained by NyashaVision22. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_model_nyashavision22_en_5.1.1_3.0_1694576589229.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_model_nyashavision22_en_5.1.1_3.0_1694576589229.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_model_nyashavision22","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_model_nyashavision22", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_model_nyashavision22| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/NyashaVision22/bert_model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_nlp_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_nlp_en.md new file mode 100644 index 00000000000000..39e3cba70f042e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_nlp_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_nlp BertEmbeddings from subbareddyiiit +author: John Snow Labs +name: bert_nlp +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_nlp` is a English model originally trained by subbareddyiiit. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_nlp_en_5.1.1_3.0_1694575401616.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_nlp_en_5.1.1_3.0_1694575401616.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_nlp","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_nlp", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_nlp| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/subbareddyiiit/BERT-NLP \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_portuguese_inf_corpus_v.1_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_portuguese_inf_corpus_v.1_en.md new file mode 100644 index 00000000000000..2ece59f5ffa5d1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_portuguese_inf_corpus_v.1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_portuguese_inf_corpus_v.1 BertEmbeddings from ricardo-filho +author: John Snow Labs +name: bert_portuguese_inf_corpus_v.1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_portuguese_inf_corpus_v.1` is a English model originally trained by ricardo-filho. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_portuguese_inf_corpus_v.1_en_5.1.1_3.0_1694568024545.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_portuguese_inf_corpus_v.1_en_5.1.1_3.0_1694568024545.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_portuguese_inf_corpus_v.1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_portuguese_inf_corpus_v.1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_portuguese_inf_corpus_v.1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/ricardo-filho/BERT-pt-inf-corpus-v.1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_portuguese_institutional_corpus_v.1_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_portuguese_institutional_corpus_v.1_en.md new file mode 100644 index 00000000000000..85eb5332731eeb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_portuguese_institutional_corpus_v.1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_portuguese_institutional_corpus_v.1 BertEmbeddings from ricardo-filho +author: John Snow Labs +name: bert_portuguese_institutional_corpus_v.1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_portuguese_institutional_corpus_v.1` is a English model originally trained by ricardo-filho. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_portuguese_institutional_corpus_v.1_en_5.1.1_3.0_1694568183725.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_portuguese_institutional_corpus_v.1_en_5.1.1_3.0_1694568183725.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_portuguese_institutional_corpus_v.1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_portuguese_institutional_corpus_v.1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_portuguese_institutional_corpus_v.1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/ricardo-filho/BERT-pt-institutional-corpus-v.1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_portuguese_institutional_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_portuguese_institutional_en.md new file mode 100644 index 00000000000000..7b1160dc5c975a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_portuguese_institutional_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_portuguese_institutional BertEmbeddings from ricardo-filho +author: John Snow Labs +name: bert_portuguese_institutional +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_portuguese_institutional` is a English model originally trained by ricardo-filho. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_portuguese_institutional_en_5.1.1_3.0_1694568312532.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_portuguese_institutional_en_5.1.1_3.0_1694568312532.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_portuguese_institutional","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_portuguese_institutional", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_portuguese_institutional| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/ricardo-filho/BERT-pt-institutional \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_pt_laptop_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_pt_laptop_en.md new file mode 100644 index 00000000000000..0d8598c932a835 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_pt_laptop_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_pt_laptop BertEmbeddings from activebus +author: John Snow Labs +name: bert_pt_laptop +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_pt_laptop` is a English model originally trained by activebus. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_pt_laptop_en_5.1.1_3.0_1694575945954.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_pt_laptop_en_5.1.1_3.0_1694575945954.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_pt_laptop","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_pt_laptop", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_pt_laptop| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|258.4 MB| + +## References + +https://huggingface.co/activebus/BERT-PT_laptop \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_pt_rest_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_pt_rest_en.md new file mode 100644 index 00000000000000..e5a75c271c4afc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_pt_rest_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_pt_rest BertEmbeddings from activebus +author: John Snow Labs +name: bert_pt_rest +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_pt_rest` is a English model originally trained by activebus. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_pt_rest_en_5.1.1_3.0_1694576170608.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_pt_rest_en_5.1.1_3.0_1694576170608.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_pt_rest","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_pt_rest", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_pt_rest| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|258.4 MB| + +## References + +https://huggingface.co/activebus/BERT-PT_rest \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_small_cord19_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_small_cord19_en.md new file mode 100644 index 00000000000000..0daf7b1a83b9d1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_small_cord19_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_small_cord19 BertEmbeddings from NeuML +author: John Snow Labs +name: bert_small_cord19 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_small_cord19` is a English model originally trained by NeuML. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_small_cord19_en_5.1.1_3.0_1694569266803.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_small_cord19_en_5.1.1_3.0_1694569266803.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_small_cord19","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_small_cord19", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_small_cord19| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|130.5 MB| + +## References + +https://huggingface.co/NeuML/bert-small-cord19 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_small_finer_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finer_en.md new file mode 100644 index 00000000000000..d0fdcade4480da --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_small_finer BertEmbeddings from muhtasham +author: John Snow Labs +name: bert_small_finer +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_small_finer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_small_finer_en_5.1.1_3.0_1694578634265.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_small_finer_en_5.1.1_3.0_1694578634265.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_small_finer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_small_finer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_small_finer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/bert-small-finer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_small_finer_longer_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finer_longer_en.md new file mode 100644 index 00000000000000..597ad6f196b6b8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finer_longer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_small_finer_longer BertEmbeddings from muhtasham +author: John Snow Labs +name: bert_small_finer_longer +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_small_finer_longer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_small_finer_longer_en_5.1.1_3.0_1694578725327.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_small_finer_longer_en_5.1.1_3.0_1694578725327.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_small_finer_longer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_small_finer_longer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_small_finer_longer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/bert-small-finer-longer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_eurlex_longer_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_eurlex_longer_en.md new file mode 100644 index 00000000000000..f93de11cb3d516 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_eurlex_longer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_small_finetuned_eurlex_longer BertEmbeddings from muhtasham +author: John Snow Labs +name: bert_small_finetuned_eurlex_longer +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_small_finetuned_eurlex_longer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_eurlex_longer_en_5.1.1_3.0_1694569982036.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_eurlex_longer_en_5.1.1_3.0_1694569982036.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_small_finetuned_eurlex_longer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_small_finetuned_eurlex_longer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_small_finetuned_eurlex_longer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/bert-small-finetuned-eurlex-longer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_finer_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_finer_en.md new file mode 100644 index 00000000000000..0370444f921f6d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_finer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_small_finetuned_finer BertEmbeddings from muhtasham +author: John Snow Labs +name: bert_small_finetuned_finer +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_small_finetuned_finer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_finer_en_5.1.1_3.0_1694577758798.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_finer_en_5.1.1_3.0_1694577758798.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_small_finetuned_finer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_small_finetuned_finer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_small_finetuned_finer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/bert-small-finetuned-finer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_finer_longer10_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_finer_longer10_en.md new file mode 100644 index 00000000000000..5bc0cf5e011fac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_finer_longer10_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_small_finetuned_finer_longer10 BertEmbeddings from muhtasham +author: John Snow Labs +name: bert_small_finetuned_finer_longer10 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_small_finetuned_finer_longer10` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_finer_longer10_en_5.1.1_3.0_1694577859721.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_finer_longer10_en_5.1.1_3.0_1694577859721.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_small_finetuned_finer_longer10","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_small_finetuned_finer_longer10", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_small_finetuned_finer_longer10| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/bert-small-finetuned-finer-longer10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_small_nan_labels_500_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_small_nan_labels_500_en.md new file mode 100644 index 00000000000000..b6ca774d2c0695 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_small_nan_labels_500_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_small_nan_labels_500 BertEmbeddings from muhtasham +author: John Snow Labs +name: bert_small_nan_labels_500 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_small_nan_labels_500` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_small_nan_labels_500_en_5.1.1_3.0_1694574048493.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_small_nan_labels_500_en_5.1.1_3.0_1694574048493.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_small_nan_labels_500","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_small_nan_labels_500", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_small_nan_labels_500| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/bert-small-nan-labels-500 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_small_pretrained_on_squad_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_small_pretrained_on_squad_en.md new file mode 100644 index 00000000000000..9c78abf0796c12 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_small_pretrained_on_squad_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_small_pretrained_on_squad BertEmbeddings from anas-awadalla +author: John Snow Labs +name: bert_small_pretrained_on_squad +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_small_pretrained_on_squad` is a English model originally trained by anas-awadalla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_small_pretrained_on_squad_en_5.1.1_3.0_1694579945463.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_small_pretrained_on_squad_en_5.1.1_3.0_1694579945463.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_small_pretrained_on_squad","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_small_pretrained_on_squad", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_small_pretrained_on_squad| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/anas-awadalla/bert-small-pretrained-on-squad \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_tiny_finetuned_legal_definitions_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_tiny_finetuned_legal_definitions_en.md new file mode 100644 index 00000000000000..e30e903e9dfbb6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_tiny_finetuned_legal_definitions_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_tiny_finetuned_legal_definitions BertEmbeddings from muhtasham +author: John Snow Labs +name: bert_tiny_finetuned_legal_definitions +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tiny_finetuned_legal_definitions` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tiny_finetuned_legal_definitions_en_5.1.1_3.0_1694564433910.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tiny_finetuned_legal_definitions_en_5.1.1_3.0_1694564433910.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_tiny_finetuned_legal_definitions","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_tiny_finetuned_legal_definitions", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tiny_finetuned_legal_definitions| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/muhtasham/bert-tiny-finetuned-legal-definitions \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_tiny_finetuned_nan_labels_nepal_bhasa_longer_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_tiny_finetuned_nan_labels_nepal_bhasa_longer_en.md new file mode 100644 index 00000000000000..f0ff6a6c7c3782 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_tiny_finetuned_nan_labels_nepal_bhasa_longer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_tiny_finetuned_nan_labels_nepal_bhasa_longer BertEmbeddings from muhtasham +author: John Snow Labs +name: bert_tiny_finetuned_nan_labels_nepal_bhasa_longer +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tiny_finetuned_nan_labels_nepal_bhasa_longer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tiny_finetuned_nan_labels_nepal_bhasa_longer_en_5.1.1_3.0_1694565410527.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tiny_finetuned_nan_labels_nepal_bhasa_longer_en_5.1.1_3.0_1694565410527.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_tiny_finetuned_nan_labels_nepal_bhasa_longer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_tiny_finetuned_nan_labels_nepal_bhasa_longer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tiny_finetuned_nan_labels_nepal_bhasa_longer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/muhtasham/bert-tiny-finetuned-nan-labels-new-longer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_truncate_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_truncate_en.md new file mode 100644 index 00000000000000..ab00b088985163 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_truncate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_truncate BertEmbeddings from NasimB +author: John Snow Labs +name: bert_truncate +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_truncate` is a English model originally trained by NasimB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_truncate_en_5.1.1_3.0_1694587157781.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_truncate_en_5.1.1_3.0_1694587157781.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_truncate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_truncate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_truncate| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|400.3 MB| + +## References + +https://huggingface.co/NasimB/bert-truncate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_ucb_5_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_ucb_5_en.md new file mode 100644 index 00000000000000..0f4f477fd701a5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_ucb_5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_ucb_5 BertEmbeddings from Diegomejia +author: John Snow Labs +name: bert_ucb_5 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ucb_5` is a English model originally trained by Diegomejia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ucb_5_en_5.1.1_3.0_1694570353048.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ucb_5_en_5.1.1_3.0_1694570353048.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_ucb_5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_ucb_5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ucb_5| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/Diegomejia/bert-ucb-5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_10_h_512_a_8_cord19_200616_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_10_h_512_a_8_cord19_200616_en.md new file mode 100644 index 00000000000000..5fac0d66ef244d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_10_h_512_a_8_cord19_200616_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_uncased_l_10_h_512_a_8_cord19_200616 BertEmbeddings from aodiniz +author: John Snow Labs +name: bert_uncased_l_10_h_512_a_8_cord19_200616 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_uncased_l_10_h_512_a_8_cord19_200616` is a English model originally trained by aodiniz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_uncased_l_10_h_512_a_8_cord19_200616_en_5.1.1_3.0_1694580662292.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_uncased_l_10_h_512_a_8_cord19_200616_en_5.1.1_3.0_1694580662292.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_uncased_l_10_h_512_a_8_cord19_200616","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_uncased_l_10_h_512_a_8_cord19_200616", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_uncased_l_10_h_512_a_8_cord19_200616| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|177.4 MB| + +## References + +https://huggingface.co/aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_2_h_256_a_4_mlm_multi_emails_hq_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_2_h_256_a_4_mlm_multi_emails_hq_en.md new file mode 100644 index 00000000000000..5faa852e2c8546 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_2_h_256_a_4_mlm_multi_emails_hq_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_uncased_l_2_h_256_a_4_mlm_multi_emails_hq BertEmbeddings from postbot +author: John Snow Labs +name: bert_uncased_l_2_h_256_a_4_mlm_multi_emails_hq +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_uncased_l_2_h_256_a_4_mlm_multi_emails_hq` is a English model originally trained by postbot. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_uncased_l_2_h_256_a_4_mlm_multi_emails_hq_en_5.1.1_3.0_1694575813152.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_uncased_l_2_h_256_a_4_mlm_multi_emails_hq_en_5.1.1_3.0_1694575813152.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_uncased_l_2_h_256_a_4_mlm_multi_emails_hq","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_uncased_l_2_h_256_a_4_mlm_multi_emails_hq", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_uncased_l_2_h_256_a_4_mlm_multi_emails_hq| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|35.9 MB| + +## References + +https://huggingface.co/postbot/bert_uncased_L-2_H-256_A-4-mlm-multi-emails-hq \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_2_h_512_a_8_cord19_200616_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_2_h_512_a_8_cord19_200616_en.md new file mode 100644 index 00000000000000..2ab2f5f66731e5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_2_h_512_a_8_cord19_200616_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_uncased_l_2_h_512_a_8_cord19_200616 BertEmbeddings from aodiniz +author: John Snow Labs +name: bert_uncased_l_2_h_512_a_8_cord19_200616 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_uncased_l_2_h_512_a_8_cord19_200616` is a English model originally trained by aodiniz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_uncased_l_2_h_512_a_8_cord19_200616_en_5.1.1_3.0_1694580817170.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_uncased_l_2_h_512_a_8_cord19_200616_en_5.1.1_3.0_1694580817170.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_uncased_l_2_h_512_a_8_cord19_200616","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_uncased_l_2_h_512_a_8_cord19_200616", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_uncased_l_2_h_512_a_8_cord19_200616| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|83.3 MB| + +## References + +https://huggingface.co/aodiniz/bert_uncased_L-2_H-512_A-8_cord19-200616 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_4_h_512_a_8_cord19_200616_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_4_h_512_a_8_cord19_200616_en.md new file mode 100644 index 00000000000000..0efa00cf262be3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_4_h_512_a_8_cord19_200616_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_uncased_l_4_h_512_a_8_cord19_200616 BertEmbeddings from aodiniz +author: John Snow Labs +name: bert_uncased_l_4_h_512_a_8_cord19_200616 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_uncased_l_4_h_512_a_8_cord19_200616` is a English model originally trained by aodiniz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_uncased_l_4_h_512_a_8_cord19_200616_en_5.1.1_3.0_1694580980693.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_uncased_l_4_h_512_a_8_cord19_200616_en_5.1.1_3.0_1694580980693.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_uncased_l_4_h_512_a_8_cord19_200616","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_uncased_l_4_h_512_a_8_cord19_200616", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_uncased_l_4_h_512_a_8_cord19_200616| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/aodiniz/bert_uncased_L-4_H-512_A-8_cord19-200616 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_6_h_128_a_2_cord19_200616_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_6_h_128_a_2_cord19_200616_en.md new file mode 100644 index 00000000000000..6045aee085b47f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_6_h_128_a_2_cord19_200616_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_uncased_l_6_h_128_a_2_cord19_200616 BertEmbeddings from aodiniz +author: John Snow Labs +name: bert_uncased_l_6_h_128_a_2_cord19_200616 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_uncased_l_6_h_128_a_2_cord19_200616` is a English model originally trained by aodiniz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_uncased_l_6_h_128_a_2_cord19_200616_en_5.1.1_3.0_1694581174056.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_uncased_l_6_h_128_a_2_cord19_200616_en_5.1.1_3.0_1694581174056.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_uncased_l_6_h_128_a_2_cord19_200616","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_uncased_l_6_h_128_a_2_cord19_200616", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_uncased_l_6_h_128_a_2_cord19_200616| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|19.6 MB| + +## References + +https://huggingface.co/aodiniz/bert_uncased_L-6_H-128_A-2_cord19-200616 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_tiny_2xthicc_multi_emails_hq_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_tiny_2xthicc_multi_emails_hq_en.md new file mode 100644 index 00000000000000..01094daa40f30b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_tiny_2xthicc_multi_emails_hq_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_uncased_tiny_2xthicc_multi_emails_hq BertEmbeddings from postbot +author: John Snow Labs +name: bert_uncased_tiny_2xthicc_multi_emails_hq +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_uncased_tiny_2xthicc_multi_emails_hq` is a English model originally trained by postbot. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_uncased_tiny_2xthicc_multi_emails_hq_en_5.1.1_3.0_1694575965085.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_uncased_tiny_2xthicc_multi_emails_hq_en_5.1.1_3.0_1694575965085.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_uncased_tiny_2xthicc_multi_emails_hq","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_uncased_tiny_2xthicc_multi_emails_hq", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_uncased_tiny_2xthicc_multi_emails_hq| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|18.1 MB| + +## References + +https://huggingface.co/postbot/bert_uncased_tiny_2xthicc-multi-emails-hq \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_tiny_multi_emails_hq_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_tiny_multi_emails_hq_en.md new file mode 100644 index 00000000000000..1bc9cd4fed9901 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_tiny_multi_emails_hq_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_uncased_tiny_multi_emails_hq BertEmbeddings from postbot +author: John Snow Labs +name: bert_uncased_tiny_multi_emails_hq +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_uncased_tiny_multi_emails_hq` is a English model originally trained by postbot. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_uncased_tiny_multi_emails_hq_en_5.1.1_3.0_1694575894615.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_uncased_tiny_multi_emails_hq_en_5.1.1_3.0_1694575894615.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_uncased_tiny_multi_emails_hq","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_uncased_tiny_multi_emails_hq", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_uncased_tiny_multi_emails_hq| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/postbot/bert_uncased_tiny-multi-emails-hq \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_wwm_words_law_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_wwm_words_law_en.md new file mode 100644 index 00000000000000..b6d165d851dc51 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_wwm_words_law_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_wwm_words_law BertEmbeddings from ssbuild +author: John Snow Labs +name: bert_wwm_words_law +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_wwm_words_law` is a English model originally trained by ssbuild. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_wwm_words_law_en_5.1.1_3.0_1694583048594.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_wwm_words_law_en_5.1.1_3.0_1694583048594.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_wwm_words_law","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_wwm_words_law", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_wwm_words_law| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|424.2 MB| + +## References + +https://huggingface.co/ssbuild/bert_wwm_words_law \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_yelp_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_yelp_en.md new file mode 100644 index 00000000000000..20781554b69aa9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_yelp_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_yelp BertEmbeddings from spicecloud +author: John Snow Labs +name: bert_yelp +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_yelp` is a English model originally trained by spicecloud. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_yelp_en_5.1.1_3.0_1694578158390.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_yelp_en_5.1.1_3.0_1694578158390.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_yelp","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_yelp", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_yelp| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/spicecloud/bert-yelp \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_yelp_local_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_yelp_local_en.md new file mode 100644 index 00000000000000..34590cb2b70605 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_yelp_local_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_yelp_local BertEmbeddings from spicecloud +author: John Snow Labs +name: bert_yelp_local +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_yelp_local` is a English model originally trained by spicecloud. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_yelp_local_en_5.1.1_3.0_1694578003743.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_yelp_local_en_5.1.1_3.0_1694578003743.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_yelp_local","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_yelp_local", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_yelp_local| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/spicecloud/bert-yelp-local \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bertimbau_base_finetuned_lener_breton_pt.md b/docs/_posts/ahmedlone127/2023-09-13-bertimbau_base_finetuned_lener_breton_pt.md new file mode 100644 index 00000000000000..a5e45323acaf3c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bertimbau_base_finetuned_lener_breton_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese bertimbau_base_finetuned_lener_breton BertEmbeddings from Luciano +author: John Snow Labs +name: bertimbau_base_finetuned_lener_breton +date: 2023-09-13 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertimbau_base_finetuned_lener_breton` is a Portuguese model originally trained by Luciano. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertimbau_base_finetuned_lener_breton_pt_5.1.1_3.0_1694580858415.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertimbau_base_finetuned_lener_breton_pt_5.1.1_3.0_1694580858415.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertimbau_base_finetuned_lener_breton","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertimbau_base_finetuned_lener_breton", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertimbau_base_finetuned_lener_breton| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|405.9 MB| + +## References + +https://huggingface.co/Luciano/bertimbau-base-finetuned-lener-br \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bertjewdialdataallqonly128_en.md b/docs/_posts/ahmedlone127/2023-09-13-bertjewdialdataallqonly128_en.md new file mode 100644 index 00000000000000..cb61fc45eb9617 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bertjewdialdataallqonly128_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertjewdialdataallqonly128 BertEmbeddings from Jeska +author: John Snow Labs +name: bertjewdialdataallqonly128 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertjewdialdataallqonly128` is a English model originally trained by Jeska. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly128_en_5.1.1_3.0_1694563422405.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly128_en_5.1.1_3.0_1694563422405.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertjewdialdataallqonly128","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertjewdialdataallqonly128", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertjewdialdataallqonly128| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/Jeska/BertjeWDialDataALLQonly128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-biblitbert_1_en.md b/docs/_posts/ahmedlone127/2023-09-13-biblitbert_1_en.md new file mode 100644 index 00000000000000..8824d2a4111687 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-biblitbert_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English biblitbert_1 BertEmbeddings from vppvgit +author: John Snow Labs +name: biblitbert_1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biblitbert_1` is a English model originally trained by vppvgit. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biblitbert_1_en_5.1.1_3.0_1694582201983.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biblitbert_1_en_5.1.1_3.0_1694582201983.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biblitbert_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biblitbert_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biblitbert_1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|411.7 MB| + +## References + +https://huggingface.co/vppvgit/BiblItBERT-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bio_minialbert_128_en.md b/docs/_posts/ahmedlone127/2023-09-13-bio_minialbert_128_en.md new file mode 100644 index 00000000000000..735a049c1999b9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bio_minialbert_128_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bio_minialbert_128 BertEmbeddings from nlpie +author: John Snow Labs +name: bio_minialbert_128 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bio_minialbert_128` is a English model originally trained by nlpie. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bio_minialbert_128_en_5.1.1_3.0_1694568055117.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bio_minialbert_128_en_5.1.1_3.0_1694568055117.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bio_minialbert_128","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bio_minialbert_128", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bio_minialbert_128| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|243.6 MB| + +## References + +https://huggingface.co/nlpie/bio-miniALBERT-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bio_tinybert_en.md b/docs/_posts/ahmedlone127/2023-09-13-bio_tinybert_en.md new file mode 100644 index 00000000000000..a25dbbbc345763 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bio_tinybert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bio_tinybert BertEmbeddings from nlpie +author: John Snow Labs +name: bio_tinybert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bio_tinybert` is a English model originally trained by nlpie. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bio_tinybert_en_5.1.1_3.0_1694577599069.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bio_tinybert_en_5.1.1_3.0_1694577599069.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bio_tinybert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bio_tinybert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bio_tinybert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|53.8 MB| + +## References + +https://huggingface.co/nlpie/bio-tinybert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-biobert_base_1.2_en.md b/docs/_posts/ahmedlone127/2023-09-13-biobert_base_1.2_en.md new file mode 100644 index 00000000000000..37f3e529a1179b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-biobert_base_1.2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English biobert_base_1.2 BertEmbeddings from abnuel +author: John Snow Labs +name: biobert_base_1.2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biobert_base_1.2` is a English model originally trained by abnuel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biobert_base_1.2_en_5.1.1_3.0_1694573075905.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biobert_base_1.2_en_5.1.1_3.0_1694573075905.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biobert_base_1.2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biobert_base_1.2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biobert_base_1.2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/abnuel/biobert-base_1.2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-biobertpt_all_pt.md b/docs/_posts/ahmedlone127/2023-09-13-biobertpt_all_pt.md new file mode 100644 index 00000000000000..4c949a0e8a3ce2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-biobertpt_all_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese biobertpt_all BertEmbeddings from pucpr +author: John Snow Labs +name: biobertpt_all +date: 2023-09-13 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biobertpt_all` is a Portuguese model originally trained by pucpr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biobertpt_all_pt_5.1.1_3.0_1694564346908.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biobertpt_all_pt_5.1.1_3.0_1694564346908.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biobertpt_all","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biobertpt_all", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biobertpt_all| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|664.8 MB| + +## References + +https://huggingface.co/pucpr/biobertpt-all \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-biobertpt_bio_pt.md b/docs/_posts/ahmedlone127/2023-09-13-biobertpt_bio_pt.md new file mode 100644 index 00000000000000..a99ea40f5089cb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-biobertpt_bio_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese biobertpt_bio BertEmbeddings from pucpr +author: John Snow Labs +name: biobertpt_bio +date: 2023-09-13 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biobertpt_bio` is a Portuguese model originally trained by pucpr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biobertpt_bio_pt_5.1.1_3.0_1694564511348.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biobertpt_bio_pt_5.1.1_3.0_1694564511348.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biobertpt_bio","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biobertpt_bio", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biobertpt_bio| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|665.0 MB| + +## References + +https://huggingface.co/pucpr/biobertpt-bio \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-biobertpt_clin_pt.md b/docs/_posts/ahmedlone127/2023-09-13-biobertpt_clin_pt.md new file mode 100644 index 00000000000000..f1500d423fab4f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-biobertpt_clin_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese biobertpt_clin BertEmbeddings from pucpr +author: John Snow Labs +name: biobertpt_clin +date: 2023-09-13 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biobertpt_clin` is a Portuguese model originally trained by pucpr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biobertpt_clin_pt_5.1.1_3.0_1694564736648.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biobertpt_clin_pt_5.1.1_3.0_1694564736648.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biobertpt_clin","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biobertpt_clin", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biobertpt_clin| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|665.0 MB| + +## References + +https://huggingface.co/pucpr/biobertpt-clin \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bioformer_16l_en.md b/docs/_posts/ahmedlone127/2023-09-13-bioformer_16l_en.md new file mode 100644 index 00000000000000..fadb7c01fd2fbc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bioformer_16l_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bioformer_16l BertEmbeddings from bioformers +author: John Snow Labs +name: bioformer_16l +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bioformer_16l` is a English model originally trained by bioformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bioformer_16l_en_5.1.1_3.0_1694566235721.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bioformer_16l_en_5.1.1_3.0_1694566235721.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bioformer_16l","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bioformer_16l", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bioformer_16l| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|155.3 MB| + +## References + +https://huggingface.co/bioformers/bioformer-16L \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-biomedical_en.md b/docs/_posts/ahmedlone127/2023-09-13-biomedical_en.md new file mode 100644 index 00000000000000..b913be3cc32eba --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-biomedical_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English biomedical BertEmbeddings from ajitrajasekharan +author: John Snow Labs +name: biomedical +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biomedical` is a English model originally trained by ajitrajasekharan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biomedical_en_5.1.1_3.0_1694577740537.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biomedical_en_5.1.1_3.0_1694577740537.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biomedical","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biomedical", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biomedical| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/ajitrajasekharan/biomedical \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bioptimus_en.md b/docs/_posts/ahmedlone127/2023-09-13-bioptimus_en.md new file mode 100644 index 00000000000000..9338e76033d79b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bioptimus_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bioptimus BertEmbeddings from rttl-ai +author: John Snow Labs +name: bioptimus +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bioptimus` is a English model originally trained by rttl-ai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bioptimus_en_5.1.1_3.0_1694587762594.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bioptimus_en_5.1.1_3.0_1694587762594.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bioptimus","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bioptimus", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bioptimus| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/rttl-ai/BIOptimus \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-burmese_awesome_eli5_mlm_model_en.md b/docs/_posts/ahmedlone127/2023-09-13-burmese_awesome_eli5_mlm_model_en.md new file mode 100644 index 00000000000000..5bab73d8dc62fd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-burmese_awesome_eli5_mlm_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English burmese_awesome_eli5_mlm_model BertEmbeddings from JackWolfard +author: John Snow Labs +name: burmese_awesome_eli5_mlm_model +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`burmese_awesome_eli5_mlm_model` is a English model originally trained by JackWolfard. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/burmese_awesome_eli5_mlm_model_en_5.1.1_3.0_1694583256576.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/burmese_awesome_eli5_mlm_model_en_5.1.1_3.0_1694583256576.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("burmese_awesome_eli5_mlm_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("burmese_awesome_eli5_mlm_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|burmese_awesome_eli5_mlm_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|523.9 KB| + +## References + +https://huggingface.co/JackWolfard/my_awesome_eli5_mlm_model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-burmese_awesome_model_alexyalunin_en.md b/docs/_posts/ahmedlone127/2023-09-13-burmese_awesome_model_alexyalunin_en.md new file mode 100644 index 00000000000000..7a02b6b052b48a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-burmese_awesome_model_alexyalunin_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English burmese_awesome_model_alexyalunin BertEmbeddings from alexyalunin +author: John Snow Labs +name: burmese_awesome_model_alexyalunin +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`burmese_awesome_model_alexyalunin` is a English model originally trained by alexyalunin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/burmese_awesome_model_alexyalunin_en_5.1.1_3.0_1694578520145.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/burmese_awesome_model_alexyalunin_en_5.1.1_3.0_1694578520145.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("burmese_awesome_model_alexyalunin","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("burmese_awesome_model_alexyalunin", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|burmese_awesome_model_alexyalunin| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|664.3 MB| + +## References + +https://huggingface.co/alexyalunin/my-awesome-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-chefberto_italian_cased_it.md b/docs/_posts/ahmedlone127/2023-09-13-chefberto_italian_cased_it.md new file mode 100644 index 00000000000000..89b3dbd0bf80a9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-chefberto_italian_cased_it.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Italian chefberto_italian_cased BertEmbeddings from vinhood +author: John Snow Labs +name: chefberto_italian_cased +date: 2023-09-13 +tags: [bert, it, open_source, fill_mask, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`chefberto_italian_cased` is a Italian model originally trained by vinhood. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/chefberto_italian_cased_it_5.1.1_3.0_1694581754685.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/chefberto_italian_cased_it_5.1.1_3.0_1694581754685.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("chefberto_italian_cased","it") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("chefberto_italian_cased", "it") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|chefberto_italian_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|it| +|Size:|412.6 MB| + +## References + +https://huggingface.co/vinhood/chefberto-italian-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-chemical_bert_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-chemical_bert_uncased_en.md new file mode 100644 index 00000000000000..8fcb986dd8d999 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-chemical_bert_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English chemical_bert_uncased BertEmbeddings from recobo +author: John Snow Labs +name: chemical_bert_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`chemical_bert_uncased` is a English model originally trained by recobo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/chemical_bert_uncased_en_5.1.1_3.0_1694566856839.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/chemical_bert_uncased_en_5.1.1_3.0_1694566856839.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("chemical_bert_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("chemical_bert_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|chemical_bert_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.1 MB| + +## References + +https://huggingface.co/recobo/chemical-bert-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-chupeto_en.md b/docs/_posts/ahmedlone127/2023-09-13-chupeto_en.md new file mode 100644 index 00000000000000..c3acf86a8e73d3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-chupeto_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English chupeto BertEmbeddings from justinian336 +author: John Snow Labs +name: chupeto +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`chupeto` is a English model originally trained by justinian336. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/chupeto_en_5.1.1_3.0_1694577833102.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/chupeto_en_5.1.1_3.0_1694577833102.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("chupeto","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("chupeto", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|chupeto| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/justinian336/chupeto \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-clinical_minialbert_312_en.md b/docs/_posts/ahmedlone127/2023-09-13-clinical_minialbert_312_en.md new file mode 100644 index 00000000000000..33b21c2e528c93 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-clinical_minialbert_312_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English clinical_minialbert_312 BertEmbeddings from nlpie +author: John Snow Labs +name: clinical_minialbert_312 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`clinical_minialbert_312` is a English model originally trained by nlpie. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clinical_minialbert_312_en_5.1.1_3.0_1694574036589.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clinical_minialbert_312_en_5.1.1_3.0_1694574036589.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("clinical_minialbert_312","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("clinical_minialbert_312", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinical_minialbert_312| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|243.6 MB| + +## References + +https://huggingface.co/nlpie/clinical-miniALBERT-312 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-clinical_pubmed_bert_base_128_en.md b/docs/_posts/ahmedlone127/2023-09-13-clinical_pubmed_bert_base_128_en.md new file mode 100644 index 00000000000000..9a61fb8060b94f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-clinical_pubmed_bert_base_128_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English clinical_pubmed_bert_base_128 BertEmbeddings from Tsubasaz +author: John Snow Labs +name: clinical_pubmed_bert_base_128 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`clinical_pubmed_bert_base_128` is a English model originally trained by Tsubasaz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clinical_pubmed_bert_base_128_en_5.1.1_3.0_1694573024229.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clinical_pubmed_bert_base_128_en_5.1.1_3.0_1694573024229.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("clinical_pubmed_bert_base_128","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("clinical_pubmed_bert_base_128", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinical_pubmed_bert_base_128| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.0 MB| + +## References + +https://huggingface.co/Tsubasaz/clinical-pubmed-bert-base-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-clinical_pubmed_bert_base_512_en.md b/docs/_posts/ahmedlone127/2023-09-13-clinical_pubmed_bert_base_512_en.md new file mode 100644 index 00000000000000..0240fd62528afd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-clinical_pubmed_bert_base_512_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English clinical_pubmed_bert_base_512 BertEmbeddings from Tsubasaz +author: John Snow Labs +name: clinical_pubmed_bert_base_512 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`clinical_pubmed_bert_base_512` is a English model originally trained by Tsubasaz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clinical_pubmed_bert_base_512_en_5.1.1_3.0_1694573181218.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clinical_pubmed_bert_base_512_en_5.1.1_3.0_1694573181218.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("clinical_pubmed_bert_base_512","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("clinical_pubmed_bert_base_512", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinical_pubmed_bert_base_512| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.0 MB| + +## References + +https://huggingface.co/Tsubasaz/clinical-pubmed-bert-base-512 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-clr_finetuned_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-clr_finetuned_bert_base_uncased_en.md new file mode 100644 index 00000000000000..3837138d40d8e0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-clr_finetuned_bert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English clr_finetuned_bert_base_uncased BertEmbeddings from SauravMaheshkar +author: John Snow Labs +name: clr_finetuned_bert_base_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`clr_finetuned_bert_base_uncased` is a English model originally trained by SauravMaheshkar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clr_finetuned_bert_base_uncased_en_5.1.1_3.0_1694570611508.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clr_finetuned_bert_base_uncased_en_5.1.1_3.0_1694570611508.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("clr_finetuned_bert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("clr_finetuned_bert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clr_finetuned_bert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.7 MB| + +## References + +https://huggingface.co/SauravMaheshkar/clr-finetuned-bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-clr_finetuned_bert_large_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-clr_finetuned_bert_large_uncased_en.md new file mode 100644 index 00000000000000..065b6c5b7f9ed8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-clr_finetuned_bert_large_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English clr_finetuned_bert_large_uncased BertEmbeddings from SauravMaheshkar +author: John Snow Labs +name: clr_finetuned_bert_large_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`clr_finetuned_bert_large_uncased` is a English model originally trained by SauravMaheshkar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clr_finetuned_bert_large_uncased_en_5.1.1_3.0_1694570948330.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clr_finetuned_bert_large_uncased_en_5.1.1_3.0_1694570948330.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("clr_finetuned_bert_large_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("clr_finetuned_bert_large_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clr_finetuned_bert_large_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/SauravMaheshkar/clr-finetuned-bert-large-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-clr_pretrained_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-clr_pretrained_bert_base_uncased_en.md new file mode 100644 index 00000000000000..a95a1ffb937be3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-clr_pretrained_bert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English clr_pretrained_bert_base_uncased BertEmbeddings from SauravMaheshkar +author: John Snow Labs +name: clr_pretrained_bert_base_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`clr_pretrained_bert_base_uncased` is a English model originally trained by SauravMaheshkar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clr_pretrained_bert_base_uncased_en_5.1.1_3.0_1694571112395.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clr_pretrained_bert_base_uncased_en_5.1.1_3.0_1694571112395.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("clr_pretrained_bert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("clr_pretrained_bert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clr_pretrained_bert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/SauravMaheshkar/clr-pretrained-bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-compact_biobert_en.md b/docs/_posts/ahmedlone127/2023-09-13-compact_biobert_en.md new file mode 100644 index 00000000000000..144968fab29a3c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-compact_biobert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English compact_biobert BertEmbeddings from nlpie +author: John Snow Labs +name: compact_biobert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`compact_biobert` is a English model originally trained by nlpie. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/compact_biobert_en_5.1.1_3.0_1694574999951.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/compact_biobert_en_5.1.1_3.0_1694574999951.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("compact_biobert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("compact_biobert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|compact_biobert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|244.4 MB| + +## References + +https://huggingface.co/nlpie/compact-biobert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-condenser_en.md b/docs/_posts/ahmedlone127/2023-09-13-condenser_en.md new file mode 100644 index 00000000000000..7f1dac0527eb84 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-condenser_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English condenser BertEmbeddings from Luyu +author: John Snow Labs +name: condenser +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`condenser` is a English model originally trained by Luyu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/condenser_en_5.1.1_3.0_1694567000935.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/condenser_en_5.1.1_3.0_1694567000935.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("condenser","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("condenser", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|condenser| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.3 MB| + +## References + +https://huggingface.co/Luyu/condenser \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-continue_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-13-continue_mlm_en.md new file mode 100644 index 00000000000000..fc267d90feb3fa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-continue_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English continue_mlm BertEmbeddings from researchaccount +author: John Snow Labs +name: continue_mlm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`continue_mlm` is a English model originally trained by researchaccount. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/continue_mlm_en_5.1.1_3.0_1694567886435.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/continue_mlm_en_5.1.1_3.0_1694567886435.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("continue_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("continue_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|continue_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|608.4 MB| + +## References + +https://huggingface.co/researchaccount/continue_mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-contractbr_bert_base_portuguese_en.md b/docs/_posts/ahmedlone127/2023-09-13-contractbr_bert_base_portuguese_en.md new file mode 100644 index 00000000000000..7755c4473c57b0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-contractbr_bert_base_portuguese_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English contractbr_bert_base_portuguese BertEmbeddings from gacosta +author: John Snow Labs +name: contractbr_bert_base_portuguese +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`contractbr_bert_base_portuguese` is a English model originally trained by gacosta. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/contractbr_bert_base_portuguese_en_5.1.1_3.0_1694582459489.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/contractbr_bert_base_portuguese_en_5.1.1_3.0_1694582459489.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("contractbr_bert_base_portuguese","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("contractbr_bert_base_portuguese", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|contractbr_bert_base_portuguese| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/gacosta/contractbr-bert-base-portuguese \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cordbert_1000_v1_en.md b/docs/_posts/ahmedlone127/2023-09-13-cordbert_1000_v1_en.md new file mode 100644 index 00000000000000..77c0c28060c6d3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cordbert_1000_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cordbert_1000_v1 BertEmbeddings from tanvir21 +author: John Snow Labs +name: cordbert_1000_v1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cordbert_1000_v1` is a English model originally trained by tanvir21. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cordbert_1000_v1_en_5.1.1_3.0_1694574593447.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cordbert_1000_v1_en_5.1.1_3.0_1694574593447.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cordbert_1000_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cordbert_1000_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cordbert_1000_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/tanvir21/cordBERT-1000-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-corsican_condenser_marco_en.md b/docs/_posts/ahmedlone127/2023-09-13-corsican_condenser_marco_en.md new file mode 100644 index 00000000000000..c37cd0b2fc911c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-corsican_condenser_marco_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English corsican_condenser_marco BertEmbeddings from Luyu +author: John Snow Labs +name: corsican_condenser_marco +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`corsican_condenser_marco` is a English model originally trained by Luyu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/corsican_condenser_marco_en_5.1.1_3.0_1694566669130.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/corsican_condenser_marco_en_5.1.1_3.0_1694566669130.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("corsican_condenser_marco","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("corsican_condenser_marco", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|corsican_condenser_marco| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.4 MB| + +## References + +https://huggingface.co/Luyu/co-condenser-marco \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-corsican_condenser_wiki_en.md b/docs/_posts/ahmedlone127/2023-09-13-corsican_condenser_wiki_en.md new file mode 100644 index 00000000000000..1f65c52df534b7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-corsican_condenser_wiki_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English corsican_condenser_wiki BertEmbeddings from Luyu +author: John Snow Labs +name: corsican_condenser_wiki +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`corsican_condenser_wiki` is a English model originally trained by Luyu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/corsican_condenser_wiki_en_5.1.1_3.0_1694566835743.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/corsican_condenser_wiki_en_5.1.1_3.0_1694566835743.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("corsican_condenser_wiki","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("corsican_condenser_wiki", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|corsican_condenser_wiki| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.4 MB| + +## References + +https://huggingface.co/Luyu/co-condenser-wiki \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-czert_b_base_cased_cs.md b/docs/_posts/ahmedlone127/2023-09-13-czert_b_base_cased_cs.md new file mode 100644 index 00000000000000..75cb46fa7c6a55 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-czert_b_base_cased_cs.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Czech czert_b_base_cased BertEmbeddings from UWB-AIR +author: John Snow Labs +name: czert_b_base_cased +date: 2023-09-13 +tags: [bert, cs, open_source, fill_mask, onnx] +task: Embeddings +language: cs +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`czert_b_base_cased` is a Czech model originally trained by UWB-AIR. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/czert_b_base_cased_cs_5.1.1_3.0_1694574332325.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/czert_b_base_cased_cs_5.1.1_3.0_1694574332325.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("czert_b_base_cased","cs") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("czert_b_base_cased", "cs") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|czert_b_base_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|cs| +|Size:|408.3 MB| + +## References + +https://huggingface.co/UWB-AIR/Czert-B-base-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_en.md b/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_en.md new file mode 100644 index 00000000000000..adfd1bdd103345 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dam_bert_base_mlm_msmarco BertEmbeddings from jingtao +author: John Snow Labs +name: dam_bert_base_mlm_msmarco +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dam_bert_base_mlm_msmarco` is a English model originally trained by jingtao. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dam_bert_base_mlm_msmarco_en_5.1.1_3.0_1694572069064.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dam_bert_base_mlm_msmarco_en_5.1.1_3.0_1694572069064.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dam_bert_base_mlm_msmarco","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dam_bert_base_mlm_msmarco", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dam_bert_base_mlm_msmarco| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/jingtao/DAM-bert_base-mlm-msmarco \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_lotte_life_test_en.md b/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_lotte_life_test_en.md new file mode 100644 index 00000000000000..d17418592ff471 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_lotte_life_test_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dam_bert_base_mlm_msmarco_lotte_life_test BertEmbeddings from jingtao +author: John Snow Labs +name: dam_bert_base_mlm_msmarco_lotte_life_test +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dam_bert_base_mlm_msmarco_lotte_life_test` is a English model originally trained by jingtao. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dam_bert_base_mlm_msmarco_lotte_life_test_en_5.1.1_3.0_1694570882292.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dam_bert_base_mlm_msmarco_lotte_life_test_en_5.1.1_3.0_1694570882292.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dam_bert_base_mlm_msmarco_lotte_life_test","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dam_bert_base_mlm_msmarco_lotte_life_test", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dam_bert_base_mlm_msmarco_lotte_life_test| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/jingtao/DAM-bert_base-mlm-msmarco-lotte_life_test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_lotte_rec_test_en.md b/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_lotte_rec_test_en.md new file mode 100644 index 00000000000000..99c897c087e9d6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_lotte_rec_test_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dam_bert_base_mlm_msmarco_lotte_rec_test BertEmbeddings from jingtao +author: John Snow Labs +name: dam_bert_base_mlm_msmarco_lotte_rec_test +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dam_bert_base_mlm_msmarco_lotte_rec_test` is a English model originally trained by jingtao. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dam_bert_base_mlm_msmarco_lotte_rec_test_en_5.1.1_3.0_1694571547494.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dam_bert_base_mlm_msmarco_lotte_rec_test_en_5.1.1_3.0_1694571547494.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dam_bert_base_mlm_msmarco_lotte_rec_test","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dam_bert_base_mlm_msmarco_lotte_rec_test", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dam_bert_base_mlm_msmarco_lotte_rec_test| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/jingtao/DAM-bert_base-mlm-msmarco-lotte_rec_test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_lotte_sci_test_en.md b/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_lotte_sci_test_en.md new file mode 100644 index 00000000000000..3010fc78c3ea32 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_lotte_sci_test_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dam_bert_base_mlm_msmarco_lotte_sci_test BertEmbeddings from jingtao +author: John Snow Labs +name: dam_bert_base_mlm_msmarco_lotte_sci_test +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dam_bert_base_mlm_msmarco_lotte_sci_test` is a English model originally trained by jingtao. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dam_bert_base_mlm_msmarco_lotte_sci_test_en_5.1.1_3.0_1694571388033.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dam_bert_base_mlm_msmarco_lotte_sci_test_en_5.1.1_3.0_1694571388033.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dam_bert_base_mlm_msmarco_lotte_sci_test","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dam_bert_base_mlm_msmarco_lotte_sci_test", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dam_bert_base_mlm_msmarco_lotte_sci_test| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/jingtao/DAM-bert_base-mlm-msmarco-lotte_sci_test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_lotte_tech_test_en.md b/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_lotte_tech_test_en.md new file mode 100644 index 00000000000000..71ea0695d2c733 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_lotte_tech_test_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dam_bert_base_mlm_msmarco_lotte_tech_test BertEmbeddings from jingtao +author: John Snow Labs +name: dam_bert_base_mlm_msmarco_lotte_tech_test +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dam_bert_base_mlm_msmarco_lotte_tech_test` is a English model originally trained by jingtao. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dam_bert_base_mlm_msmarco_lotte_tech_test_en_5.1.1_3.0_1694571198236.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dam_bert_base_mlm_msmarco_lotte_tech_test_en_5.1.1_3.0_1694571198236.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dam_bert_base_mlm_msmarco_lotte_tech_test","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dam_bert_base_mlm_msmarco_lotte_tech_test", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dam_bert_base_mlm_msmarco_lotte_tech_test| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/jingtao/DAM-bert_base-mlm-msmarco-lotte_tech_test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_lotte_write_test_en.md b/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_lotte_write_test_en.md new file mode 100644 index 00000000000000..1d6ad632de2181 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_lotte_write_test_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dam_bert_base_mlm_msmarco_lotte_write_test BertEmbeddings from jingtao +author: John Snow Labs +name: dam_bert_base_mlm_msmarco_lotte_write_test +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dam_bert_base_mlm_msmarco_lotte_write_test` is a English model originally trained by jingtao. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dam_bert_base_mlm_msmarco_lotte_write_test_en_5.1.1_3.0_1694571044394.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dam_bert_base_mlm_msmarco_lotte_write_test_en_5.1.1_3.0_1694571044394.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dam_bert_base_mlm_msmarco_lotte_write_test","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dam_bert_base_mlm_msmarco_lotte_write_test", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dam_bert_base_mlm_msmarco_lotte_write_test| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/jingtao/DAM-bert_base-mlm-msmarco-lotte_write_test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_trec_covid_en.md b/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_trec_covid_en.md new file mode 100644 index 00000000000000..956dfdf9c5a66f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dam_bert_base_mlm_msmarco_trec_covid_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dam_bert_base_mlm_msmarco_trec_covid BertEmbeddings from jingtao +author: John Snow Labs +name: dam_bert_base_mlm_msmarco_trec_covid +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dam_bert_base_mlm_msmarco_trec_covid` is a English model originally trained by jingtao. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dam_bert_base_mlm_msmarco_trec_covid_en_5.1.1_3.0_1694571695715.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dam_bert_base_mlm_msmarco_trec_covid_en_5.1.1_3.0_1694571695715.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dam_bert_base_mlm_msmarco_trec_covid","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dam_bert_base_mlm_msmarco_trec_covid", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dam_bert_base_mlm_msmarco_trec_covid| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/jingtao/DAM-bert_base-mlm-msmarco-trec_covid \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dapt_bert_ko.md b/docs/_posts/ahmedlone127/2023-09-13-dapt_bert_ko.md new file mode 100644 index 00000000000000..9a282a35eb3c46 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dapt_bert_ko.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Korean dapt_bert BertEmbeddings from Kdogs +author: John Snow Labs +name: dapt_bert +date: 2023-09-13 +tags: [bert, ko, open_source, fill_mask, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dapt_bert` is a Korean model originally trained by Kdogs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dapt_bert_ko_5.1.1_3.0_1694576688190.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dapt_bert_ko_5.1.1_3.0_1694576688190.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dapt_bert","ko") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dapt_bert", "ko") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dapt_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ko| +|Size:|412.4 MB| + +## References + +https://huggingface.co/Kdogs/dapt_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-darijabert_arabizi_ar.md b/docs/_posts/ahmedlone127/2023-09-13-darijabert_arabizi_ar.md new file mode 100644 index 00000000000000..48a8755bfb50a2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-darijabert_arabizi_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic darijabert_arabizi BertEmbeddings from SI2M-Lab +author: John Snow Labs +name: darijabert_arabizi +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`darijabert_arabizi` is a Arabic model originally trained by SI2M-Lab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/darijabert_arabizi_ar_5.1.1_3.0_1694564181705.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/darijabert_arabizi_ar_5.1.1_3.0_1694564181705.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("darijabert_arabizi","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("darijabert_arabizi", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|darijabert_arabizi| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|634.9 MB| + +## References + +https://huggingface.co/SI2M-Lab/DarijaBERT-arabizi \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dbbert_el.md b/docs/_posts/ahmedlone127/2023-09-13-dbbert_el.md new file mode 100644 index 00000000000000..64b4def3a1c89d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dbbert_el.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Modern Greek (1453-) dbbert BertEmbeddings from colinswaelens +author: John Snow Labs +name: dbbert +date: 2023-09-13 +tags: [bert, el, open_source, fill_mask, onnx] +task: Embeddings +language: el +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dbbert` is a Modern Greek (1453-) model originally trained by colinswaelens. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dbbert_el_5.1.1_3.0_1694581792538.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dbbert_el_5.1.1_3.0_1694581792538.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dbbert","el") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dbbert", "el") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dbbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|el| +|Size:|408.3 MB| + +## References + +https://huggingface.co/colinswaelens/DBBErt \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dictbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-dictbert_en.md new file mode 100644 index 00000000000000..7fd09a811d9552 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dictbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dictbert BertEmbeddings from wyu1 +author: John Snow Labs +name: dictbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dictbert` is a English model originally trained by wyu1. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dictbert_en_5.1.1_3.0_1694574371852.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dictbert_en_5.1.1_3.0_1694574371852.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dictbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dictbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dictbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/wyu1/DictBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-distil_biobert_en.md b/docs/_posts/ahmedlone127/2023-09-13-distil_biobert_en.md new file mode 100644 index 00000000000000..e0a3a57abe0059 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-distil_biobert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distil_biobert BertEmbeddings from nlpie +author: John Snow Labs +name: distil_biobert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distil_biobert` is a English model originally trained by nlpie. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distil_biobert_en_5.1.1_3.0_1694574845011.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distil_biobert_en_5.1.1_3.0_1694574845011.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("distil_biobert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("distil_biobert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distil_biobert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|244.4 MB| + +## References + +https://huggingface.co/nlpie/distil-biobert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-domain_adapted_contriever_en.md b/docs/_posts/ahmedlone127/2023-09-13-domain_adapted_contriever_en.md new file mode 100644 index 00000000000000..d1e06eeaea4573 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-domain_adapted_contriever_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English domain_adapted_contriever BertEmbeddings from secilozksen +author: John Snow Labs +name: domain_adapted_contriever +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`domain_adapted_contriever` is a English model originally trained by secilozksen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/domain_adapted_contriever_en_5.1.1_3.0_1694567650087.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/domain_adapted_contriever_en_5.1.1_3.0_1694567650087.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("domain_adapted_contriever","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("domain_adapted_contriever", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|domain_adapted_contriever| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.3 MB| + +## References + +https://huggingface.co/secilozksen/domain-adapted-contriever \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_catalan_passage_encoder_viquiquad_base_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_catalan_passage_encoder_viquiquad_base_en.md new file mode 100644 index 00000000000000..b53404b799c571 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_catalan_passage_encoder_viquiquad_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_catalan_passage_encoder_viquiquad_base BertEmbeddings from Koslav +author: John Snow Labs +name: dpr_catalan_passage_encoder_viquiquad_base +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_catalan_passage_encoder_viquiquad_base` is a English model originally trained by Koslav. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_catalan_passage_encoder_viquiquad_base_en_5.1.1_3.0_1694580713399.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_catalan_passage_encoder_viquiquad_base_en_5.1.1_3.0_1694580713399.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_catalan_passage_encoder_viquiquad_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_catalan_passage_encoder_viquiquad_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_catalan_passage_encoder_viquiquad_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/Koslav/dpr-catalan-passage_encoder-viquiquad-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_catalan_question_encoder_viquiquad_base_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_catalan_question_encoder_viquiquad_base_en.md new file mode 100644 index 00000000000000..546745425754ff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_catalan_question_encoder_viquiquad_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_catalan_question_encoder_viquiquad_base BertEmbeddings from Koslav +author: John Snow Labs +name: dpr_catalan_question_encoder_viquiquad_base +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_catalan_question_encoder_viquiquad_base` is a English model originally trained by Koslav. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_catalan_question_encoder_viquiquad_base_en_5.1.1_3.0_1694579879042.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_catalan_question_encoder_viquiquad_base_en_5.1.1_3.0_1694579879042.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_catalan_question_encoder_viquiquad_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_catalan_question_encoder_viquiquad_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_catalan_question_encoder_viquiquad_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/Koslav/dpr-catalan-question_encoder-viquiquad-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dragon_plus_context_encoder_en.md b/docs/_posts/ahmedlone127/2023-09-13-dragon_plus_context_encoder_en.md new file mode 100644 index 00000000000000..1b1ccc964427bd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dragon_plus_context_encoder_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dragon_plus_context_encoder BertEmbeddings from facebook +author: John Snow Labs +name: dragon_plus_context_encoder +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dragon_plus_context_encoder` is a English model originally trained by facebook. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dragon_plus_context_encoder_en_5.1.1_3.0_1694576995913.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dragon_plus_context_encoder_en_5.1.1_3.0_1694576995913.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dragon_plus_context_encoder","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dragon_plus_context_encoder", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dragon_plus_context_encoder| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.5 MB| + +## References + +https://huggingface.co/facebook/dragon-plus-context-encoder \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dragon_plus_query_encoder_en.md b/docs/_posts/ahmedlone127/2023-09-13-dragon_plus_query_encoder_en.md new file mode 100644 index 00000000000000..fb4aea1535cc65 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dragon_plus_query_encoder_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dragon_plus_query_encoder BertEmbeddings from facebook +author: John Snow Labs +name: dragon_plus_query_encoder +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dragon_plus_query_encoder` is a English model originally trained by facebook. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dragon_plus_query_encoder_en_5.1.1_3.0_1694576871066.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dragon_plus_query_encoder_en_5.1.1_3.0_1694576871066.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dragon_plus_query_encoder","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dragon_plus_query_encoder", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dragon_plus_query_encoder| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.4 MB| + +## References + +https://huggingface.co/facebook/dragon-plus-query-encoder \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dummy_model_aripo99_en.md b/docs/_posts/ahmedlone127/2023-09-13-dummy_model_aripo99_en.md new file mode 100644 index 00000000000000..520d13d7a60b50 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dummy_model_aripo99_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dummy_model_aripo99 BertEmbeddings from aripo99 +author: John Snow Labs +name: dummy_model_aripo99 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dummy_model_aripo99` is a English model originally trained by aripo99. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dummy_model_aripo99_en_5.1.1_3.0_1694581314642.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dummy_model_aripo99_en_5.1.1_3.0_1694581314642.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dummy_model_aripo99","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dummy_model_aripo99", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dummy_model_aripo99| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/aripo99/dummy_model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dummy_prac_model_1_en.md b/docs/_posts/ahmedlone127/2023-09-13-dummy_prac_model_1_en.md new file mode 100644 index 00000000000000..6c27cd0264995b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dummy_prac_model_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dummy_prac_model_1 BertEmbeddings from fayez94 +author: John Snow Labs +name: dummy_prac_model_1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dummy_prac_model_1` is a English model originally trained by fayez94. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dummy_prac_model_1_en_5.1.1_3.0_1694564301534.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dummy_prac_model_1_en_5.1.1_3.0_1694564301534.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dummy_prac_model_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dummy_prac_model_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dummy_prac_model_1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/fayez94/dummy_prac_model_1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dummy_prac_model_en.md b/docs/_posts/ahmedlone127/2023-09-13-dummy_prac_model_en.md new file mode 100644 index 00000000000000..85213af1c02129 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dummy_prac_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dummy_prac_model BertEmbeddings from fayez94 +author: John Snow Labs +name: dummy_prac_model +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dummy_prac_model` is a English model originally trained by fayez94. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dummy_prac_model_en_5.1.1_3.0_1694564149966.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dummy_prac_model_en_5.1.1_3.0_1694564149966.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dummy_prac_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dummy_prac_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dummy_prac_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/fayez94/dummy_prac_model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dziribert_ar.md b/docs/_posts/ahmedlone127/2023-09-13-dziribert_ar.md new file mode 100644 index 00000000000000..8b9d37ffceb42d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dziribert_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic dziribert BertEmbeddings from alger-ia +author: John Snow Labs +name: dziribert +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dziribert` is a Arabic model originally trained by alger-ia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dziribert_ar_5.1.1_3.0_1694578688485.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dziribert_ar_5.1.1_3.0_1694578688485.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dziribert","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dziribert", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dziribert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|462.5 MB| + +## References + +https://huggingface.co/alger-ia/dziribert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-e4a_covid_bert_base_romanian_cased_v1_en.md b/docs/_posts/ahmedlone127/2023-09-13-e4a_covid_bert_base_romanian_cased_v1_en.md new file mode 100644 index 00000000000000..e993f7f2b6522b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-e4a_covid_bert_base_romanian_cased_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English e4a_covid_bert_base_romanian_cased_v1 BertEmbeddings from racai +author: John Snow Labs +name: e4a_covid_bert_base_romanian_cased_v1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`e4a_covid_bert_base_romanian_cased_v1` is a English model originally trained by racai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e4a_covid_bert_base_romanian_cased_v1_en_5.1.1_3.0_1694580152465.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e4a_covid_bert_base_romanian_cased_v1_en_5.1.1_3.0_1694580152465.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("e4a_covid_bert_base_romanian_cased_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("e4a_covid_bert_base_romanian_cased_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e4a_covid_bert_base_romanian_cased_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.0 MB| + +## References + +https://huggingface.co/racai/e4a-covid-bert-base-romanian-cased-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-e4a_permits_bert_base_romanian_cased_v1_en.md b/docs/_posts/ahmedlone127/2023-09-13-e4a_permits_bert_base_romanian_cased_v1_en.md new file mode 100644 index 00000000000000..3560d137a409ff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-e4a_permits_bert_base_romanian_cased_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English e4a_permits_bert_base_romanian_cased_v1 BertEmbeddings from racai +author: John Snow Labs +name: e4a_permits_bert_base_romanian_cased_v1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`e4a_permits_bert_base_romanian_cased_v1` is a English model originally trained by racai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e4a_permits_bert_base_romanian_cased_v1_en_5.1.1_3.0_1694580307122.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e4a_permits_bert_base_romanian_cased_v1_en_5.1.1_3.0_1694580307122.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("e4a_permits_bert_base_romanian_cased_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("e4a_permits_bert_base_romanian_cased_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e4a_permits_bert_base_romanian_cased_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.0 MB| + +## References + +https://huggingface.co/racai/e4a-permits-bert-base-romanian-cased-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-ecomm_sbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-ecomm_sbert_en.md new file mode 100644 index 00000000000000..b8cb1125cb7fad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-ecomm_sbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ecomm_sbert BertEmbeddings from Maunish +author: John Snow Labs +name: ecomm_sbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ecomm_sbert` is a English model originally trained by Maunish. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ecomm_sbert_en_5.1.1_3.0_1694567607524.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ecomm_sbert_en_5.1.1_3.0_1694567607524.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ecomm_sbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ecomm_sbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ecomm_sbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|84.6 MB| + +## References + +https://huggingface.co/Maunish/ecomm-sbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-energybert_en.md b/docs/_posts/ahmedlone127/2023-09-13-energybert_en.md new file mode 100644 index 00000000000000..2c3804620bf30c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-energybert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English energybert BertEmbeddings from UNSW-MasterAI +author: John Snow Labs +name: energybert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`energybert` is a English model originally trained by UNSW-MasterAI. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/energybert_en_5.1.1_3.0_1694577639489.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/energybert_en_5.1.1_3.0_1694577639489.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("energybert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("energybert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|energybert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/UNSW-MasterAI/EnergyBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-ern3_en.md b/docs/_posts/ahmedlone127/2023-09-13-ern3_en.md new file mode 100644 index 00000000000000..d3a2d264656ffe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-ern3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ern3 BertEmbeddings from linyi +author: John Snow Labs +name: ern3 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ern3` is a English model originally trained by linyi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ern3_en_5.1.1_3.0_1694582155435.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ern3_en_5.1.1_3.0_1694582155435.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ern3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ern3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ern3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|440.8 MB| + +## References + +https://huggingface.co/linyi/ern3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-esci_mlm_alllang_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-esci_mlm_alllang_bert_base_uncased_en.md new file mode 100644 index 00000000000000..dc0421d1284e58 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-esci_mlm_alllang_bert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English esci_mlm_alllang_bert_base_uncased BertEmbeddings from spacemanidol +author: John Snow Labs +name: esci_mlm_alllang_bert_base_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`esci_mlm_alllang_bert_base_uncased` is a English model originally trained by spacemanidol. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/esci_mlm_alllang_bert_base_uncased_en_5.1.1_3.0_1694576764282.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/esci_mlm_alllang_bert_base_uncased_en_5.1.1_3.0_1694576764282.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("esci_mlm_alllang_bert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("esci_mlm_alllang_bert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|esci_mlm_alllang_bert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.9 MB| + +## References + +https://huggingface.co/spacemanidol/esci-mlm-alllang-bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-estbert_512_et.md b/docs/_posts/ahmedlone127/2023-09-13-estbert_512_et.md new file mode 100644 index 00000000000000..58c6c881cb2ace --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-estbert_512_et.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Estonian estbert_512 BertEmbeddings from tartuNLP +author: John Snow Labs +name: estbert_512 +date: 2023-09-13 +tags: [bert, et, open_source, fill_mask, onnx] +task: Embeddings +language: et +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`estbert_512` is a Estonian model originally trained by tartuNLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/estbert_512_et_5.1.1_3.0_1694576625464.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/estbert_512_et_5.1.1_3.0_1694576625464.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("estbert_512","et") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("estbert_512", "et") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|estbert_512| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|et| +|Size:|463.4 MB| + +## References + +https://huggingface.co/tartuNLP/EstBERT_512 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-estbert_et.md b/docs/_posts/ahmedlone127/2023-09-13-estbert_et.md new file mode 100644 index 00000000000000..170d8aafd0938f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-estbert_et.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Estonian estbert BertEmbeddings from tartuNLP +author: John Snow Labs +name: estbert +date: 2023-09-13 +tags: [bert, et, open_source, fill_mask, onnx] +task: Embeddings +language: et +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`estbert` is a Estonian model originally trained by tartuNLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/estbert_et_5.1.1_3.0_1694576444603.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/estbert_et_5.1.1_3.0_1694576444603.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("estbert","et") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("estbert", "et") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|estbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|et| +|Size:|463.4 MB| + +## References + +https://huggingface.co/tartuNLP/EstBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-evalconvqa_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-evalconvqa_bert_en.md new file mode 100644 index 00000000000000..57fc7a4aea9d94 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-evalconvqa_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English evalconvqa_bert BertEmbeddings from princeton-nlp +author: John Snow Labs +name: evalconvqa_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`evalconvqa_bert` is a English model originally trained by princeton-nlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/evalconvqa_bert_en_5.1.1_3.0_1694565341212.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/evalconvqa_bert_en_5.1.1_3.0_1694565341212.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("evalconvqa_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("evalconvqa_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|evalconvqa_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/princeton-nlp/EvalConvQA_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-financialbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-financialbert_en.md new file mode 100644 index 00000000000000..edd59bfd4bb5af --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-financialbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English financialbert BertEmbeddings from ahmedrachid +author: John Snow Labs +name: financialbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`financialbert` is a English model originally trained by ahmedrachid. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/financialbert_en_5.1.1_3.0_1694577402809.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/financialbert_en_5.1.1_3.0_1694577402809.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("financialbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("financialbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|financialbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/ahmedrachid/FinancialBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-first_model_en.md b/docs/_posts/ahmedlone127/2023-09-13-first_model_en.md new file mode 100644 index 00000000000000..b22012612473b8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-first_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English first_model BertEmbeddings from songqian +author: John Snow Labs +name: first_model +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`first_model` is a English model originally trained by songqian. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/first_model_en_5.1.1_3.0_1694575037544.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/first_model_en_5.1.1_3.0_1694575037544.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("first_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("first_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|first_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/songqian/first_model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-further_train_original_10_en.md b/docs/_posts/ahmedlone127/2023-09-13-further_train_original_10_en.md new file mode 100644 index 00000000000000..fbad8893e69a78 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-further_train_original_10_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English further_train_original_10 BertEmbeddings from onlydj96 +author: John Snow Labs +name: further_train_original_10 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`further_train_original_10` is a English model originally trained by onlydj96. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/further_train_original_10_en_5.1.1_3.0_1694588052671.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/further_train_original_10_en_5.1.1_3.0_1694588052671.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("further_train_original_10","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("further_train_original_10", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|further_train_original_10| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|441.2 MB| + +## References + +https://huggingface.co/onlydj96/further_train_original_10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-german_bert_base_german_cased_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-13-german_bert_base_german_cased_finetuned_en.md new file mode 100644 index 00000000000000..8f88cccaac2903 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-german_bert_base_german_cased_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English german_bert_base_german_cased_finetuned BertEmbeddings from rodrigotuna +author: John Snow Labs +name: german_bert_base_german_cased_finetuned +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`german_bert_base_german_cased_finetuned` is a English model originally trained by rodrigotuna. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/german_bert_base_german_cased_finetuned_en_5.1.1_3.0_1694565721535.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/german_bert_base_german_cased_finetuned_en_5.1.1_3.0_1694565721535.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("german_bert_base_german_cased_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("german_bert_base_german_cased_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|german_bert_base_german_cased_finetuned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/rodrigotuna/de-bert-base-german-cased-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-german_medbert_de.md b/docs/_posts/ahmedlone127/2023-09-13-german_medbert_de.md new file mode 100644 index 00000000000000..2613b3e2831b95 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-german_medbert_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German german_medbert BertEmbeddings from smanjil +author: John Snow Labs +name: german_medbert +date: 2023-09-13 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`german_medbert` is a German model originally trained by smanjil. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/german_medbert_de_5.1.1_3.0_1694574616438.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/german_medbert_de_5.1.1_3.0_1694574616438.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("german_medbert","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("german_medbert", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|german_medbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|406.9 MB| + +## References + +https://huggingface.co/smanjil/German-MedBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-gujibert_fan_en.md b/docs/_posts/ahmedlone127/2023-09-13-gujibert_fan_en.md new file mode 100644 index 00000000000000..974ed93ec27adc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-gujibert_fan_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English gujibert_fan BertEmbeddings from hsc748NLP +author: John Snow Labs +name: gujibert_fan +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`gujibert_fan` is a English model originally trained by hsc748NLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gujibert_fan_en_5.1.1_3.0_1694564628936.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gujibert_fan_en_5.1.1_3.0_1694564628936.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("gujibert_fan","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("gujibert_fan", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gujibert_fan| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|420.2 MB| + +## References + +https://huggingface.co/hsc748NLP/GujiBERT_fan \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hebert_en.md b/docs/_posts/ahmedlone127/2023-09-13-hebert_en.md new file mode 100644 index 00000000000000..c005513098f274 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hebert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English hebert BertEmbeddings from avichr +author: John Snow Labs +name: hebert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hebert` is a English model originally trained by avichr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hebert_en_5.1.1_3.0_1694584681748.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hebert_en_5.1.1_3.0_1694584681748.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hebert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hebert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hebert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/avichr/heBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hindi_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-hindi_bert_en.md new file mode 100644 index 00000000000000..562516ed5fe44a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hindi_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English hindi_bert BertEmbeddings from sukritin +author: John Snow Labs +name: hindi_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hindi_bert` is a English model originally trained by sukritin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hindi_bert_en_5.1.1_3.0_1694575763223.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hindi_bert_en_5.1.1_3.0_1694575763223.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hindi_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hindi_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hindi_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|609.2 MB| + +## References + +https://huggingface.co/sukritin/hindi-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hindi_bert_scratch_hi.md b/docs/_posts/ahmedlone127/2023-09-13-hindi_bert_scratch_hi.md new file mode 100644 index 00000000000000..a6f3495e42142e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hindi_bert_scratch_hi.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hindi hindi_bert_scratch BertEmbeddings from l3cube-pune +author: John Snow Labs +name: hindi_bert_scratch +date: 2023-09-13 +tags: [bert, hi, open_source, fill_mask, onnx] +task: Embeddings +language: hi +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hindi_bert_scratch` is a Hindi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hindi_bert_scratch_hi_5.1.1_3.0_1694579566926.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hindi_bert_scratch_hi_5.1.1_3.0_1694579566926.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hindi_bert_scratch","hi") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hindi_bert_scratch", "hi") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hindi_bert_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|hi| +|Size:|470.6 MB| + +## References + +https://huggingface.co/l3cube-pune/hindi-bert-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hindi_bert_v1_hi.md b/docs/_posts/ahmedlone127/2023-09-13-hindi_bert_v1_hi.md new file mode 100644 index 00000000000000..3389947f94522e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hindi_bert_v1_hi.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hindi hindi_bert_v1 BertEmbeddings from l3cube-pune +author: John Snow Labs +name: hindi_bert_v1 +date: 2023-09-13 +tags: [bert, hi, open_source, fill_mask, onnx] +task: Embeddings +language: hi +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hindi_bert_v1` is a Hindi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hindi_bert_v1_hi_5.1.1_3.0_1694575771399.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hindi_bert_v1_hi_5.1.1_3.0_1694575771399.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hindi_bert_v1","hi") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hindi_bert_v1", "hi") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hindi_bert_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|hi| +|Size:|663.8 MB| + +## References + +https://huggingface.co/l3cube-pune/hindi-bert-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hindi_least_haitian_1m_hi.md b/docs/_posts/ahmedlone127/2023-09-13-hindi_least_haitian_1m_hi.md new file mode 100644 index 00000000000000..da1c809c5ea558 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hindi_least_haitian_1m_hi.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hindi hindi_least_haitian_1m BertEmbeddings from l3cube-pune +author: John Snow Labs +name: hindi_least_haitian_1m +date: 2023-09-13 +tags: [bert, hi, open_source, fill_mask, onnx] +task: Embeddings +language: hi +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hindi_least_haitian_1m` is a Hindi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hindi_least_haitian_1m_hi_5.1.1_3.0_1694584509062.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hindi_least_haitian_1m_hi_5.1.1_3.0_1694584509062.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hindi_least_haitian_1m","hi") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hindi_least_haitian_1m", "hi") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hindi_least_haitian_1m| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|hi| +|Size:|890.6 MB| + +## References + +https://huggingface.co/l3cube-pune/hi-least-ht-1m \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hindi_marathi_dev_bert_hi.md b/docs/_posts/ahmedlone127/2023-09-13-hindi_marathi_dev_bert_hi.md new file mode 100644 index 00000000000000..9e3e31dd3ed22f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hindi_marathi_dev_bert_hi.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hindi hindi_marathi_dev_bert BertEmbeddings from l3cube-pune +author: John Snow Labs +name: hindi_marathi_dev_bert +date: 2023-09-13 +tags: [bert, hi, open_source, fill_mask, onnx] +task: Embeddings +language: hi +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hindi_marathi_dev_bert` is a Hindi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hindi_marathi_dev_bert_hi_5.1.1_3.0_1694576361222.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hindi_marathi_dev_bert_hi_5.1.1_3.0_1694576361222.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hindi_marathi_dev_bert","hi") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hindi_marathi_dev_bert", "hi") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hindi_marathi_dev_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|hi| +|Size:|890.6 MB| + +## References + +https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hindi_marathi_dev_bert_scratch_hi.md b/docs/_posts/ahmedlone127/2023-09-13-hindi_marathi_dev_bert_scratch_hi.md new file mode 100644 index 00000000000000..05db5ed8b72c67 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hindi_marathi_dev_bert_scratch_hi.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hindi hindi_marathi_dev_bert_scratch BertEmbeddings from l3cube-pune +author: John Snow Labs +name: hindi_marathi_dev_bert_scratch +date: 2023-09-13 +tags: [bert, hi, open_source, fill_mask, onnx] +task: Embeddings +language: hi +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hindi_marathi_dev_bert_scratch` is a Hindi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hindi_marathi_dev_bert_scratch_hi_5.1.1_3.0_1694579727628.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hindi_marathi_dev_bert_scratch_hi_5.1.1_3.0_1694579727628.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hindi_marathi_dev_bert_scratch","hi") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hindi_marathi_dev_bert_scratch", "hi") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hindi_marathi_dev_bert_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|hi| +|Size:|608.7 MB| + +## References + +https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hindi_tweets_bert_hateful_hi.md b/docs/_posts/ahmedlone127/2023-09-13-hindi_tweets_bert_hateful_hi.md new file mode 100644 index 00000000000000..da69bfdddb0cb7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hindi_tweets_bert_hateful_hi.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hindi hindi_tweets_bert_hateful BertEmbeddings from l3cube-pune +author: John Snow Labs +name: hindi_tweets_bert_hateful +date: 2023-09-13 +tags: [bert, hi, open_source, fill_mask, onnx] +task: Embeddings +language: hi +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hindi_tweets_bert_hateful` is a Hindi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hindi_tweets_bert_hateful_hi_5.1.1_3.0_1694584250203.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hindi_tweets_bert_hateful_hi_5.1.1_3.0_1694584250203.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hindi_tweets_bert_hateful","hi") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hindi_tweets_bert_hateful", "hi") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hindi_tweets_bert_hateful| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|hi| +|Size:|890.6 MB| + +## References + +https://huggingface.co/l3cube-pune/hindi-tweets-bert-hateful \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hindi_tweets_bert_hi.md b/docs/_posts/ahmedlone127/2023-09-13-hindi_tweets_bert_hi.md new file mode 100644 index 00000000000000..e059fc94d31bb5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hindi_tweets_bert_hi.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hindi hindi_tweets_bert BertEmbeddings from l3cube-pune +author: John Snow Labs +name: hindi_tweets_bert +date: 2023-09-13 +tags: [bert, hi, open_source, fill_mask, onnx] +task: Embeddings +language: hi +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hindi_tweets_bert` is a Hindi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hindi_tweets_bert_hi_5.1.1_3.0_1694581273510.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hindi_tweets_bert_hi_5.1.1_3.0_1694581273510.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hindi_tweets_bert","hi") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hindi_tweets_bert", "hi") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hindi_tweets_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|hi| +|Size:|890.7 MB| + +## References + +https://huggingface.co/l3cube-pune/hindi-tweets-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-improvedabg_20_epochs_en.md b/docs/_posts/ahmedlone127/2023-09-13-improvedabg_20_epochs_en.md new file mode 100644 index 00000000000000..0b353761ff19b1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-improvedabg_20_epochs_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English improvedabg_20_epochs BertEmbeddings from Embible +author: John Snow Labs +name: improvedabg_20_epochs +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`improvedabg_20_epochs` is a English model originally trained by Embible. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/improvedabg_20_epochs_en_5.1.1_3.0_1694563461257.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/improvedabg_20_epochs_en_5.1.1_3.0_1694563461257.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("improvedabg_20_epochs","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("improvedabg_20_epochs", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|improvedabg_20_epochs| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|689.6 MB| + +## References + +https://huggingface.co/Embible/improvedABG-20-epochs \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-improvedabg_50_epochs_en.md b/docs/_posts/ahmedlone127/2023-09-13-improvedabg_50_epochs_en.md new file mode 100644 index 00000000000000..7198fd3847eb15 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-improvedabg_50_epochs_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English improvedabg_50_epochs BertEmbeddings from Embible +author: John Snow Labs +name: improvedabg_50_epochs +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`improvedabg_50_epochs` is a English model originally trained by Embible. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/improvedabg_50_epochs_en_5.1.1_3.0_1694563658734.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/improvedabg_50_epochs_en_5.1.1_3.0_1694563658734.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("improvedabg_50_epochs","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("improvedabg_50_epochs", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|improvedabg_50_epochs| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|689.4 MB| + +## References + +https://huggingface.co/Embible/improvedABG-50-epochs \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-inlegalbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-inlegalbert_en.md new file mode 100644 index 00000000000000..e589b3dc1280a4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-inlegalbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English inlegalbert BertEmbeddings from law-ai +author: John Snow Labs +name: inlegalbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`inlegalbert` is a English model originally trained by law-ai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/inlegalbert_en_5.1.1_3.0_1694582770345.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/inlegalbert_en_5.1.1_3.0_1694582770345.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("inlegalbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("inlegalbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|inlegalbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/law-ai/InLegalBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-javanese_bert_small_imdb_jv.md b/docs/_posts/ahmedlone127/2023-09-13-javanese_bert_small_imdb_jv.md new file mode 100644 index 00000000000000..498e4e60dc789f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-javanese_bert_small_imdb_jv.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Javanese javanese_bert_small_imdb BertEmbeddings from w11wo +author: John Snow Labs +name: javanese_bert_small_imdb +date: 2023-09-13 +tags: [bert, jv, open_source, fill_mask, onnx] +task: Embeddings +language: jv +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`javanese_bert_small_imdb` is a Javanese model originally trained by w11wo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/javanese_bert_small_imdb_jv_5.1.1_3.0_1694582477865.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/javanese_bert_small_imdb_jv_5.1.1_3.0_1694582477865.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("javanese_bert_small_imdb","jv") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("javanese_bert_small_imdb", "jv") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|javanese_bert_small_imdb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|jv| +|Size:|407.3 MB| + +## References + +https://huggingface.co/w11wo/javanese-bert-small-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kazakhbertmulti_kk.md b/docs/_posts/ahmedlone127/2023-09-13-kazakhbertmulti_kk.md new file mode 100644 index 00000000000000..d19355b01557e5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kazakhbertmulti_kk.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Kazakh kazakhbertmulti BertEmbeddings from amandyk +author: John Snow Labs +name: kazakhbertmulti +date: 2023-09-13 +tags: [bert, kk, open_source, fill_mask, onnx] +task: Embeddings +language: kk +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kazakhbertmulti` is a Kazakh model originally trained by amandyk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kazakhbertmulti_kk_5.1.1_3.0_1694567001040.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kazakhbertmulti_kk_5.1.1_3.0_1694567001040.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kazakhbertmulti","kk") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kazakhbertmulti", "kk") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kazakhbertmulti| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|kk| +|Size:|609.9 MB| + +## References + +https://huggingface.co/amandyk/KazakhBERTmulti \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kcbert_base_dev_en.md b/docs/_posts/ahmedlone127/2023-09-13-kcbert_base_dev_en.md new file mode 100644 index 00000000000000..e8fc285327efa2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kcbert_base_dev_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kcbert_base_dev BertEmbeddings from beomi +author: John Snow Labs +name: kcbert_base_dev +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kcbert_base_dev` is a English model originally trained by beomi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kcbert_base_dev_en_5.1.1_3.0_1694586571113.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kcbert_base_dev_en_5.1.1_3.0_1694586571113.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kcbert_base_dev","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kcbert_base_dev", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kcbert_base_dev| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.3 MB| + +## References + +https://huggingface.co/beomi/kcbert-base-dev \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kcbert_base_petition_en.md b/docs/_posts/ahmedlone127/2023-09-13-kcbert_base_petition_en.md new file mode 100644 index 00000000000000..e3dfe66d543d72 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kcbert_base_petition_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kcbert_base_petition BertEmbeddings from Kyoungmin +author: John Snow Labs +name: kcbert_base_petition +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kcbert_base_petition` is a English model originally trained by Kyoungmin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kcbert_base_petition_en_5.1.1_3.0_1694565357198.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kcbert_base_petition_en_5.1.1_3.0_1694565357198.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kcbert_base_petition","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kcbert_base_petition", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kcbert_base_petition| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.2 MB| + +## References + +https://huggingface.co/Kyoungmin/kcbert-base-petition \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kcbert_large_dev_en.md b/docs/_posts/ahmedlone127/2023-09-13-kcbert_large_dev_en.md new file mode 100644 index 00000000000000..3231fe67c06f5f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kcbert_large_dev_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kcbert_large_dev BertEmbeddings from beomi +author: John Snow Labs +name: kcbert_large_dev +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kcbert_large_dev` is a English model originally trained by beomi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kcbert_large_dev_en_5.1.1_3.0_1694587013172.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kcbert_large_dev_en_5.1.1_3.0_1694587013172.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kcbert_large_dev","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kcbert_large_dev", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kcbert_large_dev| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/beomi/kcbert-large-dev \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kcbert_large_en.md b/docs/_posts/ahmedlone127/2023-09-13-kcbert_large_en.md new file mode 100644 index 00000000000000..4750d48d788aef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kcbert_large_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kcbert_large BertEmbeddings from beomi +author: John Snow Labs +name: kcbert_large +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kcbert_large` is a English model originally trained by beomi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kcbert_large_en_5.1.1_3.0_1694587353127.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kcbert_large_en_5.1.1_3.0_1694587353127.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kcbert_large","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kcbert_large", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kcbert_large| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/beomi/kcbert-large \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kcbert_mlm_finetune_en.md b/docs/_posts/ahmedlone127/2023-09-13-kcbert_mlm_finetune_en.md new file mode 100644 index 00000000000000..ab9f10ea0b1bb9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kcbert_mlm_finetune_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kcbert_mlm_finetune BertEmbeddings from stresscaptor +author: John Snow Labs +name: kcbert_mlm_finetune +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kcbert_mlm_finetune` is a English model originally trained by stresscaptor. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kcbert_mlm_finetune_en_5.1.1_3.0_1694567744835.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kcbert_mlm_finetune_en_5.1.1_3.0_1694567744835.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kcbert_mlm_finetune","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kcbert_mlm_finetune", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kcbert_mlm_finetune| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.4 MB| + +## References + +https://huggingface.co/stresscaptor/kcbert-mlm-finetune \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kpfbert_base_en.md b/docs/_posts/ahmedlone127/2023-09-13-kpfbert_base_en.md new file mode 100644 index 00000000000000..5f84ee9050008c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kpfbert_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kpfbert_base BertEmbeddings from yunaissance +author: John Snow Labs +name: kpfbert_base +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kpfbert_base` is a English model originally trained by yunaissance. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kpfbert_base_en_5.1.1_3.0_1694584038349.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kpfbert_base_en_5.1.1_3.0_1694584038349.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kpfbert_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kpfbert_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kpfbert_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|425.1 MB| + +## References + +https://huggingface.co/yunaissance/kpfbert-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-labse_english_russian_erzya_v1_ru.md b/docs/_posts/ahmedlone127/2023-09-13-labse_english_russian_erzya_v1_ru.md new file mode 100644 index 00000000000000..f3fb0cffb9aa05 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-labse_english_russian_erzya_v1_ru.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Russian labse_english_russian_erzya_v1 BertEmbeddings from slone +author: John Snow Labs +name: labse_english_russian_erzya_v1 +date: 2023-09-13 +tags: [bert, ru, open_source, fill_mask, onnx] +task: Embeddings +language: ru +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`labse_english_russian_erzya_v1` is a Russian model originally trained by slone. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/labse_english_russian_erzya_v1_ru_5.1.1_3.0_1694583966275.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/labse_english_russian_erzya_v1_ru_5.1.1_3.0_1694583966275.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("labse_english_russian_erzya_v1","ru") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("labse_english_russian_erzya_v1", "ru") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|labse_english_russian_erzya_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ru| +|Size:|535.5 MB| + +## References + +https://huggingface.co/slone/LaBSE-en-ru-myv-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-legal_bert_base_uncased_finetuned_rramicus_en.md b/docs/_posts/ahmedlone127/2023-09-13-legal_bert_base_uncased_finetuned_rramicus_en.md new file mode 100644 index 00000000000000..29fa712a5d4e73 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-legal_bert_base_uncased_finetuned_rramicus_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English legal_bert_base_uncased_finetuned_rramicus BertEmbeddings from repro-rights-amicus-briefs +author: John Snow Labs +name: legal_bert_base_uncased_finetuned_rramicus +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legal_bert_base_uncased_finetuned_rramicus` is a English model originally trained by repro-rights-amicus-briefs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legal_bert_base_uncased_finetuned_rramicus_en_5.1.1_3.0_1694567706023.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legal_bert_base_uncased_finetuned_rramicus_en_5.1.1_3.0_1694567706023.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legal_bert_base_uncased_finetuned_rramicus","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legal_bert_base_uncased_finetuned_rramicus", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legal_bert_base_uncased_finetuned_rramicus| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/repro-rights-amicus-briefs/legal-bert-base-uncased-finetuned-RRamicus \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-legalbert_adept_en.md b/docs/_posts/ahmedlone127/2023-09-13-legalbert_adept_en.md new file mode 100644 index 00000000000000..30fc217e2ac076 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-legalbert_adept_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English legalbert_adept BertEmbeddings from hatemestinbejaia +author: John Snow Labs +name: legalbert_adept +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legalbert_adept` is a English model originally trained by hatemestinbejaia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legalbert_adept_en_5.1.1_3.0_1694567799021.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legalbert_adept_en_5.1.1_3.0_1694567799021.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legalbert_adept","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legalbert_adept", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legalbert_adept| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/hatemestinbejaia/legalbert-adept \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-legalnlp_bert_pt.md b/docs/_posts/ahmedlone127/2023-09-13-legalnlp_bert_pt.md new file mode 100644 index 00000000000000..ac59b8243b994d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-legalnlp_bert_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese legalnlp_bert BertEmbeddings from felipemaiapolo +author: John Snow Labs +name: legalnlp_bert +date: 2023-09-13 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legalnlp_bert` is a Portuguese model originally trained by felipemaiapolo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legalnlp_bert_pt_5.1.1_3.0_1694580772789.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legalnlp_bert_pt_5.1.1_3.0_1694580772789.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legalnlp_bert","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legalnlp_bert", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legalnlp_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|405.8 MB| + +## References + +https://huggingface.co/felipemaiapolo/legalnlp-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-lesssexistbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-lesssexistbert_en.md new file mode 100644 index 00000000000000..b24b0324e69c1e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-lesssexistbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English lesssexistbert BertEmbeddings from clincolnoz +author: John Snow Labs +name: lesssexistbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lesssexistbert` is a English model originally trained by clincolnoz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lesssexistbert_en_5.1.1_3.0_1694578655498.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lesssexistbert_en_5.1.1_3.0_1694578655498.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("lesssexistbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("lesssexistbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lesssexistbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.3 MB| + +## References + +https://huggingface.co/clincolnoz/LessSexistBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-lsg_bert_base_uncased_4096_en.md b/docs/_posts/ahmedlone127/2023-09-13-lsg_bert_base_uncased_4096_en.md new file mode 100644 index 00000000000000..1645fb87513f11 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-lsg_bert_base_uncased_4096_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English lsg_bert_base_uncased_4096 BertEmbeddings from ccdv +author: John Snow Labs +name: lsg_bert_base_uncased_4096 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lsg_bert_base_uncased_4096` is a English model originally trained by ccdv. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lsg_bert_base_uncased_4096_en_5.1.1_3.0_1694564185857.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lsg_bert_base_uncased_4096_en_5.1.1_3.0_1694564185857.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("lsg_bert_base_uncased_4096","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("lsg_bert_base_uncased_4096", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lsg_bert_base_uncased_4096| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|417.5 MB| + +## References + +https://huggingface.co/ccdv/lsg-bert-base-uncased-4096 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mabepa_sts_es.md b/docs/_posts/ahmedlone127/2023-09-13-mabepa_sts_es.md new file mode 100644 index 00000000000000..46fd6e911ad2a3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mabepa_sts_es.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Castilian, Spanish mabepa_sts BertEmbeddings from Brendar +author: John Snow Labs +name: mabepa_sts +date: 2023-09-13 +tags: [bert, es, open_source, fill_mask, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mabepa_sts` is a Castilian, Spanish model originally trained by Brendar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mabepa_sts_es_5.1.1_3.0_1694584433265.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mabepa_sts_es_5.1.1_3.0_1694584433265.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mabepa_sts","es") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mabepa_sts", "es") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mabepa_sts| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|es| +|Size:|409.6 MB| + +## References + +https://huggingface.co/Brendar/MaBePa_STS \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-malay_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-malay_bert_en.md new file mode 100644 index 00000000000000..b8a4aa84b861b5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-malay_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English malay_bert BertEmbeddings from NLP4H +author: John Snow Labs +name: malay_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`malay_bert` is a English model originally trained by NLP4H. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/malay_bert_en_5.1.1_3.0_1694568227304.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/malay_bert_en_5.1.1_3.0_1694568227304.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("malay_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("malay_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|malay_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.2 MB| + +## References + +https://huggingface.co/NLP4H/ms_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-marathi_bert_scratch_mr.md b/docs/_posts/ahmedlone127/2023-09-13-marathi_bert_scratch_mr.md new file mode 100644 index 00000000000000..5de0ded2a4897b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-marathi_bert_scratch_mr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Marathi marathi_bert_scratch BertEmbeddings from l3cube-pune +author: John Snow Labs +name: marathi_bert_scratch +date: 2023-09-13 +tags: [bert, mr, open_source, fill_mask, onnx] +task: Embeddings +language: mr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`marathi_bert_scratch` is a Marathi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/marathi_bert_scratch_mr_5.1.1_3.0_1694579363809.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/marathi_bert_scratch_mr_5.1.1_3.0_1694579363809.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("marathi_bert_scratch","mr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("marathi_bert_scratch", "mr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|marathi_bert_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|mr| +|Size:|470.3 MB| + +## References + +https://huggingface.co/l3cube-pune/marathi-bert-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-marathi_tweets_bert_hateful_mr.md b/docs/_posts/ahmedlone127/2023-09-13-marathi_tweets_bert_hateful_mr.md new file mode 100644 index 00000000000000..0be66bec861ee8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-marathi_tweets_bert_hateful_mr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Marathi marathi_tweets_bert_hateful BertEmbeddings from l3cube-pune +author: John Snow Labs +name: marathi_tweets_bert_hateful +date: 2023-09-13 +tags: [bert, mr, open_source, fill_mask, onnx] +task: Embeddings +language: mr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`marathi_tweets_bert_hateful` is a Marathi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/marathi_tweets_bert_hateful_mr_5.1.1_3.0_1694578089840.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/marathi_tweets_bert_hateful_mr_5.1.1_3.0_1694578089840.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("marathi_tweets_bert_hateful","mr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("marathi_tweets_bert_hateful", "mr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|marathi_tweets_bert_hateful| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|mr| +|Size:|890.7 MB| + +## References + +https://huggingface.co/l3cube-pune/marathi-tweets-bert-hateful \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-marathi_tweets_bert_mr.md b/docs/_posts/ahmedlone127/2023-09-13-marathi_tweets_bert_mr.md new file mode 100644 index 00000000000000..018c729848ea80 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-marathi_tweets_bert_mr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Marathi marathi_tweets_bert BertEmbeddings from l3cube-pune +author: John Snow Labs +name: marathi_tweets_bert +date: 2023-09-13 +tags: [bert, mr, open_source, fill_mask, onnx] +task: Embeddings +language: mr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`marathi_tweets_bert` is a Marathi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/marathi_tweets_bert_mr_5.1.1_3.0_1694576608611.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/marathi_tweets_bert_mr_5.1.1_3.0_1694576608611.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("marathi_tweets_bert","mr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("marathi_tweets_bert", "mr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|marathi_tweets_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|mr| +|Size:|890.6 MB| + +## References + +https://huggingface.co/l3cube-pune/marathi-tweets-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-marbert_adept_en.md b/docs/_posts/ahmedlone127/2023-09-13-marbert_adept_en.md new file mode 100644 index 00000000000000..a944df7dd957a9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-marbert_adept_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English marbert_adept BertEmbeddings from hatemestinbejaia +author: John Snow Labs +name: marbert_adept +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`marbert_adept` is a English model originally trained by hatemestinbejaia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/marbert_adept_en_5.1.1_3.0_1694570997383.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/marbert_adept_en_5.1.1_3.0_1694570997383.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("marbert_adept","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("marbert_adept", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|marbert_adept| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|606.7 MB| + +## References + +https://huggingface.co/hatemestinbejaia/MARBERT-adept \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-marbert_ar.md b/docs/_posts/ahmedlone127/2023-09-13-marbert_ar.md new file mode 100644 index 00000000000000..1fd0a24bafa01c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-marbert_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic marbert BertEmbeddings from UBC-NLP +author: John Snow Labs +name: marbert +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`marbert` is a Arabic model originally trained by UBC-NLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/marbert_ar_5.1.1_3.0_1694573980919.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/marbert_ar_5.1.1_3.0_1694573980919.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("marbert","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("marbert", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|marbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|608.7 MB| + +## References + +https://huggingface.co/UBC-NLP/MARBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mathbert_custom_en.md b/docs/_posts/ahmedlone127/2023-09-13-mathbert_custom_en.md new file mode 100644 index 00000000000000..54ba238f87fdf5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mathbert_custom_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mathbert_custom BertEmbeddings from tbs17 +author: John Snow Labs +name: mathbert_custom +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mathbert_custom` is a English model originally trained by tbs17. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mathbert_custom_en_5.1.1_3.0_1694576775346.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mathbert_custom_en_5.1.1_3.0_1694576775346.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mathbert_custom","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mathbert_custom", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mathbert_custom| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.2 MB| + +## References + +https://huggingface.co/tbs17/MathBERT-custom \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mbert_finetuned_pytorch_en.md b/docs/_posts/ahmedlone127/2023-09-13-mbert_finetuned_pytorch_en.md new file mode 100644 index 00000000000000..063c04f6f55d97 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mbert_finetuned_pytorch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mbert_finetuned_pytorch BertEmbeddings from fimu-docproc-research +author: John Snow Labs +name: mbert_finetuned_pytorch +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbert_finetuned_pytorch` is a English model originally trained by fimu-docproc-research. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbert_finetuned_pytorch_en_5.1.1_3.0_1694583639590.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbert_finetuned_pytorch_en_5.1.1_3.0_1694583639590.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbert_finetuned_pytorch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbert_finetuned_pytorch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbert_finetuned_pytorch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/fimu-docproc-research/mbert-finetuned-pytorch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mbertu_arabic_en.md b/docs/_posts/ahmedlone127/2023-09-13-mbertu_arabic_en.md new file mode 100644 index 00000000000000..34acc71ebced77 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mbertu_arabic_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mbertu_arabic BertEmbeddings from Zappandy +author: John Snow Labs +name: mbertu_arabic +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbertu_arabic` is a English model originally trained by Zappandy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbertu_arabic_en_5.1.1_3.0_1694576350158.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbertu_arabic_en_5.1.1_3.0_1694576350158.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbertu_arabic","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbertu_arabic", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbertu_arabic| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|664.5 MB| + +## References + +https://huggingface.co/Zappandy/mBERTu-arabic \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-minialbert_128_en.md b/docs/_posts/ahmedlone127/2023-09-13-minialbert_128_en.md new file mode 100644 index 00000000000000..9947e5777491b8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-minialbert_128_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English minialbert_128 BertEmbeddings from nlpie +author: John Snow Labs +name: minialbert_128 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`minialbert_128` is a English model originally trained by nlpie. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/minialbert_128_en_5.1.1_3.0_1694567917349.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/minialbert_128_en_5.1.1_3.0_1694567917349.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("minialbert_128","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("minialbert_128", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|minialbert_128| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.0 MB| + +## References + +https://huggingface.co/nlpie/miniALBERT-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mlm_gh_issues_en.md b/docs/_posts/ahmedlone127/2023-09-13-mlm_gh_issues_en.md new file mode 100644 index 00000000000000..a9ed8a4ce17be7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mlm_gh_issues_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_gh_issues BertEmbeddings from ericntay +author: John Snow Labs +name: mlm_gh_issues +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_gh_issues` is a English model originally trained by ericntay. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_gh_issues_en_5.1.1_3.0_1694577513579.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_gh_issues_en_5.1.1_3.0_1694577513579.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_gh_issues","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_gh_issues", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_gh_issues| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/ericntay/mlm_gh_issues \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-model_65000_20ep_en.md b/docs/_posts/ahmedlone127/2023-09-13-model_65000_20ep_en.md new file mode 100644 index 00000000000000..1ab4e5fa53572f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-model_65000_20ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English model_65000_20ep BertEmbeddings from sergiyvl +author: John Snow Labs +name: model_65000_20ep +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`model_65000_20ep` is a English model originally trained by sergiyvl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/model_65000_20ep_en_5.1.1_3.0_1694573722433.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/model_65000_20ep_en_5.1.1_3.0_1694573722433.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("model_65000_20ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("model_65000_20ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|model_65000_20ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|664.5 MB| + +## References + +https://huggingface.co/sergiyvl/model_65000_20ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-model_ankai_en.md b/docs/_posts/ahmedlone127/2023-09-13-model_ankai_en.md new file mode 100644 index 00000000000000..0474e5a33c124b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-model_ankai_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English model_ankai BertEmbeddings from wudi7758521521 +author: John Snow Labs +name: model_ankai +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`model_ankai` is a English model originally trained by wudi7758521521. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/model_ankai_en_5.1.1_3.0_1694586066096.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/model_ankai_en_5.1.1_3.0_1694586066096.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("model_ankai","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("model_ankai", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|model_ankai| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/wudi7758521521/model_ankai \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-model_bangla_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-model_bangla_bert_en.md new file mode 100644 index 00000000000000..29a3dc0f14d069 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-model_bangla_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English model_bangla_bert BertEmbeddings from Kowsher +author: John Snow Labs +name: model_bangla_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`model_bangla_bert` is a English model originally trained by Kowsher. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/model_bangla_bert_en_5.1.1_3.0_1694565028072.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/model_bangla_bert_en_5.1.1_3.0_1694565028072.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("model_bangla_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("model_bangla_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|model_bangla_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|612.1 MB| + +## References + +https://huggingface.co/Kowsher/model-bangla-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-moresexistbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-moresexistbert_en.md new file mode 100644 index 00000000000000..15e51bcc31404a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-moresexistbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English moresexistbert BertEmbeddings from clincolnoz +author: John Snow Labs +name: moresexistbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`moresexistbert` is a English model originally trained by clincolnoz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/moresexistbert_en_5.1.1_3.0_1694577057922.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/moresexistbert_en_5.1.1_3.0_1694577057922.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("moresexistbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("moresexistbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|moresexistbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|404.7 MB| + +## References + +https://huggingface.co/clincolnoz/MoreSexistBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-muril_with_mlm_cased_temp_en.md b/docs/_posts/ahmedlone127/2023-09-13-muril_with_mlm_cased_temp_en.md new file mode 100644 index 00000000000000..bed1b3b903c55e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-muril_with_mlm_cased_temp_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English muril_with_mlm_cased_temp BertEmbeddings from simran-kh +author: John Snow Labs +name: muril_with_mlm_cased_temp +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`muril_with_mlm_cased_temp` is a English model originally trained by simran-kh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/muril_with_mlm_cased_temp_en_5.1.1_3.0_1694574360108.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/muril_with_mlm_cased_temp_en_5.1.1_3.0_1694574360108.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("muril_with_mlm_cased_temp","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("muril_with_mlm_cased_temp", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|muril_with_mlm_cased_temp| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|890.4 MB| + +## References + +https://huggingface.co/simran-kh/muril-with-mlm-cased-temp \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mwp_bert_english_en.md b/docs/_posts/ahmedlone127/2023-09-13-mwp_bert_english_en.md new file mode 100644 index 00000000000000..a773bb355fd881 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mwp_bert_english_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mwp_bert_english BertEmbeddings from invokerliang +author: John Snow Labs +name: mwp_bert_english +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mwp_bert_english` is a English model originally trained by invokerliang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mwp_bert_english_en_5.1.1_3.0_1694575296759.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mwp_bert_english_en_5.1.1_3.0_1694575296759.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mwp_bert_english","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mwp_bert_english", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mwp_bert_english| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/invokerliang/MWP-BERT-en \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mymodel005_en.md b/docs/_posts/ahmedlone127/2023-09-13-mymodel005_en.md new file mode 100644 index 00000000000000..bb998c2ce0980e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mymodel005_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mymodel005 BertEmbeddings from wbmitcast +author: John Snow Labs +name: mymodel005 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mymodel005` is a English model originally trained by wbmitcast. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mymodel005_en_5.1.1_3.0_1694583366404.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mymodel005_en_5.1.1_3.0_1694583366404.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mymodel005","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mymodel005", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mymodel005| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/wbmitcast/mymodel005 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mymodel007_wbmitcast_en.md b/docs/_posts/ahmedlone127/2023-09-13-mymodel007_wbmitcast_en.md new file mode 100644 index 00000000000000..181bd8341e32df --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mymodel007_wbmitcast_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mymodel007_wbmitcast BertEmbeddings from wbmitcast +author: John Snow Labs +name: mymodel007_wbmitcast +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mymodel007_wbmitcast` is a English model originally trained by wbmitcast. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mymodel007_wbmitcast_en_5.1.1_3.0_1694583570220.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mymodel007_wbmitcast_en_5.1.1_3.0_1694583570220.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mymodel007_wbmitcast","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mymodel007_wbmitcast", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mymodel007_wbmitcast| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/wbmitcast/mymodel007 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mymodel1007_en.md b/docs/_posts/ahmedlone127/2023-09-13-mymodel1007_en.md new file mode 100644 index 00000000000000..22775a1a5aa98b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mymodel1007_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mymodel1007 BertEmbeddings from Wilson2021 +author: John Snow Labs +name: mymodel1007 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mymodel1007` is a English model originally trained by Wilson2021. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mymodel1007_en_5.1.1_3.0_1694574662440.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mymodel1007_en_5.1.1_3.0_1694574662440.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mymodel1007","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mymodel1007", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mymodel1007| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Wilson2021/mymodel1007 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-nepalibert_en.md b/docs/_posts/ahmedlone127/2023-09-13-nepalibert_en.md new file mode 100644 index 00000000000000..df8acc60b5f81c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-nepalibert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English nepalibert BertEmbeddings from Rajan +author: John Snow Labs +name: nepalibert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nepalibert` is a English model originally trained by Rajan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nepalibert_en_5.1.1_3.0_1694569792967.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nepalibert_en_5.1.1_3.0_1694569792967.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("nepalibert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("nepalibert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nepalibert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|305.4 MB| + +## References + +https://huggingface.co/Rajan/NepaliBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-nepalibert_ne.md b/docs/_posts/ahmedlone127/2023-09-13-nepalibert_ne.md new file mode 100644 index 00000000000000..704887a504fc5a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-nepalibert_ne.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Nepali (macrolanguage) nepalibert BertEmbeddings from Shushant +author: John Snow Labs +name: nepalibert +date: 2023-09-13 +tags: [bert, ne, open_source, fill_mask, onnx] +task: Embeddings +language: ne +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nepalibert` is a Nepali (macrolanguage) model originally trained by Shushant. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nepalibert_ne_5.1.1_3.0_1694571473601.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nepalibert_ne_5.1.1_3.0_1694571473601.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("nepalibert","ne") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("nepalibert", "ne") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nepalibert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ne| +|Size:|408.5 MB| + +## References + +https://huggingface.co/Shushant/nepaliBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-neuba_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-neuba_bert_en.md new file mode 100644 index 00000000000000..b2a00258eba302 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-neuba_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English neuba_bert BertEmbeddings from thunlp +author: John Snow Labs +name: neuba_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`neuba_bert` is a English model originally trained by thunlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/neuba_bert_en_5.1.1_3.0_1694577955856.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/neuba_bert_en_5.1.1_3.0_1694577955856.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("neuba_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("neuba_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|neuba_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/thunlp/neuba-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-nl1_en.md b/docs/_posts/ahmedlone127/2023-09-13-nl1_en.md new file mode 100644 index 00000000000000..7c685451ff7262 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-nl1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English nl1 BertEmbeddings from willemjan +author: John Snow Labs +name: nl1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nl1` is a English model originally trained by willemjan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nl1_en_5.1.1_3.0_1694584898456.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nl1_en_5.1.1_3.0_1694584898456.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("nl1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("nl1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nl1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/willemjan/nl1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-notram_bert_norwegian_cased_080321_no.md b/docs/_posts/ahmedlone127/2023-09-13-notram_bert_norwegian_cased_080321_no.md new file mode 100644 index 00000000000000..b7ede65d1edf53 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-notram_bert_norwegian_cased_080321_no.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Norwegian notram_bert_norwegian_cased_080321 BertEmbeddings from NbAiLab +author: John Snow Labs +name: notram_bert_norwegian_cased_080321 +date: 2023-09-13 +tags: [bert, "no", open_source, fill_mask, onnx] +task: Embeddings +language: "no" +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`notram_bert_norwegian_cased_080321` is a Norwegian model originally trained by NbAiLab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/notram_bert_norwegian_cased_080321_no_5.1.1_3.0_1694569128176.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/notram_bert_norwegian_cased_080321_no_5.1.1_3.0_1694569128176.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("notram_bert_norwegian_cased_080321","no") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("notram_bert_norwegian_cased_080321", "no") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|notram_bert_norwegian_cased_080321| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|no| +|Size:|663.0 MB| + +## References + +https://huggingface.co/NbAiLab/notram-bert-norwegian-cased-080321 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-opticalbert_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-opticalbert_cased_en.md new file mode 100644 index 00000000000000..63d40cbc3aaeaf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-opticalbert_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English opticalbert_cased BertEmbeddings from opticalmaterials +author: John Snow Labs +name: opticalbert_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`opticalbert_cased` is a English model originally trained by opticalmaterials. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/opticalbert_cased_en_5.1.1_3.0_1694581051911.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/opticalbert_cased_en_5.1.1_3.0_1694581051911.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("opticalbert_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("opticalbert_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|opticalbert_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.5 MB| + +## References + +https://huggingface.co/opticalmaterials/opticalbert_cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-opticalbert_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-opticalbert_uncased_en.md new file mode 100644 index 00000000000000..89048fd4589948 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-opticalbert_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English opticalbert_uncased BertEmbeddings from opticalmaterials +author: John Snow Labs +name: opticalbert_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`opticalbert_uncased` is a English model originally trained by opticalmaterials. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/opticalbert_uncased_en_5.1.1_3.0_1694581416416.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/opticalbert_uncased_en_5.1.1_3.0_1694581416416.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("opticalbert_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("opticalbert_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|opticalbert_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/opticalmaterials/opticalbert_uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-opticalpurebert_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-opticalpurebert_cased_en.md new file mode 100644 index 00000000000000..a6bf328fba73bd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-opticalpurebert_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English opticalpurebert_cased BertEmbeddings from opticalmaterials +author: John Snow Labs +name: opticalpurebert_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`opticalpurebert_cased` is a English model originally trained by opticalmaterials. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/opticalpurebert_cased_en_5.1.1_3.0_1694581559636.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/opticalpurebert_cased_en_5.1.1_3.0_1694581559636.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("opticalpurebert_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("opticalpurebert_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|opticalpurebert_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.7 MB| + +## References + +https://huggingface.co/opticalmaterials/opticalpurebert_cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-opticalpurebert_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-opticalpurebert_uncased_en.md new file mode 100644 index 00000000000000..9f396c3edfa399 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-opticalpurebert_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English opticalpurebert_uncased BertEmbeddings from opticalmaterials +author: John Snow Labs +name: opticalpurebert_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`opticalpurebert_uncased` is a English model originally trained by opticalmaterials. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/opticalpurebert_uncased_en_5.1.1_3.0_1694581713185.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/opticalpurebert_uncased_en_5.1.1_3.0_1694581713185.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("opticalpurebert_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("opticalpurebert_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|opticalpurebert_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/opticalmaterials/opticalpurebert_uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-output_miladfa7_en.md b/docs/_posts/ahmedlone127/2023-09-13-output_miladfa7_en.md new file mode 100644 index 00000000000000..7ebbc4d5b7b831 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-output_miladfa7_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English output_miladfa7 BertEmbeddings from miladfa7 +author: John Snow Labs +name: output_miladfa7 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`output_miladfa7` is a English model originally trained by miladfa7. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/output_miladfa7_en_5.1.1_3.0_1694569708274.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/output_miladfa7_en_5.1.1_3.0_1694569708274.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("output_miladfa7","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("output_miladfa7", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|output_miladfa7| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|606.0 MB| + +## References + +https://huggingface.co/miladfa7/output \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-paraphraserplus_1epoch_en.md b/docs/_posts/ahmedlone127/2023-09-13-paraphraserplus_1epoch_en.md new file mode 100644 index 00000000000000..20335bd56f296d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-paraphraserplus_1epoch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English paraphraserplus_1epoch BertEmbeddings from sergiyvl +author: John Snow Labs +name: paraphraserplus_1epoch +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`paraphraserplus_1epoch` is a English model originally trained by sergiyvl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/paraphraserplus_1epoch_en_5.1.1_3.0_1694573041191.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/paraphraserplus_1epoch_en_5.1.1_3.0_1694573041191.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("paraphraserplus_1epoch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("paraphraserplus_1epoch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|paraphraserplus_1epoch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|664.4 MB| + +## References + +https://huggingface.co/sergiyvl/ParaPhraserPlus_1epoch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-parlamint_en.md b/docs/_posts/ahmedlone127/2023-09-13-parlamint_en.md new file mode 100644 index 00000000000000..65579e5335403e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-parlamint_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English parlamint BertEmbeddings from IneG +author: John Snow Labs +name: parlamint +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`parlamint` is a English model originally trained by IneG. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/parlamint_en_5.1.1_3.0_1694584898291.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/parlamint_en_5.1.1_3.0_1694584898291.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("parlamint","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("parlamint", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|parlamint| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/IneG/parlamint \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-parsbert_base_sanay_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-parsbert_base_sanay_uncased_en.md new file mode 100644 index 00000000000000..ebf072adbafbf1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-parsbert_base_sanay_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English parsbert_base_sanay_uncased BertEmbeddings from miladfa7 +author: John Snow Labs +name: parsbert_base_sanay_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`parsbert_base_sanay_uncased` is a English model originally trained by miladfa7. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/parsbert_base_sanay_uncased_en_5.1.1_3.0_1694569942308.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/parsbert_base_sanay_uncased_en_5.1.1_3.0_1694569942308.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("parsbert_base_sanay_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("parsbert_base_sanay_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|parsbert_base_sanay_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|608.9 MB| + +## References + +https://huggingface.co/miladfa7/parsbert-base-sanay-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-pcscibert_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-pcscibert_cased_en.md new file mode 100644 index 00000000000000..607352ec2d16c8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-pcscibert_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pcscibert_cased BertEmbeddings from jmzk96 +author: John Snow Labs +name: pcscibert_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pcscibert_cased` is a English model originally trained by jmzk96. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pcscibert_cased_en_5.1.1_3.0_1694586987079.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pcscibert_cased_en_5.1.1_3.0_1694586987079.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("pcscibert_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("pcscibert_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pcscibert_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/jmzk96/PCSciBERT_cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-protaugment_lm_banking77_en.md b/docs/_posts/ahmedlone127/2023-09-13-protaugment_lm_banking77_en.md new file mode 100644 index 00000000000000..0bf59c249e99db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-protaugment_lm_banking77_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English protaugment_lm_banking77 BertEmbeddings from tdopierre +author: John Snow Labs +name: protaugment_lm_banking77 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`protaugment_lm_banking77` is a English model originally trained by tdopierre. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/protaugment_lm_banking77_en_5.1.1_3.0_1694576921524.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/protaugment_lm_banking77_en_5.1.1_3.0_1694576921524.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("protaugment_lm_banking77","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("protaugment_lm_banking77", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|protaugment_lm_banking77| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.5 MB| + +## References + +https://huggingface.co/tdopierre/ProtAugment-LM-BANKING77 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-pubmedbert_abstract_cord19_en.md b/docs/_posts/ahmedlone127/2023-09-13-pubmedbert_abstract_cord19_en.md new file mode 100644 index 00000000000000..1dfebc410806ac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-pubmedbert_abstract_cord19_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pubmedbert_abstract_cord19 BertEmbeddings from pritamdeka +author: John Snow Labs +name: pubmedbert_abstract_cord19 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pubmedbert_abstract_cord19` is a English model originally trained by pritamdeka. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pubmedbert_abstract_cord19_en_5.1.1_3.0_1694564002029.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pubmedbert_abstract_cord19_en_5.1.1_3.0_1694564002029.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("pubmedbert_abstract_cord19","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("pubmedbert_abstract_cord19", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pubmedbert_abstract_cord19| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/pritamdeka/PubMedBert-abstract-cord19 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-pubmedbert_abstract_cord19_v2_en.md b/docs/_posts/ahmedlone127/2023-09-13-pubmedbert_abstract_cord19_v2_en.md new file mode 100644 index 00000000000000..a5c4d15b137480 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-pubmedbert_abstract_cord19_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pubmedbert_abstract_cord19_v2 BertEmbeddings from pritamdeka +author: John Snow Labs +name: pubmedbert_abstract_cord19_v2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pubmedbert_abstract_cord19_v2` is a English model originally trained by pritamdeka. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pubmedbert_abstract_cord19_v2_en_5.1.1_3.0_1694563849710.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pubmedbert_abstract_cord19_v2_en_5.1.1_3.0_1694563849710.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("pubmedbert_abstract_cord19_v2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("pubmedbert_abstract_cord19_v2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pubmedbert_abstract_cord19_v2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/pritamdeka/PubMedBert-abstract-cord19-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-pubmedbert_fulltext_cord19_en.md b/docs/_posts/ahmedlone127/2023-09-13-pubmedbert_fulltext_cord19_en.md new file mode 100644 index 00000000000000..3b7bc95189a5b5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-pubmedbert_fulltext_cord19_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pubmedbert_fulltext_cord19 BertEmbeddings from pritamdeka +author: John Snow Labs +name: pubmedbert_fulltext_cord19 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pubmedbert_fulltext_cord19` is a English model originally trained by pritamdeka. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pubmedbert_fulltext_cord19_en_5.1.1_3.0_1694564166623.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pubmedbert_fulltext_cord19_en_5.1.1_3.0_1694564166623.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("pubmedbert_fulltext_cord19","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("pubmedbert_fulltext_cord19", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pubmedbert_fulltext_cord19| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/pritamdeka/PubMedBert-fulltext-cord19 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-qaideptmodel_en.md b/docs/_posts/ahmedlone127/2023-09-13-qaideptmodel_en.md new file mode 100644 index 00000000000000..91ba2dea30bd77 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-qaideptmodel_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English qaideptmodel BertEmbeddings from Razan +author: John Snow Labs +name: qaideptmodel +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`qaideptmodel` is a English model originally trained by Razan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/qaideptmodel_en_5.1.1_3.0_1694569996396.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/qaideptmodel_en_5.1.1_3.0_1694569996396.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("qaideptmodel","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("qaideptmodel", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|qaideptmodel| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|504.8 MB| + +## References + +https://huggingface.co/Razan/QAIDeptModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-qe3_ar.md b/docs/_posts/ahmedlone127/2023-09-13-qe3_ar.md new file mode 100644 index 00000000000000..7fbf063d360c7e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-qe3_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic qe3 BertEmbeddings from NLP-EXP +author: John Snow Labs +name: qe3 +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`qe3` is a Arabic model originally trained by NLP-EXP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/qe3_ar_5.1.1_3.0_1694580295901.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/qe3_ar_5.1.1_3.0_1694580295901.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("qe3","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("qe3", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|qe3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|504.1 MB| + +## References + +https://huggingface.co/NLP-EXP/QE3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-qe6_ar.md b/docs/_posts/ahmedlone127/2023-09-13-qe6_ar.md new file mode 100644 index 00000000000000..b92c874a716461 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-qe6_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic qe6 BertEmbeddings from NLP-EXP +author: John Snow Labs +name: qe6 +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`qe6` is a Arabic model originally trained by NLP-EXP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/qe6_ar_5.1.1_3.0_1694580457879.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/qe6_ar_5.1.1_3.0_1694580457879.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("qe6","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("qe6", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|qe6| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|504.2 MB| + +## References + +https://huggingface.co/NLP-EXP/QE6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-qse_en.md b/docs/_posts/ahmedlone127/2023-09-13-qse_en.md new file mode 100644 index 00000000000000..ac1c35f3a24fce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-qse_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English qse BertEmbeddings from NLP-EXP +author: John Snow Labs +name: qse +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`qse` is a English model originally trained by NLP-EXP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/qse_en_5.1.1_3.0_1694582574251.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/qse_en_5.1.1_3.0_1694582574251.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("qse","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("qse", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|qse| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|504.0 MB| + +## References + +https://huggingface.co/NLP-EXP/QSE \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-qsr_ar.md b/docs/_posts/ahmedlone127/2023-09-13-qsr_ar.md new file mode 100644 index 00000000000000..18104a5f339a1a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-qsr_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic qsr BertEmbeddings from NLP-EXP +author: John Snow Labs +name: qsr +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`qsr` is a Arabic model originally trained by NLP-EXP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/qsr_ar_5.1.1_3.0_1694580143110.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/qsr_ar_5.1.1_3.0_1694580143110.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("qsr","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("qsr", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|qsr| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|504.0 MB| + +## References + +https://huggingface.co/NLP-EXP/QSR \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-qsrt_ar.md b/docs/_posts/ahmedlone127/2023-09-13-qsrt_ar.md new file mode 100644 index 00000000000000..80bf5b476a74de --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-qsrt_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic qsrt BertEmbeddings from NLP-EXP +author: John Snow Labs +name: qsrt +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`qsrt` is a Arabic model originally trained by NLP-EXP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/qsrt_ar_5.1.1_3.0_1694579804623.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/qsrt_ar_5.1.1_3.0_1694579804623.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("qsrt","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("qsrt", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|qsrt| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|504.2 MB| + +## References + +https://huggingface.co/NLP-EXP/QSRT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-qst_ar.md b/docs/_posts/ahmedlone127/2023-09-13-qst_ar.md new file mode 100644 index 00000000000000..34eb8c95c873e7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-qst_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic qst BertEmbeddings from NLP-EXP +author: John Snow Labs +name: qst +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`qst` is a Arabic model originally trained by NLP-EXP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/qst_ar_5.1.1_3.0_1694579959539.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/qst_ar_5.1.1_3.0_1694579959539.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("qst","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("qst", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|qst| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|504.0 MB| + +## References + +https://huggingface.co/NLP-EXP/QST \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-recipe_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-recipe_bert_base_uncased_en.md new file mode 100644 index 00000000000000..e3eae7db825d7a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-recipe_bert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English recipe_bert_base_uncased BertEmbeddings from AnonymousSub +author: John Snow Labs +name: recipe_bert_base_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`recipe_bert_base_uncased` is a English model originally trained by AnonymousSub. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/recipe_bert_base_uncased_en_5.1.1_3.0_1694577388934.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/recipe_bert_base_uncased_en_5.1.1_3.0_1694577388934.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("recipe_bert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("recipe_bert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|recipe_bert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/AnonymousSub/recipe-bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-retbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-retbert_en.md new file mode 100644 index 00000000000000..a04449fd7fe3b0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-retbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English retbert BertEmbeddings from ThePixOne +author: John Snow Labs +name: retbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`retbert` is a English model originally trained by ThePixOne. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/retbert_en_5.1.1_3.0_1694572305505.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/retbert_en_5.1.1_3.0_1694572305505.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("retbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("retbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|retbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/ThePixOne/retBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-rubert_base_ru.md b/docs/_posts/ahmedlone127/2023-09-13-rubert_base_ru.md new file mode 100644 index 00000000000000..890f7c7e49be59 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-rubert_base_ru.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Russian rubert_base BertEmbeddings from ai-forever +author: John Snow Labs +name: rubert_base +date: 2023-09-13 +tags: [bert, ru, open_source, fill_mask, onnx] +task: Embeddings +language: ru +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`rubert_base` is a Russian model originally trained by ai-forever. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/rubert_base_ru_5.1.1_3.0_1694571215568.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/rubert_base_ru_5.1.1_3.0_1694571215568.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("rubert_base","ru") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("rubert_base", "ru") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rubert_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ru| +|Size:|667.0 MB| + +## References + +https://huggingface.co/ai-forever/ruBert-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-rubert_large_ru.md b/docs/_posts/ahmedlone127/2023-09-13-rubert_large_ru.md new file mode 100644 index 00000000000000..4215dc9aff15ed --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-rubert_large_ru.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Russian rubert_large BertEmbeddings from ai-forever +author: John Snow Labs +name: rubert_large +date: 2023-09-13 +tags: [bert, ru, open_source, fill_mask, onnx] +task: Embeddings +language: ru +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`rubert_large` is a Russian model originally trained by ai-forever. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/rubert_large_ru_5.1.1_3.0_1694571586736.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/rubert_large_ru_5.1.1_3.0_1694571586736.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("rubert_large","ru") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("rubert_large", "ru") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rubert_large| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ru| +|Size:|1.6 GB| + +## References + +https://huggingface.co/ai-forever/ruBert-large \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-rubiobert_en.md b/docs/_posts/ahmedlone127/2023-09-13-rubiobert_en.md new file mode 100644 index 00000000000000..709b81962d538b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-rubiobert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English rubiobert BertEmbeddings from alexyalunin +author: John Snow Labs +name: rubiobert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`rubiobert` is a English model originally trained by alexyalunin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/rubiobert_en_5.1.1_3.0_1694578324460.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/rubiobert_en_5.1.1_3.0_1694578324460.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("rubiobert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("rubiobert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rubiobert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|667.1 MB| + +## References + +https://huggingface.co/alexyalunin/RuBioBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-scibert_lm_v1_finetuned_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-scibert_lm_v1_finetuned_20_en.md new file mode 100644 index 00000000000000..c5e0519e83ef15 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-scibert_lm_v1_finetuned_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English scibert_lm_v1_finetuned_20 BertEmbeddings from ariesutiono +author: John Snow Labs +name: scibert_lm_v1_finetuned_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scibert_lm_v1_finetuned_20` is a English model originally trained by ariesutiono. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scibert_lm_v1_finetuned_20_en_5.1.1_3.0_1694564841995.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scibert_lm_v1_finetuned_20_en_5.1.1_3.0_1694564841995.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("scibert_lm_v1_finetuned_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("scibert_lm_v1_finetuned_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scibert_lm_v1_finetuned_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|499.5 MB| + +## References + +https://huggingface.co/ariesutiono/scibert-lm-v1-finetuned-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-scibert_lm_v2_finetuned_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-scibert_lm_v2_finetuned_20_en.md new file mode 100644 index 00000000000000..ee7b79a92a3f4f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-scibert_lm_v2_finetuned_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English scibert_lm_v2_finetuned_20 BertEmbeddings from ariesutiono +author: John Snow Labs +name: scibert_lm_v2_finetuned_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scibert_lm_v2_finetuned_20` is a English model originally trained by ariesutiono. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scibert_lm_v2_finetuned_20_en_5.1.1_3.0_1694565081199.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scibert_lm_v2_finetuned_20_en_5.1.1_3.0_1694565081199.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("scibert_lm_v2_finetuned_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("scibert_lm_v2_finetuned_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scibert_lm_v2_finetuned_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|499.5 MB| + +## References + +https://huggingface.co/ariesutiono/scibert-lm-v2-finetuned-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-scibert_scivocab_uncased_finetuned_scibero_en.md b/docs/_posts/ahmedlone127/2023-09-13-scibert_scivocab_uncased_finetuned_scibero_en.md new file mode 100644 index 00000000000000..777c51d6e921d2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-scibert_scivocab_uncased_finetuned_scibero_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English scibert_scivocab_uncased_finetuned_scibero BertEmbeddings from Transabrar +author: John Snow Labs +name: scibert_scivocab_uncased_finetuned_scibero +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scibert_scivocab_uncased_finetuned_scibero` is a English model originally trained by Transabrar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scibert_scivocab_uncased_finetuned_scibero_en_5.1.1_3.0_1694572669211.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scibert_scivocab_uncased_finetuned_scibero_en_5.1.1_3.0_1694572669211.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("scibert_scivocab_uncased_finetuned_scibero","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("scibert_scivocab_uncased_finetuned_scibero", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scibert_scivocab_uncased_finetuned_scibero| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.0 MB| + +## References + +https://huggingface.co/Transabrar/scibert_scivocab_uncased-finetuned-scibero \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-scibert_scivocab_uncased_finetuned_scibert_agu_abstracts_en.md b/docs/_posts/ahmedlone127/2023-09-13-scibert_scivocab_uncased_finetuned_scibert_agu_abstracts_en.md new file mode 100644 index 00000000000000..4170c091360902 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-scibert_scivocab_uncased_finetuned_scibert_agu_abstracts_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English scibert_scivocab_uncased_finetuned_scibert_agu_abstracts BertEmbeddings from arminmehrabian +author: John Snow Labs +name: scibert_scivocab_uncased_finetuned_scibert_agu_abstracts +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scibert_scivocab_uncased_finetuned_scibert_agu_abstracts` is a English model originally trained by arminmehrabian. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scibert_scivocab_uncased_finetuned_scibert_agu_abstracts_en_5.1.1_3.0_1694563751306.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scibert_scivocab_uncased_finetuned_scibert_agu_abstracts_en_5.1.1_3.0_1694563751306.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("scibert_scivocab_uncased_finetuned_scibert_agu_abstracts","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("scibert_scivocab_uncased_finetuned_scibert_agu_abstracts", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scibert_scivocab_uncased_finetuned_scibert_agu_abstracts| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/arminmehrabian/scibert_scivocab_uncased-finetuned-scibert-agu-abstracts \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-scibert_scivocab_uncased_long_4096_en.md b/docs/_posts/ahmedlone127/2023-09-13-scibert_scivocab_uncased_long_4096_en.md new file mode 100644 index 00000000000000..56b07713c1bea8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-scibert_scivocab_uncased_long_4096_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English scibert_scivocab_uncased_long_4096 BertEmbeddings from yorko +author: John Snow Labs +name: scibert_scivocab_uncased_long_4096 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scibert_scivocab_uncased_long_4096` is a English model originally trained by yorko. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scibert_scivocab_uncased_long_4096_en_5.1.1_3.0_1694587393144.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scibert_scivocab_uncased_long_4096_en_5.1.1_3.0_1694587393144.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("scibert_scivocab_uncased_long_4096","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("scibert_scivocab_uncased_long_4096", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scibert_scivocab_uncased_long_4096| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|420.3 MB| + +## References + +https://huggingface.co/yorko/scibert_scivocab_uncased_long_4096 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-scibert_wechsel_korean_en.md b/docs/_posts/ahmedlone127/2023-09-13-scibert_wechsel_korean_en.md new file mode 100644 index 00000000000000..3ee8667dc06922 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-scibert_wechsel_korean_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English scibert_wechsel_korean BertEmbeddings from LeverageX +author: John Snow Labs +name: scibert_wechsel_korean +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scibert_wechsel_korean` is a English model originally trained by LeverageX. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scibert_wechsel_korean_en_5.1.1_3.0_1694566166641.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scibert_wechsel_korean_en_5.1.1_3.0_1694566166641.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("scibert_wechsel_korean","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("scibert_wechsel_korean", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scibert_wechsel_korean| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.4 MB| + +## References + +https://huggingface.co/LeverageX/scibert-wechsel-korean \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-segabert_large_en.md b/docs/_posts/ahmedlone127/2023-09-13-segabert_large_en.md new file mode 100644 index 00000000000000..d47852fa81c05c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-segabert_large_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English segabert_large BertEmbeddings from rsvp-AI-ca +author: John Snow Labs +name: segabert_large +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`segabert_large` is a English model originally trained by rsvp-AI-ca. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/segabert_large_en_5.1.1_3.0_1694568994445.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/segabert_large_en_5.1.1_3.0_1694568994445.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("segabert_large","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("segabert_large", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|segabert_large| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|941.1 MB| + +## References + +https://huggingface.co/rsvp-AI-ca/segabert-large \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-sentmae_beir_en.md b/docs/_posts/ahmedlone127/2023-09-13-sentmae_beir_en.md new file mode 100644 index 00000000000000..b797ac70350b25 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-sentmae_beir_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sentmae_beir BertEmbeddings from SamuelYang +author: John Snow Labs +name: sentmae_beir +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sentmae_beir` is a English model originally trained by SamuelYang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sentmae_beir_en_5.1.1_3.0_1694567238490.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sentmae_beir_en_5.1.1_3.0_1694567238490.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sentmae_beir","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sentmae_beir", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sentmae_beir| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.6 MB| + +## References + +https://huggingface.co/SamuelYang/SentMAE_BEIR \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-sentmae_en.md b/docs/_posts/ahmedlone127/2023-09-13-sentmae_en.md new file mode 100644 index 00000000000000..c71ac5be26922e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-sentmae_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sentmae BertEmbeddings from SamuelYang +author: John Snow Labs +name: sentmae +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sentmae` is a English model originally trained by SamuelYang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sentmae_en_5.1.1_3.0_1694567054783.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sentmae_en_5.1.1_3.0_1694567054783.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sentmae","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sentmae", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sentmae| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.6 MB| + +## References + +https://huggingface.co/SamuelYang/SentMAE \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-sentmae_msmarco_en.md b/docs/_posts/ahmedlone127/2023-09-13-sentmae_msmarco_en.md new file mode 100644 index 00000000000000..a26ae4ff2a2120 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-sentmae_msmarco_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sentmae_msmarco BertEmbeddings from SamuelYang +author: John Snow Labs +name: sentmae_msmarco +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sentmae_msmarco` is a English model originally trained by SamuelYang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sentmae_msmarco_en_5.1.1_3.0_1694567599891.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sentmae_msmarco_en_5.1.1_3.0_1694567599891.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sentmae_msmarco","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sentmae_msmarco", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sentmae_msmarco| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.7 MB| + +## References + +https://huggingface.co/SamuelYang/SentMAE_MSMARCO \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-sikubert_zh.md b/docs/_posts/ahmedlone127/2023-09-13-sikubert_zh.md new file mode 100644 index 00000000000000..1f8bbc0e6e1c9a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-sikubert_zh.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Chinese sikubert BertEmbeddings from SIKU-BERT +author: John Snow Labs +name: sikubert +date: 2023-09-13 +tags: [bert, zh, open_source, fill_mask, onnx] +task: Embeddings +language: zh +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sikubert` is a Chinese model originally trained by SIKU-BERT. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sikubert_zh_5.1.1_3.0_1694570261032.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sikubert_zh_5.1.1_3.0_1694570261032.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sikubert","zh") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sikubert", "zh") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sikubert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|zh| +|Size:|406.0 MB| + +## References + +https://huggingface.co/SIKU-BERT/sikubert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-simlm_base_msmarco_en.md b/docs/_posts/ahmedlone127/2023-09-13-simlm_base_msmarco_en.md new file mode 100644 index 00000000000000..bd9a20c9fe4cd8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-simlm_base_msmarco_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English simlm_base_msmarco BertEmbeddings from intfloat +author: John Snow Labs +name: simlm_base_msmarco +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`simlm_base_msmarco` is a English model originally trained by intfloat. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/simlm_base_msmarco_en_5.1.1_3.0_1694566597314.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/simlm_base_msmarco_en_5.1.1_3.0_1694566597314.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("simlm_base_msmarco","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("simlm_base_msmarco", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|simlm_base_msmarco| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/intfloat/simlm-base-msmarco \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-simlm_base_wiki100w_en.md b/docs/_posts/ahmedlone127/2023-09-13-simlm_base_wiki100w_en.md new file mode 100644 index 00000000000000..5d653265f03a17 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-simlm_base_wiki100w_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English simlm_base_wiki100w BertEmbeddings from intfloat +author: John Snow Labs +name: simlm_base_wiki100w +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`simlm_base_wiki100w` is a English model originally trained by intfloat. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/simlm_base_wiki100w_en_5.1.1_3.0_1694566769454.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/simlm_base_wiki100w_en_5.1.1_3.0_1694566769454.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("simlm_base_wiki100w","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("simlm_base_wiki100w", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|simlm_base_wiki100w| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.8 MB| + +## References + +https://huggingface.co/intfloat/simlm-base-wiki100w \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-slim_beir_scifact_old_en.md b/docs/_posts/ahmedlone127/2023-09-13-slim_beir_scifact_old_en.md new file mode 100644 index 00000000000000..994317be139b8b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-slim_beir_scifact_old_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English slim_beir_scifact_old BertEmbeddings from kinoo +author: John Snow Labs +name: slim_beir_scifact_old +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`slim_beir_scifact_old` is a English model originally trained by kinoo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/slim_beir_scifact_old_en_5.1.1_3.0_1694578412848.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/slim_beir_scifact_old_en_5.1.1_3.0_1694578412848.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("slim_beir_scifact_old","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("slim_beir_scifact_old", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|slim_beir_scifact_old| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/kinoo/slim_beir-scifact-old \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-slimr_msmarco_passage_en.md b/docs/_posts/ahmedlone127/2023-09-13-slimr_msmarco_passage_en.md new file mode 100644 index 00000000000000..d8f59ad8aa8f32 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-slimr_msmarco_passage_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English slimr_msmarco_passage BertEmbeddings from castorini +author: John Snow Labs +name: slimr_msmarco_passage +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`slimr_msmarco_passage` is a English model originally trained by castorini. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/slimr_msmarco_passage_en_5.1.1_3.0_1694577246538.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/slimr_msmarco_passage_en_5.1.1_3.0_1694577246538.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("slimr_msmarco_passage","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("slimr_msmarco_passage", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|slimr_msmarco_passage| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/castorini/slimr-msmarco-passage \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..9f23d6657fee5d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563331949.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563331949.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_qnli_from_scratch_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|135.2 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-qnli-from-scratch-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..0c2d846bfce26d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563524788.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563524788.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|122.8 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-qqp-from-scratch-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_rte_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_rte_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..69cca9ff8c53e6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_rte_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_rte_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_rte_custom_tokenizer_expand_vocab +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_rte_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_rte_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563639389.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_rte_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563639389.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_rte_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_rte_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_rte_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|126.2 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-rte-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..2bd82070b0129b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563734576.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563734576.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_rte_from_scratch_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|126.3 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-rte-from-scratch-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_sst2_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_sst2_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..5a93507c7457ed --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_sst2_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_sst2_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_sst2_custom_tokenizer_expand_vocab +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_sst2_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_sst2_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563829652.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_sst2_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563829652.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_sst2_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_sst2_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_sst2_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|120.8 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-sst2-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..826dafefb44cac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563918718.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563918718.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_sst2_from_scratch_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|120.8 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-sst2-from-scratch-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_stsb_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_stsb_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..11d672c756d5fd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_stsb_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_stsb_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_stsb_custom_tokenizer_expand_vocab +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_stsb_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_stsb_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694564136292.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_stsb_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694564136292.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_stsb_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_stsb_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_stsb_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|117.4 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-stsb-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..6eac2463e75411 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694564017601.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694564017601.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_stsb_from_scratch_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|117.4 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-stsb-from-scratch-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..18c0b2d5019597 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694564212872.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694564212872.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|108.1 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-wnli-from-scratch-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-spbert_mlm_base_en.md b/docs/_posts/ahmedlone127/2023-09-13-spbert_mlm_base_en.md new file mode 100644 index 00000000000000..dc85b7d7c9991e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-spbert_mlm_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English spbert_mlm_base BertEmbeddings from razent +author: John Snow Labs +name: spbert_mlm_base +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`spbert_mlm_base` is a English model originally trained by razent. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/spbert_mlm_base_en_5.1.1_3.0_1694566338968.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/spbert_mlm_base_en_5.1.1_3.0_1694566338968.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("spbert_mlm_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("spbert_mlm_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|spbert_mlm_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/razent/spbert-mlm-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-spbert_mlm_wso_base_en.md b/docs/_posts/ahmedlone127/2023-09-13-spbert_mlm_wso_base_en.md new file mode 100644 index 00000000000000..36b6d2733147e3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-spbert_mlm_wso_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English spbert_mlm_wso_base BertEmbeddings from razent +author: John Snow Labs +name: spbert_mlm_wso_base +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`spbert_mlm_wso_base` is a English model originally trained by razent. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/spbert_mlm_wso_base_en_5.1.1_3.0_1694566556828.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/spbert_mlm_wso_base_en_5.1.1_3.0_1694566556828.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("spbert_mlm_wso_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("spbert_mlm_wso_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|spbert_mlm_wso_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.5 MB| + +## References + +https://huggingface.co/razent/spbert-mlm-wso-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-splade_cocondenser_selfdistil_baseplate_en.md b/docs/_posts/ahmedlone127/2023-09-13-splade_cocondenser_selfdistil_baseplate_en.md new file mode 100644 index 00000000000000..a6d5e2441fc700 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-splade_cocondenser_selfdistil_baseplate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English splade_cocondenser_selfdistil_baseplate BertEmbeddings from baseplate +author: John Snow Labs +name: splade_cocondenser_selfdistil_baseplate +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`splade_cocondenser_selfdistil_baseplate` is a English model originally trained by baseplate. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/splade_cocondenser_selfdistil_baseplate_en_5.1.1_3.0_1694585076462.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/splade_cocondenser_selfdistil_baseplate_en_5.1.1_3.0_1694585076462.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("splade_cocondenser_selfdistil_baseplate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("splade_cocondenser_selfdistil_baseplate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|splade_cocondenser_selfdistil_baseplate| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.4 MB| + +## References + +https://huggingface.co/baseplate/splade-cocondenser-selfdistil \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-sw_v1_sw.md b/docs/_posts/ahmedlone127/2023-09-13-sw_v1_sw.md new file mode 100644 index 00000000000000..1b5b9b390cacf3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-sw_v1_sw.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Swahili (macrolanguage) sw_v1 BertEmbeddings from eolang +author: John Snow Labs +name: sw_v1 +date: 2023-09-13 +tags: [bert, sw, open_source, fill_mask, onnx] +task: Embeddings +language: sw +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sw_v1` is a Swahili (macrolanguage) model originally trained by eolang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sw_v1_sw_5.1.1_3.0_1694578806650.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sw_v1_sw_5.1.1_3.0_1694578806650.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sw_v1","sw") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sw_v1", "sw") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sw_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|sw| +|Size:|408.0 MB| + +## References + +https://huggingface.co/eolang/SW-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-test_telsayed_en.md b/docs/_posts/ahmedlone127/2023-09-13-test_telsayed_en.md new file mode 100644 index 00000000000000..a4d1b32d58d99d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-test_telsayed_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English test_telsayed BertEmbeddings from telsayed +author: John Snow Labs +name: test_telsayed +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`test_telsayed` is a English model originally trained by telsayed. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/test_telsayed_en_5.1.1_3.0_1694576902353.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/test_telsayed_en_5.1.1_3.0_1694576902353.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("test_telsayed","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("test_telsayed", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|test_telsayed| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/telsayed/test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tiny_a_2_h_2_en.md b/docs/_posts/ahmedlone127/2023-09-13-tiny_a_2_h_2_en.md new file mode 100644 index 00000000000000..e9217ac1e45314 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tiny_a_2_h_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_a_2_h_2 BertEmbeddings from KamrusSamad +author: John Snow Labs +name: tiny_a_2_h_2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_a_2_h_2` is a English model originally trained by KamrusSamad. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_a_2_h_2_en_5.1.1_3.0_1694564331760.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_a_2_h_2_en_5.1.1_3.0_1694564331760.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_a_2_h_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_a_2_h_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_a_2_h_2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/KamrusSamad/tiny_A-2_H-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tiny_clinicalbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-tiny_clinicalbert_en.md new file mode 100644 index 00000000000000..47f42829007e45 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tiny_clinicalbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_clinicalbert BertEmbeddings from nlpie +author: John Snow Labs +name: tiny_clinicalbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_clinicalbert` is a English model originally trained by nlpie. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_clinicalbert_en_5.1.1_3.0_1694573934228.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_clinicalbert_en_5.1.1_3.0_1694573934228.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_clinicalbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_clinicalbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_clinicalbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|51.9 MB| + +## References + +https://huggingface.co/nlpie/tiny-clinicalbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tiny_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-13-tiny_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..4fa075f99a585a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tiny_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563249016.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563249016.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_wnli_from_scratch_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-wnli-from-scratch-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_ag_medium_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_ag_medium_scale_en.md new file mode 100644 index 00000000000000..49b628bcf918ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_ag_medium_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_ag_medium_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_ag_medium_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_ag_medium_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_ag_medium_scale_en_5.1.1_3.0_1694588336137.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_ag_medium_scale_en_5.1.1_3.0_1694588336137.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_ag_medium_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_ag_medium_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_ag_medium_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.3 MB| + +## References + +https://huggingface.co/yxchar/tlm-ag-medium-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-topic_erica_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-topic_erica_bert_en.md new file mode 100644 index 00000000000000..5cd4d3f913f1b4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-topic_erica_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English topic_erica_bert BertEmbeddings from Kdogs +author: John Snow Labs +name: topic_erica_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`topic_erica_bert` is a English model originally trained by Kdogs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/topic_erica_bert_en_5.1.1_3.0_1694565067616.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/topic_erica_bert_en_5.1.1_3.0_1694565067616.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("topic_erica_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("topic_erica_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|topic_erica_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.4 MB| + +## References + +https://huggingface.co/Kdogs/topic_erica_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-transformer_exercise_01_en.md b/docs/_posts/ahmedlone127/2023-09-13-transformer_exercise_01_en.md new file mode 100644 index 00000000000000..45e31f24879095 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-transformer_exercise_01_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English transformer_exercise_01 BertEmbeddings from alangganggang +author: John Snow Labs +name: transformer_exercise_01 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`transformer_exercise_01` is a English model originally trained by alangganggang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/transformer_exercise_01_en_5.1.1_3.0_1694577887359.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/transformer_exercise_01_en_5.1.1_3.0_1694577887359.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("transformer_exercise_01","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("transformer_exercise_01", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|transformer_exercise_01| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/alangganggang/transformer_exercise_01 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tsonga_test_en.md b/docs/_posts/ahmedlone127/2023-09-13-tsonga_test_en.md new file mode 100644 index 00000000000000..acb9374ea0b290 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tsonga_test_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tsonga_test BertEmbeddings from taesu +author: John Snow Labs +name: tsonga_test +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tsonga_test` is a English model originally trained by taesu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tsonga_test_en_5.1.1_3.0_1694576071612.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tsonga_test_en_5.1.1_3.0_1694576071612.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tsonga_test","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tsonga_test", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tsonga_test| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.7 MB| + +## References + +https://huggingface.co/taesu/ts-test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tulio_bert_es.md b/docs/_posts/ahmedlone127/2023-09-13-tulio_bert_es.md new file mode 100644 index 00000000000000..7d68cf213c0215 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tulio_bert_es.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Castilian, Spanish tulio_bert BertEmbeddings from jorgeortizfuentes +author: John Snow Labs +name: tulio_bert +date: 2023-09-13 +tags: [bert, es, open_source, fill_mask, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tulio_bert` is a Castilian, Spanish model originally trained by jorgeortizfuentes. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tulio_bert_es_5.1.1_3.0_1694570756756.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tulio_bert_es_5.1.1_3.0_1694570756756.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tulio_bert","es") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tulio_bert", "es") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tulio_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|es| +|Size:|409.0 MB| + +## References + +https://huggingface.co/jorgeortizfuentes/tulio-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-wineberto_italian_cased_it.md b/docs/_posts/ahmedlone127/2023-09-13-wineberto_italian_cased_it.md new file mode 100644 index 00000000000000..3909a41f8d79b2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-wineberto_italian_cased_it.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Italian wineberto_italian_cased BertEmbeddings from vinhood +author: John Snow Labs +name: wineberto_italian_cased +date: 2023-09-13 +tags: [bert, it, open_source, fill_mask, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`wineberto_italian_cased` is a Italian model originally trained by vinhood. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/wineberto_italian_cased_it_5.1.1_3.0_1694581896429.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/wineberto_italian_cased_it_5.1.1_3.0_1694581896429.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("wineberto_italian_cased","it") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("wineberto_italian_cased", "it") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|wineberto_italian_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|it| +|Size:|412.6 MB| + +## References + +https://huggingface.co/vinhood/wineberto-italian-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-wobert_chinese_plus_zh.md b/docs/_posts/ahmedlone127/2023-09-13-wobert_chinese_plus_zh.md new file mode 100644 index 00000000000000..1959e2400b1f9d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-wobert_chinese_plus_zh.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Chinese wobert_chinese_plus BertEmbeddings from qinluo +author: John Snow Labs +name: wobert_chinese_plus +date: 2023-09-13 +tags: [bert, zh, open_source, fill_mask, onnx] +task: Embeddings +language: zh +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`wobert_chinese_plus` is a Chinese model originally trained by qinluo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/wobert_chinese_plus_zh_5.1.1_3.0_1694565904418.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/wobert_chinese_plus_zh_5.1.1_3.0_1694565904418.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("wobert_chinese_plus","zh") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("wobert_chinese_plus", "zh") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|wobert_chinese_plus| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|zh| +|Size:|464.5 MB| + +## References + +https://huggingface.co/qinluo/wobert-chinese-plus \ No newline at end of file From 6ec2297cce6fece670d3a2f69f11dea38ebe867e Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Wed, 13 Sep 2023 20:36:30 +0700 Subject: [PATCH 42/55] 2023-09-13-tlm_ag_small_scale_en (#13980) * Add model 2023-09-13-lb_mbert_en * Add model 2023-09-13-bert_telugu_23_en * Add model 2023-09-13-bert_web_bulgarian_bg * Add model 2023-09-13-bert_base_italian_uncased_dbmdz_it * Add model 2023-09-12-cord19_bert_en * Add model 2023-09-13-bert_base_spanish_wwm_uncased_finetuned_imdb_spanish_en * Add model 2023-09-13-bangla_bert_base_finetuned_tweets_en * Add model 2023-09-13-bert_embding_finetuned_spmlm_en * Add model 2023-09-12-bert_base_xsum_en * Add model 2023-09-13-nepal_bhasa_model_en * Add model 2023-09-13-multi_dialect_bert_base_arabic_ar * Add model 2023-09-13-morrbert_en * Add model 2023-09-13-bert_base_italian_xxl_cased_it * Add model 2023-09-13-me_bert_mixed_v2_mr * Add model 2023-09-13-datafinder_scibert_dutch_queries_en * Add model 2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_1_en * Add model 2023-09-13-wisdomify_en * Add model 2023-09-12-original_topic_sports_kcbert_en * Add model 2023-09-13-rubert_tiny_ru * Add model 2023-09-13-lexbert_turkish_uncased_en * Add model 2023-09-13-bert_base_dutch_cased_mlm_visio_en * Add model 2023-09-13-first_try_rubert_200_16_16_25ep_en * Add model 2023-09-13-helloworld_en * Add model 2023-09-13-parlbert_german_v2_de * Add model 2023-09-13-bertjewdialdataqa20k_en * Add model 2023-09-13-kinyabert_small_finetuned_kintweetsb_en * Add model 2023-09-13-bert_medium_pretrained_on_squad_en * Add model 2023-09-13-bert_review_en * Add model 2023-09-13-distilbertu_base_cased_1.0_en * Add model 2023-09-13-bert_double_en * Add model 2023-09-13-bert_base_german_cased_domain_adaptation_accelerate_en * Add model 2023-09-13-bert_base_uncased_copy_en * Add model 2023-09-13-kinyabert_large_finetuned_kintweetsc_en * Add model 2023-09-13-arabert_quran_large_en * Add model 2023-09-13-batteryscibert_uncased_en * Add model 2023-09-13-bert_mini_historic_multilingual_cased_xx * Add model 2023-09-13-tlm_hyp_small_scale_en * Add model 2023-09-13-bert_base_multilingual_cased_finetuned_am_shb_xx * Add model 2023-09-13-bert_finetuning_test_zqf03118_en * Add model 2023-09-13-bert_small_finetuned_legal_contracts_larger20_5_1_en * Add model 2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_15_en * Add model 2023-09-13-bert_medium_mlsm_en * Add model 2023-09-13-hindi_random_twt_1m_hi * Add model 2023-09-13-tfhbert_en * Add model 2023-09-13-bert_base_uncased_byeongal_en * Add model 2023-09-13-kinyabert_small_finetuned_kintweetsc_en * Add model 2023-09-13-bert_base_greek_uncased_v1_finetuned_imdb_en * Add model 2023-09-13-bert_large_cased_sigir_support_refute_evidence_norwegian_label_40_en * Add model 2023-09-13-defsent_bert_base_uncased_mean_en * Add model 2023-09-13-bert_large_cased_sigir_lr10_1_prepend_20_en * Add model 2023-09-13-danish_legal_bert_base_da * Add model 2023-09-13-bert_base_greek_uncased_v1_finetuned_polylex_en * Add model 2023-09-12-mbert_tlm_sent_english_italian_en * Add model 2023-09-13-sanaybert_model_v1_en * Add model 2023-09-13-finbert_pretrain_tianzhou_en * Add model 2023-09-13-bert_base_greek_uncased_v2_finetuned_polylex_en * Add model 2023-09-13-first_try_rubert_200_16_16_10ep_en * Add model 2023-09-13-batteryonlybert_cased_en * Add model 2023-09-13-distilbertu_base_cased_anneal_en * Add model 2023-09-13-bert_base_uncased_new_data_bert1_en * Add model 2023-09-13-indo1_en * Add model 2023-09-13-bertimbau_legal_pt * Add model 2023-09-13-batteryonlybert_uncased_en * Add model 2023-09-13-bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr10_labelled_40_en * Add model 2023-09-13-bert_semeval_env_en * Add model 2023-09-13-bert_base_german_cased_oldvocab_de * Add model 2023-09-12-dummy_model_bigtimecodersean_en * Add model 2023-09-13-berel_dicta_il_he * Add model 2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_22_en * Add model 2023-09-13-bert_medium_historic_multilingual_cased_xx * Add model 2023-09-12-bertjewdialdataallqonly04_en * Add model 2023-09-13-quant_bert_en * Add model 2023-09-13-netbert_en * Add model 2023-09-13-tlm_chemprot_large_scale_en * Add model 2023-09-13-covid_bert_base_en * Add model 2023-09-13-cdgp_chilean_sign_language_scibert_dgen_en * Add model 2023-09-13-mbertv2.0_en * Add model 2023-09-13-kinyabert_large_finetuned_kintweetsd_en * Add model 2023-09-13-bert_small_finetuned_eurlex_en * Add model 2023-09-13-dal_bert_finetuned_medical_v3_en * Add model 2023-09-13-cms_rx_language_correction_en * Add model 2023-09-13-spa_en * Add model 2023-09-13-bert_base_uncased_2022_nvidia_test_2_en * Add model 2023-09-13-bert_base_uncased_2022_nvidia_test_3_en * Add model 2023-09-13-further_train_2_domain_10_en * Add model 2023-09-13-custom_legalbert_en * Add model 2023-09-13-melayubert_ms * Add model 2023-09-13-agriculture_bert_uncased_en * Add model 2023-09-13-tlm_rct_20k_small_scale_en * Add model 2023-09-13-wiki_bert_en * Add model 2023-09-13-bert_large_cased_sigir_lr100_0_prepend_40_en * Add model 2023-09-13-detox_kcbert_base_en * Add model 2023-09-13-bert_base_multilingual_uncased_finetuned_xx * Add model 2023-09-13-kinyabert_small_finetuned_kintweetsd_en * Add model 2023-09-12-bert_base_english_turkish_cased_en * Add model 2023-09-13-bert_large_cased_sigir_lr100_1_cased_40_en * Add model 2023-09-12-tiny_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab_en * Add model 2023-09-13-bibert_20_epochs_en * Add model 2023-09-13-bert_finetuning_test_0925_en * Add model 2023-09-13-tlm_chemprot_small_scale_en * Add model 2023-09-13-carlbert_webex_en * Add model 2023-09-13-bert_based_restaurant_review_en * Add model 2023-09-13-bert_large_nli_en * Add model 2023-09-13-dummy_model_jammm1412_en * Add model 2023-09-13-bert_large_arabic_ar * Add model 2023-09-13-finbert_lm_finetuned_news_en * Add model 2023-09-12-topic_it_science_bert_en * Add model 2023-09-13-defsent_bert_large_uncased_cls_en * Add model 2023-09-13-hing_bert_hi * Add model 2023-09-13-bert_base_uncased_finetuned_kintweetse_en * Add model 2023-09-13-bert_base_historic_multilingual_cased_xx * Add model 2023-09-13-marathi_bert_smaller_mr * Add model 2023-09-13-tlm_citation_intent_medium_scale_en * Add model 2023-09-13-bert_base_indonesian_1.5g_id * Add model 2023-09-12-hatebert_en * Add model 2023-09-13-tiny_biobert_en * Add model 2023-09-13-gujibert_jian_fan_en * Add model 2023-09-13-danish_bert_botxo_da * Add model 2023-09-13-bert_small_finetuned_parsed_longer100_en * Add model 2023-09-12-bert_base_cased_portuguese_ccorpus_en * Add model 2023-09-13-biblitbert_en * Add model 2023-09-13-bert_medium_arabic_ar * Add model 2023-09-13-bertjewdialdataallqonly09_en * Add model 2023-09-13-legal_bertimbau_base_pt * Add model 2023-09-13-bert_base_uncased_issues_128_roscoyoon_en * Add model 2023-09-13-indojave_codemixed_bert_base_id * Add model 2023-09-13-eng_en * Add model 2023-09-13-arabertmo_base_v10_en * Add model 2023-09-12-legal_bert_base_uncased_en * Add model 2023-09-13-dlub_2022_mlm_full_muug_en * Add model 2023-09-12-bertjewdialdataall_en * Add model 2023-09-12-mlm_20230503_indobert_base_p2_002_en * Add model 2023-09-12-sec_bert_base_en * Add model 2023-09-13-german_financial_statements_bert_de * Add model 2023-09-13-carlbert_webex_mlm_spatial_en * Add model 2023-09-13-bert_base_indonesian_522m_id * Add model 2023-09-13-dbert_ko * Add model 2023-09-13-hing_mbert_hi * Add model 2023-09-13-greek_media_bert_base_uncased_el * Add model 2023-09-13-mlm_20230403_002_1_en * Add model 2023-09-13-tsonga_test2_en * Add model 2023-09-13-bert_base_italian_xxl_uncased_it * Add model 2023-09-13-distilbertu_base_cased_0.5_en * Add model 2023-09-13-spanish_bert_base_spanish_wwm_cased_en * Add model 2023-09-13-bert_dp_4_en * Add model 2023-09-12-mlm_20230513_indobert_large_p1_002_pt1_en * Add model 2023-09-13-bert_small_finetuned_legal_definitions_en * Add model 2023-09-12-tiny_mlm_glue_mrpc_custom_tokenizer_expand_vocab_en * Add model 2023-09-13-sci_summary_2_en * Add model 2023-09-13-nepnewsbert_en * Add model 2023-09-13-bert_small_finetuned_legal_definitions_longer_en * Add model 2023-09-13-bert_base_greek_uncased_v3_finetuned_polylex_en * Add model 2023-09-13-beto_chile_politico_1990_2019_es * Add model 2023-09-13-retromae_msmarco_finetune_en * Add model 2023-09-13-bert_base_arabertv2_ar * Add model 2023-09-13-tlm_sciie_medium_scale_en * Add model 2023-09-13-further_train_domain_10_en * Add model 2023-09-13-mbert_rom_arabic_en * Add model 2023-09-12-biomednlp_pubmedbert_base_uncased_abstract_fulltext_en * Add model 2023-09-13-distil_clinicalbert_en * Add model 2023-09-13-kinyabert_small_finetuned_kintweetsa_en * Add model 2023-09-13-lernnavibert_en * Add model 2023-09-13-absa_mlm_2_en * Add model 2023-09-13-tlm_sciie_small_scale_en * Add model 2023-09-13-javanese_bert_small_jv * Add model 2023-09-13-incaselawbert_en * Add model 2023-09-13-mymodel001_en * Add model 2023-09-12-akeylegalbert6_en * Add model 2023-09-13-bert_base_low_resource_wellness_en * Add model 2023-09-12-bert_base_english_russian_cased_en * Add model 2023-09-13-carlbert_webex_mlm_aditeyabaral_en * Add model 2023-09-13-distilbertu_base_cased_en * Add model 2023-09-13-mlm_20230403_001_1_en * Add model 2023-09-13-test_itamarl_en * Add model 2023-09-13-cms_ext_bio_clinicalbert_en * Add model 2023-09-13-knowbias_bert_base_uncased_race_en * Add model 2023-09-13-film98991bert_base_uncased_en * Add model 2023-09-12-romanian_bert_tweet_large_ro * Add model 2023-09-13-bioformer_8l_en * Add model 2023-09-12-efficient_splade_vi_bt_large_query_en * Add model 2023-09-13-bert_small_historic_multilingual_cased_xx * Add model 2023-09-13-bert_dk_rest_en * Add model 2023-09-13-all_minilm_l6_v2_finetuned_wikitext2_en * Add model 2023-09-13-gujiroberta_jian_fan_en * Add model 2023-09-13-bert_hs_idpt_en * Add model 2023-09-13-itcast_nlp_base_en * Add model 2023-09-13-bert_base_swedish_europeana_cased_en * Add model 2023-09-13-nlpload_en * Add model 2023-09-13-bert_base_uncased_issues_128_shenghao1993_en * Add model 2023-09-12-minilmv2_l6_h768_distilled_from_bert_large_en * Add model 2023-09-13-newmodel_en * Add model 2023-09-13-cocodr_base_en * Add model 2023-09-13-marathi_bert_small_mr * Add model 2023-09-13-slimr_pp_msmarco_passage_en * Add model 2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_8_en * Add model 2023-09-13-bert_base_uncased_issues_128_synpjh_en * Add model 2023-09-13-twteval_pretrained_en * Add model 2023-09-13-mlm_20230404_001_2_en * Add model 2023-09-13-sanay_bert_en * Add model 2023-09-13-kinyabert_large_finetuned_kintweets_en * Add model 2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_11_en * Add model 2023-09-12-muril_adapted_local_xx * Add model 2023-09-13-bert_base_uncased_ifedrigo_en * Add model 2023-09-13-bert_base_uncased_issues_128_haesun_en * Add model 2023-09-13-bert_base_spanish_wwm_cased_dccuchile_es * Add model 2023-09-13-kinyabert_small_finetuned_kintweets_en * Add model 2023-09-13-retromae_en * Add model 2023-09-13-indo2_en * Add model 2023-09-13-bert_base_turkish_128k_cased_offensive_mlm_tr * Add model 2023-09-13-tod_bert_jnt_v1_en * Add model 2023-09-12-prop_marco_en * Add model 2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_en * Add model 2023-09-13-biovocabbert_en * Add model 2023-09-13-tlm_citation_intent_small_scale_en * Add model 2023-09-13-bert_base_uncased_finetuned_lexglue_en * Add model 2023-09-13-cocodr_large_en * Add model 2023-09-13-bert_base_german_cased_archaeo_de * Add model 2023-09-13-bert_large_cased_finetuned_low100_0_cased_da_20_en * Add model 2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_2_en * Add model 2023-09-13-tulio_chilean_spanish_bert_es * Add model 2023-09-12-bertjewdialdataall04_en * Add model 2023-09-13-protaugment_lm_hwu64_en * Add model 2023-09-13-tlm_rct_20k_medium_scale_en * Add model 2023-09-13-drug_combinations_lm_pubmedbert_en * Add model 2023-09-13-btfhbert_en * Add model 2023-09-13-mrf_bert_en * Add model 2023-09-13-bert_base_uncased_issues_128_xxr_en * Add model 2023-09-13-bert_base_turkish_uncased_offensive_mlm_tr * Add model 2023-09-12-mbert_xdm_english_chinese_en * Add model 2023-09-12-legal_bertimbau_large_pt * Add model 2023-09-13-bert_base_spanish_wwm_cased_plai_edp_test_es * Add model 2023-09-13-bert_base_multilingual_cased_urgency_xx * Add model 2023-09-13-bert_large_retrained_4_epochs_en * Add model 2023-09-13-bert_base_historic_dutch_cased_en * Add model 2023-09-12-bert_c1_english_only_en * Add model 2023-09-13-bert_base_portuguese_cased_finetuned_tcu_acordaos_pt * Add model 2023-09-13-bert_base_spanish_wwm_uncased_es * Add model 2023-09-13-hindi_tweets_bert_v2_hi * Add model 2023-09-12-mlm_20230513_indobert_large_p1_002_pt2_en * Add model 2023-09-13-inlegalbert_cbp_lkg_finetuned_en * Add model 2023-09-13-bert_base_cased_portuguese_lenerbr_alynneoya_en * Add model 2023-09-13-sci_summary_3_en * Add model 2023-09-13-protaugment_lm_clinic150_en * Add model 2023-09-13-aave_bert_en * Add model 2023-09-13-tlm_amazon_small_scale_en * Add model 2023-09-13-ai12_en * Add model 2023-09-13-kinyabert_large_finetuned_kintweetsa_en * Add model 2023-09-13-bert_base_turkish_128k_uncased_offensive_mlm_tr * Add model 2023-09-13-small_mlm_glue_qqp_custom_tokenizer_expand_vocab_en * Add model 2023-09-13-nl2_en --------- Co-authored-by: ahmedlone127 --- .../2023-09-12-akeylegalbert6_en.md | 93 +++++++++++++++++++ ...2-bert_base_cased_portuguese_ccorpus_en.md | 93 +++++++++++++++++++ ...-09-12-bert_base_english_dutch_cased_en.md | 93 +++++++++++++++++++ ...9-12-bert_base_english_russian_cased_en.md | 93 +++++++++++++++++++ ...9-12-bert_base_english_turkish_cased_en.md | 93 +++++++++++++++++++ ...ert_base_uncased_issues_128_kjunelee_en.md | 93 +++++++++++++++++++ ...2023-09-12-bert_base_uncased_mwesner_en.md | 93 +++++++++++++++++++ .../2023-09-12-bert_base_xsum_en.md | 93 +++++++++++++++++++ .../2023-09-12-bert_c1_english_only_en.md | 93 +++++++++++++++++++ .../2023-09-12-bertjewdialdataall04_en.md | 93 +++++++++++++++++++ .../2023-09-12-bertjewdialdataall_en.md | 93 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docs/_posts/ahmedlone127/2023-09-13-twteval_pretrained_en.md create mode 100644 docs/_posts/ahmedlone127/2023-09-13-uzbert_base_uncased_uz.md create mode 100644 docs/_posts/ahmedlone127/2023-09-13-wiki_bert_en.md create mode 100644 docs/_posts/ahmedlone127/2023-09-13-wisdomify_en.md diff --git a/docs/_posts/ahmedlone127/2023-09-12-akeylegalbert6_en.md b/docs/_posts/ahmedlone127/2023-09-12-akeylegalbert6_en.md new file mode 100644 index 00000000000000..410b1b7c629140 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-akeylegalbert6_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English akeylegalbert6 BertEmbeddings from hatemestinbejaia +author: John Snow Labs +name: akeylegalbert6 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`akeylegalbert6` is a English model originally trained by hatemestinbejaia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/akeylegalbert6_en_5.1.1_3.0_1694557526003.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/akeylegalbert6_en_5.1.1_3.0_1694557526003.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("akeylegalbert6","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("akeylegalbert6", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|akeylegalbert6| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/hatemestinbejaia/AkeyLegalBert6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_cased_portuguese_ccorpus_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_cased_portuguese_ccorpus_en.md new file mode 100644 index 00000000000000..4683925cb34f5b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_cased_portuguese_ccorpus_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_portuguese_ccorpus BertEmbeddings from rosimeirecosta +author: John Snow Labs +name: bert_base_cased_portuguese_ccorpus +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_portuguese_ccorpus` is a English model originally trained by rosimeirecosta. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_portuguese_ccorpus_en_5.1.1_3.0_1694553390707.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_portuguese_ccorpus_en_5.1.1_3.0_1694553390707.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_portuguese_ccorpus","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_portuguese_ccorpus", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_portuguese_ccorpus| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/rosimeirecosta/bert-base-cased-pt-ccorpus \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_dutch_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_dutch_cased_en.md new file mode 100644 index 00000000000000..b37e0aeaf11003 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_dutch_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_dutch_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_dutch_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_dutch_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_dutch_cased_en_5.1.1_3.0_1694554396036.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_dutch_cased_en_5.1.1_3.0_1694554396036.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_dutch_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_dutch_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_dutch_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|415.9 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-nl-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_russian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_russian_cased_en.md new file mode 100644 index 00000000000000..d67d3c6d1b9729 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_russian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_russian_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_russian_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_russian_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_russian_cased_en_5.1.1_3.0_1694555197411.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_russian_cased_en_5.1.1_3.0_1694555197411.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_russian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_russian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_russian_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|428.3 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-ru-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_turkish_cased_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_turkish_cased_en.md new file mode 100644 index 00000000000000..2da371c0b7b62d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_english_turkish_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_english_turkish_cased BertEmbeddings from Geotrend +author: John Snow Labs +name: bert_base_english_turkish_cased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_english_turkish_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_english_turkish_cased_en_5.1.1_3.0_1694555686626.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_english_turkish_cased_en_5.1.1_3.0_1694555686626.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_english_turkish_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_english_turkish_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_english_turkish_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.7 MB| + +## References + +https://huggingface.co/Geotrend/bert-base-en-tr-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_kjunelee_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_kjunelee_en.md new file mode 100644 index 00000000000000..970d677c362d5a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_issues_128_kjunelee_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_kjunelee BertEmbeddings from kjunelee +author: John Snow Labs +name: bert_base_uncased_issues_128_kjunelee +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_kjunelee` is a English model originally trained by kjunelee. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_kjunelee_en_5.1.1_3.0_1694552426494.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_kjunelee_en_5.1.1_3.0_1694552426494.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_kjunelee","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_kjunelee", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_kjunelee| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/kjunelee/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_mwesner_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_mwesner_en.md new file mode 100644 index 00000000000000..83f70642955b72 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_uncased_mwesner_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_mwesner BertEmbeddings from mwesner +author: John Snow Labs +name: bert_base_uncased_mwesner +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_mwesner` is a English model originally trained by mwesner. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mwesner_en_5.1.1_3.0_1694556928581.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mwesner_en_5.1.1_3.0_1694556928581.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_mwesner","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_mwesner", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_mwesner| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/mwesner/bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_xsum_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_xsum_en.md new file mode 100644 index 00000000000000..0d79b071cbcc10 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_xsum_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_xsum BertEmbeddings from Aktsvigun +author: John Snow Labs +name: bert_base_xsum +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_xsum` is a English model originally trained by Aktsvigun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_xsum_en_5.1.1_3.0_1694552050336.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_xsum_en_5.1.1_3.0_1694552050336.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_xsum","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_xsum", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_xsum| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Aktsvigun/bert-base-xsum \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_c1_english_only_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_c1_english_only_en.md new file mode 100644 index 00000000000000..efbc94a0ffb839 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_c1_english_only_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_c1_english_only BertEmbeddings from OpenSemShift +author: John Snow Labs +name: bert_c1_english_only +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_c1_english_only` is a English model originally trained by OpenSemShift. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_c1_english_only_en_5.1.1_3.0_1694558574758.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_c1_english_only_en_5.1.1_3.0_1694558574758.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_c1_english_only","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_c1_english_only", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_c1_english_only| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.1 MB| + +## References + +https://huggingface.co/OpenSemShift/bert-c1-en-only \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataall04_en.md b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataall04_en.md new file mode 100644 index 00000000000000..82a05b455c516f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataall04_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertjewdialdataall04 BertEmbeddings from Jeska +author: John Snow Labs +name: bertjewdialdataall04 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertjewdialdataall04` is a English model originally trained by Jeska. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertjewdialdataall04_en_5.1.1_3.0_1694561966824.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertjewdialdataall04_en_5.1.1_3.0_1694561966824.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertjewdialdataall04","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertjewdialdataall04", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertjewdialdataall04| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/Jeska/BertjeWDialDataALL04 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataall_en.md b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataall_en.md new file mode 100644 index 00000000000000..423677d03ebd6f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataall_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertjewdialdataall BertEmbeddings from Jeska +author: John Snow Labs +name: bertjewdialdataall +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertjewdialdataall` is a English model originally trained by Jeska. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertjewdialdataall_en_5.1.1_3.0_1694561562918.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertjewdialdataall_en_5.1.1_3.0_1694561562918.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertjewdialdataall","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertjewdialdataall", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertjewdialdataall| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/Jeska/BertjeWDialDataALL \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly04_en.md b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly04_en.md new file mode 100644 index 00000000000000..3b08bfc73ced60 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bertjewdialdataallqonly04_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertjewdialdataallqonly04 BertEmbeddings from Jeska +author: John Snow Labs +name: bertjewdialdataallqonly04 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertjewdialdataallqonly04` is a English model originally trained by Jeska. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly04_en_5.1.1_3.0_1694562548301.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly04_en_5.1.1_3.0_1694562548301.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertjewdialdataallqonly04","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertjewdialdataallqonly04", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertjewdialdataallqonly04| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/Jeska/BertjeWDialDataALLQonly04 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-biomednlp_pubmedbert_base_uncased_abstract_fulltext_en.md b/docs/_posts/ahmedlone127/2023-09-12-biomednlp_pubmedbert_base_uncased_abstract_fulltext_en.md new file mode 100644 index 00000000000000..1196d738573394 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-biomednlp_pubmedbert_base_uncased_abstract_fulltext_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English biomednlp_pubmedbert_base_uncased_abstract_fulltext BertEmbeddings from microsoft +author: John Snow Labs +name: biomednlp_pubmedbert_base_uncased_abstract_fulltext +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biomednlp_pubmedbert_base_uncased_abstract_fulltext` is a English model originally trained by microsoft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biomednlp_pubmedbert_base_uncased_abstract_fulltext_en_5.1.1_3.0_1694553416047.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biomednlp_pubmedbert_base_uncased_abstract_fulltext_en_5.1.1_3.0_1694553416047.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biomednlp_pubmedbert_base_uncased_abstract_fulltext","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biomednlp_pubmedbert_base_uncased_abstract_fulltext", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biomednlp_pubmedbert_base_uncased_abstract_fulltext| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.2 MB| + +## References + +https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-cord19_bert_en.md b/docs/_posts/ahmedlone127/2023-09-12-cord19_bert_en.md new file mode 100644 index 00000000000000..0b81945bf7d886 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-cord19_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cord19_bert BertEmbeddings from CovRelex-SE +author: John Snow Labs +name: cord19_bert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cord19_bert` is a English model originally trained by CovRelex-SE. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cord19_bert_en_5.1.1_3.0_1694559956829.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cord19_bert_en_5.1.1_3.0_1694559956829.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cord19_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cord19_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cord19_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/CovRelex-SE/CORD19-BERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-dummy_model_bigtimecodersean_en.md b/docs/_posts/ahmedlone127/2023-09-12-dummy_model_bigtimecodersean_en.md new file mode 100644 index 00000000000000..698d345c4e10f6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-dummy_model_bigtimecodersean_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dummy_model_bigtimecodersean BertEmbeddings from BigTimeCoderSean +author: John Snow Labs +name: dummy_model_bigtimecodersean +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dummy_model_bigtimecodersean` is a English model originally trained by BigTimeCoderSean. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dummy_model_bigtimecodersean_en_5.1.1_3.0_1694557914292.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dummy_model_bigtimecodersean_en_5.1.1_3.0_1694557914292.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dummy_model_bigtimecodersean","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dummy_model_bigtimecodersean", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dummy_model_bigtimecodersean| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/BigTimeCoderSean/dummy-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-efficient_splade_vi_bt_large_query_en.md b/docs/_posts/ahmedlone127/2023-09-12-efficient_splade_vi_bt_large_query_en.md new file mode 100644 index 00000000000000..c03dfb7943213b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-efficient_splade_vi_bt_large_query_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English efficient_splade_vi_bt_large_query BertEmbeddings from naver +author: John Snow Labs +name: efficient_splade_vi_bt_large_query +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`efficient_splade_vi_bt_large_query` is a English model originally trained by naver. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/efficient_splade_vi_bt_large_query_en_5.1.1_3.0_1694557934858.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/efficient_splade_vi_bt_large_query_en_5.1.1_3.0_1694557934858.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("efficient_splade_vi_bt_large_query","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("efficient_splade_vi_bt_large_query", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|efficient_splade_vi_bt_large_query| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/naver/efficient-splade-VI-BT-large-query \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-finbert_pretrain_financeinc_en.md b/docs/_posts/ahmedlone127/2023-09-12-finbert_pretrain_financeinc_en.md new file mode 100644 index 00000000000000..ca737e852f38be --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-finbert_pretrain_financeinc_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English finbert_pretrain_financeinc BertEmbeddings from FinanceInc +author: John Snow Labs +name: finbert_pretrain_financeinc +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finbert_pretrain_financeinc` is a English model originally trained by FinanceInc. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finbert_pretrain_financeinc_en_5.1.1_3.0_1694562283178.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finbert_pretrain_financeinc_en_5.1.1_3.0_1694562283178.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("finbert_pretrain_financeinc","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("finbert_pretrain_financeinc", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finbert_pretrain_financeinc| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/FinanceInc/finbert-pretrain \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-finetuned_test_1_en.md b/docs/_posts/ahmedlone127/2023-09-12-finetuned_test_1_en.md new file mode 100644 index 00000000000000..2f7df4ad42604e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-finetuned_test_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English finetuned_test_1 BertEmbeddings from ariesutiono +author: John Snow Labs +name: finetuned_test_1 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finetuned_test_1` is a English model originally trained by ariesutiono. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finetuned_test_1_en_5.1.1_3.0_1694559280850.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finetuned_test_1_en_5.1.1_3.0_1694559280850.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("finetuned_test_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("finetuned_test_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finetuned_test_1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/ariesutiono/finetuned-test-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-hatebert_en.md b/docs/_posts/ahmedlone127/2023-09-12-hatebert_en.md new file mode 100644 index 00000000000000..816b594a5abb7a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-hatebert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English hatebert BertEmbeddings from GroNLP +author: John Snow Labs +name: hatebert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hatebert` is a English model originally trained by GroNLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hatebert_en_5.1.1_3.0_1694559158477.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hatebert_en_5.1.1_3.0_1694559158477.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hatebert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hatebert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hatebert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.1 MB| + +## References + +https://huggingface.co/GroNLP/hateBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-kkmultbert_2e_en.md b/docs/_posts/ahmedlone127/2023-09-12-kkmultbert_2e_en.md new file mode 100644 index 00000000000000..16e41a1f5b7257 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-kkmultbert_2e_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kkmultbert_2e BertEmbeddings from TurkLangsTeamURFU +author: John Snow Labs +name: kkmultbert_2e +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kkmultbert_2e` is a English model originally trained by TurkLangsTeamURFU. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kkmultbert_2e_en_5.1.1_3.0_1694553427917.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kkmultbert_2e_en_5.1.1_3.0_1694553427917.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kkmultbert_2e","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kkmultbert_2e", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kkmultbert_2e| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|494.2 MB| + +## References + +https://huggingface.co/TurkLangsTeamURFU/KKMultBert_2e \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-kw_pubmed_vanilla_document_10000_0.0003_2_en.md b/docs/_posts/ahmedlone127/2023-09-12-kw_pubmed_vanilla_document_10000_0.0003_2_en.md new file mode 100644 index 00000000000000..125180ba2abf59 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-kw_pubmed_vanilla_document_10000_0.0003_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kw_pubmed_vanilla_document_10000_0.0003_2 BertEmbeddings from enoriega +author: John Snow Labs +name: kw_pubmed_vanilla_document_10000_0.0003_2 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kw_pubmed_vanilla_document_10000_0.0003_2` is a English model originally trained by enoriega. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kw_pubmed_vanilla_document_10000_0.0003_2_en_5.1.1_3.0_1694553795601.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kw_pubmed_vanilla_document_10000_0.0003_2_en_5.1.1_3.0_1694553795601.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kw_pubmed_vanilla_document_10000_0.0003_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kw_pubmed_vanilla_document_10000_0.0003_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kw_pubmed_vanilla_document_10000_0.0003_2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/enoriega/kw_pubmed_vanilla_document_10000_0.0003_2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-legal_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-12-legal_bert_base_uncased_en.md new file mode 100644 index 00000000000000..f6ab8e6b0aea41 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-legal_bert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English legal_bert_base_uncased BertEmbeddings from nlpaueb +author: John Snow Labs +name: legal_bert_base_uncased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legal_bert_base_uncased` is a English model originally trained by nlpaueb. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legal_bert_base_uncased_en_5.1.1_3.0_1694561549951.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legal_bert_base_uncased_en_5.1.1_3.0_1694561549951.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legal_bert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legal_bert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legal_bert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/nlpaueb/legal-bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-legal_bertimbau_large_pt.md b/docs/_posts/ahmedlone127/2023-09-12-legal_bertimbau_large_pt.md new file mode 100644 index 00000000000000..2b9ea254a999c3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-legal_bertimbau_large_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese legal_bertimbau_large BertEmbeddings from rufimelo +author: John Snow Labs +name: legal_bertimbau_large +date: 2023-09-12 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legal_bertimbau_large` is a Portuguese model originally trained by rufimelo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legal_bertimbau_large_pt_5.1.1_3.0_1694562841145.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legal_bertimbau_large_pt_5.1.1_3.0_1694562841145.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legal_bertimbau_large","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legal_bertimbau_large", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legal_bertimbau_large| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|1.2 GB| + +## References + +https://huggingface.co/rufimelo/Legal-BERTimbau-large \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mbert_tlm_sent_english_italian_en.md b/docs/_posts/ahmedlone127/2023-09-12-mbert_tlm_sent_english_italian_en.md new file mode 100644 index 00000000000000..67e15d0000953f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mbert_tlm_sent_english_italian_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mbert_tlm_sent_english_italian BertEmbeddings from nikitam +author: John Snow Labs +name: mbert_tlm_sent_english_italian +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbert_tlm_sent_english_italian` is a English model originally trained by nikitam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbert_tlm_sent_english_italian_en_5.1.1_3.0_1694559911755.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbert_tlm_sent_english_italian_en_5.1.1_3.0_1694559911755.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbert_tlm_sent_english_italian","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbert_tlm_sent_english_italian", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbert_tlm_sent_english_italian| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|622.4 MB| + +## References + +https://huggingface.co/nikitam/mbert-tlm-sent-en-it \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mbert_xdm_english_chinese_en.md b/docs/_posts/ahmedlone127/2023-09-12-mbert_xdm_english_chinese_en.md new file mode 100644 index 00000000000000..07ec58f81d5be0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mbert_xdm_english_chinese_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mbert_xdm_english_chinese BertEmbeddings from nikitam +author: John Snow Labs +name: mbert_xdm_english_chinese +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbert_xdm_english_chinese` is a English model originally trained by nikitam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbert_xdm_english_chinese_en_5.1.1_3.0_1694560769570.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbert_xdm_english_chinese_en_5.1.1_3.0_1694560769570.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbert_xdm_english_chinese","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbert_xdm_english_chinese", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbert_xdm_english_chinese| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|624.6 MB| + +## References + +https://huggingface.co/nikitam/mbert-xdm-en-zh \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-minilmv2_l6_h768_distilled_from_bert_large_en.md b/docs/_posts/ahmedlone127/2023-09-12-minilmv2_l6_h768_distilled_from_bert_large_en.md new file mode 100644 index 00000000000000..f95a02a6a5034f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-minilmv2_l6_h768_distilled_from_bert_large_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English minilmv2_l6_h768_distilled_from_bert_large BertEmbeddings from nreimers +author: John Snow Labs +name: minilmv2_l6_h768_distilled_from_bert_large +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`minilmv2_l6_h768_distilled_from_bert_large` is a English model originally trained by nreimers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/minilmv2_l6_h768_distilled_from_bert_large_en_5.1.1_3.0_1694562531906.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/minilmv2_l6_h768_distilled_from_bert_large_en_5.1.1_3.0_1694562531906.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("minilmv2_l6_h768_distilled_from_bert_large","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("minilmv2_l6_h768_distilled_from_bert_large", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|minilmv2_l6_h768_distilled_from_bert_large| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|158.8 MB| + +## References + +https://huggingface.co/nreimers/MiniLMv2-L6-H768-distilled-from-BERT-Large \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mlm_20230503_indobert_base_p2_002_en.md b/docs/_posts/ahmedlone127/2023-09-12-mlm_20230503_indobert_base_p2_002_en.md new file mode 100644 index 00000000000000..2076a704a9435c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mlm_20230503_indobert_base_p2_002_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230503_indobert_base_p2_002 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230503_indobert_base_p2_002 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230503_indobert_base_p2_002` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230503_indobert_base_p2_002_en_5.1.1_3.0_1694550333162.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230503_indobert_base_p2_002_en_5.1.1_3.0_1694550333162.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230503_indobert_base_p2_002","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230503_indobert_base_p2_002", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230503_indobert_base_p2_002| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.2 MB| + +## References + +https://huggingface.co/intanm/mlm-20230503-indobert-base-p2-002 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mlm_20230513_indobert_large_p1_002_pt1_en.md b/docs/_posts/ahmedlone127/2023-09-12-mlm_20230513_indobert_large_p1_002_pt1_en.md new file mode 100644 index 00000000000000..8f532d96fde757 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mlm_20230513_indobert_large_p1_002_pt1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230513_indobert_large_p1_002_pt1 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230513_indobert_large_p1_002_pt1 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230513_indobert_large_p1_002_pt1` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230513_indobert_large_p1_002_pt1_en_5.1.1_3.0_1694560785628.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230513_indobert_large_p1_002_pt1_en_5.1.1_3.0_1694560785628.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230513_indobert_large_p1_002_pt1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230513_indobert_large_p1_002_pt1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230513_indobert_large_p1_002_pt1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/intanm/mlm-20230513-indobert-large-p1-002-pt1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-mlm_20230513_indobert_large_p1_002_pt2_en.md b/docs/_posts/ahmedlone127/2023-09-12-mlm_20230513_indobert_large_p1_002_pt2_en.md new file mode 100644 index 00000000000000..595e95f4a87ad0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-mlm_20230513_indobert_large_p1_002_pt2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230513_indobert_large_p1_002_pt2 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230513_indobert_large_p1_002_pt2 +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230513_indobert_large_p1_002_pt2` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230513_indobert_large_p1_002_pt2_en_5.1.1_3.0_1694561058491.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230513_indobert_large_p1_002_pt2_en_5.1.1_3.0_1694561058491.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230513_indobert_large_p1_002_pt2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230513_indobert_large_p1_002_pt2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230513_indobert_large_p1_002_pt2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/intanm/mlm-20230513-indobert-large-p1-002-pt2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-muril_adapted_local_xx.md b/docs/_posts/ahmedlone127/2023-09-12-muril_adapted_local_xx.md new file mode 100644 index 00000000000000..dab1e578462ca2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-muril_adapted_local_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual muril_adapted_local BertEmbeddings from monsoon-nlp +author: John Snow Labs +name: muril_adapted_local +date: 2023-09-12 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`muril_adapted_local` is a Multilingual model originally trained by monsoon-nlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/muril_adapted_local_xx_5.1.1_3.0_1694555822467.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/muril_adapted_local_xx_5.1.1_3.0_1694555822467.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("muril_adapted_local","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("muril_adapted_local", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|muril_adapted_local| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|885.8 MB| + +## References + +https://huggingface.co/monsoon-nlp/muril-adapted-local \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-original_topic_sports_kcbert_en.md b/docs/_posts/ahmedlone127/2023-09-12-original_topic_sports_kcbert_en.md new file mode 100644 index 00000000000000..18a9ab6eb73789 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-original_topic_sports_kcbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English original_topic_sports_kcbert BertEmbeddings from Kdogs +author: John Snow Labs +name: original_topic_sports_kcbert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`original_topic_sports_kcbert` is a English model originally trained by Kdogs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/original_topic_sports_kcbert_en_5.1.1_3.0_1694561238255.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/original_topic_sports_kcbert_en_5.1.1_3.0_1694561238255.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("original_topic_sports_kcbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("original_topic_sports_kcbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|original_topic_sports_kcbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.3 MB| + +## References + +https://huggingface.co/Kdogs/original_topic-sports_kcbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-prop_marco_en.md b/docs/_posts/ahmedlone127/2023-09-12-prop_marco_en.md new file mode 100644 index 00000000000000..66090ef8f1d566 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-prop_marco_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English prop_marco BertEmbeddings from xyma +author: John Snow Labs +name: prop_marco +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`prop_marco` is a English model originally trained by xyma. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/prop_marco_en_5.1.1_3.0_1694559633981.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/prop_marco_en_5.1.1_3.0_1694559633981.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("prop_marco","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("prop_marco", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|prop_marco| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/xyma/PROP-marco \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-romanian_bert_tweet_large_ro.md b/docs/_posts/ahmedlone127/2023-09-12-romanian_bert_tweet_large_ro.md new file mode 100644 index 00000000000000..81cd4e646f5662 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-romanian_bert_tweet_large_ro.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Moldavian, Moldovan, Romanian romanian_bert_tweet_large BertEmbeddings from Iulian277 +author: John Snow Labs +name: romanian_bert_tweet_large +date: 2023-09-12 +tags: [bert, ro, open_source, fill_mask, onnx] +task: Embeddings +language: ro +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`romanian_bert_tweet_large` is a Moldavian, Moldovan, Romanian model originally trained by Iulian277. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/romanian_bert_tweet_large_ro_5.1.1_3.0_1694557360243.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/romanian_bert_tweet_large_ro_5.1.1_3.0_1694557360243.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("romanian_bert_tweet_large","ro") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("romanian_bert_tweet_large", "ro") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|romanian_bert_tweet_large| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ro| +|Size:|1.3 GB| + +## References + +https://huggingface.co/Iulian277/ro-bert-tweet-large \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-sec_bert_base_en.md b/docs/_posts/ahmedlone127/2023-09-12-sec_bert_base_en.md new file mode 100644 index 00000000000000..c9b1aca7e0f539 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-sec_bert_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sec_bert_base BertEmbeddings from nlpaueb +author: John Snow Labs +name: sec_bert_base +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sec_bert_base` is a English model originally trained by nlpaueb. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sec_bert_base_en_5.1.1_3.0_1694561781228.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sec_bert_base_en_5.1.1_3.0_1694561781228.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sec_bert_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sec_bert_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sec_bert_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.5 MB| + +## References + +https://huggingface.co/nlpaueb/sec-bert-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_qnli_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_qnli_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..7eb6df9e905eec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-small_mlm_glue_qnli_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_qnli_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_qnli_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_qnli_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_qnli_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563099212.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_qnli_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563099212.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_qnli_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_qnli_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_qnli_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|135.1 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-qnli-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_mrpc_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_mrpc_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..7d838dab658e0e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_mrpc_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_mrpc_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_mrpc_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_mrpc_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_mrpc_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561680337.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_mrpc_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694561680337.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_mrpc_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_mrpc_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_mrpc_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|19.9 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-mrpc-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..2e3bb3e26abfc2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-tiny_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562311451.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694562311451.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_glue_qqp_from_scratch_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|20.2 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-glue-qqp-from-scratch-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-topic_it_science_bert_en.md b/docs/_posts/ahmedlone127/2023-09-12-topic_it_science_bert_en.md new file mode 100644 index 00000000000000..7111c6abb236fa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-topic_it_science_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English topic_it_science_bert BertEmbeddings from Kdogs +author: John Snow Labs +name: topic_it_science_bert +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`topic_it_science_bert` is a English model originally trained by Kdogs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/topic_it_science_bert_en_5.1.1_3.0_1694562722460.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/topic_it_science_bert_en_5.1.1_3.0_1694562722460.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("topic_it_science_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("topic_it_science_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|topic_it_science_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.4 MB| + +## References + +https://huggingface.co/Kdogs/topic_IT-Science_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-transformer_nlp_en.md b/docs/_posts/ahmedlone127/2023-09-12-transformer_nlp_en.md new file mode 100644 index 00000000000000..f27af25c1bb53d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-transformer_nlp_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English transformer_nlp BertEmbeddings from onon214 +author: John Snow Labs +name: transformer_nlp +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`transformer_nlp` is a English model originally trained by onon214. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/transformer_nlp_en_5.1.1_3.0_1694562498875.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/transformer_nlp_en_5.1.1_3.0_1694562498875.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("transformer_nlp","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("transformer_nlp", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|transformer_nlp| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.6 MB| + +## References + +https://huggingface.co/onon214/transformer-NLP \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-2022_02_10_en.md b/docs/_posts/ahmedlone127/2023-09-13-2022_02_10_en.md new file mode 100644 index 00000000000000..202256d38246f1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-2022_02_10_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English 2022_02_10 BertEmbeddings from wuyanzu +author: John Snow Labs +name: 2022_02_10 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`2022_02_10` is a English model originally trained by wuyanzu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/2022_02_10_en_5.1.1_3.0_1694586410513.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/2022_02_10_en_5.1.1_3.0_1694586410513.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("2022_02_10","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("2022_02_10", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|2022_02_10| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/wuyanzu/2022_02_10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-a_different_bert_model_en.md b/docs/_posts/ahmedlone127/2023-09-13-a_different_bert_model_en.md new file mode 100644 index 00000000000000..491e2bbd31cf0e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-a_different_bert_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English a_different_bert_model BertEmbeddings from bstad +author: John Snow Labs +name: a_different_bert_model +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`a_different_bert_model` is a English model originally trained by bstad. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/a_different_bert_model_en_5.1.1_3.0_1694589202062.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/a_different_bert_model_en_5.1.1_3.0_1694589202062.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("a_different_bert_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("a_different_bert_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|a_different_bert_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/bstad/a-different-bert-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-aave_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-aave_bert_en.md new file mode 100644 index 00000000000000..ab0b5409aaef14 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-aave_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English aave_bert BertEmbeddings from csalaam +author: John Snow Labs +name: aave_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`aave_bert` is a English model originally trained by csalaam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/aave_bert_en_5.1.1_3.0_1694611678460.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/aave_bert_en_5.1.1_3.0_1694611678460.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("aave_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("aave_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|aave_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.4 MB| + +## References + +https://huggingface.co/csalaam/AAVE-BERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-absa_mlm_2_en.md b/docs/_posts/ahmedlone127/2023-09-13-absa_mlm_2_en.md new file mode 100644 index 00000000000000..bf605a09be386c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-absa_mlm_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English absa_mlm_2 BertEmbeddings from UchihaMadara +author: John Snow Labs +name: absa_mlm_2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`absa_mlm_2` is a English model originally trained by UchihaMadara. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/absa_mlm_2_en_5.1.1_3.0_1694579368670.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/absa_mlm_2_en_5.1.1_3.0_1694579368670.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("absa_mlm_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("absa_mlm_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|absa_mlm_2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/UchihaMadara/absa-mlm-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-agriculture_bert_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-agriculture_bert_uncased_en.md new file mode 100644 index 00000000000000..ab77f2d4f15401 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-agriculture_bert_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English agriculture_bert_uncased BertEmbeddings from recobo +author: John Snow Labs +name: agriculture_bert_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`agriculture_bert_uncased` is a English model originally trained by recobo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/agriculture_bert_uncased_en_5.1.1_3.0_1694566700185.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/agriculture_bert_uncased_en_5.1.1_3.0_1694566700185.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("agriculture_bert_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("agriculture_bert_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|agriculture_bert_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/recobo/agriculture-bert-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-ai12_en.md b/docs/_posts/ahmedlone127/2023-09-13-ai12_en.md new file mode 100644 index 00000000000000..ca5f33564417ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-ai12_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ai12 BertEmbeddings from zzecf +author: John Snow Labs +name: ai12 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ai12` is a English model originally trained by zzecf. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ai12_en_5.1.1_3.0_1694599540494.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ai12_en_5.1.1_3.0_1694599540494.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ai12","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ai12", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ai12| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/zzecf/AI12 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-ai12nlp_en.md b/docs/_posts/ahmedlone127/2023-09-13-ai12nlp_en.md new file mode 100644 index 00000000000000..6f5f5eac6a1457 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-ai12nlp_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ai12nlp BertEmbeddings from zhizihuabai +author: John Snow Labs +name: ai12nlp +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ai12nlp` is a English model originally trained by zhizihuabai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ai12nlp_en_5.1.1_3.0_1694593503233.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ai12nlp_en_5.1.1_3.0_1694593503233.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ai12nlp","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ai12nlp", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ai12nlp| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/zhizihuabai/ai12nlp \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-ai12one_en.md b/docs/_posts/ahmedlone127/2023-09-13-ai12one_en.md new file mode 100644 index 00000000000000..416607b63561aa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-ai12one_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ai12one BertEmbeddings from zhizihuabai +author: John Snow Labs +name: ai12one +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ai12one` is a English model originally trained by zhizihuabai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ai12one_en_5.1.1_3.0_1694593660881.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ai12one_en_5.1.1_3.0_1694593660881.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ai12one","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ai12one", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ai12one| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/zhizihuabai/ai12one \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-alephbertgimmel_base_en.md b/docs/_posts/ahmedlone127/2023-09-13-alephbertgimmel_base_en.md new file mode 100644 index 00000000000000..3d4cf3b3466d62 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-alephbertgimmel_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English alephbertgimmel_base BertEmbeddings from dicta-il +author: John Snow Labs +name: alephbertgimmel_base +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`alephbertgimmel_base` is a English model originally trained by dicta-il. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/alephbertgimmel_base_en_5.1.1_3.0_1694594556756.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/alephbertgimmel_base_en_5.1.1_3.0_1694594556756.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("alephbertgimmel_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("alephbertgimmel_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|alephbertgimmel_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|690.4 MB| + +## References + +https://huggingface.co/dicta-il/alephbertgimmel-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-alephbertgimmel_small_en.md b/docs/_posts/ahmedlone127/2023-09-13-alephbertgimmel_small_en.md new file mode 100644 index 00000000000000..977afc12fb0680 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-alephbertgimmel_small_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English alephbertgimmel_small BertEmbeddings from dicta-il +author: John Snow Labs +name: alephbertgimmel_small +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`alephbertgimmel_small` is a English model originally trained by dicta-il. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/alephbertgimmel_small_en_5.1.1_3.0_1694594680695.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/alephbertgimmel_small_en_5.1.1_3.0_1694594680695.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("alephbertgimmel_small","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("alephbertgimmel_small", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|alephbertgimmel_small| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|295.5 MB| + +## References + +https://huggingface.co/dicta-il/alephbertgimmel-small \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-all_minilm_l6_v2_finetuned_wikitext2_en.md b/docs/_posts/ahmedlone127/2023-09-13-all_minilm_l6_v2_finetuned_wikitext2_en.md new file mode 100644 index 00000000000000..f95a0dce3bab16 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-all_minilm_l6_v2_finetuned_wikitext2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English all_minilm_l6_v2_finetuned_wikitext2 BertEmbeddings from Shimiao +author: John Snow Labs +name: all_minilm_l6_v2_finetuned_wikitext2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`all_minilm_l6_v2_finetuned_wikitext2` is a English model originally trained by Shimiao. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/all_minilm_l6_v2_finetuned_wikitext2_en_5.1.1_3.0_1694600245310.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/all_minilm_l6_v2_finetuned_wikitext2_en_5.1.1_3.0_1694600245310.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("all_minilm_l6_v2_finetuned_wikitext2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("all_minilm_l6_v2_finetuned_wikitext2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|all_minilm_l6_v2_finetuned_wikitext2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|84.6 MB| + +## References + +https://huggingface.co/Shimiao/all-MiniLM-L6-v2-finetuned-wikitext2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-apachebertbasecase_en.md b/docs/_posts/ahmedlone127/2023-09-13-apachebertbasecase_en.md new file mode 100644 index 00000000000000..fa72d217cb6063 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-apachebertbasecase_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English apachebertbasecase BertEmbeddings from EgilKarlsen +author: John Snow Labs +name: apachebertbasecase +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`apachebertbasecase` is a English model originally trained by EgilKarlsen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/apachebertbasecase_en_5.1.1_3.0_1694572112065.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/apachebertbasecase_en_5.1.1_3.0_1694572112065.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("apachebertbasecase","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("apachebertbasecase", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|apachebertbasecase| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/EgilKarlsen/ApacheBertBaseCase \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-arabert_quran_large_en.md b/docs/_posts/ahmedlone127/2023-09-13-arabert_quran_large_en.md new file mode 100644 index 00000000000000..ef998da95b03a3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-arabert_quran_large_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English arabert_quran_large BertEmbeddings from omarelsayeed +author: John Snow Labs +name: arabert_quran_large +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabert_quran_large` is a English model originally trained by omarelsayeed. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabert_quran_large_en_5.1.1_3.0_1694590848206.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabert_quran_large_en_5.1.1_3.0_1694590848206.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabert_quran_large","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabert_quran_large", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabert_quran_large| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.4 GB| + +## References + +https://huggingface.co/omarelsayeed/arabert_quran_large \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-arabertmo_base_v10_en.md b/docs/_posts/ahmedlone127/2023-09-13-arabertmo_base_v10_en.md new file mode 100644 index 00000000000000..47d65bda6cc454 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-arabertmo_base_v10_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English arabertmo_base_v10 BertEmbeddings from Ebtihal +author: John Snow Labs +name: arabertmo_base_v10 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabertmo_base_v10` is a English model originally trained by Ebtihal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabertmo_base_v10_en_5.1.1_3.0_1694604536787.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabertmo_base_v10_en_5.1.1_3.0_1694604536787.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabertmo_base_v10","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabertmo_base_v10", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabertmo_base_v10| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.9 MB| + +## References + +https://huggingface.co/Ebtihal/AraBertMo_base_V10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-arabic_hd_en.md b/docs/_posts/ahmedlone127/2023-09-13-arabic_hd_en.md new file mode 100644 index 00000000000000..c212d933f5b95f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-arabic_hd_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English arabic_hd BertEmbeddings from avichr +author: John Snow Labs +name: arabic_hd +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabic_hd` is a English model originally trained by avichr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabic_hd_en_5.1.1_3.0_1694584520930.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabic_hd_en_5.1.1_3.0_1694584520930.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabic_hd","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabic_hd", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabic_hd| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.0 MB| + +## References + +https://huggingface.co/avichr/ar_hd \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-azbert_base_en.md b/docs/_posts/ahmedlone127/2023-09-13-azbert_base_en.md new file mode 100644 index 00000000000000..dec6b8e17faa32 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-azbert_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English azbert_base BertEmbeddings from castorini +author: John Snow Labs +name: azbert_base +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`azbert_base` is a English model originally trained by castorini. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/azbert_base_en_5.1.1_3.0_1694590742738.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/azbert_base_en_5.1.1_3.0_1694590742738.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("azbert_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("azbert_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|azbert_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.7 MB| + +## References + +https://huggingface.co/castorini/azbert-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bangla_bert_base_finetuned_tweets_en.md b/docs/_posts/ahmedlone127/2023-09-13-bangla_bert_base_finetuned_tweets_en.md new file mode 100644 index 00000000000000..b987da576ab2c0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bangla_bert_base_finetuned_tweets_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bangla_bert_base_finetuned_tweets BertEmbeddings from myahan007 +author: John Snow Labs +name: bangla_bert_base_finetuned_tweets +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bangla_bert_base_finetuned_tweets` is a English model originally trained by myahan007. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bangla_bert_base_finetuned_tweets_en_5.1.1_3.0_1694589153688.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bangla_bert_base_finetuned_tweets_en_5.1.1_3.0_1694589153688.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bangla_bert_base_finetuned_tweets","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bangla_bert_base_finetuned_tweets", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bangla_bert_base_finetuned_tweets| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|613.8 MB| + +## References + +https://huggingface.co/myahan007/bangla-bert-base-finetuned-tweets \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-batteryonlybert_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-batteryonlybert_cased_en.md new file mode 100644 index 00000000000000..10374191eff46a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-batteryonlybert_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English batteryonlybert_cased BertEmbeddings from batterydata +author: John Snow Labs +name: batteryonlybert_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`batteryonlybert_cased` is a English model originally trained by batterydata. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/batteryonlybert_cased_en_5.1.1_3.0_1694600323117.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/batteryonlybert_cased_en_5.1.1_3.0_1694600323117.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("batteryonlybert_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("batteryonlybert_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|batteryonlybert_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.7 MB| + +## References + +https://huggingface.co/batterydata/batteryonlybert-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-batteryonlybert_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-batteryonlybert_uncased_en.md new file mode 100644 index 00000000000000..82aee1dcea6004 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-batteryonlybert_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English batteryonlybert_uncased BertEmbeddings from batterydata +author: John Snow Labs +name: batteryonlybert_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`batteryonlybert_uncased` is a English model originally trained by batterydata. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/batteryonlybert_uncased_en_5.1.1_3.0_1694600650112.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/batteryonlybert_uncased_en_5.1.1_3.0_1694600650112.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("batteryonlybert_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("batteryonlybert_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|batteryonlybert_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/batterydata/batteryonlybert-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-batteryscibert_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-batteryscibert_uncased_en.md new file mode 100644 index 00000000000000..f96644f9fe74c2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-batteryscibert_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English batteryscibert_uncased BertEmbeddings from batterydata +author: John Snow Labs +name: batteryscibert_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`batteryscibert_uncased` is a English model originally trained by batterydata. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/batteryscibert_uncased_en_5.1.1_3.0_1694585738175.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/batteryscibert_uncased_en_5.1.1_3.0_1694585738175.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("batteryscibert_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("batteryscibert_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|batteryscibert_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.0 MB| + +## References + +https://huggingface.co/batterydata/batteryscibert-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-berel_2.0_he.md b/docs/_posts/ahmedlone127/2023-09-13-berel_2.0_he.md new file mode 100644 index 00000000000000..5ffbe53c0946db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-berel_2.0_he.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hebrew berel_2.0 BertEmbeddings from dicta-il +author: John Snow Labs +name: berel_2.0 +date: 2023-09-13 +tags: [bert, he, open_source, fill_mask, onnx] +task: Embeddings +language: he +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`berel_2.0` is a Hebrew model originally trained by dicta-il. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/berel_2.0_he_5.1.1_3.0_1694589736279.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/berel_2.0_he_5.1.1_3.0_1694589736279.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("berel_2.0","he") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("berel_2.0", "he") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|berel_2.0| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|he| +|Size:|690.0 MB| + +## References + +https://huggingface.co/dicta-il/BEREL_2.0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-berel_dicta_il_he.md b/docs/_posts/ahmedlone127/2023-09-13-berel_dicta_il_he.md new file mode 100644 index 00000000000000..ea270841690679 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-berel_dicta_il_he.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hebrew berel_dicta_il BertEmbeddings from dicta-il +author: John Snow Labs +name: berel_dicta_il +date: 2023-09-13 +tags: [bert, he, open_source, fill_mask, onnx] +task: Embeddings +language: he +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`berel_dicta_il` is a Hebrew model originally trained by dicta-il. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/berel_dicta_il_he_5.1.1_3.0_1694589503563.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/berel_dicta_il_he_5.1.1_3.0_1694589503563.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("berel_dicta_il","he") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("berel_dicta_il", "he") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|berel_dicta_il| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|he| +|Size:|690.1 MB| + +## References + +https://huggingface.co/dicta-il/BEREL \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabertv01_ar.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabertv01_ar.md new file mode 100644 index 00000000000000..cfed471a9af0b3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabertv01_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_base_arabertv01 BertEmbeddings from aubmindlab +author: John Snow Labs +name: bert_base_arabertv01 +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabertv01` is a Arabic model originally trained by aubmindlab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabertv01_ar_5.1.1_3.0_1694582857463.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabertv01_ar_5.1.1_3.0_1694582857463.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_arabertv01","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_arabertv01", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabertv01| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|505.0 MB| + +## References + +https://huggingface.co/aubmindlab/bert-base-arabertv01 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabertv2_ar.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabertv2_ar.md new file mode 100644 index 00000000000000..df33f02fcf9d63 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabertv2_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_base_arabertv2 BertEmbeddings from aubmindlab +author: John Snow Labs +name: bert_base_arabertv2 +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabertv2` is a Arabic model originally trained by aubmindlab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabertv2_ar_5.1.1_3.0_1694583313889.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabertv2_ar_5.1.1_3.0_1694583313889.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_arabertv2","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_arabertv2", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabertv2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|504.8 MB| + +## References + +https://huggingface.co/aubmindlab/bert-base-arabertv2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabic_ar.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabic_ar.md new file mode 100644 index 00000000000000..442db9215c95da --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabic_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_base_arabic BertEmbeddings from asafaya +author: John Snow Labs +name: bert_base_arabic +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabic` is a Arabic model originally trained by asafaya. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabic_ar_5.1.1_3.0_1694581997506.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabic_ar_5.1.1_3.0_1694581997506.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_arabic","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_arabic", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabic| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|412.0 MB| + +## References + +https://huggingface.co/asafaya/bert-base-arabic \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_finetuned_semeval2010_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_finetuned_semeval2010_mlm_en.md new file mode 100644 index 00000000000000..6838dc89fc1dce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_finetuned_semeval2010_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_semeval2010_mlm BertEmbeddings from TimShieh +author: John Snow Labs +name: bert_base_cased_finetuned_semeval2010_mlm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_semeval2010_mlm` is a English model originally trained by TimShieh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_semeval2010_mlm_en_5.1.1_3.0_1694581084119.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_semeval2010_mlm_en_5.1.1_3.0_1694581084119.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_semeval2010_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_semeval2010_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_semeval2010_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/TimShieh/bert-base-cased-finetuned-semeval2010-MLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_portuguese_lenerbr_alynneoya_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_portuguese_lenerbr_alynneoya_en.md new file mode 100644 index 00000000000000..c2afc25f465d07 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_portuguese_lenerbr_alynneoya_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_portuguese_lenerbr_alynneoya BertEmbeddings from alynneoya +author: John Snow Labs +name: bert_base_cased_portuguese_lenerbr_alynneoya +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_portuguese_lenerbr_alynneoya` is a English model originally trained by alynneoya. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_portuguese_lenerbr_alynneoya_en_5.1.1_3.0_1694611495066.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_portuguese_lenerbr_alynneoya_en_5.1.1_3.0_1694611495066.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_portuguese_lenerbr_alynneoya","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_portuguese_lenerbr_alynneoya", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_portuguese_lenerbr_alynneoya| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/alynneoya/bert-base-cased-pt-lenerbr \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_portuguese_lenerbr_luciolrv_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_portuguese_lenerbr_luciolrv_en.md new file mode 100644 index 00000000000000..494def19cc4d4a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_portuguese_lenerbr_luciolrv_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_portuguese_lenerbr_luciolrv BertEmbeddings from luciolrv +author: John Snow Labs +name: bert_base_cased_portuguese_lenerbr_luciolrv +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_portuguese_lenerbr_luciolrv` is a English model originally trained by luciolrv. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_portuguese_lenerbr_luciolrv_en_5.1.1_3.0_1694588906119.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_portuguese_lenerbr_luciolrv_en_5.1.1_3.0_1694588906119.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_portuguese_lenerbr_luciolrv","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_portuguese_lenerbr_luciolrv", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_portuguese_lenerbr_luciolrv| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/luciolrv/bert-base-cased-pt-lenerbr \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_dutch_cased_mlm_visio_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_dutch_cased_mlm_visio_en.md new file mode 100644 index 00000000000000..863fea572e1288 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_dutch_cased_mlm_visio_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_dutch_cased_mlm_visio BertEmbeddings from jegormeister +author: John Snow Labs +name: bert_base_dutch_cased_mlm_visio +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_dutch_cased_mlm_visio` is a English model originally trained by jegormeister. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_dutch_cased_mlm_visio_en_5.1.1_3.0_1694598191567.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_dutch_cased_mlm_visio_en_5.1.1_3.0_1694598191567.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_dutch_cased_mlm_visio","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_dutch_cased_mlm_visio", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_dutch_cased_mlm_visio| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/jegormeister/bert-base-dutch-cased-mlm-visio \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_finnish_europeana_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_finnish_europeana_cased_en.md new file mode 100644 index 00000000000000..95edef13405007 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_finnish_europeana_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_finnish_europeana_cased BertEmbeddings from dbmdz +author: John Snow Labs +name: bert_base_finnish_europeana_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_finnish_europeana_cased` is a English model originally trained by dbmdz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_finnish_europeana_cased_en_5.1.1_3.0_1694594988308.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_finnish_europeana_cased_en_5.1.1_3.0_1694594988308.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_finnish_europeana_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_finnish_europeana_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_finnish_europeana_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|411.3 MB| + +## References + +https://huggingface.co/dbmdz/bert-base-finnish-europeana-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_german_cased_archaeo_de.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_german_cased_archaeo_de.md new file mode 100644 index 00000000000000..ee417e4188bab9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_german_cased_archaeo_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German bert_base_german_cased_archaeo BertEmbeddings from alexbrandsen +author: John Snow Labs +name: bert_base_german_cased_archaeo +date: 2023-09-13 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_german_cased_archaeo` is a German model originally trained by alexbrandsen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_german_cased_archaeo_de_5.1.1_3.0_1694608920923.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_german_cased_archaeo_de_5.1.1_3.0_1694608920923.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_german_cased_archaeo","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_german_cased_archaeo", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_german_cased_archaeo| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|406.9 MB| + +## References + +https://huggingface.co/alexbrandsen/bert-base-german-cased-archaeo \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_german_cased_dbmdz_de.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_german_cased_dbmdz_de.md new file mode 100644 index 00000000000000..347955729e9737 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_german_cased_dbmdz_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German bert_base_german_cased_dbmdz BertEmbeddings from dbmdz +author: John Snow Labs +name: bert_base_german_cased_dbmdz +date: 2023-09-13 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_german_cased_dbmdz` is a German model originally trained by dbmdz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_german_cased_dbmdz_de_5.1.1_3.0_1694595151979.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_german_cased_dbmdz_de_5.1.1_3.0_1694595151979.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_german_cased_dbmdz","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_german_cased_dbmdz", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_german_cased_dbmdz| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|409.9 MB| + +## References + +https://huggingface.co/dbmdz/bert-base-german-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_german_cased_domain_adaptation_accelerate_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_german_cased_domain_adaptation_accelerate_en.md new file mode 100644 index 00000000000000..8f2dcd6db4c97a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_german_cased_domain_adaptation_accelerate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_german_cased_domain_adaptation_accelerate BertEmbeddings from rodrigotuna +author: John Snow Labs +name: bert_base_german_cased_domain_adaptation_accelerate +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_german_cased_domain_adaptation_accelerate` is a English model originally trained by rodrigotuna. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_german_cased_domain_adaptation_accelerate_en_5.1.1_3.0_1694565556342.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_german_cased_domain_adaptation_accelerate_en_5.1.1_3.0_1694565556342.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_german_cased_domain_adaptation_accelerate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_german_cased_domain_adaptation_accelerate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_german_cased_domain_adaptation_accelerate| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/rodrigotuna/bert-base-german-cased-domain-adaptation-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_german_cased_issues_128_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_german_cased_issues_128_en.md new file mode 100644 index 00000000000000..53132de0920d36 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_german_cased_issues_128_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_german_cased_issues_128 BertEmbeddings from ogimgio +author: John Snow Labs +name: bert_base_german_cased_issues_128 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_german_cased_issues_128` is a English model originally trained by ogimgio. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_german_cased_issues_128_en_5.1.1_3.0_1694595672332.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_german_cased_issues_128_en_5.1.1_3.0_1694595672332.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_german_cased_issues_128","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_german_cased_issues_128", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_german_cased_issues_128| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/ogimgio/bert-base-german-cased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_german_cased_oldvocab_de.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_german_cased_oldvocab_de.md new file mode 100644 index 00000000000000..4ba0dcbbacb836 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_german_cased_oldvocab_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German bert_base_german_cased_oldvocab BertEmbeddings from deepset +author: John Snow Labs +name: bert_base_german_cased_oldvocab +date: 2023-09-13 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_german_cased_oldvocab` is a German model originally trained by deepset. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_german_cased_oldvocab_de_5.1.1_3.0_1694600841728.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_german_cased_oldvocab_de_5.1.1_3.0_1694600841728.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_german_cased_oldvocab","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_german_cased_oldvocab", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_german_cased_oldvocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|406.9 MB| + +## References + +https://huggingface.co/deepset/bert-base-german-cased-oldvocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_greek_uncased_v1_finetuned_imdb_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_greek_uncased_v1_finetuned_imdb_en.md new file mode 100644 index 00000000000000..df25665fc434e0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_greek_uncased_v1_finetuned_imdb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_greek_uncased_v1_finetuned_imdb BertEmbeddings from snousias +author: John Snow Labs +name: bert_base_greek_uncased_v1_finetuned_imdb +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_greek_uncased_v1_finetuned_imdb` is a English model originally trained by snousias. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_greek_uncased_v1_finetuned_imdb_en_5.1.1_3.0_1694599489863.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_greek_uncased_v1_finetuned_imdb_en_5.1.1_3.0_1694599489863.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_greek_uncased_v1_finetuned_imdb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_greek_uncased_v1_finetuned_imdb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_greek_uncased_v1_finetuned_imdb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|421.1 MB| + +## References + +https://huggingface.co/snousias/bert-base-greek-uncased-v1-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_greek_uncased_v1_finetuned_polylex_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_greek_uncased_v1_finetuned_polylex_en.md new file mode 100644 index 00000000000000..9cea6160babbcb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_greek_uncased_v1_finetuned_polylex_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_greek_uncased_v1_finetuned_polylex BertEmbeddings from snousias +author: John Snow Labs +name: bert_base_greek_uncased_v1_finetuned_polylex +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_greek_uncased_v1_finetuned_polylex` is a English model originally trained by snousias. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_greek_uncased_v1_finetuned_polylex_en_5.1.1_3.0_1694599881004.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_greek_uncased_v1_finetuned_polylex_en_5.1.1_3.0_1694599881004.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_greek_uncased_v1_finetuned_polylex","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_greek_uncased_v1_finetuned_polylex", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_greek_uncased_v1_finetuned_polylex| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|421.1 MB| + +## References + +https://huggingface.co/snousias/bert-base-greek-uncased-v1-finetuned-polylex \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_greek_uncased_v2_finetuned_polylex_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_greek_uncased_v2_finetuned_polylex_en.md new file mode 100644 index 00000000000000..b3aec13782eb31 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_greek_uncased_v2_finetuned_polylex_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_greek_uncased_v2_finetuned_polylex BertEmbeddings from snousias +author: John Snow Labs +name: bert_base_greek_uncased_v2_finetuned_polylex +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_greek_uncased_v2_finetuned_polylex` is a English model originally trained by snousias. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_greek_uncased_v2_finetuned_polylex_en_5.1.1_3.0_1694600262879.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_greek_uncased_v2_finetuned_polylex_en_5.1.1_3.0_1694600262879.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_greek_uncased_v2_finetuned_polylex","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_greek_uncased_v2_finetuned_polylex", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_greek_uncased_v2_finetuned_polylex| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|421.1 MB| + +## References + +https://huggingface.co/snousias/bert-base-greek-uncased-v2-finetuned-polylex \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_greek_uncased_v3_finetuned_polylex_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_greek_uncased_v3_finetuned_polylex_en.md new file mode 100644 index 00000000000000..eab4bc337bfed2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_greek_uncased_v3_finetuned_polylex_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_greek_uncased_v3_finetuned_polylex BertEmbeddings from snousias +author: John Snow Labs +name: bert_base_greek_uncased_v3_finetuned_polylex +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_greek_uncased_v3_finetuned_polylex` is a English model originally trained by snousias. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_greek_uncased_v3_finetuned_polylex_en_5.1.1_3.0_1694605011287.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_greek_uncased_v3_finetuned_polylex_en_5.1.1_3.0_1694605011287.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_greek_uncased_v3_finetuned_polylex","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_greek_uncased_v3_finetuned_polylex", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_greek_uncased_v3_finetuned_polylex| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|421.1 MB| + +## References + +https://huggingface.co/snousias/bert-base-greek-uncased-v3-finetuned-polylex \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_historic_dutch_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_historic_dutch_cased_en.md new file mode 100644 index 00000000000000..4b86243905530f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_historic_dutch_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_historic_dutch_cased BertEmbeddings from dbmdz +author: John Snow Labs +name: bert_base_historic_dutch_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_historic_dutch_cased` is a English model originally trained by dbmdz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_historic_dutch_cased_en_5.1.1_3.0_1694595509546.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_historic_dutch_cased_en_5.1.1_3.0_1694595509546.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_historic_dutch_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_historic_dutch_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_historic_dutch_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|411.9 MB| + +## References + +https://huggingface.co/dbmdz/bert-base-historic-dutch-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_historic_english_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_historic_english_cased_en.md new file mode 100644 index 00000000000000..427a4b95ed78f4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_historic_english_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_historic_english_cased BertEmbeddings from dbmdz +author: John Snow Labs +name: bert_base_historic_english_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_historic_english_cased` is a English model originally trained by dbmdz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_historic_english_cased_en_5.1.1_3.0_1694595905919.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_historic_english_cased_en_5.1.1_3.0_1694595905919.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_historic_english_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_historic_english_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_historic_english_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|411.7 MB| + +## References + +https://huggingface.co/dbmdz/bert-base-historic-english-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_historic_multilingual_cased_xx.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_historic_multilingual_cased_xx.md new file mode 100644 index 00000000000000..bccbc9b331cdaa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_historic_multilingual_cased_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_historic_multilingual_cased BertEmbeddings from dbmdz +author: John Snow Labs +name: bert_base_historic_multilingual_cased +date: 2023-09-13 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_historic_multilingual_cased` is a Multilingual model originally trained by dbmdz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_historic_multilingual_cased_xx_5.1.1_3.0_1694596352201.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_historic_multilingual_cased_xx_5.1.1_3.0_1694596352201.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_historic_multilingual_cased","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_historic_multilingual_cased", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_historic_multilingual_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|412.1 MB| + +## References + +https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_indonesian_1.5g_id.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_indonesian_1.5g_id.md new file mode 100644 index 00000000000000..93254d6d1cc62b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_indonesian_1.5g_id.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Indonesian bert_base_indonesian_1.5g BertEmbeddings from cahya +author: John Snow Labs +name: bert_base_indonesian_1.5g +date: 2023-09-13 +tags: [bert, id, open_source, fill_mask, onnx] +task: Embeddings +language: id +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_indonesian_1.5g` is a Indonesian model originally trained by cahya. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_indonesian_1.5g_id_5.1.1_3.0_1694590366871.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_indonesian_1.5g_id_5.1.1_3.0_1694590366871.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_indonesian_1.5g","id") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_indonesian_1.5g", "id") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_indonesian_1.5g| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|id| +|Size:|412.6 MB| + +## References + +https://huggingface.co/cahya/bert-base-indonesian-1.5G \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_indonesian_522m_id.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_indonesian_522m_id.md new file mode 100644 index 00000000000000..12c7e0e54371da --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_indonesian_522m_id.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Indonesian bert_base_indonesian_522m BertEmbeddings from cahya +author: John Snow Labs +name: bert_base_indonesian_522m +date: 2023-09-13 +tags: [bert, id, open_source, fill_mask, onnx] +task: Embeddings +language: id +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_indonesian_522m` is a Indonesian model originally trained by cahya. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_indonesian_522m_id_5.1.1_3.0_1694590524543.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_indonesian_522m_id_5.1.1_3.0_1694590524543.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_indonesian_522m","id") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_indonesian_522m", "id") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_indonesian_522m| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|id| +|Size:|412.5 MB| + +## References + +https://huggingface.co/cahya/bert-base-indonesian-522M \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_italian_cased_dbmdz_it.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_italian_cased_dbmdz_it.md new file mode 100644 index 00000000000000..926f2019ac7794 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_italian_cased_dbmdz_it.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Italian bert_base_italian_cased_dbmdz BertEmbeddings from dbmdz +author: John Snow Labs +name: bert_base_italian_cased_dbmdz +date: 2023-09-13 +tags: [bert, it, open_source, fill_mask, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_italian_cased_dbmdz` is a Italian model originally trained by dbmdz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_italian_cased_dbmdz_it_5.1.1_3.0_1694596864624.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_italian_cased_dbmdz_it_5.1.1_3.0_1694596864624.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_italian_cased_dbmdz","it") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_italian_cased_dbmdz", "it") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_italian_cased_dbmdz| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|it| +|Size:|409.6 MB| + +## References + +https://huggingface.co/dbmdz/bert-base-italian-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_italian_uncased_dbmdz_it.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_italian_uncased_dbmdz_it.md new file mode 100644 index 00000000000000..f0206e9a3f2d72 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_italian_uncased_dbmdz_it.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Italian bert_base_italian_uncased_dbmdz BertEmbeddings from dbmdz +author: John Snow Labs +name: bert_base_italian_uncased_dbmdz +date: 2023-09-13 +tags: [bert, it, open_source, fill_mask, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_italian_uncased_dbmdz` is a Italian model originally trained by dbmdz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_italian_uncased_dbmdz_it_5.1.1_3.0_1694597192371.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_italian_uncased_dbmdz_it_5.1.1_3.0_1694597192371.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_italian_uncased_dbmdz","it") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_italian_uncased_dbmdz", "it") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_italian_uncased_dbmdz| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|it| +|Size:|409.7 MB| + +## References + +https://huggingface.co/dbmdz/bert-base-italian-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_italian_xxl_cased_it.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_italian_xxl_cased_it.md new file mode 100644 index 00000000000000..6dacfaa0825933 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_italian_xxl_cased_it.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Italian bert_base_italian_xxl_cased BertEmbeddings from dbmdz +author: John Snow Labs +name: bert_base_italian_xxl_cased +date: 2023-09-13 +tags: [bert, it, open_source, fill_mask, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_italian_xxl_cased` is a Italian model originally trained by dbmdz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_italian_xxl_cased_it_5.1.1_3.0_1694597718333.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_italian_xxl_cased_it_5.1.1_3.0_1694597718333.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_italian_xxl_cased","it") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_italian_xxl_cased", "it") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_italian_xxl_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|it| +|Size:|412.6 MB| + +## References + +https://huggingface.co/dbmdz/bert-base-italian-xxl-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_italian_xxl_uncased_it.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_italian_xxl_uncased_it.md new file mode 100644 index 00000000000000..2ddd032300c379 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_italian_xxl_uncased_it.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Italian bert_base_italian_xxl_uncased BertEmbeddings from dbmdz +author: John Snow Labs +name: bert_base_italian_xxl_uncased +date: 2023-09-13 +tags: [bert, it, open_source, fill_mask, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_italian_xxl_uncased` is a Italian model originally trained by dbmdz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_italian_xxl_uncased_it_5.1.1_3.0_1694598081351.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_italian_xxl_uncased_it_5.1.1_3.0_1694598081351.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_italian_xxl_uncased","it") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_italian_xxl_uncased", "it") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_italian_xxl_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|it| +|Size:|412.6 MB| + +## References + +https://huggingface.co/dbmdz/bert-base-italian-xxl-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_low_resource_wellness_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_low_resource_wellness_en.md new file mode 100644 index 00000000000000..606af3b8d46f10 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_low_resource_wellness_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_low_resource_wellness BertEmbeddings from gaeunseo +author: John Snow Labs +name: bert_base_low_resource_wellness +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_low_resource_wellness` is a English model originally trained by gaeunseo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_low_resource_wellness_en_5.1.1_3.0_1694590425595.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_low_resource_wellness_en_5.1.1_3.0_1694590425595.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_low_resource_wellness","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_low_resource_wellness", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_low_resource_wellness| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.4 MB| + +## References + +https://huggingface.co/gaeunseo/bert-base-low_resource-wellness \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_multilingual_cased_finetuned_am_shb_xx.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_multilingual_cased_finetuned_am_shb_xx.md new file mode 100644 index 00000000000000..75b7edd1e0c4e6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_multilingual_cased_finetuned_am_shb_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_cased_finetuned_am_shb BertEmbeddings from am-shb +author: John Snow Labs +name: bert_base_multilingual_cased_finetuned_am_shb +date: 2023-09-13 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_cased_finetuned_am_shb` is a Multilingual model originally trained by am-shb. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_am_shb_xx_5.1.1_3.0_1694579098188.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_am_shb_xx_5.1.1_3.0_1694579098188.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_multilingual_cased_finetuned_am_shb","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_multilingual_cased_finetuned_am_shb", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_cased_finetuned_am_shb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|665.0 MB| + +## References + +https://huggingface.co/am-shb/bert-base-multilingual-cased-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_multilingual_cased_urgency_xx.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_multilingual_cased_urgency_xx.md new file mode 100644 index 00000000000000..54f481e3105cb0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_multilingual_cased_urgency_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_cased_urgency BertEmbeddings from Narshion +author: John Snow Labs +name: bert_base_multilingual_cased_urgency +date: 2023-09-13 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_cased_urgency` is a Multilingual model originally trained by Narshion. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_urgency_xx_5.1.1_3.0_1694568412458.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_urgency_xx_5.1.1_3.0_1694568412458.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_multilingual_cased_urgency","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_multilingual_cased_urgency", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_cased_urgency| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|662.2 MB| + +## References + +https://huggingface.co/Narshion/bert-base-multilingual-cased-urgency \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_multilingual_uncased_finetuned_xx.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_multilingual_uncased_finetuned_xx.md new file mode 100644 index 00000000000000..965f449592bc25 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_multilingual_uncased_finetuned_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_uncased_finetuned BertEmbeddings from am-shb +author: John Snow Labs +name: bert_base_multilingual_uncased_finetuned +date: 2023-09-13 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_uncased_finetuned` is a Multilingual model originally trained by am-shb. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_uncased_finetuned_xx_5.1.1_3.0_1694579287846.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_uncased_finetuned_xx_5.1.1_3.0_1694579287846.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_multilingual_uncased_finetuned","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_multilingual_uncased_finetuned", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_uncased_finetuned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|625.5 MB| + +## References + +https://huggingface.co/am-shb/bert-base-multilingual-uncased-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_portuguese_cased_finetuned_chico_xavier_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_portuguese_cased_finetuned_chico_xavier_en.md new file mode 100644 index 00000000000000..dbc92962015805 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_portuguese_cased_finetuned_chico_xavier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_portuguese_cased_finetuned_chico_xavier BertEmbeddings from gabrielgmendonca +author: John Snow Labs +name: bert_base_portuguese_cased_finetuned_chico_xavier +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_portuguese_cased_finetuned_chico_xavier` is a English model originally trained by gabrielgmendonca. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_portuguese_cased_finetuned_chico_xavier_en_5.1.1_3.0_1694587483813.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_portuguese_cased_finetuned_chico_xavier_en_5.1.1_3.0_1694587483813.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_portuguese_cased_finetuned_chico_xavier","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_portuguese_cased_finetuned_chico_xavier", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_portuguese_cased_finetuned_chico_xavier| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/gabrielgmendonca/bert-base-portuguese-cased-finetuned-chico-xavier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_portuguese_cased_finetuned_tcu_acordaos_pt.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_portuguese_cased_finetuned_tcu_acordaos_pt.md new file mode 100644 index 00000000000000..59ef6cbf3b777a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_portuguese_cased_finetuned_tcu_acordaos_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese bert_base_portuguese_cased_finetuned_tcu_acordaos BertEmbeddings from Luciano +author: John Snow Labs +name: bert_base_portuguese_cased_finetuned_tcu_acordaos +date: 2023-09-13 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_portuguese_cased_finetuned_tcu_acordaos` is a Portuguese model originally trained by Luciano. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_portuguese_cased_finetuned_tcu_acordaos_pt_5.1.1_3.0_1694566507028.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_portuguese_cased_finetuned_tcu_acordaos_pt_5.1.1_3.0_1694566507028.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_portuguese_cased_finetuned_tcu_acordaos","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_portuguese_cased_finetuned_tcu_acordaos", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_portuguese_cased_finetuned_tcu_acordaos| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|405.9 MB| + +## References + +https://huggingface.co/Luciano/bert-base-portuguese-cased-finetuned-tcu-acordaos \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_cased_dccuchile_es.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_cased_dccuchile_es.md new file mode 100644 index 00000000000000..7c551b16413123 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_cased_dccuchile_es.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Castilian, Spanish bert_base_spanish_wwm_cased_dccuchile BertEmbeddings from dccuchile +author: John Snow Labs +name: bert_base_spanish_wwm_cased_dccuchile +date: 2023-09-13 +tags: [bert, es, open_source, fill_mask, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_spanish_wwm_cased_dccuchile` is a Castilian, Spanish model originally trained by dccuchile. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_dccuchile_es_5.1.1_3.0_1694600005028.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_dccuchile_es_5.1.1_3.0_1694600005028.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_spanish_wwm_cased_dccuchile","es") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_spanish_wwm_cased_dccuchile", "es") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_spanish_wwm_cased_dccuchile| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|es| +|Size:|409.5 MB| + +## References + +https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_cased_finetuned_tweets_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_cased_finetuned_tweets_en.md new file mode 100644 index 00000000000000..bc43e9ef6b52f9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_cased_finetuned_tweets_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_spanish_wwm_cased_finetuned_tweets BertEmbeddings from myahan007 +author: John Snow Labs +name: bert_base_spanish_wwm_cased_finetuned_tweets +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_spanish_wwm_cased_finetuned_tweets` is a English model originally trained by myahan007. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_tweets_en_5.1.1_3.0_1694588752414.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_tweets_en_5.1.1_3.0_1694588752414.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_spanish_wwm_cased_finetuned_tweets","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_spanish_wwm_cased_finetuned_tweets", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_spanish_wwm_cased_finetuned_tweets| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/myahan007/bert-base-spanish-wwm-cased-finetuned-tweets \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_cased_finetuned_tweets_es.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_cased_finetuned_tweets_es.md new file mode 100644 index 00000000000000..78f8e86f7a7aed --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_cased_finetuned_tweets_es.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Castilian, Spanish bert_base_spanish_wwm_cased_finetuned_tweets BertEmbeddings from mariav +author: John Snow Labs +name: bert_base_spanish_wwm_cased_finetuned_tweets +date: 2023-09-13 +tags: [bert, es, open_source, fill_mask, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_spanish_wwm_cased_finetuned_tweets` is a Castilian, Spanish model originally trained by mariav. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_tweets_es_5.1.1_3.0_1694586672799.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_tweets_es_5.1.1_3.0_1694586672799.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_spanish_wwm_cased_finetuned_tweets","es") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_spanish_wwm_cased_finetuned_tweets", "es") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_spanish_wwm_cased_finetuned_tweets| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|es| +|Size:|409.4 MB| + +## References + +https://huggingface.co/mariav/bert-base-spanish-wwm-cased-finetuned-tweets \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_cased_plai_edp_test_es.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_cased_plai_edp_test_es.md new file mode 100644 index 00000000000000..dc85095149dca8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_cased_plai_edp_test_es.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Castilian, Spanish bert_base_spanish_wwm_cased_plai_edp_test BertEmbeddings from plai-edp-test +author: John Snow Labs +name: bert_base_spanish_wwm_cased_plai_edp_test +date: 2023-09-13 +tags: [bert, es, open_source, fill_mask, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_spanish_wwm_cased_plai_edp_test` is a Castilian, Spanish model originally trained by plai-edp-test. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_plai_edp_test_es_5.1.1_3.0_1694572546577.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_plai_edp_test_es_5.1.1_3.0_1694572546577.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_spanish_wwm_cased_plai_edp_test","es") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_spanish_wwm_cased_plai_edp_test", "es") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_spanish_wwm_cased_plai_edp_test| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|es| +|Size:|409.5 MB| + +## References + +https://huggingface.co/plai-edp-test/bert_base_spanish_wwm_cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_uncased_es.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_uncased_es.md new file mode 100644 index 00000000000000..e4a6622f220fdb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_uncased_es.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Castilian, Spanish bert_base_spanish_wwm_uncased BertEmbeddings from dccuchile +author: John Snow Labs +name: bert_base_spanish_wwm_uncased +date: 2023-09-13 +tags: [bert, es, open_source, fill_mask, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_spanish_wwm_uncased` is a Castilian, Spanish model originally trained by dccuchile. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_uncased_es_5.1.1_3.0_1694600405744.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_uncased_es_5.1.1_3.0_1694600405744.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_spanish_wwm_uncased","es") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_spanish_wwm_uncased", "es") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_spanish_wwm_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|es| +|Size:|409.6 MB| + +## References + +https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_uncased_finetuned_imdb_spanish_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_uncased_finetuned_imdb_spanish_en.md new file mode 100644 index 00000000000000..6262eb8343343f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_wwm_uncased_finetuned_imdb_spanish_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_spanish_wwm_uncased_finetuned_imdb_spanish BertEmbeddings from GabrielDGC +author: John Snow Labs +name: bert_base_spanish_wwm_uncased_finetuned_imdb_spanish +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_spanish_wwm_uncased_finetuned_imdb_spanish` is a English model originally trained by GabrielDGC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_uncased_finetuned_imdb_spanish_en_5.1.1_3.0_1694578377936.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_uncased_finetuned_imdb_spanish_en_5.1.1_3.0_1694578377936.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_spanish_wwm_uncased_finetuned_imdb_spanish","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_spanish_wwm_uncased_finetuned_imdb_spanish", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_spanish_wwm_uncased_finetuned_imdb_spanish| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.6 MB| + +## References + +https://huggingface.co/GabrielDGC/bert-base-spanish-wwm-uncased-finetuned-imdb-spanish \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_swedish_europeana_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_swedish_europeana_cased_en.md new file mode 100644 index 00000000000000..f5ea2258f15131 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_swedish_europeana_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_swedish_europeana_cased BertEmbeddings from dbmdz +author: John Snow Labs +name: bert_base_swedish_europeana_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_swedish_europeana_cased` is a English model originally trained by dbmdz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_swedish_europeana_cased_en_5.1.1_3.0_1694598474238.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_swedish_europeana_cased_en_5.1.1_3.0_1694598474238.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_swedish_europeana_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_swedish_europeana_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_swedish_europeana_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|411.9 MB| + +## References + +https://huggingface.co/dbmdz/bert-base-swedish-europeana-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_swedish_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_swedish_uncased_en.md new file mode 100644 index 00000000000000..84c35b4ed9b18c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_swedish_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_swedish_uncased BertEmbeddings from af-ai-center +author: John Snow Labs +name: bert_base_swedish_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_swedish_uncased` is a English model originally trained by af-ai-center. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_swedish_uncased_en_5.1.1_3.0_1694576951355.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_swedish_uncased_en_5.1.1_3.0_1694576951355.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_swedish_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_swedish_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_swedish_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.2 MB| + +## References + +https://huggingface.co/af-ai-center/bert-base-swedish-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_test_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_test_en.md new file mode 100644 index 00000000000000..90977abfd25993 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_test_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_test BertEmbeddings from Exqrch +author: John Snow Labs +name: bert_base_test +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_test` is a English model originally trained by Exqrch. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_test_en_5.1.1_3.0_1694593295884.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_test_en_5.1.1_3.0_1694593295884.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_test","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_test", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_test| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.6 MB| + +## References + +https://huggingface.co/Exqrch/bert-base-test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_turkish_128k_cased_offensive_mlm_tr.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_turkish_128k_cased_offensive_mlm_tr.md new file mode 100644 index 00000000000000..11bb47a13d09b4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_turkish_128k_cased_offensive_mlm_tr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Turkish bert_base_turkish_128k_cased_offensive_mlm BertEmbeddings from Overfit-GM +author: John Snow Labs +name: bert_base_turkish_128k_cased_offensive_mlm +date: 2023-09-13 +tags: [bert, tr, open_source, fill_mask, onnx] +task: Embeddings +language: tr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_turkish_128k_cased_offensive_mlm` is a Turkish model originally trained by Overfit-GM. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_turkish_128k_cased_offensive_mlm_tr_5.1.1_3.0_1694606938576.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_turkish_128k_cased_offensive_mlm_tr_5.1.1_3.0_1694606938576.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_turkish_128k_cased_offensive_mlm","tr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_turkish_128k_cased_offensive_mlm", "tr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_turkish_128k_cased_offensive_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|tr| +|Size:|688.9 MB| + +## References + +https://huggingface.co/Overfit-GM/bert-base-turkish-128k-cased-offensive-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_turkish_128k_uncased_offensive_mlm_tr.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_turkish_128k_uncased_offensive_mlm_tr.md new file mode 100644 index 00000000000000..499de2f13074a3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_turkish_128k_uncased_offensive_mlm_tr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Turkish bert_base_turkish_128k_uncased_offensive_mlm BertEmbeddings from Overfit-GM +author: John Snow Labs +name: bert_base_turkish_128k_uncased_offensive_mlm +date: 2023-09-13 +tags: [bert, tr, open_source, fill_mask, onnx] +task: Embeddings +language: tr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_turkish_128k_uncased_offensive_mlm` is a Turkish model originally trained by Overfit-GM. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_turkish_128k_uncased_offensive_mlm_tr_5.1.1_3.0_1694607873975.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_turkish_128k_uncased_offensive_mlm_tr_5.1.1_3.0_1694607873975.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_turkish_128k_uncased_offensive_mlm","tr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_turkish_128k_uncased_offensive_mlm", "tr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_turkish_128k_uncased_offensive_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|tr| +|Size:|689.4 MB| + +## References + +https://huggingface.co/Overfit-GM/bert-base-turkish-128k-uncased-offensive-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_turkish_uncased_offensive_mlm_tr.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_turkish_uncased_offensive_mlm_tr.md new file mode 100644 index 00000000000000..b0f6e5c7475c82 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_turkish_uncased_offensive_mlm_tr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Turkish bert_base_turkish_uncased_offensive_mlm BertEmbeddings from Overfit-GM +author: John Snow Labs +name: bert_base_turkish_uncased_offensive_mlm +date: 2023-09-13 +tags: [bert, tr, open_source, fill_mask, onnx] +task: Embeddings +language: tr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_turkish_uncased_offensive_mlm` is a Turkish model originally trained by Overfit-GM. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_turkish_uncased_offensive_mlm_tr_5.1.1_3.0_1694609304508.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_turkish_uncased_offensive_mlm_tr_5.1.1_3.0_1694609304508.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_turkish_uncased_offensive_mlm","tr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_turkish_uncased_offensive_mlm", "tr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_turkish_uncased_offensive_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|tr| +|Size:|412.5 MB| + +## References + +https://huggingface.co/Overfit-GM/bert-base-turkish-uncased-offensive-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_nvidia_test_2_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_nvidia_test_2_en.md new file mode 100644 index 00000000000000..43f03aba5e7704 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_nvidia_test_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_2022_nvidia_test_2 BertEmbeddings from philschmid +author: John Snow Labs +name: bert_base_uncased_2022_nvidia_test_2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_2022_nvidia_test_2` is a English model originally trained by philschmid. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_2022_nvidia_test_2_en_5.1.1_3.0_1694571908191.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_2022_nvidia_test_2_en_5.1.1_3.0_1694571908191.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_2022_nvidia_test_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_2022_nvidia_test_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_2022_nvidia_test_2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.1 MB| + +## References + +https://huggingface.co/philschmid/bert-base-uncased-2022-nvidia-test-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_nvidia_test_3_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_nvidia_test_3_en.md new file mode 100644 index 00000000000000..8e27228b1e0818 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2022_nvidia_test_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_2022_nvidia_test_3 BertEmbeddings from philschmid +author: John Snow Labs +name: bert_base_uncased_2022_nvidia_test_3 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_2022_nvidia_test_3` is a English model originally trained by philschmid. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_2022_nvidia_test_3_en_5.1.1_3.0_1694569617047.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_2022_nvidia_test_3_en_5.1.1_3.0_1694569617047.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_2022_nvidia_test_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_2022_nvidia_test_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_2022_nvidia_test_3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.1 MB| + +## References + +https://huggingface.co/philschmid/bert-base-uncased-2022-nvidia-test-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_byeongal_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_byeongal_en.md new file mode 100644 index 00000000000000..36d8b118330731 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_byeongal_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_byeongal BertEmbeddings from byeongal +author: John Snow Labs +name: bert_base_uncased_byeongal +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_byeongal` is a English model originally trained by byeongal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_byeongal_en_5.1.1_3.0_1694590179339.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_byeongal_en_5.1.1_3.0_1694590179339.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_byeongal","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_byeongal", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_byeongal| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/byeongal/bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_copy_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_copy_en.md new file mode 100644 index 00000000000000..26732073ef077e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_copy_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_copy BertEmbeddings from osanseviero +author: John Snow Labs +name: bert_base_uncased_copy +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_copy` is a English model originally trained by osanseviero. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_copy_en_5.1.1_3.0_1694598665795.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_copy_en_5.1.1_3.0_1694598665795.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_copy","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_copy", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_copy| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/osanseviero/bert-base-uncased-copy \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_kintweetse_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_kintweetse_en.md new file mode 100644 index 00000000000000..a8e01998a10993 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_kintweetse_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_kintweetse BertEmbeddings from RogerB +author: John Snow Labs +name: bert_base_uncased_finetuned_kintweetse +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_kintweetse` is a English model originally trained by RogerB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_kintweetse_en_5.1.1_3.0_1694603450967.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_kintweetse_en_5.1.1_3.0_1694603450967.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_kintweetse","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_kintweetse", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_kintweetse| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/RogerB/bert-base-uncased-finetuned-kintweetsE \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_lexglue_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_lexglue_en.md new file mode 100644 index 00000000000000..8bdea571ad9092 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_lexglue_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_lexglue BertEmbeddings from mayankb96 +author: John Snow Labs +name: bert_base_uncased_finetuned_lexglue +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_lexglue` is a English model originally trained by mayankb96. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_lexglue_en_5.1.1_3.0_1694600610326.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_lexglue_en_5.1.1_3.0_1694600610326.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_lexglue","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_lexglue", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_lexglue| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/mayankb96/bert-base-uncased-finetuned-lexglue \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_ifedrigo_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_ifedrigo_en.md new file mode 100644 index 00000000000000..918290da2ad879 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_ifedrigo_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_ifedrigo BertEmbeddings from ifedrigo +author: John Snow Labs +name: bert_base_uncased_ifedrigo +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_ifedrigo` is a English model originally trained by ifedrigo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_ifedrigo_en_5.1.1_3.0_1694576407119.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_ifedrigo_en_5.1.1_3.0_1694576407119.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_ifedrigo","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_ifedrigo", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_ifedrigo| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|413.2 MB| + +## References + +https://huggingface.co/ifedrigo/bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_coldfir3_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_coldfir3_en.md new file mode 100644 index 00000000000000..948d3d2800fd03 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_coldfir3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_coldfir3 BertEmbeddings from coldfir3 +author: John Snow Labs +name: bert_base_uncased_issues_128_coldfir3 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_coldfir3` is a English model originally trained by coldfir3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_coldfir3_en_5.1.1_3.0_1694594084400.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_coldfir3_en_5.1.1_3.0_1694594084400.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_coldfir3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_coldfir3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_coldfir3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/coldfir3/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_danielvelaj_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_danielvelaj_en.md new file mode 100644 index 00000000000000..0e424c288033d6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_danielvelaj_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_danielvelaj BertEmbeddings from DanielVelaJ +author: John Snow Labs +name: bert_base_uncased_issues_128_danielvelaj +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_danielvelaj` is a English model originally trained by DanielVelaJ. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_danielvelaj_en_5.1.1_3.0_1694593430068.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_danielvelaj_en_5.1.1_3.0_1694593430068.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_danielvelaj","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_danielvelaj", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_danielvelaj| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/DanielVelaJ/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_haesun_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_haesun_en.md new file mode 100644 index 00000000000000..5900144020ac72 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_haesun_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_haesun BertEmbeddings from haesun +author: John Snow Labs +name: bert_base_uncased_issues_128_haesun +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_haesun` is a English model originally trained by haesun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_haesun_en_5.1.1_3.0_1694579131163.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_haesun_en_5.1.1_3.0_1694579131163.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_haesun","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_haesun", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_haesun| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/haesun/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_jmassot_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_jmassot_en.md new file mode 100644 index 00000000000000..8009f6b354f5cc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_jmassot_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_jmassot BertEmbeddings from jmassot +author: John Snow Labs +name: bert_base_uncased_issues_128_jmassot +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_jmassot` is a English model originally trained by jmassot. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_jmassot_en_5.1.1_3.0_1694587856633.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_jmassot_en_5.1.1_3.0_1694587856633.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_jmassot","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_jmassot", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_jmassot| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/jmassot/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_roscoyoon_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_roscoyoon_en.md new file mode 100644 index 00000000000000..81e2a4883fdef9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_roscoyoon_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_roscoyoon BertEmbeddings from roscoyoon +author: John Snow Labs +name: bert_base_uncased_issues_128_roscoyoon +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_roscoyoon` is a English model originally trained by roscoyoon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_roscoyoon_en_5.1.1_3.0_1694575118085.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_roscoyoon_en_5.1.1_3.0_1694575118085.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_roscoyoon","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_roscoyoon", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_roscoyoon| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/roscoyoon/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_shenghao1993_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_shenghao1993_en.md new file mode 100644 index 00000000000000..ca206b9f9f651b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_shenghao1993_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_shenghao1993 BertEmbeddings from Shenghao1993 +author: John Snow Labs +name: bert_base_uncased_issues_128_shenghao1993 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_shenghao1993` is a English model originally trained by Shenghao1993. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_shenghao1993_en_5.1.1_3.0_1694567929037.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_shenghao1993_en_5.1.1_3.0_1694567929037.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_shenghao1993","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_shenghao1993", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_shenghao1993| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Shenghao1993/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_synpjh_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_synpjh_en.md new file mode 100644 index 00000000000000..3fa0e07aac9da5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_synpjh_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_synpjh BertEmbeddings from synpjh +author: John Snow Labs +name: bert_base_uncased_issues_128_synpjh +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_synpjh` is a English model originally trained by synpjh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_synpjh_en_5.1.1_3.0_1694585902258.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_synpjh_en_5.1.1_3.0_1694585902258.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_synpjh","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_synpjh", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_synpjh| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/synpjh/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_transformersbook_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_transformersbook_en.md new file mode 100644 index 00000000000000..8e4a59eeea3d16 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_transformersbook_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_transformersbook BertEmbeddings from transformersbook +author: John Snow Labs +name: bert_base_uncased_issues_128_transformersbook +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_transformersbook` is a English model originally trained by transformersbook. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_transformersbook_en_5.1.1_3.0_1694578903746.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_transformersbook_en_5.1.1_3.0_1694578903746.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_transformersbook","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_transformersbook", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_transformersbook| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/transformersbook/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_xxr_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_xxr_en.md new file mode 100644 index 00000000000000..1ba71ee2d8e2c9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_xxr_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_xxr BertEmbeddings from xxr +author: John Snow Labs +name: bert_base_uncased_issues_128_xxr +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_xxr` is a English model originally trained by xxr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_xxr_en_5.1.1_3.0_1694586835551.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_xxr_en_5.1.1_3.0_1694586835551.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_xxr","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_xxr", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_xxr| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/xxr/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_new_data_bert1_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_new_data_bert1_en.md new file mode 100644 index 00000000000000..e9875facf050b1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_new_data_bert1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_new_data_bert1 BertEmbeddings from Billwzl +author: John Snow Labs +name: bert_base_uncased_new_data_bert1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_new_data_bert1` is a English model originally trained by Billwzl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_new_data_bert1_en_5.1.1_3.0_1694574719260.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_new_data_bert1_en_5.1.1_3.0_1694574719260.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_new_data_bert1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_new_data_bert1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_new_data_bert1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/Billwzl/bert-base-uncased-New_data_bert1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_based_restaurant_review_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_based_restaurant_review_en.md new file mode 100644 index 00000000000000..bcda9e00f0aade --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_based_restaurant_review_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_based_restaurant_review BertEmbeddings from River-jh +author: John Snow Labs +name: bert_based_restaurant_review +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_based_restaurant_review` is a English model originally trained by River-jh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_based_restaurant_review_en_5.1.1_3.0_1694572475763.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_based_restaurant_review_en_5.1.1_3.0_1694572475763.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_based_restaurant_review","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_based_restaurant_review", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_based_restaurant_review| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/River-jh/bert-based-restaurant-review \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_concat_2_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_concat_2_en.md new file mode 100644 index 00000000000000..1d1bd83784da47 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_concat_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_concat_2 BertEmbeddings from NasimB +author: John Snow Labs +name: bert_concat_2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_concat_2` is a English model originally trained by NasimB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_concat_2_en_5.1.1_3.0_1694586672241.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_concat_2_en_5.1.1_3.0_1694586672241.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_concat_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_concat_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_concat_2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.4 MB| + +## References + +https://huggingface.co/NasimB/bert-concat-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_dk_rest_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_dk_rest_en.md new file mode 100644 index 00000000000000..478cf5f6fe2faa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_dk_rest_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_dk_rest BertEmbeddings from activebus +author: John Snow Labs +name: bert_dk_rest +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_dk_rest` is a English model originally trained by activebus. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_dk_rest_en_5.1.1_3.0_1694575729183.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_dk_rest_en_5.1.1_3.0_1694575729183.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_dk_rest","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_dk_rest", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_dk_rest| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|258.3 MB| + +## References + +https://huggingface.co/activebus/BERT-DK_rest \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_double_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_double_en.md new file mode 100644 index 00000000000000..4346874aebf859 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_double_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_double BertEmbeddings from casehold +author: John Snow Labs +name: bert_double +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_double` is a English model originally trained by casehold. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_double_en_5.1.1_3.0_1694597248105.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_double_en_5.1.1_3.0_1694597248105.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_double","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_double", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_double| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/casehold/bert-double \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_dp_4_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_dp_4_en.md new file mode 100644 index 00000000000000..bbe1ef3293485c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_dp_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_dp_4 BertEmbeddings from NasimB +author: John Snow Labs +name: bert_dp_4 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_dp_4` is a English model originally trained by NasimB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_dp_4_en_5.1.1_3.0_1694588456919.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_dp_4_en_5.1.1_3.0_1694588456919.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_dp_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_dp_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_dp_4| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.9 MB| + +## References + +https://huggingface.co/NasimB/bert-dp-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_embding_finetuned_spmlm_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_embding_finetuned_spmlm_en.md new file mode 100644 index 00000000000000..4a7dda31e29a2d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_embding_finetuned_spmlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_embding_finetuned_spmlm BertEmbeddings from ashwathjadhav23 +author: John Snow Labs +name: bert_embding_finetuned_spmlm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_embding_finetuned_spmlm` is a English model originally trained by ashwathjadhav23. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embding_finetuned_spmlm_en_5.1.1_3.0_1694585297333.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embding_finetuned_spmlm_en_5.1.1_3.0_1694585297333.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_embding_finetuned_spmlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_embding_finetuned_spmlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embding_finetuned_spmlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/ashwathjadhav23/Bert_Embding_Finetuned_SpMLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_finetuning_test_0925_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_finetuning_test_0925_en.md new file mode 100644 index 00000000000000..13bdf930b47782 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_finetuning_test_0925_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_finetuning_test_0925 BertEmbeddings from wbmitcast +author: John Snow Labs +name: bert_finetuning_test_0925 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuning_test_0925` is a English model originally trained by wbmitcast. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuning_test_0925_en_5.1.1_3.0_1694583057346.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuning_test_0925_en_5.1.1_3.0_1694583057346.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_finetuning_test_0925","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_finetuning_test_0925", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuning_test_0925| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/wbmitcast/bert_finetuning_test_0925 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_finetuning_test_zqf03118_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_finetuning_test_zqf03118_en.md new file mode 100644 index 00000000000000..7b641eeeba3434 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_finetuning_test_zqf03118_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_finetuning_test_zqf03118 BertEmbeddings from zqf03118 +author: John Snow Labs +name: bert_finetuning_test_zqf03118 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuning_test_zqf03118` is a English model originally trained by zqf03118. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuning_test_zqf03118_en_5.1.1_3.0_1694599186944.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuning_test_zqf03118_en_5.1.1_3.0_1694599186944.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_finetuning_test_zqf03118","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_finetuning_test_zqf03118", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuning_test_zqf03118| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/zqf03118/bert_finetuning_test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_hs_idpt_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_hs_idpt_en.md new file mode 100644 index 00000000000000..a0c3a1346cf118 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_hs_idpt_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_hs_idpt BertEmbeddings from SmartPy +author: John Snow Labs +name: bert_hs_idpt +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_hs_idpt` is a English model originally trained by SmartPy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_hs_idpt_en_5.1.1_3.0_1694607979355.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_hs_idpt_en_5.1.1_3.0_1694607979355.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_hs_idpt","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_hs_idpt", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_hs_idpt| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/SmartPy/bert-hs-idpt \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_labor_space_token_512_batch_8_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_labor_space_token_512_batch_8_en.md new file mode 100644 index 00000000000000..cef4e1056540e2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_labor_space_token_512_batch_8_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_labor_space_token_512_batch_8 BertEmbeddings from seongwoon +author: John Snow Labs +name: bert_labor_space_token_512_batch_8 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_labor_space_token_512_batch_8` is a English model originally trained by seongwoon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_labor_space_token_512_batch_8_en_5.1.1_3.0_1694591397056.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_labor_space_token_512_batch_8_en_5.1.1_3.0_1694591397056.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_labor_space_token_512_batch_8","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_labor_space_token_512_batch_8", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_labor_space_token_512_batch_8| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/seongwoon/Bert_labor_space_token_512_batch_8 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_arabertv02_ar.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_arabertv02_ar.md new file mode 100644 index 00000000000000..d9d37240f60859 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_arabertv02_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_large_arabertv02 BertEmbeddings from aubmindlab +author: John Snow Labs +name: bert_large_arabertv02 +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_arabertv02` is a Arabic model originally trained by aubmindlab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_arabertv02_ar_5.1.1_3.0_1694584023166.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_arabertv02_ar_5.1.1_3.0_1694584023166.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_arabertv02","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_arabertv02", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_arabertv02| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|1.4 GB| + +## References + +https://huggingface.co/aubmindlab/bert-large-arabertv02 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_arabertv02_twitter_ar.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_arabertv02_twitter_ar.md new file mode 100644 index 00000000000000..3b4cdb91d7e071 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_arabertv02_twitter_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_large_arabertv02_twitter BertEmbeddings from aubmindlab +author: John Snow Labs +name: bert_large_arabertv02_twitter +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_arabertv02_twitter` is a Arabic model originally trained by aubmindlab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_arabertv02_twitter_ar_5.1.1_3.0_1694583674089.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_arabertv02_twitter_ar_5.1.1_3.0_1694583674089.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_arabertv02_twitter","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_arabertv02_twitter", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_arabertv02_twitter| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|1.4 GB| + +## References + +https://huggingface.co/aubmindlab/bert-large-arabertv02-twitter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_arabic_ar.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_arabic_ar.md new file mode 100644 index 00000000000000..48d1326005cf80 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_arabic_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_large_arabic BertEmbeddings from asafaya +author: John Snow Labs +name: bert_large_arabic +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_arabic` is a Arabic model originally trained by asafaya. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_arabic_ar_5.1.1_3.0_1694582290621.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_arabic_ar_5.1.1_3.0_1694582290621.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_arabic","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_arabic", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_arabic| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|1.3 GB| + +## References + +https://huggingface.co/asafaya/bert-large-arabic \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_low100_0_cased_da_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_low100_0_cased_da_20_en.md new file mode 100644 index 00000000000000..1d95bcf3764694 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_low100_0_cased_da_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_finetuned_low100_0_cased_da_20 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_finetuned_low100_0_cased_da_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_finetuned_low100_0_cased_da_20` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_low100_0_cased_da_20_en_5.1.1_3.0_1694565992407.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_low100_0_cased_da_20_en_5.1.1_3.0_1694565992407.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_finetuned_low100_0_cased_da_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_finetuned_low100_0_cased_da_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_finetuned_low100_0_cased_da_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-finetuned-low100-0-cased-DA-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_low10_0_cased_da_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_low10_0_cased_da_20_en.md new file mode 100644 index 00000000000000..79f3eb938bfb36 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_low10_0_cased_da_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_finetuned_low10_0_cased_da_20 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_finetuned_low10_0_cased_da_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_finetuned_low10_0_cased_da_20` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_low10_0_cased_da_20_en_5.1.1_3.0_1694565663480.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_low10_0_cased_da_20_en_5.1.1_3.0_1694565663480.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_finetuned_low10_0_cased_da_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_finetuned_low10_0_cased_da_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_finetuned_low10_0_cased_da_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-finetuned-low10-0-cased-DA-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_lowr100_0_cased_da_40_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_lowr100_0_cased_da_40_en.md new file mode 100644 index 00000000000000..7c957e0565e050 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_lowr100_0_cased_da_40_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_finetuned_lowr100_0_cased_da_40 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_finetuned_lowr100_0_cased_da_40 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_finetuned_lowr100_0_cased_da_40` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_lowr100_0_cased_da_40_en_5.1.1_3.0_1694567194862.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_lowr100_0_cased_da_40_en_5.1.1_3.0_1694567194862.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_finetuned_lowr100_0_cased_da_40","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_finetuned_lowr100_0_cased_da_40", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_finetuned_lowr100_0_cased_da_40| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-finetuned-lowR100-0-cased-DA-40 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_portuguese_lenerbr_luciolrv_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_portuguese_lenerbr_luciolrv_en.md new file mode 100644 index 00000000000000..fc2d653e3bd167 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_portuguese_lenerbr_luciolrv_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_portuguese_lenerbr_luciolrv BertEmbeddings from luciolrv +author: John Snow Labs +name: bert_large_cased_portuguese_lenerbr_luciolrv +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_portuguese_lenerbr_luciolrv` is a English model originally trained by luciolrv. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_portuguese_lenerbr_luciolrv_en_5.1.1_3.0_1694584926995.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_portuguese_lenerbr_luciolrv_en_5.1.1_3.0_1694584926995.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_portuguese_lenerbr_luciolrv","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_portuguese_lenerbr_luciolrv", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_portuguese_lenerbr_luciolrv| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/luciolrv/bert-large-cased-pt-lenerbr \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_lr100_0_prepend_40_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_lr100_0_prepend_40_en.md new file mode 100644 index 00000000000000..9714c08d2e10eb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_lr100_0_prepend_40_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_lr100_0_prepend_40 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_lr100_0_prepend_40 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_lr100_0_prepend_40` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_lr100_0_prepend_40_en_5.1.1_3.0_1694570580287.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_lr100_0_prepend_40_en_5.1.1_3.0_1694570580287.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_lr100_0_prepend_40","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_lr100_0_prepend_40", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_lr100_0_prepend_40| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-LR100-0-prepend-40 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_lr100_1_cased_40_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_lr100_1_cased_40_en.md new file mode 100644 index 00000000000000..15e5bc25cdec34 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_lr100_1_cased_40_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_lr100_1_cased_40 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_lr100_1_cased_40 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_lr100_1_cased_40` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_lr100_1_cased_40_en_5.1.1_3.0_1694569306149.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_lr100_1_cased_40_en_5.1.1_3.0_1694569306149.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_lr100_1_cased_40","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_lr100_1_cased_40", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_lr100_1_cased_40| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-LR100-1-cased-40 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_lr10_1_prepend_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_lr10_1_prepend_20_en.md new file mode 100644 index 00000000000000..937bf1fbe4ffc6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_lr10_1_prepend_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_lr10_1_prepend_20 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_lr10_1_prepend_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_lr10_1_prepend_20` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_lr10_1_prepend_20_en_5.1.1_3.0_1694570247498.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_lr10_1_prepend_20_en_5.1.1_3.0_1694570247498.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_lr10_1_prepend_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_lr10_1_prepend_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_lr10_1_prepend_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-LR10-1-prepend-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_norwegian_label_40_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_norwegian_label_40_en.md new file mode 100644 index 00000000000000..b5c2321004f89c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_norwegian_label_40_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_norwegian_label_40 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_norwegian_label_40 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_norwegian_label_40` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_norwegian_label_40_en_5.1.1_3.0_1694572396505.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_norwegian_label_40_en_5.1.1_3.0_1694572396505.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_norwegian_label_40","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_norwegian_label_40", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_norwegian_label_40| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-no-label-40 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr10_labelled_40_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr10_labelled_40_en.md new file mode 100644 index 00000000000000..1077a6ead45398 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr10_labelled_40_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr10_labelled_40 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr10_labelled_40 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr10_labelled_40` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr10_labelled_40_en_5.1.1_3.0_1694573805864.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr10_labelled_40_en_5.1.1_3.0_1694573805864.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr10_labelled_40","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr10_labelled_40", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_norwegian_label_40_sigir_tune2nd_lr10_labelled_40| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-no-label-40-sigir-tune2nd-LR10-labelled-40 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_evidence_norwegian_label_40_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_evidence_norwegian_label_40_en.md new file mode 100644 index 00000000000000..6f531b6f584c91 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_evidence_norwegian_label_40_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_evidence_norwegian_label_40 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_evidence_norwegian_label_40 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_evidence_norwegian_label_40` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_evidence_norwegian_label_40_en_5.1.1_3.0_1694575387153.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_evidence_norwegian_label_40_en_5.1.1_3.0_1694575387153.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_evidence_norwegian_label_40","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_evidence_norwegian_label_40", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_evidence_norwegian_label_40| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-evidence-no-label-40 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_0_prompt_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_0_prompt_en.md new file mode 100644 index 00000000000000..21f9d653f98d1f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_0_prompt_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_0_prompt BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_0_prompt +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_0_prompt` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_0_prompt_en_5.1.1_3.0_1694583930419.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_0_prompt_en_5.1.1_3.0_1694583930419.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_0_prompt","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_0_prompt", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_0_prompt| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-40-0-prompt \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_1_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_1_en.md new file mode 100644 index 00000000000000..262851f34c3b21 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_1 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_1` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_1_en_5.1.1_3.0_1694582866236.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_1_en_5.1.1_3.0_1694582866236.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-40-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_en.md new file mode 100644 index 00000000000000..bd78c8deee6cda --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_en_5.1.1_3.0_1694578940076.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40_en_5.1.1_3.0_1694578940076.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_40| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-40 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_2_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_2_en.md new file mode 100644 index 00000000000000..2a5c92934ce453 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_2 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_2` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_2_en_5.1.1_3.0_1694588193598.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_2_en_5.1.1_3.0_1694588193598.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_3_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_3_en.md new file mode 100644 index 00000000000000..1a29158803d7ea --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_3 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_3 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_3` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_3_en_5.1.1_3.0_1694588891046.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_3_en_5.1.1_3.0_1694588891046.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_4_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_4_en.md new file mode 100644 index 00000000000000..c72524aed9307d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_4 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_4 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_4` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_4_en_5.1.1_3.0_1694589244932.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_4_en_5.1.1_3.0_1694589244932.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_4| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_21_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_21_en.md new file mode 100644 index 00000000000000..e2891bffbdaa6d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_21_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_21 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_21 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_21` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_21_en_5.1.1_3.0_1694569649905.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_21_en_5.1.1_3.0_1694569649905.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_21","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_21", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_21| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-21 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_22_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_22_en.md new file mode 100644 index 00000000000000..b3a053447a112c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_22_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_22 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_22 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_22` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_22_en_5.1.1_3.0_1694569966201.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_22_en_5.1.1_3.0_1694569966201.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_22","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_22", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_22| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-22 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_10_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_10_en.md new file mode 100644 index 00000000000000..fe7aae847bd184 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_10_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_10 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_10 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_10` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_10_en_5.1.1_3.0_1694573423912.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_10_en_5.1.1_3.0_1694573423912.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_10","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_10", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_10| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR50-8-fast-10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_11_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_11_en.md new file mode 100644 index 00000000000000..fedff5398add0b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_11_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_11 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_11 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_11` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_11_en_5.1.1_3.0_1694573761232.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_11_en_5.1.1_3.0_1694573761232.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_11","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_11", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_11| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR50-8-fast-11 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_15_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_15_en.md new file mode 100644 index 00000000000000..9f62efd21eb560 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_15_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_15 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_15 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_15` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_15_en_5.1.1_3.0_1694575016746.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_15_en_5.1.1_3.0_1694575016746.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_15","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_15", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_15| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR50-8-fast-15 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_4_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_4_en.md new file mode 100644 index 00000000000000..c86bf1d18a8015 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_4 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_4 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_4` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_4_en_5.1.1_3.0_1694571499195.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_4_en_5.1.1_3.0_1694571499195.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_4| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR50-8-fast-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_8_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_8_en.md new file mode 100644 index 00000000000000..74318af2756bbc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_8_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_8 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_8 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_8` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_8_en_5.1.1_3.0_1694572788274.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_8_en_5.1.1_3.0_1694572788274.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_8","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_8", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_8| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR50-8-fast-8 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_nli_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_nli_en.md new file mode 100644 index 00000000000000..052fd12c99b54a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_nli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_nli BertEmbeddings from binwang +author: John Snow Labs +name: bert_large_nli +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_nli` is a English model originally trained by binwang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_nli_en_5.1.1_3.0_1694588802340.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_nli_en_5.1.1_3.0_1694588802340.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_nli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_nli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_nli| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/binwang/bert-large-nli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_nli_stsb_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_nli_stsb_en.md new file mode 100644 index 00000000000000..d0d80f42a38572 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_nli_stsb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_nli_stsb BertEmbeddings from binwang +author: John Snow Labs +name: bert_large_nli_stsb +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_nli_stsb` is a English model originally trained by binwang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_nli_stsb_en_5.1.1_3.0_1694588441505.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_nli_stsb_en_5.1.1_3.0_1694588441505.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_nli_stsb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_nli_stsb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_nli_stsb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/binwang/bert-large-nli-stsb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_retrained_2_epochs_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_retrained_2_epochs_en.md new file mode 100644 index 00000000000000..2b808c2f7fab8c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_retrained_2_epochs_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_retrained_2_epochs BertEmbeddings from trangdieu +author: John Snow Labs +name: bert_large_retrained_2_epochs +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_retrained_2_epochs` is a English model originally trained by trangdieu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_retrained_2_epochs_en_5.1.1_3.0_1694578433457.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_retrained_2_epochs_en_5.1.1_3.0_1694578433457.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_retrained_2_epochs","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_retrained_2_epochs", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_retrained_2_epochs| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/trangdieu/bert-large-retrained-2-epochs \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_retrained_4_epochs_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_retrained_4_epochs_en.md new file mode 100644 index 00000000000000..31407c22b6f5ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_retrained_4_epochs_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_retrained_4_epochs BertEmbeddings from trangdieu +author: John Snow Labs +name: bert_large_retrained_4_epochs +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_retrained_4_epochs` is a English model originally trained by trangdieu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_retrained_4_epochs_en_5.1.1_3.0_1694578721586.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_retrained_4_epochs_en_5.1.1_3.0_1694578721586.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_retrained_4_epochs","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_retrained_4_epochs", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_retrained_4_epochs| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/trangdieu/bert-large-retrained-4-epochs \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_swedish_nordic_pile_320_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_swedish_nordic_pile_320_en.md new file mode 100644 index 00000000000000..549d1f84090c57 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_swedish_nordic_pile_320_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_swedish_nordic_pile_320 BertEmbeddings from timpal0l +author: John Snow Labs +name: bert_large_swedish_nordic_pile_320 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_swedish_nordic_pile_320` is a English model originally trained by timpal0l. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_swedish_nordic_pile_320_en_5.1.1_3.0_1694583683417.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_swedish_nordic_pile_320_en_5.1.1_3.0_1694583683417.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_swedish_nordic_pile_320","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_swedish_nordic_pile_320", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_swedish_nordic_pile_320| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.4 GB| + +## References + +https://huggingface.co/timpal0l/bert_large_sv_nordic_pile_320 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_medium_arabic_ar.md b/docs/_posts/ahmedlone127/2023-09-13-bert_medium_arabic_ar.md new file mode 100644 index 00000000000000..633e34076de7d8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_medium_arabic_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_medium_arabic BertEmbeddings from asafaya +author: John Snow Labs +name: bert_medium_arabic +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_medium_arabic` is a Arabic model originally trained by asafaya. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_medium_arabic_ar_5.1.1_3.0_1694582427477.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_medium_arabic_ar_5.1.1_3.0_1694582427477.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_medium_arabic","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_medium_arabic", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_medium_arabic| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|157.2 MB| + +## References + +https://huggingface.co/asafaya/bert-medium-arabic \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_medium_historic_multilingual_cased_xx.md b/docs/_posts/ahmedlone127/2023-09-13-bert_medium_historic_multilingual_cased_xx.md new file mode 100644 index 00000000000000..abfbd0ff6933c7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_medium_historic_multilingual_cased_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_medium_historic_multilingual_cased BertEmbeddings from dbmdz +author: John Snow Labs +name: bert_medium_historic_multilingual_cased +date: 2023-09-13 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_medium_historic_multilingual_cased` is a Multilingual model originally trained by dbmdz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_medium_historic_multilingual_cased_xx_5.1.1_3.0_1694598781999.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_medium_historic_multilingual_cased_xx_5.1.1_3.0_1694598781999.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_medium_historic_multilingual_cased","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_medium_historic_multilingual_cased", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_medium_historic_multilingual_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|156.8 MB| + +## References + +https://huggingface.co/dbmdz/bert-medium-historic-multilingual-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_medium_mlsm_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_medium_mlsm_en.md new file mode 100644 index 00000000000000..5395d4019074b9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_medium_mlsm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_medium_mlsm BertEmbeddings from SzegedAI +author: John Snow Labs +name: bert_medium_mlsm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_medium_mlsm` is a English model originally trained by SzegedAI. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_medium_mlsm_en_5.1.1_3.0_1694598153281.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_medium_mlsm_en_5.1.1_3.0_1694598153281.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_medium_mlsm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_medium_mlsm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_medium_mlsm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|157.1 MB| + +## References + +https://huggingface.co/SzegedAI/bert-medium-mlsm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_medium_pretrained_on_squad_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_medium_pretrained_on_squad_en.md new file mode 100644 index 00000000000000..ed9774d369d15a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_medium_pretrained_on_squad_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_medium_pretrained_on_squad BertEmbeddings from anas-awadalla +author: John Snow Labs +name: bert_medium_pretrained_on_squad +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_medium_pretrained_on_squad` is a English model originally trained by anas-awadalla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_medium_pretrained_on_squad_en_5.1.1_3.0_1694579855883.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_medium_pretrained_on_squad_en_5.1.1_3.0_1694579855883.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_medium_pretrained_on_squad","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_medium_pretrained_on_squad", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_medium_pretrained_on_squad| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|154.2 MB| + +## References + +https://huggingface.co/anas-awadalla/bert-medium-pretrained-on-squad \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_mini_historic_multilingual_cased_xx.md b/docs/_posts/ahmedlone127/2023-09-13-bert_mini_historic_multilingual_cased_xx.md new file mode 100644 index 00000000000000..40be9370125ff1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_mini_historic_multilingual_cased_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_mini_historic_multilingual_cased BertEmbeddings from dbmdz +author: John Snow Labs +name: bert_mini_historic_multilingual_cased +date: 2023-09-13 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_mini_historic_multilingual_cased` is a Multilingual model originally trained by dbmdz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_mini_historic_multilingual_cased_xx_5.1.1_3.0_1694599073613.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_mini_historic_multilingual_cased_xx_5.1.1_3.0_1694599073613.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_mini_historic_multilingual_cased","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_mini_historic_multilingual_cased", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_mini_historic_multilingual_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|43.3 MB| + +## References + +https://huggingface.co/dbmdz/bert-mini-historic-multilingual-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_model_bstad_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_model_bstad_en.md new file mode 100644 index 00000000000000..d81b3f1f1f4af1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_model_bstad_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_model_bstad BertEmbeddings from bstad +author: John Snow Labs +name: bert_model_bstad +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_model_bstad` is a English model originally trained by bstad. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_model_bstad_en_5.1.1_3.0_1694589393853.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_model_bstad_en_5.1.1_3.0_1694589393853.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_model_bstad","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_model_bstad", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_model_bstad| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/bstad/bert-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_pretrain_btk_mufi_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_pretrain_btk_mufi_en.md new file mode 100644 index 00000000000000..65e9b63b26a662 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_pretrain_btk_mufi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_pretrain_btk_mufi BertEmbeddings from btk-mufi +author: John Snow Labs +name: bert_pretrain_btk_mufi +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_pretrain_btk_mufi` is a English model originally trained by btk-mufi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_pretrain_btk_mufi_en_5.1.1_3.0_1694589810242.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_pretrain_btk_mufi_en_5.1.1_3.0_1694589810242.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_pretrain_btk_mufi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_pretrain_btk_mufi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_pretrain_btk_mufi| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.5 MB| + +## References + +https://huggingface.co/btk-mufi/bert-pretrain \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_review_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_review_en.md new file mode 100644 index 00000000000000..481b86ea562085 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_review_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_review BertEmbeddings from activebus +author: John Snow Labs +name: bert_review +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_review` is a English model originally trained by activebus. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_review_en_5.1.1_3.0_1694576401917.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_review_en_5.1.1_3.0_1694576401917.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_review","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_review", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_review| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|258.5 MB| + +## References + +https://huggingface.co/activebus/BERT_Review \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_semeval_env_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_semeval_env_en.md new file mode 100644 index 00000000000000..cf0aff26c2994a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_semeval_env_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_semeval_env BertEmbeddings from Indraa99 +author: John Snow Labs +name: bert_semeval_env +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_semeval_env` is a English model originally trained by Indraa99. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_semeval_env_en_5.1.1_3.0_1694600701019.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_semeval_env_en_5.1.1_3.0_1694600701019.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_semeval_env","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_semeval_env", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_semeval_env| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Indraa99/bert_semeval_env \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_eurlex_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_eurlex_en.md new file mode 100644 index 00000000000000..b8717811c8441f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_eurlex_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_small_finetuned_eurlex BertEmbeddings from muhtasham +author: John Snow Labs +name: bert_small_finetuned_eurlex +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_small_finetuned_eurlex` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_eurlex_en_5.1.1_3.0_1694569896945.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_eurlex_en_5.1.1_3.0_1694569896945.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_small_finetuned_eurlex","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_small_finetuned_eurlex", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_small_finetuned_eurlex| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/bert-small-finetuned-eurlex \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_legal_contracts10train10val_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_legal_contracts10train10val_en.md new file mode 100644 index 00000000000000..074215c3c9423f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_legal_contracts10train10val_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_small_finetuned_legal_contracts10train10val BertEmbeddings from muhtasham +author: John Snow Labs +name: bert_small_finetuned_legal_contracts10train10val +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_small_finetuned_legal_contracts10train10val` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_legal_contracts10train10val_en_5.1.1_3.0_1694569317652.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_legal_contracts10train10val_en_5.1.1_3.0_1694569317652.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_small_finetuned_legal_contracts10train10val","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_small_finetuned_legal_contracts10train10val", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_small_finetuned_legal_contracts10train10val| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/bert-small-finetuned-legal-contracts10train10val \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_legal_contracts_larger20_5_1_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_legal_contracts_larger20_5_1_en.md new file mode 100644 index 00000000000000..435aeeb1d244bc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_legal_contracts_larger20_5_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_small_finetuned_legal_contracts_larger20_5_1 BertEmbeddings from muhtasham +author: John Snow Labs +name: bert_small_finetuned_legal_contracts_larger20_5_1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_small_finetuned_legal_contracts_larger20_5_1` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_legal_contracts_larger20_5_1_en_5.1.1_3.0_1694572830320.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_legal_contracts_larger20_5_1_en_5.1.1_3.0_1694572830320.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_small_finetuned_legal_contracts_larger20_5_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_small_finetuned_legal_contracts_larger20_5_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_small_finetuned_legal_contracts_larger20_5_1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/bert-small-finetuned-legal-contracts-larger20-5-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_legal_definitions_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_legal_definitions_en.md new file mode 100644 index 00000000000000..ed5356442609bd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_legal_definitions_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_small_finetuned_legal_definitions BertEmbeddings from muhtasham +author: John Snow Labs +name: bert_small_finetuned_legal_definitions +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_small_finetuned_legal_definitions` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_legal_definitions_en_5.1.1_3.0_1694568934992.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_legal_definitions_en_5.1.1_3.0_1694568934992.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_small_finetuned_legal_definitions","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_small_finetuned_legal_definitions", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_small_finetuned_legal_definitions| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/bert-small-finetuned-legal-definitions \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_legal_definitions_longer_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_legal_definitions_longer_en.md new file mode 100644 index 00000000000000..c3274873f0bb6c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_legal_definitions_longer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_small_finetuned_legal_definitions_longer BertEmbeddings from muhtasham +author: John Snow Labs +name: bert_small_finetuned_legal_definitions_longer +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_small_finetuned_legal_definitions_longer` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_legal_definitions_longer_en_5.1.1_3.0_1694569019352.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_legal_definitions_longer_en_5.1.1_3.0_1694569019352.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_small_finetuned_legal_definitions_longer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_small_finetuned_legal_definitions_longer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_small_finetuned_legal_definitions_longer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/bert-small-finetuned-legal-definitions-longer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_parsed20_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_parsed20_en.md new file mode 100644 index 00000000000000..7ba28d136dcab8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_parsed20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_small_finetuned_parsed20 BertEmbeddings from muhtasham +author: John Snow Labs +name: bert_small_finetuned_parsed20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_small_finetuned_parsed20` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_parsed20_en_5.1.1_3.0_1694573762439.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_parsed20_en_5.1.1_3.0_1694573762439.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_small_finetuned_parsed20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_small_finetuned_parsed20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_small_finetuned_parsed20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/bert-small-finetuned-parsed20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_parsed_longer100_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_parsed_longer100_en.md new file mode 100644 index 00000000000000..3049691293e264 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_parsed_longer100_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_small_finetuned_parsed_longer100 BertEmbeddings from muhtasham +author: John Snow Labs +name: bert_small_finetuned_parsed_longer100 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_small_finetuned_parsed_longer100` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_parsed_longer100_en_5.1.1_3.0_1694573963051.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_parsed_longer100_en_5.1.1_3.0_1694573963051.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_small_finetuned_parsed_longer100","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_small_finetuned_parsed_longer100", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_small_finetuned_parsed_longer100| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/bert-small-finetuned-parsed-longer100 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_small_historic_multilingual_cased_xx.md b/docs/_posts/ahmedlone127/2023-09-13-bert_small_historic_multilingual_cased_xx.md new file mode 100644 index 00000000000000..0fe24dcc60b249 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_small_historic_multilingual_cased_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_small_historic_multilingual_cased BertEmbeddings from dbmdz +author: John Snow Labs +name: bert_small_historic_multilingual_cased +date: 2023-09-13 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_small_historic_multilingual_cased` is a Multilingual model originally trained by dbmdz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_small_historic_multilingual_cased_xx_5.1.1_3.0_1694599387528.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_small_historic_multilingual_cased_xx_5.1.1_3.0_1694599387528.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_small_historic_multilingual_cased","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_small_historic_multilingual_cased", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_small_historic_multilingual_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|109.5 MB| + +## References + +https://huggingface.co/dbmdz/bert-small-historic-multilingual-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_telugu_23_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_telugu_23_en.md new file mode 100644 index 00000000000000..ce4153198ca85c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_telugu_23_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_telugu_23 BertEmbeddings from swadesh7 +author: John Snow Labs +name: bert_telugu_23 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_telugu_23` is a English model originally trained by swadesh7. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_telugu_23_en_5.1.1_3.0_1694596933667.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_telugu_23_en_5.1.1_3.0_1694596933667.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_telugu_23","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_telugu_23", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_telugu_23| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.1 MB| + +## References + +https://huggingface.co/swadesh7/bert_telugu_23 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_tiny_standard_bahasa_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_tiny_standard_bahasa_cased_en.md new file mode 100644 index 00000000000000..9d2f7d91d43067 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_tiny_standard_bahasa_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_tiny_standard_bahasa_cased BertEmbeddings from mesolitica +author: John Snow Labs +name: bert_tiny_standard_bahasa_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tiny_standard_bahasa_cased` is a English model originally trained by mesolitica. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tiny_standard_bahasa_cased_en_5.1.1_3.0_1694586694738.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tiny_standard_bahasa_cased_en_5.1.1_3.0_1694586694738.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_tiny_standard_bahasa_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_tiny_standard_bahasa_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tiny_standard_bahasa_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|61.5 MB| + +## References + +https://huggingface.co/mesolitica/bert-tiny-standard-bahasa-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_2_h_128_a_2_cord19_200616_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_2_h_128_a_2_cord19_200616_en.md new file mode 100644 index 00000000000000..64cf2614b5e33e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_2_h_128_a_2_cord19_200616_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_uncased_l_2_h_128_a_2_cord19_200616 BertEmbeddings from aodiniz +author: John Snow Labs +name: bert_uncased_l_2_h_128_a_2_cord19_200616 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_uncased_l_2_h_128_a_2_cord19_200616` is a English model originally trained by aodiniz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_uncased_l_2_h_128_a_2_cord19_200616_en_5.1.1_3.0_1694580732283.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_uncased_l_2_h_128_a_2_cord19_200616_en_5.1.1_3.0_1694580732283.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_uncased_l_2_h_128_a_2_cord19_200616","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_uncased_l_2_h_128_a_2_cord19_200616", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_uncased_l_2_h_128_a_2_cord19_200616| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.6 MB| + +## References + +https://huggingface.co/aodiniz/bert_uncased_L-2_H-128_A-2_cord19-200616 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_4_h_256_a_4_cord19_200616_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_4_h_256_a_4_cord19_200616_en.md new file mode 100644 index 00000000000000..c970369a6154e3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_4_h_256_a_4_cord19_200616_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_uncased_l_4_h_256_a_4_cord19_200616 BertEmbeddings from aodiniz +author: John Snow Labs +name: bert_uncased_l_4_h_256_a_4_cord19_200616 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_uncased_l_4_h_256_a_4_cord19_200616` is a English model originally trained by aodiniz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_uncased_l_4_h_256_a_4_cord19_200616_en_5.1.1_3.0_1694580897916.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_uncased_l_4_h_256_a_4_cord19_200616_en_5.1.1_3.0_1694580897916.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_uncased_l_4_h_256_a_4_cord19_200616","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_uncased_l_4_h_256_a_4_cord19_200616", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_uncased_l_4_h_256_a_4_cord19_200616| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|41.9 MB| + +## References + +https://huggingface.co/aodiniz/bert_uncased_L-4_H-256_A-4_cord19-200616 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_4_h_768_a_12_cord19_200616_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_4_h_768_a_12_cord19_200616_en.md new file mode 100644 index 00000000000000..e07948da9a1681 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_uncased_l_4_h_768_a_12_cord19_200616_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_uncased_l_4_h_768_a_12_cord19_200616 BertEmbeddings from aodiniz +author: John Snow Labs +name: bert_uncased_l_4_h_768_a_12_cord19_200616 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_uncased_l_4_h_768_a_12_cord19_200616` is a English model originally trained by aodiniz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_uncased_l_4_h_768_a_12_cord19_200616_en_5.1.1_3.0_1694581077990.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_uncased_l_4_h_768_a_12_cord19_200616_en_5.1.1_3.0_1694581077990.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_uncased_l_4_h_768_a_12_cord19_200616","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_uncased_l_4_h_768_a_12_cord19_200616", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_uncased_l_4_h_768_a_12_cord19_200616| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|194.4 MB| + +## References + +https://huggingface.co/aodiniz/bert_uncased_L-4_H-768_A-12_cord19-200616 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_web_bulgarian_bg.md b/docs/_posts/ahmedlone127/2023-09-13-bert_web_bulgarian_bg.md new file mode 100644 index 00000000000000..5dd4d50c9edd33 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_web_bulgarian_bg.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Bulgarian bert_web_bulgarian BertEmbeddings from usmiva +author: John Snow Labs +name: bert_web_bulgarian +date: 2023-09-13 +tags: [bert, bg, open_source, fill_mask, onnx] +task: Embeddings +language: bg +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_web_bulgarian` is a Bulgarian model originally trained by usmiva. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_web_bulgarian_bg_5.1.1_3.0_1694590200921.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_web_bulgarian_bg_5.1.1_3.0_1694590200921.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_web_bulgarian","bg") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_web_bulgarian", "bg") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_web_bulgarian| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|bg| +|Size:|406.9 MB| + +## References + +https://huggingface.co/usmiva/bert-web-bg \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bertbek_news_big_cased_uz.md b/docs/_posts/ahmedlone127/2023-09-13-bertbek_news_big_cased_uz.md new file mode 100644 index 00000000000000..70e811cdbd63a1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bertbek_news_big_cased_uz.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Uzbek bertbek_news_big_cased BertEmbeddings from elmurod1202 +author: John Snow Labs +name: bertbek_news_big_cased +date: 2023-09-13 +tags: [bert, uz, open_source, fill_mask, onnx] +task: Embeddings +language: uz +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertbek_news_big_cased` is a Uzbek model originally trained by elmurod1202. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertbek_news_big_cased_uz_5.1.1_3.0_1694583891350.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertbek_news_big_cased_uz_5.1.1_3.0_1694583891350.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertbek_news_big_cased","uz") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertbek_news_big_cased", "uz") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertbek_news_big_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|uz| +|Size:|405.5 MB| + +## References + +https://huggingface.co/elmurod1202/bertbek-news-big-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bertformalityclassificiation_en.md b/docs/_posts/ahmedlone127/2023-09-13-bertformalityclassificiation_en.md new file mode 100644 index 00000000000000..b0719c19dfc7ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bertformalityclassificiation_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertformalityclassificiation BertEmbeddings from qdenisq +author: John Snow Labs +name: bertformalityclassificiation +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertformalityclassificiation` is a English model originally trained by qdenisq. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertformalityclassificiation_en_5.1.1_3.0_1694565715751.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertformalityclassificiation_en_5.1.1_3.0_1694565715751.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertformalityclassificiation","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertformalityclassificiation", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertformalityclassificiation| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/qdenisq/BertFormalityClassificiation \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bertimbau_legal_pt.md b/docs/_posts/ahmedlone127/2023-09-13-bertimbau_legal_pt.md new file mode 100644 index 00000000000000..6bfe1d93037752 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bertimbau_legal_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese bertimbau_legal BertEmbeddings from mynoguti +author: John Snow Labs +name: bertimbau_legal +date: 2023-09-13 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertimbau_legal` is a Portuguese model originally trained by mynoguti. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertimbau_legal_pt_5.1.1_3.0_1694571645436.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertimbau_legal_pt_5.1.1_3.0_1694571645436.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertimbau_legal","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertimbau_legal", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertimbau_legal| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|405.9 MB| + +## References + +https://huggingface.co/mynoguti/BERTimbau_Legal \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bertjewdialdataallqonly09_en.md b/docs/_posts/ahmedlone127/2023-09-13-bertjewdialdataallqonly09_en.md new file mode 100644 index 00000000000000..39b35de7a1c1b4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bertjewdialdataallqonly09_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertjewdialdataallqonly09 BertEmbeddings from Jeska +author: John Snow Labs +name: bertjewdialdataallqonly09 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertjewdialdataallqonly09` is a English model originally trained by Jeska. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly09_en_5.1.1_3.0_1694563274040.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertjewdialdataallqonly09_en_5.1.1_3.0_1694563274040.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertjewdialdataallqonly09","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertjewdialdataallqonly09", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertjewdialdataallqonly09| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/Jeska/BertjeWDialDataALLQonly09 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bertjewdialdataqa20k_en.md b/docs/_posts/ahmedlone127/2023-09-13-bertjewdialdataqa20k_en.md new file mode 100644 index 00000000000000..c0589fdb8f0967 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bertjewdialdataqa20k_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertjewdialdataqa20k BertEmbeddings from Jeska +author: John Snow Labs +name: bertjewdialdataqa20k +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertjewdialdataqa20k` is a English model originally trained by Jeska. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertjewdialdataqa20k_en_5.1.1_3.0_1694563599531.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertjewdialdataqa20k_en_5.1.1_3.0_1694563599531.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertjewdialdataqa20k","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertjewdialdataqa20k", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertjewdialdataqa20k| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/Jeska/BertjeWDialDataQA20k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-beto_chile_politico_1990_2019_es.md b/docs/_posts/ahmedlone127/2023-09-13-beto_chile_politico_1990_2019_es.md new file mode 100644 index 00000000000000..25b0dce4ddca9e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-beto_chile_politico_1990_2019_es.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Castilian, Spanish beto_chile_politico_1990_2019 BertEmbeddings from lucas-valenzuela-everke +author: John Snow Labs +name: beto_chile_politico_1990_2019 +date: 2023-09-13 +tags: [bert, es, open_source, fill_mask, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`beto_chile_politico_1990_2019` is a Castilian, Spanish model originally trained by lucas-valenzuela-everke. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/beto_chile_politico_1990_2019_es_5.1.1_3.0_1694593619363.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/beto_chile_politico_1990_2019_es_5.1.1_3.0_1694593619363.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("beto_chile_politico_1990_2019","es") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("beto_chile_politico_1990_2019", "es") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|beto_chile_politico_1990_2019| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|es| +|Size:|409.7 MB| + +## References + +https://huggingface.co/lucas-valenzuela-everke/BETO-chile-politico-1990-2019 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bibert_20_epochs_en.md b/docs/_posts/ahmedlone127/2023-09-13-bibert_20_epochs_en.md new file mode 100644 index 00000000000000..6a39e1db9237cf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bibert_20_epochs_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bibert_20_epochs BertEmbeddings from Embible +author: John Snow Labs +name: bibert_20_epochs +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bibert_20_epochs` is a English model originally trained by Embible. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bibert_20_epochs_en_5.1.1_3.0_1694563935647.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bibert_20_epochs_en_5.1.1_3.0_1694563935647.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bibert_20_epochs","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bibert_20_epochs", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bibert_20_epochs| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|432.5 MB| + +## References + +https://huggingface.co/Embible/bibert-20-epochs \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-biblitbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-biblitbert_en.md new file mode 100644 index 00000000000000..0add19e1696bfe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-biblitbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English biblitbert BertEmbeddings from vppvgit +author: John Snow Labs +name: biblitbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biblitbert` is a English model originally trained by vppvgit. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biblitbert_en_5.1.1_3.0_1694582340360.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biblitbert_en_5.1.1_3.0_1694582340360.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biblitbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biblitbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biblitbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.6 MB| + +## References + +https://huggingface.co/vppvgit/BiblItBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bio_clinicalbert_finetuning_data_en.md b/docs/_posts/ahmedlone127/2023-09-13-bio_clinicalbert_finetuning_data_en.md new file mode 100644 index 00000000000000..53b2f8386e25a9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bio_clinicalbert_finetuning_data_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bio_clinicalbert_finetuning_data BertEmbeddings from Dinithi +author: John Snow Labs +name: bio_clinicalbert_finetuning_data +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bio_clinicalbert_finetuning_data` is a English model originally trained by Dinithi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bio_clinicalbert_finetuning_data_en_5.1.1_3.0_1694589997412.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bio_clinicalbert_finetuning_data_en_5.1.1_3.0_1694589997412.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bio_clinicalbert_finetuning_data","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bio_clinicalbert_finetuning_data", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bio_clinicalbert_finetuning_data| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.3 MB| + +## References + +https://huggingface.co/Dinithi/Bio_ClinicalBERT-finetuning-data \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bioformer_8l_en.md b/docs/_posts/ahmedlone127/2023-09-13-bioformer_8l_en.md new file mode 100644 index 00000000000000..f46bd2eebccb21 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bioformer_8l_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bioformer_8l BertEmbeddings from bioformers +author: John Snow Labs +name: bioformer_8l +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bioformer_8l` is a English model originally trained by bioformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bioformer_8l_en_5.1.1_3.0_1694588927608.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bioformer_8l_en_5.1.1_3.0_1694588927608.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bioformer_8l","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bioformer_8l", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bioformer_8l| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|158.5 MB| + +## References + +https://huggingface.co/bioformers/bioformer-8L \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-biovocabbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-biovocabbert_en.md new file mode 100644 index 00000000000000..c27bf67c5f0b7a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-biovocabbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English biovocabbert BertEmbeddings from osunlp +author: John Snow Labs +name: biovocabbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biovocabbert` is a English model originally trained by osunlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biovocabbert_en_5.1.1_3.0_1694577552564.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biovocabbert_en_5.1.1_3.0_1694577552564.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biovocabbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biovocabbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biovocabbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|352.0 MB| + +## References + +https://huggingface.co/osunlp/BioVocabBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bnbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-bnbert_en.md new file mode 100644 index 00000000000000..06ab195de4a6c7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bnbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bnbert BertEmbeddings from KamrusSamad +author: John Snow Labs +name: bnbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bnbert` is a English model originally trained by KamrusSamad. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bnbert_en_5.1.1_3.0_1694564272370.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bnbert_en_5.1.1_3.0_1694564272370.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bnbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bnbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bnbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|53.8 MB| + +## References + +https://huggingface.co/KamrusSamad/bnbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-btfhbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-btfhbert_en.md new file mode 100644 index 00000000000000..f733c2d13b55ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-btfhbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English btfhbert BertEmbeddings from hsc748NLP +author: John Snow Labs +name: btfhbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`btfhbert` is a English model originally trained by hsc748NLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/btfhbert_en_5.1.1_3.0_1694599994851.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/btfhbert_en_5.1.1_3.0_1694599994851.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("btfhbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("btfhbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|btfhbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/hsc748NLP/BtfhBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-carlbert_webex_en.md b/docs/_posts/ahmedlone127/2023-09-13-carlbert_webex_en.md new file mode 100644 index 00000000000000..411a6b8bd0f3a5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-carlbert_webex_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English carlbert_webex BertEmbeddings from aditeyabaral +author: John Snow Labs +name: carlbert_webex +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`carlbert_webex` is a English model originally trained by aditeyabaral. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/carlbert_webex_en_5.1.1_3.0_1694602888545.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/carlbert_webex_en_5.1.1_3.0_1694602888545.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("carlbert_webex","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("carlbert_webex", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|carlbert_webex| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/aditeyabaral/carlbert-webex \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-carlbert_webex_mlm_aditeyabaral_en.md b/docs/_posts/ahmedlone127/2023-09-13-carlbert_webex_mlm_aditeyabaral_en.md new file mode 100644 index 00000000000000..824c548082c3a4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-carlbert_webex_mlm_aditeyabaral_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English carlbert_webex_mlm_aditeyabaral BertEmbeddings from aditeyabaral +author: John Snow Labs +name: carlbert_webex_mlm_aditeyabaral +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`carlbert_webex_mlm_aditeyabaral` is a English model originally trained by aditeyabaral. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/carlbert_webex_mlm_aditeyabaral_en_5.1.1_3.0_1694606195386.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/carlbert_webex_mlm_aditeyabaral_en_5.1.1_3.0_1694606195386.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("carlbert_webex_mlm_aditeyabaral","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("carlbert_webex_mlm_aditeyabaral", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|carlbert_webex_mlm_aditeyabaral| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/aditeyabaral/carlbert-webex-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-carlbert_webex_mlm_spatial_en.md b/docs/_posts/ahmedlone127/2023-09-13-carlbert_webex_mlm_spatial_en.md new file mode 100644 index 00000000000000..cf439778d38e8a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-carlbert_webex_mlm_spatial_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English carlbert_webex_mlm_spatial BertEmbeddings from aditeyabaral +author: John Snow Labs +name: carlbert_webex_mlm_spatial +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`carlbert_webex_mlm_spatial` is a English model originally trained by aditeyabaral. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/carlbert_webex_mlm_spatial_en_5.1.1_3.0_1694604986013.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/carlbert_webex_mlm_spatial_en_5.1.1_3.0_1694604986013.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("carlbert_webex_mlm_spatial","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("carlbert_webex_mlm_spatial", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|carlbert_webex_mlm_spatial| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/aditeyabaral/carlbert-webex-mlm-spatial \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cdgp_chilean_sign_language_scibert_cloth_en.md b/docs/_posts/ahmedlone127/2023-09-13-cdgp_chilean_sign_language_scibert_cloth_en.md new file mode 100644 index 00000000000000..1faa563f14cc99 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cdgp_chilean_sign_language_scibert_cloth_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cdgp_chilean_sign_language_scibert_cloth BertEmbeddings from AndyChiang +author: John Snow Labs +name: cdgp_chilean_sign_language_scibert_cloth +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cdgp_chilean_sign_language_scibert_cloth` is a English model originally trained by AndyChiang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cdgp_chilean_sign_language_scibert_cloth_en_5.1.1_3.0_1694593597348.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cdgp_chilean_sign_language_scibert_cloth_en_5.1.1_3.0_1694593597348.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cdgp_chilean_sign_language_scibert_cloth","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cdgp_chilean_sign_language_scibert_cloth", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cdgp_chilean_sign_language_scibert_cloth| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.0 MB| + +## References + +https://huggingface.co/AndyChiang/cdgp-csg-scibert-cloth \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cdgp_chilean_sign_language_scibert_dgen_en.md b/docs/_posts/ahmedlone127/2023-09-13-cdgp_chilean_sign_language_scibert_dgen_en.md new file mode 100644 index 00000000000000..aff679a8f5bb12 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cdgp_chilean_sign_language_scibert_dgen_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cdgp_chilean_sign_language_scibert_dgen BertEmbeddings from AndyChiang +author: John Snow Labs +name: cdgp_chilean_sign_language_scibert_dgen +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cdgp_chilean_sign_language_scibert_dgen` is a English model originally trained by AndyChiang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cdgp_chilean_sign_language_scibert_dgen_en_5.1.1_3.0_1694593731248.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cdgp_chilean_sign_language_scibert_dgen_en_5.1.1_3.0_1694593731248.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cdgp_chilean_sign_language_scibert_dgen","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cdgp_chilean_sign_language_scibert_dgen", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cdgp_chilean_sign_language_scibert_dgen| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.0 MB| + +## References + +https://huggingface.co/AndyChiang/cdgp-csg-scibert-dgen \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-childbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-childbert_en.md new file mode 100644 index 00000000000000..d1c973a23cbb25 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-childbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English childbert BertEmbeddings from Aunsiels +author: John Snow Labs +name: childbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`childbert` is a English model originally trained by Aunsiels. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/childbert_en_5.1.1_3.0_1694595021953.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/childbert_en_5.1.1_3.0_1694595021953.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("childbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("childbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|childbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/Aunsiels/ChildBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cms_ext_bio_clinicalbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-cms_ext_bio_clinicalbert_en.md new file mode 100644 index 00000000000000..3f8b71da9b25dd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cms_ext_bio_clinicalbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cms_ext_bio_clinicalbert BertEmbeddings from lowem1 +author: John Snow Labs +name: cms_ext_bio_clinicalbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cms_ext_bio_clinicalbert` is a English model originally trained by lowem1. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cms_ext_bio_clinicalbert_en_5.1.1_3.0_1694584923196.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cms_ext_bio_clinicalbert_en_5.1.1_3.0_1694584923196.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cms_ext_bio_clinicalbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cms_ext_bio_clinicalbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cms_ext_bio_clinicalbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|642.3 MB| + +## References + +https://huggingface.co/lowem1/cms-ext-Bio_ClinicalBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cms_rx_language_correction_en.md b/docs/_posts/ahmedlone127/2023-09-13-cms_rx_language_correction_en.md new file mode 100644 index 00000000000000..8bdd347bb709e6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cms_rx_language_correction_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cms_rx_language_correction BertEmbeddings from lowem1 +author: John Snow Labs +name: cms_rx_language_correction +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cms_rx_language_correction` is a English model originally trained by lowem1. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cms_rx_language_correction_en_5.1.1_3.0_1694599191432.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cms_rx_language_correction_en_5.1.1_3.0_1694599191432.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cms_rx_language_correction","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cms_rx_language_correction", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cms_rx_language_correction| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.1 MB| + +## References + +https://huggingface.co/lowem1/cms-rx-language-correction \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cocodr_base_en.md b/docs/_posts/ahmedlone127/2023-09-13-cocodr_base_en.md new file mode 100644 index 00000000000000..49919d736de2a8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cocodr_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cocodr_base BertEmbeddings from OpenMatch +author: John Snow Labs +name: cocodr_base +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cocodr_base` is a English model originally trained by OpenMatch. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cocodr_base_en_5.1.1_3.0_1694608342393.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cocodr_base_en_5.1.1_3.0_1694608342393.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cocodr_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cocodr_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cocodr_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.4 MB| + +## References + +https://huggingface.co/OpenMatch/cocodr-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cocodr_large_en.md b/docs/_posts/ahmedlone127/2023-09-13-cocodr_large_en.md new file mode 100644 index 00000000000000..1e9f45976a95c9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cocodr_large_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cocodr_large BertEmbeddings from OpenMatch +author: John Snow Labs +name: cocodr_large +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cocodr_large` is a English model originally trained by OpenMatch. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cocodr_large_en_5.1.1_3.0_1694609184010.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cocodr_large_en_5.1.1_3.0_1694609184010.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cocodr_large","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cocodr_large", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cocodr_large| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/OpenMatch/cocodr-large \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-condenser_bert_large_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-condenser_bert_large_uncased_en.md new file mode 100644 index 00000000000000..39e5eeba0d1d56 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-condenser_bert_large_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English condenser_bert_large_uncased BertEmbeddings from ffgcc +author: John Snow Labs +name: condenser_bert_large_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`condenser_bert_large_uncased` is a English model originally trained by ffgcc. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/condenser_bert_large_uncased_en_5.1.1_3.0_1694592731465.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/condenser_bert_large_uncased_en_5.1.1_3.0_1694592731465.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("condenser_bert_large_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("condenser_bert_large_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|condenser_bert_large_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/ffgcc/condenser-bert-large-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-covid_bert_base_en.md b/docs/_posts/ahmedlone127/2023-09-13-covid_bert_base_en.md new file mode 100644 index 00000000000000..88d1d6c9e36ad2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-covid_bert_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English covid_bert_base BertEmbeddings from deepset +author: John Snow Labs +name: covid_bert_base +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`covid_bert_base` is a English model originally trained by deepset. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/covid_bert_base_en_5.1.1_3.0_1694601364340.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/covid_bert_base_en_5.1.1_3.0_1694601364340.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("covid_bert_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("covid_bert_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|covid_bert_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/deepset/covid_bert_base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cubetest_zh.md b/docs/_posts/ahmedlone127/2023-09-13-cubetest_zh.md new file mode 100644 index 00000000000000..bc289bd5bd2a15 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cubetest_zh.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Chinese cubetest BertEmbeddings from Cube +author: John Snow Labs +name: cubetest +date: 2023-09-13 +tags: [bert, zh, open_source, fill_mask, onnx] +task: Embeddings +language: zh +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cubetest` is a Chinese model originally trained by Cube. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cubetest_zh_5.1.1_3.0_1694565779939.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cubetest_zh_5.1.1_3.0_1694565779939.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cubetest","zh") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cubetest", "zh") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cubetest| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|zh| +|Size:|259.0 MB| + +## References + +https://huggingface.co/Cube/cubetest \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-custom_legalbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-custom_legalbert_en.md new file mode 100644 index 00000000000000..28074d6aa750f4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-custom_legalbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English custom_legalbert BertEmbeddings from casehold +author: John Snow Labs +name: custom_legalbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`custom_legalbert` is a English model originally trained by casehold. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/custom_legalbert_en_5.1.1_3.0_1694597799860.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/custom_legalbert_en_5.1.1_3.0_1694597799860.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("custom_legalbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("custom_legalbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|custom_legalbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|411.6 MB| + +## References + +https://huggingface.co/casehold/custom-legalbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dal_bert_finetuned_medical_v3_en.md b/docs/_posts/ahmedlone127/2023-09-13-dal_bert_finetuned_medical_v3_en.md new file mode 100644 index 00000000000000..3ac4fa97d1b892 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dal_bert_finetuned_medical_v3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dal_bert_finetuned_medical_v3 BertEmbeddings from IRI2070 +author: John Snow Labs +name: dal_bert_finetuned_medical_v3 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dal_bert_finetuned_medical_v3` is a English model originally trained by IRI2070. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dal_bert_finetuned_medical_v3_en_5.1.1_3.0_1694601639385.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dal_bert_finetuned_medical_v3_en_5.1.1_3.0_1694601639385.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dal_bert_finetuned_medical_v3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dal_bert_finetuned_medical_v3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dal_bert_finetuned_medical_v3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|432.1 MB| + +## References + +https://huggingface.co/IRI2070/dal-bert-finetuned-medical-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-danish_bert_botxo_da.md b/docs/_posts/ahmedlone127/2023-09-13-danish_bert_botxo_da.md new file mode 100644 index 00000000000000..dfaf7571f7bca9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-danish_bert_botxo_da.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Danish danish_bert_botxo BertEmbeddings from Maltehb +author: John Snow Labs +name: danish_bert_botxo +date: 2023-09-13 +tags: [bert, da, open_source, fill_mask, onnx] +task: Embeddings +language: da +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`danish_bert_botxo` is a Danish model originally trained by Maltehb. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/danish_bert_botxo_da_5.1.1_3.0_1694567180720.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/danish_bert_botxo_da_5.1.1_3.0_1694567180720.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("danish_bert_botxo","da") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("danish_bert_botxo", "da") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|danish_bert_botxo| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|da| +|Size:|412.3 MB| + +## References + +https://huggingface.co/Maltehb/danish-bert-botxo \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-danish_legal_bert_base_da.md b/docs/_posts/ahmedlone127/2023-09-13-danish_legal_bert_base_da.md new file mode 100644 index 00000000000000..bddeda23e86fba --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-danish_legal_bert_base_da.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Danish danish_legal_bert_base BertEmbeddings from coastalcph +author: John Snow Labs +name: danish_legal_bert_base +date: 2023-09-13 +tags: [bert, da, open_source, fill_mask, onnx] +task: Embeddings +language: da +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`danish_legal_bert_base` is a Danish model originally trained by coastalcph. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/danish_legal_bert_base_da_5.1.1_3.0_1694589626739.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/danish_legal_bert_base_da_5.1.1_3.0_1694589626739.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("danish_legal_bert_base","da") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("danish_legal_bert_base", "da") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|danish_legal_bert_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|da| +|Size:|411.6 MB| + +## References + +https://huggingface.co/coastalcph/danish-legal-bert-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-datafinder_scibert_dutch_queries_en.md b/docs/_posts/ahmedlone127/2023-09-13-datafinder_scibert_dutch_queries_en.md new file mode 100644 index 00000000000000..67f6b75cc25669 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-datafinder_scibert_dutch_queries_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English datafinder_scibert_dutch_queries BertEmbeddings from viswavi +author: John Snow Labs +name: datafinder_scibert_dutch_queries +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`datafinder_scibert_dutch_queries` is a English model originally trained by viswavi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/datafinder_scibert_dutch_queries_en_5.1.1_3.0_1694597768652.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/datafinder_scibert_dutch_queries_en_5.1.1_3.0_1694597768652.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("datafinder_scibert_dutch_queries","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("datafinder_scibert_dutch_queries", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|datafinder_scibert_dutch_queries| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.0 MB| + +## References + +https://huggingface.co/viswavi/datafinder-scibert-nl-queries \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dbert_ko.md b/docs/_posts/ahmedlone127/2023-09-13-dbert_ko.md new file mode 100644 index 00000000000000..1fc439d9070a33 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dbert_ko.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Korean dbert BertEmbeddings from baikal-nlp +author: John Snow Labs +name: dbert +date: 2023-09-13 +tags: [bert, ko, open_source, fill_mask, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dbert` is a Korean model originally trained by baikal-nlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dbert_ko_5.1.1_3.0_1694601729093.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dbert_ko_5.1.1_3.0_1694601729093.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dbert","ko") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dbert", "ko") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ko| +|Size:|421.2 MB| + +## References + +https://huggingface.co/baikal-nlp/dbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dce_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-dce_bert_en.md new file mode 100644 index 00000000000000..978b1aac7c81b9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dce_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dce_bert BertEmbeddings from Daniel-Saeedi +author: John Snow Labs +name: dce_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dce_bert` is a English model originally trained by Daniel-Saeedi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dce_bert_en_5.1.1_3.0_1694588634755.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dce_bert_en_5.1.1_3.0_1694588634755.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dce_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dce_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dce_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Daniel-Saeedi/DCE_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-defsent_bert_base_uncased_cls_en.md b/docs/_posts/ahmedlone127/2023-09-13-defsent_bert_base_uncased_cls_en.md new file mode 100644 index 00000000000000..ca04ab6fe345a9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-defsent_bert_base_uncased_cls_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English defsent_bert_base_uncased_cls BertEmbeddings from cl-nagoya +author: John Snow Labs +name: defsent_bert_base_uncased_cls +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`defsent_bert_base_uncased_cls` is a English model originally trained by cl-nagoya. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/defsent_bert_base_uncased_cls_en_5.1.1_3.0_1694591623874.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/defsent_bert_base_uncased_cls_en_5.1.1_3.0_1694591623874.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("defsent_bert_base_uncased_cls","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("defsent_bert_base_uncased_cls", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|defsent_bert_base_uncased_cls| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/cl-nagoya/defsent-bert-base-uncased-cls \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-defsent_bert_base_uncased_max_en.md b/docs/_posts/ahmedlone127/2023-09-13-defsent_bert_base_uncased_max_en.md new file mode 100644 index 00000000000000..5bd65bbbf6e250 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-defsent_bert_base_uncased_max_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English defsent_bert_base_uncased_max BertEmbeddings from cl-nagoya +author: John Snow Labs +name: defsent_bert_base_uncased_max +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`defsent_bert_base_uncased_max` is a English model originally trained by cl-nagoya. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/defsent_bert_base_uncased_max_en_5.1.1_3.0_1694591807875.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/defsent_bert_base_uncased_max_en_5.1.1_3.0_1694591807875.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("defsent_bert_base_uncased_max","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("defsent_bert_base_uncased_max", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|defsent_bert_base_uncased_max| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/cl-nagoya/defsent-bert-base-uncased-max \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-defsent_bert_base_uncased_mean_en.md b/docs/_posts/ahmedlone127/2023-09-13-defsent_bert_base_uncased_mean_en.md new file mode 100644 index 00000000000000..299e8f7b886645 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-defsent_bert_base_uncased_mean_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English defsent_bert_base_uncased_mean BertEmbeddings from cl-nagoya +author: John Snow Labs +name: defsent_bert_base_uncased_mean +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`defsent_bert_base_uncased_mean` is a English model originally trained by cl-nagoya. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/defsent_bert_base_uncased_mean_en_5.1.1_3.0_1694591979137.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/defsent_bert_base_uncased_mean_en_5.1.1_3.0_1694591979137.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("defsent_bert_base_uncased_mean","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("defsent_bert_base_uncased_mean", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|defsent_bert_base_uncased_mean| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/cl-nagoya/defsent-bert-base-uncased-mean \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-defsent_bert_large_uncased_cls_en.md b/docs/_posts/ahmedlone127/2023-09-13-defsent_bert_large_uncased_cls_en.md new file mode 100644 index 00000000000000..775e11f666de9f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-defsent_bert_large_uncased_cls_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English defsent_bert_large_uncased_cls BertEmbeddings from cl-nagoya +author: John Snow Labs +name: defsent_bert_large_uncased_cls +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`defsent_bert_large_uncased_cls` is a English model originally trained by cl-nagoya. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/defsent_bert_large_uncased_cls_en_5.1.1_3.0_1694592276372.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/defsent_bert_large_uncased_cls_en_5.1.1_3.0_1694592276372.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("defsent_bert_large_uncased_cls","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("defsent_bert_large_uncased_cls", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|defsent_bert_large_uncased_cls| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/cl-nagoya/defsent-bert-large-uncased-cls \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-defsent_bert_large_uncased_max_en.md b/docs/_posts/ahmedlone127/2023-09-13-defsent_bert_large_uncased_max_en.md new file mode 100644 index 00000000000000..e1191204040027 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-defsent_bert_large_uncased_max_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English defsent_bert_large_uncased_max BertEmbeddings from cl-nagoya +author: John Snow Labs +name: defsent_bert_large_uncased_max +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`defsent_bert_large_uncased_max` is a English model originally trained by cl-nagoya. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/defsent_bert_large_uncased_max_en_5.1.1_3.0_1694592601643.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/defsent_bert_large_uncased_max_en_5.1.1_3.0_1694592601643.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("defsent_bert_large_uncased_max","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("defsent_bert_large_uncased_max", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|defsent_bert_large_uncased_max| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/cl-nagoya/defsent-bert-large-uncased-max \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-defsent_bert_large_uncased_mean_en.md b/docs/_posts/ahmedlone127/2023-09-13-defsent_bert_large_uncased_mean_en.md new file mode 100644 index 00000000000000..1fef36d1678c19 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-defsent_bert_large_uncased_mean_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English defsent_bert_large_uncased_mean BertEmbeddings from cl-nagoya +author: John Snow Labs +name: defsent_bert_large_uncased_mean +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`defsent_bert_large_uncased_mean` is a English model originally trained by cl-nagoya. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/defsent_bert_large_uncased_mean_en_5.1.1_3.0_1694592879544.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/defsent_bert_large_uncased_mean_en_5.1.1_3.0_1694592879544.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("defsent_bert_large_uncased_mean","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("defsent_bert_large_uncased_mean", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|defsent_bert_large_uncased_mean| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/cl-nagoya/defsent-bert-large-uncased-mean \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-detox_kcbert_base_en.md b/docs/_posts/ahmedlone127/2023-09-13-detox_kcbert_base_en.md new file mode 100644 index 00000000000000..49044e9a1c704f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-detox_kcbert_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English detox_kcbert_base BertEmbeddings from beomi +author: John Snow Labs +name: detox_kcbert_base +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`detox_kcbert_base` is a English model originally trained by beomi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/detox_kcbert_base_en_5.1.1_3.0_1694586418562.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/detox_kcbert_base_en_5.1.1_3.0_1694586418562.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("detox_kcbert_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("detox_kcbert_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|detox_kcbert_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.2 MB| + +## References + +https://huggingface.co/beomi/detox-kcbert-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-distil_clinicalbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-distil_clinicalbert_en.md new file mode 100644 index 00000000000000..97ae8e2fea4c56 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-distil_clinicalbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distil_clinicalbert BertEmbeddings from nlpie +author: John Snow Labs +name: distil_clinicalbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distil_clinicalbert` is a English model originally trained by nlpie. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distil_clinicalbert_en_5.1.1_3.0_1694606505752.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distil_clinicalbert_en_5.1.1_3.0_1694606505752.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("distil_clinicalbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("distil_clinicalbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distil_clinicalbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|244.5 MB| + +## References + +https://huggingface.co/nlpie/distil-clinicalbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-distilbertu_base_cased_0.0_en.md b/docs/_posts/ahmedlone127/2023-09-13-distilbertu_base_cased_0.0_en.md new file mode 100644 index 00000000000000..b07af59d141fe7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-distilbertu_base_cased_0.0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbertu_base_cased_0.0 BertEmbeddings from amitness +author: John Snow Labs +name: distilbertu_base_cased_0.0 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbertu_base_cased_0.0` is a English model originally trained by amitness. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbertu_base_cased_0.0_en_5.1.1_3.0_1694584748299.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbertu_base_cased_0.0_en_5.1.1_3.0_1694584748299.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("distilbertu_base_cased_0.0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("distilbertu_base_cased_0.0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbertu_base_cased_0.0| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|470.4 MB| + +## References + +https://huggingface.co/amitness/distilbertu-base-cased-0.0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-distilbertu_base_cased_0.5_en.md b/docs/_posts/ahmedlone127/2023-09-13-distilbertu_base_cased_0.5_en.md new file mode 100644 index 00000000000000..e775e2a5dc48fe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-distilbertu_base_cased_0.5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbertu_base_cased_0.5 BertEmbeddings from amitness +author: John Snow Labs +name: distilbertu_base_cased_0.5 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbertu_base_cased_0.5` is a English model originally trained by amitness. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbertu_base_cased_0.5_en_5.1.1_3.0_1694584594357.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbertu_base_cased_0.5_en_5.1.1_3.0_1694584594357.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("distilbertu_base_cased_0.5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("distilbertu_base_cased_0.5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbertu_base_cased_0.5| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|469.9 MB| + +## References + +https://huggingface.co/amitness/distilbertu-base-cased-0.5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-distilbertu_base_cased_1.0_en.md b/docs/_posts/ahmedlone127/2023-09-13-distilbertu_base_cased_1.0_en.md new file mode 100644 index 00000000000000..50e164eb9a64dc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-distilbertu_base_cased_1.0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbertu_base_cased_1.0 BertEmbeddings from amitness +author: John Snow Labs +name: distilbertu_base_cased_1.0 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbertu_base_cased_1.0` is a English model originally trained by amitness. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbertu_base_cased_1.0_en_5.1.1_3.0_1694585114516.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbertu_base_cased_1.0_en_5.1.1_3.0_1694585114516.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("distilbertu_base_cased_1.0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("distilbertu_base_cased_1.0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbertu_base_cased_1.0| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|470.4 MB| + +## References + +https://huggingface.co/amitness/distilbertu-base-cased-1.0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-distilbertu_base_cased_anneal_en.md b/docs/_posts/ahmedlone127/2023-09-13-distilbertu_base_cased_anneal_en.md new file mode 100644 index 00000000000000..52f56521e1552b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-distilbertu_base_cased_anneal_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbertu_base_cased_anneal BertEmbeddings from amitness +author: John Snow Labs +name: distilbertu_base_cased_anneal +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbertu_base_cased_anneal` is a English model originally trained by amitness. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbertu_base_cased_anneal_en_5.1.1_3.0_1694585303981.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbertu_base_cased_anneal_en_5.1.1_3.0_1694585303981.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("distilbertu_base_cased_anneal","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("distilbertu_base_cased_anneal", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbertu_base_cased_anneal| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|470.4 MB| + +## References + +https://huggingface.co/amitness/distilbertu-base-cased-anneal \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-distilbertu_base_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-distilbertu_base_cased_en.md new file mode 100644 index 00000000000000..b578da8e30f0b3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-distilbertu_base_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbertu_base_cased BertEmbeddings from amitness +author: John Snow Labs +name: distilbertu_base_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbertu_base_cased` is a English model originally trained by amitness. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbertu_base_cased_en_5.1.1_3.0_1694580349235.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbertu_base_cased_en_5.1.1_3.0_1694580349235.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("distilbertu_base_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("distilbertu_base_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbertu_base_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.6 MB| + +## References + +https://huggingface.co/amitness/distilbertu-base-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dlub_2022_mlm_full_muug_en.md b/docs/_posts/ahmedlone127/2023-09-13-dlub_2022_mlm_full_muug_en.md new file mode 100644 index 00000000000000..6236a746a7f382 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dlub_2022_mlm_full_muug_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dlub_2022_mlm_full_muug BertEmbeddings from Muug +author: John Snow Labs +name: dlub_2022_mlm_full_muug +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dlub_2022_mlm_full_muug` is a English model originally trained by Muug. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dlub_2022_mlm_full_muug_en_5.1.1_3.0_1694586598576.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dlub_2022_mlm_full_muug_en_5.1.1_3.0_1694586598576.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dlub_2022_mlm_full_muug","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dlub_2022_mlm_full_muug", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dlub_2022_mlm_full_muug| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.6 MB| + +## References + +https://huggingface.co/Muug/dlub-2022-mlm-full \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-drug_combinations_lm_pubmedbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-drug_combinations_lm_pubmedbert_en.md new file mode 100644 index 00000000000000..726036d9ebe635 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-drug_combinations_lm_pubmedbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English drug_combinations_lm_pubmedbert BertEmbeddings from allenai +author: John Snow Labs +name: drug_combinations_lm_pubmedbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`drug_combinations_lm_pubmedbert` is a English model originally trained by allenai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/drug_combinations_lm_pubmedbert_en_5.1.1_3.0_1694595258369.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/drug_combinations_lm_pubmedbert_en_5.1.1_3.0_1694595258369.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("drug_combinations_lm_pubmedbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("drug_combinations_lm_pubmedbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|drug_combinations_lm_pubmedbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.9 MB| + +## References + +https://huggingface.co/allenai/drug_combinations_lm_pubmedbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dummy_model_jammm1412_en.md b/docs/_posts/ahmedlone127/2023-09-13-dummy_model_jammm1412_en.md new file mode 100644 index 00000000000000..05043ba2285adc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dummy_model_jammm1412_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dummy_model_jammm1412 BertEmbeddings from jammm1412 +author: John Snow Labs +name: dummy_model_jammm1412 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dummy_model_jammm1412` is a English model originally trained by jammm1412. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dummy_model_jammm1412_en_5.1.1_3.0_1694590400887.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dummy_model_jammm1412_en_5.1.1_3.0_1694590400887.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dummy_model_jammm1412","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dummy_model_jammm1412", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dummy_model_jammm1412| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/jammm1412/dummy-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dummy_model_yangwooko_en.md b/docs/_posts/ahmedlone127/2023-09-13-dummy_model_yangwooko_en.md new file mode 100644 index 00000000000000..7ba86749517693 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dummy_model_yangwooko_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dummy_model_yangwooko BertEmbeddings from yangwooko +author: John Snow Labs +name: dummy_model_yangwooko +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dummy_model_yangwooko` is a English model originally trained by yangwooko. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dummy_model_yangwooko_en_5.1.1_3.0_1694592058468.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dummy_model_yangwooko_en_5.1.1_3.0_1694592058468.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dummy_model_yangwooko","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dummy_model_yangwooko", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dummy_model_yangwooko| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/yangwooko/dummy-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-eng_en.md b/docs/_posts/ahmedlone127/2023-09-13-eng_en.md new file mode 100644 index 00000000000000..49ae10b89a5e1e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-eng_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English eng BertEmbeddings from willemjan +author: John Snow Labs +name: eng +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`eng` is a English model originally trained by willemjan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/eng_en_5.1.1_3.0_1694584220308.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/eng_en_5.1.1_3.0_1694584220308.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("eng","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("eng", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|eng| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/willemjan/eng \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-esci_mlm_us_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-esci_mlm_us_bert_base_uncased_en.md new file mode 100644 index 00000000000000..fb350cc0a49cc4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-esci_mlm_us_bert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English esci_mlm_us_bert_base_uncased BertEmbeddings from spacemanidol +author: John Snow Labs +name: esci_mlm_us_bert_base_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`esci_mlm_us_bert_base_uncased` is a English model originally trained by spacemanidol. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/esci_mlm_us_bert_base_uncased_en_5.1.1_3.0_1694576915321.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/esci_mlm_us_bert_base_uncased_en_5.1.1_3.0_1694576915321.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("esci_mlm_us_bert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("esci_mlm_us_bert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|esci_mlm_us_bert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.5 MB| + +## References + +https://huggingface.co/spacemanidol/esci-mlm-us-bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-film95000bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-film95000bert_base_uncased_en.md new file mode 100644 index 00000000000000..dbc7a7ad03953c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-film95000bert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English film95000bert_base_uncased BertEmbeddings from AmaiaSolaun +author: John Snow Labs +name: film95000bert_base_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`film95000bert_base_uncased` is a English model originally trained by AmaiaSolaun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/film95000bert_base_uncased_en_5.1.1_3.0_1694594888745.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/film95000bert_base_uncased_en_5.1.1_3.0_1694594888745.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("film95000bert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("film95000bert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|film95000bert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/AmaiaSolaun/film95000bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-film98991bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-film98991bert_base_uncased_en.md new file mode 100644 index 00000000000000..6a2666aa9d5ed5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-film98991bert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English film98991bert_base_uncased BertEmbeddings from AmaiaSolaun +author: John Snow Labs +name: film98991bert_base_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`film98991bert_base_uncased` is a English model originally trained by AmaiaSolaun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/film98991bert_base_uncased_en_5.1.1_3.0_1694592232167.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/film98991bert_base_uncased_en_5.1.1_3.0_1694592232167.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("film98991bert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("film98991bert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|film98991bert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/AmaiaSolaun/film98991bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-finbert_lm_finetuned_news_en.md b/docs/_posts/ahmedlone127/2023-09-13-finbert_lm_finetuned_news_en.md new file mode 100644 index 00000000000000..1b746cf42f86bf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-finbert_lm_finetuned_news_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English finbert_lm_finetuned_news BertEmbeddings from HoseinPanahi +author: John Snow Labs +name: finbert_lm_finetuned_news +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finbert_lm_finetuned_news` is a English model originally trained by HoseinPanahi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finbert_lm_finetuned_news_en_5.1.1_3.0_1694585481904.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finbert_lm_finetuned_news_en_5.1.1_3.0_1694585481904.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("finbert_lm_finetuned_news","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("finbert_lm_finetuned_news", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finbert_lm_finetuned_news| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/HoseinPanahi/finbert-lm-finetuned-news \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-finbert_pretrain_tianzhou_en.md b/docs/_posts/ahmedlone127/2023-09-13-finbert_pretrain_tianzhou_en.md new file mode 100644 index 00000000000000..9b5a0a539c6ad5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-finbert_pretrain_tianzhou_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English finbert_pretrain_tianzhou BertEmbeddings from Tianzhou +author: John Snow Labs +name: finbert_pretrain_tianzhou +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finbert_pretrain_tianzhou` is a English model originally trained by Tianzhou. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finbert_pretrain_tianzhou_en_5.1.1_3.0_1694591031769.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finbert_pretrain_tianzhou_en_5.1.1_3.0_1694591031769.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("finbert_pretrain_tianzhou","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("finbert_pretrain_tianzhou", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finbert_pretrain_tianzhou| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Tianzhou/finbert-pretrain \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-finbert_wechsel_korean_en.md b/docs/_posts/ahmedlone127/2023-09-13-finbert_wechsel_korean_en.md new file mode 100644 index 00000000000000..ce009c06b284aa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-finbert_wechsel_korean_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English finbert_wechsel_korean BertEmbeddings from LeverageX +author: John Snow Labs +name: finbert_wechsel_korean +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finbert_wechsel_korean` is a English model originally trained by LeverageX. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finbert_wechsel_korean_en_5.1.1_3.0_1694566000716.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finbert_wechsel_korean_en_5.1.1_3.0_1694566000716.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("finbert_wechsel_korean","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("finbert_wechsel_korean", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finbert_wechsel_korean| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.5 MB| + +## References + +https://huggingface.co/LeverageX/finbert-wechsel-korean \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-first_try_rubert_200_16_16_10ep_en.md b/docs/_posts/ahmedlone127/2023-09-13-first_try_rubert_200_16_16_10ep_en.md new file mode 100644 index 00000000000000..6799aa0211c0ce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-first_try_rubert_200_16_16_10ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English first_try_rubert_200_16_16_10ep BertEmbeddings from sergiyvl +author: John Snow Labs +name: first_try_rubert_200_16_16_10ep +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`first_try_rubert_200_16_16_10ep` is a English model originally trained by sergiyvl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/first_try_rubert_200_16_16_10ep_en_5.1.1_3.0_1694573249695.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/first_try_rubert_200_16_16_10ep_en_5.1.1_3.0_1694573249695.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("first_try_rubert_200_16_16_10ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("first_try_rubert_200_16_16_10ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|first_try_rubert_200_16_16_10ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|664.5 MB| + +## References + +https://huggingface.co/sergiyvl/first_try_RuBERT_200_16_16_10ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-first_try_rubert_200_16_16_25ep_en.md b/docs/_posts/ahmedlone127/2023-09-13-first_try_rubert_200_16_16_25ep_en.md new file mode 100644 index 00000000000000..115a223d9974ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-first_try_rubert_200_16_16_25ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English first_try_rubert_200_16_16_25ep BertEmbeddings from sergiyvl +author: John Snow Labs +name: first_try_rubert_200_16_16_25ep +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`first_try_rubert_200_16_16_25ep` is a English model originally trained by sergiyvl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/first_try_rubert_200_16_16_25ep_en_5.1.1_3.0_1694573484331.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/first_try_rubert_200_16_16_25ep_en_5.1.1_3.0_1694573484331.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("first_try_rubert_200_16_16_25ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("first_try_rubert_200_16_16_25ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|first_try_rubert_200_16_16_25ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|664.5 MB| + +## References + +https://huggingface.co/sergiyvl/first_try_RuBERT_200_16_16_25ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-further_train_2_domain_10_en.md b/docs/_posts/ahmedlone127/2023-09-13-further_train_2_domain_10_en.md new file mode 100644 index 00000000000000..2f17d61ccd58d6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-further_train_2_domain_10_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English further_train_2_domain_10 BertEmbeddings from onlydj96 +author: John Snow Labs +name: further_train_2_domain_10 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`further_train_2_domain_10` is a English model originally trained by onlydj96. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/further_train_2_domain_10_en_5.1.1_3.0_1694592009482.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/further_train_2_domain_10_en_5.1.1_3.0_1694592009482.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("further_train_2_domain_10","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("further_train_2_domain_10", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|further_train_2_domain_10| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|441.2 MB| + +## References + +https://huggingface.co/onlydj96/further_train_2_domain_10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-further_train_2_domain_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-further_train_2_domain_20_en.md new file mode 100644 index 00000000000000..edf83ab4338b87 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-further_train_2_domain_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English further_train_2_domain_20 BertEmbeddings from onlydj96 +author: John Snow Labs +name: further_train_2_domain_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`further_train_2_domain_20` is a English model originally trained by onlydj96. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/further_train_2_domain_20_en_5.1.1_3.0_1694592413273.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/further_train_2_domain_20_en_5.1.1_3.0_1694592413273.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("further_train_2_domain_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("further_train_2_domain_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|further_train_2_domain_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|441.2 MB| + +## References + +https://huggingface.co/onlydj96/further_train_2_domain_20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-further_train_domain_10_en.md b/docs/_posts/ahmedlone127/2023-09-13-further_train_domain_10_en.md new file mode 100644 index 00000000000000..4c3169396a98f1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-further_train_domain_10_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English further_train_domain_10 BertEmbeddings from onlydj96 +author: John Snow Labs +name: further_train_domain_10 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`further_train_domain_10` is a English model originally trained by onlydj96. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/further_train_domain_10_en_5.1.1_3.0_1694588212891.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/further_train_domain_10_en_5.1.1_3.0_1694588212891.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("further_train_domain_10","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("further_train_domain_10", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|further_train_domain_10| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|441.2 MB| + +## References + +https://huggingface.co/onlydj96/further_train_domain_10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-further_train_domain_30_en.md b/docs/_posts/ahmedlone127/2023-09-13-further_train_domain_30_en.md new file mode 100644 index 00000000000000..7a1b3e9de9b684 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-further_train_domain_30_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English further_train_domain_30 BertEmbeddings from onlydj96 +author: John Snow Labs +name: further_train_domain_30 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`further_train_domain_30` is a English model originally trained by onlydj96. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/further_train_domain_30_en_5.1.1_3.0_1694589245637.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/further_train_domain_30_en_5.1.1_3.0_1694589245637.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("further_train_domain_30","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("further_train_domain_30", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|further_train_domain_30| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|441.2 MB| + +## References + +https://huggingface.co/onlydj96/further_train_domain_30 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-further_train_intent_10_en.md b/docs/_posts/ahmedlone127/2023-09-13-further_train_intent_10_en.md new file mode 100644 index 00000000000000..5456cf600ae19f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-further_train_intent_10_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English further_train_intent_10 BertEmbeddings from onlydj96 +author: John Snow Labs +name: further_train_intent_10 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`further_train_intent_10` is a English model originally trained by onlydj96. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/further_train_intent_10_en_5.1.1_3.0_1694589450357.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/further_train_intent_10_en_5.1.1_3.0_1694589450357.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("further_train_intent_10","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("further_train_intent_10", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|further_train_intent_10| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|441.2 MB| + +## References + +https://huggingface.co/onlydj96/further_train_intent_10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-futuretod_base_v1.0_en.md b/docs/_posts/ahmedlone127/2023-09-13-futuretod_base_v1.0_en.md new file mode 100644 index 00000000000000..5ca5e0339d0fa5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-futuretod_base_v1.0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English futuretod_base_v1.0 BertEmbeddings from AndrewZeng +author: John Snow Labs +name: futuretod_base_v1.0 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`futuretod_base_v1.0` is a English model originally trained by AndrewZeng. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/futuretod_base_v1.0_en_5.1.1_3.0_1694594942260.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/futuretod_base_v1.0_en_5.1.1_3.0_1694594942260.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("futuretod_base_v1.0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("futuretod_base_v1.0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|futuretod_base_v1.0| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.6 MB| + +## References + +https://huggingface.co/AndrewZeng/futuretod-base-v1.0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-german_financial_statements_bert_de.md b/docs/_posts/ahmedlone127/2023-09-13-german_financial_statements_bert_de.md new file mode 100644 index 00000000000000..b46aa402b3c54c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-german_financial_statements_bert_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German german_financial_statements_bert BertEmbeddings from fabianrausch +author: John Snow Labs +name: german_financial_statements_bert +date: 2023-09-13 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`german_financial_statements_bert` is a German model originally trained by fabianrausch. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/german_financial_statements_bert_de_5.1.1_3.0_1694604897426.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/german_financial_statements_bert_de_5.1.1_3.0_1694604897426.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("german_financial_statements_bert","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("german_financial_statements_bert", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|german_financial_statements_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|406.9 MB| + +## References + +https://huggingface.co/fabianrausch/german-financial-statements-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-greek_media_bert_base_uncased_el.md b/docs/_posts/ahmedlone127/2023-09-13-greek_media_bert_base_uncased_el.md new file mode 100644 index 00000000000000..d47f66ca464251 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-greek_media_bert_base_uncased_el.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Modern Greek (1453-) greek_media_bert_base_uncased BertEmbeddings from dimitriz +author: John Snow Labs +name: greek_media_bert_base_uncased +date: 2023-09-13 +tags: [bert, el, open_source, fill_mask, onnx] +task: Embeddings +language: el +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`greek_media_bert_base_uncased` is a Modern Greek (1453-) model originally trained by dimitriz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/greek_media_bert_base_uncased_el_5.1.1_3.0_1694581631448.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/greek_media_bert_base_uncased_el_5.1.1_3.0_1694581631448.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("greek_media_bert_base_uncased","el") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("greek_media_bert_base_uncased", "el") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|greek_media_bert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|el| +|Size:|420.8 MB| + +## References + +https://huggingface.co/dimitriz/greek-media-bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-gujibert_jian_fan_en.md b/docs/_posts/ahmedlone127/2023-09-13-gujibert_jian_fan_en.md new file mode 100644 index 00000000000000..5e085a7a7f5f49 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-gujibert_jian_fan_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English gujibert_jian_fan BertEmbeddings from hsc748NLP +author: John Snow Labs +name: gujibert_jian_fan +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`gujibert_jian_fan` is a English model originally trained by hsc748NLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gujibert_jian_fan_en_5.1.1_3.0_1694602793200.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gujibert_jian_fan_en_5.1.1_3.0_1694602793200.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("gujibert_jian_fan","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("gujibert_jian_fan", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gujibert_jian_fan| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|420.3 MB| + +## References + +https://huggingface.co/hsc748NLP/GujiBERT_jian_fan \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-gujiroberta_jian_fan_en.md b/docs/_posts/ahmedlone127/2023-09-13-gujiroberta_jian_fan_en.md new file mode 100644 index 00000000000000..54611211e4320c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-gujiroberta_jian_fan_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English gujiroberta_jian_fan BertEmbeddings from hsc748NLP +author: John Snow Labs +name: gujiroberta_jian_fan +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`gujiroberta_jian_fan` is a English model originally trained by hsc748NLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gujiroberta_jian_fan_en_5.1.1_3.0_1694603135270.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gujiroberta_jian_fan_en_5.1.1_3.0_1694603135270.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("gujiroberta_jian_fan","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("gujiroberta_jian_fan", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gujiroberta_jian_fan| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|420.2 MB| + +## References + +https://huggingface.co/hsc748NLP/GujiRoBERTa_jian_fan \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-helloworld_en.md b/docs/_posts/ahmedlone127/2023-09-13-helloworld_en.md new file mode 100644 index 00000000000000..6b1f69586a01de --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-helloworld_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English helloworld BertEmbeddings from EternalPursuit +author: John Snow Labs +name: helloworld +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`helloworld` is a English model originally trained by EternalPursuit. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/helloworld_en_5.1.1_3.0_1694579975322.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/helloworld_en_5.1.1_3.0_1694579975322.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("helloworld","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("helloworld", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|helloworld| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/EternalPursuit/HelloWorld \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hindi_bert_embed_en.md b/docs/_posts/ahmedlone127/2023-09-13-hindi_bert_embed_en.md new file mode 100644 index 00000000000000..fd28eae5bf1df2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hindi_bert_embed_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English hindi_bert_embed BertEmbeddings from l3cube-pune +author: John Snow Labs +name: hindi_bert_embed +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hindi_bert_embed` is a English model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hindi_bert_embed_en_5.1.1_3.0_1694591488555.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hindi_bert_embed_en_5.1.1_3.0_1694591488555.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hindi_bert_embed","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hindi_bert_embed", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hindi_bert_embed| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|890.8 MB| + +## References + +https://huggingface.co/l3cube-pune/hi-bert-embed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hindi_random_twt_1m_hi.md b/docs/_posts/ahmedlone127/2023-09-13-hindi_random_twt_1m_hi.md new file mode 100644 index 00000000000000..867ab58c004c9e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hindi_random_twt_1m_hi.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hindi hindi_random_twt_1m BertEmbeddings from l3cube-pune +author: John Snow Labs +name: hindi_random_twt_1m +date: 2023-09-13 +tags: [bert, hi, open_source, fill_mask, onnx] +task: Embeddings +language: hi +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hindi_random_twt_1m` is a Hindi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hindi_random_twt_1m_hi_5.1.1_3.0_1694584742371.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hindi_random_twt_1m_hi_5.1.1_3.0_1694584742371.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hindi_random_twt_1m","hi") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hindi_random_twt_1m", "hi") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hindi_random_twt_1m| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|hi| +|Size:|890.6 MB| + +## References + +https://huggingface.co/l3cube-pune/hi-random-twt-1m \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hindi_tweets_bert_v2_hi.md b/docs/_posts/ahmedlone127/2023-09-13-hindi_tweets_bert_v2_hi.md new file mode 100644 index 00000000000000..ad861f3e42188c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hindi_tweets_bert_v2_hi.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hindi hindi_tweets_bert_v2 BertEmbeddings from l3cube-pune +author: John Snow Labs +name: hindi_tweets_bert_v2 +date: 2023-09-13 +tags: [bert, hi, open_source, fill_mask, onnx] +task: Embeddings +language: hi +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hindi_tweets_bert_v2` is a Hindi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hindi_tweets_bert_v2_hi_5.1.1_3.0_1694585904644.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hindi_tweets_bert_v2_hi_5.1.1_3.0_1694585904644.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hindi_tweets_bert_v2","hi") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hindi_tweets_bert_v2", "hi") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hindi_tweets_bert_v2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|hi| +|Size:|890.7 MB| + +## References + +https://huggingface.co/l3cube-pune/hindi-tweets-bert-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hing_bert_hi.md b/docs/_posts/ahmedlone127/2023-09-13-hing_bert_hi.md new file mode 100644 index 00000000000000..a3be6df300e822 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hing_bert_hi.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hindi hing_bert BertEmbeddings from l3cube-pune +author: John Snow Labs +name: hing_bert +date: 2023-09-13 +tags: [bert, hi, open_source, fill_mask, onnx] +task: Embeddings +language: hi +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hing_bert` is a Hindi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hing_bert_hi_5.1.1_3.0_1694603455445.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hing_bert_hi_5.1.1_3.0_1694603455445.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hing_bert","hi") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hing_bert", "hi") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hing_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|hi| +|Size:|407.2 MB| + +## References + +https://huggingface.co/l3cube-pune/hing-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hing_mbert_hi.md b/docs/_posts/ahmedlone127/2023-09-13-hing_mbert_hi.md new file mode 100644 index 00000000000000..7134c5d87651b9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hing_mbert_hi.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hindi hing_mbert BertEmbeddings from l3cube-pune +author: John Snow Labs +name: hing_mbert +date: 2023-09-13 +tags: [bert, hi, open_source, fill_mask, onnx] +task: Embeddings +language: hi +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hing_mbert` is a Hindi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hing_mbert_hi_5.1.1_3.0_1694603989005.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hing_mbert_hi_5.1.1_3.0_1694603989005.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hing_mbert","hi") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hing_mbert", "hi") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hing_mbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|hi| +|Size:|664.9 MB| + +## References + +https://huggingface.co/l3cube-pune/hing-mbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hing_mbert_mixed_v2_hi.md b/docs/_posts/ahmedlone127/2023-09-13-hing_mbert_mixed_v2_hi.md new file mode 100644 index 00000000000000..9eff007ee96b4b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hing_mbert_mixed_v2_hi.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hindi hing_mbert_mixed_v2 BertEmbeddings from l3cube-pune +author: John Snow Labs +name: hing_mbert_mixed_v2 +date: 2023-09-13 +tags: [bert, hi, open_source, fill_mask, onnx] +task: Embeddings +language: hi +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hing_mbert_mixed_v2` is a Hindi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hing_mbert_mixed_v2_hi_5.1.1_3.0_1694591335498.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hing_mbert_mixed_v2_hi_5.1.1_3.0_1694591335498.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hing_mbert_mixed_v2","hi") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hing_mbert_mixed_v2", "hi") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hing_mbert_mixed_v2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|hi| +|Size:|890.6 MB| + +## References + +https://huggingface.co/l3cube-pune/hing-mbert-mixed-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hsebert_italian_cased_it.md b/docs/_posts/ahmedlone127/2023-09-13-hsebert_italian_cased_it.md new file mode 100644 index 00000000000000..a550c16626afee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hsebert_italian_cased_it.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Italian hsebert_italian_cased BertEmbeddings from bullmount +author: John Snow Labs +name: hsebert_italian_cased +date: 2023-09-13 +tags: [bert, it, open_source, fill_mask, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hsebert_italian_cased` is a Italian model originally trained by bullmount. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hsebert_italian_cased_it_5.1.1_3.0_1694589993209.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hsebert_italian_cased_it_5.1.1_3.0_1694589993209.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hsebert_italian_cased","it") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hsebert_italian_cased", "it") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hsebert_italian_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|it| +|Size:|409.9 MB| + +## References + +https://huggingface.co/bullmount/hseBert-it-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-improvedabg_10_epochs_en.md b/docs/_posts/ahmedlone127/2023-09-13-improvedabg_10_epochs_en.md new file mode 100644 index 00000000000000..820db58cbc7265 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-improvedabg_10_epochs_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English improvedabg_10_epochs BertEmbeddings from Embible +author: John Snow Labs +name: improvedabg_10_epochs +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`improvedabg_10_epochs` is a English model originally trained by Embible. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/improvedabg_10_epochs_en_5.1.1_3.0_1694563262843.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/improvedabg_10_epochs_en_5.1.1_3.0_1694563262843.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("improvedabg_10_epochs","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("improvedabg_10_epochs", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|improvedabg_10_epochs| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|689.6 MB| + +## References + +https://huggingface.co/Embible/improvedABG-10-epochs \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-incaselawbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-incaselawbert_en.md new file mode 100644 index 00000000000000..40ad49508ef8dc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-incaselawbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English incaselawbert BertEmbeddings from law-ai +author: John Snow Labs +name: incaselawbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`incaselawbert` is a English model originally trained by law-ai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/incaselawbert_en_5.1.1_3.0_1694582926461.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/incaselawbert_en_5.1.1_3.0_1694582926461.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("incaselawbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("incaselawbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|incaselawbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/law-ai/InCaseLawBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-indo1_en.md b/docs/_posts/ahmedlone127/2023-09-13-indo1_en.md new file mode 100644 index 00000000000000..a00868e5b1e601 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-indo1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English indo1 BertEmbeddings from willemjan +author: John Snow Labs +name: indo1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indo1` is a English model originally trained by willemjan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indo1_en_5.1.1_3.0_1694584468221.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indo1_en_5.1.1_3.0_1694584468221.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("indo1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("indo1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indo1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/willemjan/indo1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-indo2_en.md b/docs/_posts/ahmedlone127/2023-09-13-indo2_en.md new file mode 100644 index 00000000000000..7456ede4fd2e5d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-indo2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English indo2 BertEmbeddings from willemjan +author: John Snow Labs +name: indo2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indo2` is a English model originally trained by willemjan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indo2_en_5.1.1_3.0_1694584681956.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indo2_en_5.1.1_3.0_1694584681956.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("indo2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("indo2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indo2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/willemjan/indo2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-indojave_codemixed_bert_base_id.md b/docs/_posts/ahmedlone127/2023-09-13-indojave_codemixed_bert_base_id.md new file mode 100644 index 00000000000000..485c423bba9a53 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-indojave_codemixed_bert_base_id.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Indonesian indojave_codemixed_bert_base BertEmbeddings from fathan +author: John Snow Labs +name: indojave_codemixed_bert_base +date: 2023-09-13 +tags: [bert, id, open_source, fill_mask, onnx] +task: Embeddings +language: id +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indojave_codemixed_bert_base` is a Indonesian model originally trained by fathan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indojave_codemixed_bert_base_id_5.1.1_3.0_1694574763045.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indojave_codemixed_bert_base_id_5.1.1_3.0_1694574763045.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("indojave_codemixed_bert_base","id") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("indojave_codemixed_bert_base", "id") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indojave_codemixed_bert_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|id| +|Size:|464.6 MB| + +## References + +https://huggingface.co/fathan/indojave-codemixed-bert-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-inlegalbert_cbp_lkg_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-13-inlegalbert_cbp_lkg_finetuned_en.md new file mode 100644 index 00000000000000..12dec567dbd231 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-inlegalbert_cbp_lkg_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English inlegalbert_cbp_lkg_finetuned BertEmbeddings from kinshuk-h +author: John Snow Labs +name: inlegalbert_cbp_lkg_finetuned +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`inlegalbert_cbp_lkg_finetuned` is a English model originally trained by kinshuk-h. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/inlegalbert_cbp_lkg_finetuned_en_5.1.1_3.0_1694606435709.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/inlegalbert_cbp_lkg_finetuned_en_5.1.1_3.0_1694606435709.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("inlegalbert_cbp_lkg_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("inlegalbert_cbp_lkg_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|inlegalbert_cbp_lkg_finetuned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/kinshuk-h/InLegalBERT-cbp-lkg-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-intent_further_pretrain_en.md b/docs/_posts/ahmedlone127/2023-09-13-intent_further_pretrain_en.md new file mode 100644 index 00000000000000..7961383d850226 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-intent_further_pretrain_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English intent_further_pretrain BertEmbeddings from onlydj96 +author: John Snow Labs +name: intent_further_pretrain +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`intent_further_pretrain` is a English model originally trained by onlydj96. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/intent_further_pretrain_en_5.1.1_3.0_1694587646285.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/intent_further_pretrain_en_5.1.1_3.0_1694587646285.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("intent_further_pretrain","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("intent_further_pretrain", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|intent_further_pretrain| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|441.2 MB| + +## References + +https://huggingface.co/onlydj96/intent_further_pretrain \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-itcast_nlp_base_en.md b/docs/_posts/ahmedlone127/2023-09-13-itcast_nlp_base_en.md new file mode 100644 index 00000000000000..4338d8b6d021ec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-itcast_nlp_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English itcast_nlp_base BertEmbeddings from suojianhua +author: John Snow Labs +name: itcast_nlp_base +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`itcast_nlp_base` is a English model originally trained by suojianhua. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/itcast_nlp_base_en_5.1.1_3.0_1694575926111.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/itcast_nlp_base_en_5.1.1_3.0_1694575926111.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("itcast_nlp_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("itcast_nlp_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|itcast_nlp_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/suojianhua/itcast-nlp-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-javanese_bert_small_jv.md b/docs/_posts/ahmedlone127/2023-09-13-javanese_bert_small_jv.md new file mode 100644 index 00000000000000..0463665dbd6178 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-javanese_bert_small_jv.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Javanese javanese_bert_small BertEmbeddings from w11wo +author: John Snow Labs +name: javanese_bert_small +date: 2023-09-13 +tags: [bert, jv, open_source, fill_mask, onnx] +task: Embeddings +language: jv +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`javanese_bert_small` is a Javanese model originally trained by w11wo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/javanese_bert_small_jv_5.1.1_3.0_1694582650100.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/javanese_bert_small_jv_5.1.1_3.0_1694582650100.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("javanese_bert_small","jv") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("javanese_bert_small", "jv") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|javanese_bert_small| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|jv| +|Size:|407.3 MB| + +## References + +https://huggingface.co/w11wo/javanese-bert-small \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kcbert_base_ko.md b/docs/_posts/ahmedlone127/2023-09-13-kcbert_base_ko.md new file mode 100644 index 00000000000000..d20f4a148bf6e3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kcbert_base_ko.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Korean kcbert_base BertEmbeddings from beomi +author: John Snow Labs +name: kcbert_base +date: 2023-09-13 +tags: [bert, ko, open_source, fill_mask, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kcbert_base` is a Korean model originally trained by beomi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kcbert_base_ko_5.1.1_3.0_1694586700028.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kcbert_base_ko_5.1.1_3.0_1694586700028.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kcbert_base","ko") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kcbert_base", "ko") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kcbert_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ko| +|Size:|406.2 MB| + +## References + +https://huggingface.co/beomi/kcbert-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kinyabert_large_finetuned_kintweets_en.md b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_large_finetuned_kintweets_en.md new file mode 100644 index 00000000000000..1957d090be629a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_large_finetuned_kintweets_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kinyabert_large_finetuned_kintweets BertEmbeddings from RogerB +author: John Snow Labs +name: kinyabert_large_finetuned_kintweets +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kinyabert_large_finetuned_kintweets` is a English model originally trained by RogerB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kinyabert_large_finetuned_kintweets_en_5.1.1_3.0_1694608062216.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kinyabert_large_finetuned_kintweets_en_5.1.1_3.0_1694608062216.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kinyabert_large_finetuned_kintweets","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kinyabert_large_finetuned_kintweets", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kinyabert_large_finetuned_kintweets| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.7 MB| + +## References + +https://huggingface.co/RogerB/KinyaBERT-large-finetuned-kintweets \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kinyabert_large_finetuned_kintweetsa_en.md b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_large_finetuned_kintweetsa_en.md new file mode 100644 index 00000000000000..4404452dfc8da6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_large_finetuned_kintweetsa_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kinyabert_large_finetuned_kintweetsa BertEmbeddings from RogerB +author: John Snow Labs +name: kinyabert_large_finetuned_kintweetsa +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kinyabert_large_finetuned_kintweetsa` is a English model originally trained by RogerB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kinyabert_large_finetuned_kintweetsa_en_5.1.1_3.0_1694606829948.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kinyabert_large_finetuned_kintweetsa_en_5.1.1_3.0_1694606829948.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kinyabert_large_finetuned_kintweetsa","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kinyabert_large_finetuned_kintweetsa", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kinyabert_large_finetuned_kintweetsa| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.7 MB| + +## References + +https://huggingface.co/RogerB/KinyaBERT-large-finetuned-kintweetsA \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kinyabert_large_finetuned_kintweetsb_en.md b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_large_finetuned_kintweetsb_en.md new file mode 100644 index 00000000000000..0d6d296e28483b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_large_finetuned_kintweetsb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kinyabert_large_finetuned_kintweetsb BertEmbeddings from RogerB +author: John Snow Labs +name: kinyabert_large_finetuned_kintweetsb +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kinyabert_large_finetuned_kintweetsb` is a English model originally trained by RogerB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kinyabert_large_finetuned_kintweetsb_en_5.1.1_3.0_1694596216029.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kinyabert_large_finetuned_kintweetsb_en_5.1.1_3.0_1694596216029.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kinyabert_large_finetuned_kintweetsb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kinyabert_large_finetuned_kintweetsb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kinyabert_large_finetuned_kintweetsb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.7 MB| + +## References + +https://huggingface.co/RogerB/KinyaBERT-large-finetuned-kintweetsB \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kinyabert_large_finetuned_kintweetsc_en.md b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_large_finetuned_kintweetsc_en.md new file mode 100644 index 00000000000000..67edcdababab77 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_large_finetuned_kintweetsc_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kinyabert_large_finetuned_kintweetsc BertEmbeddings from RogerB +author: John Snow Labs +name: kinyabert_large_finetuned_kintweetsc +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kinyabert_large_finetuned_kintweetsc` is a English model originally trained by RogerB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kinyabert_large_finetuned_kintweetsc_en_5.1.1_3.0_1694598865169.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kinyabert_large_finetuned_kintweetsc_en_5.1.1_3.0_1694598865169.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kinyabert_large_finetuned_kintweetsc","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kinyabert_large_finetuned_kintweetsc", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kinyabert_large_finetuned_kintweetsc| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.7 MB| + +## References + +https://huggingface.co/RogerB/KinyaBERT-large-finetuned-kintweetsC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kinyabert_large_finetuned_kintweetsd_en.md b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_large_finetuned_kintweetsd_en.md new file mode 100644 index 00000000000000..b0f764b70467d1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_large_finetuned_kintweetsd_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kinyabert_large_finetuned_kintweetsd BertEmbeddings from RogerB +author: John Snow Labs +name: kinyabert_large_finetuned_kintweetsd +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kinyabert_large_finetuned_kintweetsd` is a English model originally trained by RogerB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kinyabert_large_finetuned_kintweetsd_en_5.1.1_3.0_1694601566724.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kinyabert_large_finetuned_kintweetsd_en_5.1.1_3.0_1694601566724.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kinyabert_large_finetuned_kintweetsd","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kinyabert_large_finetuned_kintweetsd", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kinyabert_large_finetuned_kintweetsd| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.7 MB| + +## References + +https://huggingface.co/RogerB/KinyaBERT-large-finetuned-kintweetsD \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kinyabert_small_finetuned_kintweets_en.md b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_small_finetuned_kintweets_en.md new file mode 100644 index 00000000000000..e5304d97df0562 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_small_finetuned_kintweets_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kinyabert_small_finetuned_kintweets BertEmbeddings from RogerB +author: John Snow Labs +name: kinyabert_small_finetuned_kintweets +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kinyabert_small_finetuned_kintweets` is a English model originally trained by RogerB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kinyabert_small_finetuned_kintweets_en_5.1.1_3.0_1694607452346.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kinyabert_small_finetuned_kintweets_en_5.1.1_3.0_1694607452346.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kinyabert_small_finetuned_kintweets","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kinyabert_small_finetuned_kintweets", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kinyabert_small_finetuned_kintweets| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.4 MB| + +## References + +https://huggingface.co/RogerB/KinyaBERT-small-finetuned-kintweets \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kinyabert_small_finetuned_kintweetsa_en.md b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_small_finetuned_kintweetsa_en.md new file mode 100644 index 00000000000000..c71c91a278f7a1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_small_finetuned_kintweetsa_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kinyabert_small_finetuned_kintweetsa BertEmbeddings from RogerB +author: John Snow Labs +name: kinyabert_small_finetuned_kintweetsa +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kinyabert_small_finetuned_kintweetsa` is a English model originally trained by RogerB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kinyabert_small_finetuned_kintweetsa_en_5.1.1_3.0_1694606413895.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kinyabert_small_finetuned_kintweetsa_en_5.1.1_3.0_1694606413895.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kinyabert_small_finetuned_kintweetsa","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kinyabert_small_finetuned_kintweetsa", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kinyabert_small_finetuned_kintweetsa| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.4 MB| + +## References + +https://huggingface.co/RogerB/KinyaBERT-small-finetuned-kintweetsA \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kinyabert_small_finetuned_kintweetsb_en.md b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_small_finetuned_kintweetsb_en.md new file mode 100644 index 00000000000000..3763017395ce77 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_small_finetuned_kintweetsb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kinyabert_small_finetuned_kintweetsb BertEmbeddings from RogerB +author: John Snow Labs +name: kinyabert_small_finetuned_kintweetsb +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kinyabert_small_finetuned_kintweetsb` is a English model originally trained by RogerB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kinyabert_small_finetuned_kintweetsb_en_5.1.1_3.0_1694595713007.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kinyabert_small_finetuned_kintweetsb_en_5.1.1_3.0_1694595713007.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kinyabert_small_finetuned_kintweetsb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kinyabert_small_finetuned_kintweetsb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kinyabert_small_finetuned_kintweetsb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.4 MB| + +## References + +https://huggingface.co/RogerB/KinyaBERT-small-finetuned-kintweetsB \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kinyabert_small_finetuned_kintweetsc_en.md b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_small_finetuned_kintweetsc_en.md new file mode 100644 index 00000000000000..a6c98aa2fccbba --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_small_finetuned_kintweetsc_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kinyabert_small_finetuned_kintweetsc BertEmbeddings from RogerB +author: John Snow Labs +name: kinyabert_small_finetuned_kintweetsc +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kinyabert_small_finetuned_kintweetsc` is a English model originally trained by RogerB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kinyabert_small_finetuned_kintweetsc_en_5.1.1_3.0_1694598449540.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kinyabert_small_finetuned_kintweetsc_en_5.1.1_3.0_1694598449540.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kinyabert_small_finetuned_kintweetsc","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kinyabert_small_finetuned_kintweetsc", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kinyabert_small_finetuned_kintweetsc| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.4 MB| + +## References + +https://huggingface.co/RogerB/KinyaBERT-small-finetuned-kintweetsC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kinyabert_small_finetuned_kintweetsd_en.md b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_small_finetuned_kintweetsd_en.md new file mode 100644 index 00000000000000..b44ed59ea42313 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kinyabert_small_finetuned_kintweetsd_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kinyabert_small_finetuned_kintweetsd BertEmbeddings from RogerB +author: John Snow Labs +name: kinyabert_small_finetuned_kintweetsd +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kinyabert_small_finetuned_kintweetsd` is a English model originally trained by RogerB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kinyabert_small_finetuned_kintweetsd_en_5.1.1_3.0_1694601166540.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kinyabert_small_finetuned_kintweetsd_en_5.1.1_3.0_1694601166540.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kinyabert_small_finetuned_kintweetsd","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kinyabert_small_finetuned_kintweetsd", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kinyabert_small_finetuned_kintweetsd| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.4 MB| + +## References + +https://huggingface.co/RogerB/KinyaBERT-small-finetuned-kintweetsD \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-knowbias_bert_base_uncased_race_en.md b/docs/_posts/ahmedlone127/2023-09-13-knowbias_bert_base_uncased_race_en.md new file mode 100644 index 00000000000000..63fc96b6125e6f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-knowbias_bert_base_uncased_race_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English knowbias_bert_base_uncased_race BertEmbeddings from squiduu +author: John Snow Labs +name: knowbias_bert_base_uncased_race +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`knowbias_bert_base_uncased_race` is a English model originally trained by squiduu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/knowbias_bert_base_uncased_race_en_5.1.1_3.0_1694582004965.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/knowbias_bert_base_uncased_race_en_5.1.1_3.0_1694582004965.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("knowbias_bert_base_uncased_race","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("knowbias_bert_base_uncased_race", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|knowbias_bert_base_uncased_race| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/squiduu/knowbias-bert-base-uncased-race \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-lb_mbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-lb_mbert_en.md new file mode 100644 index 00000000000000..16d13c67720cb1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-lb_mbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English lb_mbert BertEmbeddings from lothritz +author: John Snow Labs +name: lb_mbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lb_mbert` is a English model originally trained by lothritz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lb_mbert_en_5.1.1_3.0_1694596944765.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lb_mbert_en_5.1.1_3.0_1694596944765.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("lb_mbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("lb_mbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lb_mbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|621.8 MB| + +## References + +https://huggingface.co/lothritz/Lb_mBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-legal_bert_base_cased_ptbr_pt.md b/docs/_posts/ahmedlone127/2023-09-13-legal_bert_base_cased_ptbr_pt.md new file mode 100644 index 00000000000000..5584191dc3f44e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-legal_bert_base_cased_ptbr_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese legal_bert_base_cased_ptbr BertEmbeddings from dominguesm +author: John Snow Labs +name: legal_bert_base_cased_ptbr +date: 2023-09-13 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legal_bert_base_cased_ptbr` is a Portuguese model originally trained by dominguesm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legal_bert_base_cased_ptbr_pt_5.1.1_3.0_1694591089960.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legal_bert_base_cased_ptbr_pt_5.1.1_3.0_1694591089960.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legal_bert_base_cased_ptbr","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legal_bert_base_cased_ptbr", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legal_bert_base_cased_ptbr| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|470.0 MB| + +## References + +https://huggingface.co/dominguesm/legal-bert-base-cased-ptbr \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-legal_bertimbau_base_pt.md b/docs/_posts/ahmedlone127/2023-09-13-legal_bertimbau_base_pt.md new file mode 100644 index 00000000000000..28b0a0221a83b3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-legal_bertimbau_base_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese legal_bertimbau_base BertEmbeddings from rufimelo +author: John Snow Labs +name: legal_bertimbau_base +date: 2023-09-13 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legal_bertimbau_base` is a Portuguese model originally trained by rufimelo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legal_bertimbau_base_pt_5.1.1_3.0_1694563613858.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legal_bertimbau_base_pt_5.1.1_3.0_1694563613858.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legal_bertimbau_base","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legal_bertimbau_base", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legal_bertimbau_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|405.8 MB| + +## References + +https://huggingface.co/rufimelo/Legal-BERTimbau-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-lernnavibert_en.md b/docs/_posts/ahmedlone127/2023-09-13-lernnavibert_en.md new file mode 100644 index 00000000000000..e5b80355eb7be6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-lernnavibert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English lernnavibert BertEmbeddings from lucazed +author: John Snow Labs +name: lernnavibert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lernnavibert` is a English model originally trained by lucazed. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lernnavibert_en_5.1.1_3.0_1694576101007.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lernnavibert_en_5.1.1_3.0_1694576101007.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("lernnavibert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("lernnavibert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lernnavibert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/lucazed/LernnaviBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-lexbert_turkish_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-lexbert_turkish_uncased_en.md new file mode 100644 index 00000000000000..f0c4f28546d29d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-lexbert_turkish_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English lexbert_turkish_uncased BertEmbeddings from sfurkan +author: John Snow Labs +name: lexbert_turkish_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lexbert_turkish_uncased` is a English model originally trained by sfurkan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lexbert_turkish_uncased_en_5.1.1_3.0_1694569214173.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lexbert_turkish_uncased_en_5.1.1_3.0_1694569214173.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("lexbert_turkish_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("lexbert_turkish_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lexbert_turkish_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.5 MB| + +## References + +https://huggingface.co/sfurkan/LexBERT-turkish-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-lsg4k_italian_legal_bert_it.md b/docs/_posts/ahmedlone127/2023-09-13-lsg4k_italian_legal_bert_it.md new file mode 100644 index 00000000000000..f3839e77af29a0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-lsg4k_italian_legal_bert_it.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Italian lsg4k_italian_legal_bert BertEmbeddings from dlicari +author: John Snow Labs +name: lsg4k_italian_legal_bert +date: 2023-09-13 +tags: [bert, it, open_source, fill_mask, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lsg4k_italian_legal_bert` is a Italian model originally trained by dlicari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lsg4k_italian_legal_bert_it_5.1.1_3.0_1694596094098.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lsg4k_italian_legal_bert_it_5.1.1_3.0_1694596094098.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("lsg4k_italian_legal_bert","it") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("lsg4k_italian_legal_bert", "it") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lsg4k_italian_legal_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|it| +|Size:|419.2 MB| + +## References + +https://huggingface.co/dlicari/lsg4k-Italian-Legal-BERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-lsg_legal_base_uncased_4096_en.md b/docs/_posts/ahmedlone127/2023-09-13-lsg_legal_base_uncased_4096_en.md new file mode 100644 index 00000000000000..709bbed08aba51 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-lsg_legal_base_uncased_4096_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English lsg_legal_base_uncased_4096 BertEmbeddings from ccdv +author: John Snow Labs +name: lsg_legal_base_uncased_4096 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lsg_legal_base_uncased_4096` is a English model originally trained by ccdv. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lsg_legal_base_uncased_4096_en_5.1.1_3.0_1694590941145.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lsg_legal_base_uncased_4096_en_5.1.1_3.0_1694590941145.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("lsg_legal_base_uncased_4096","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("lsg_legal_base_uncased_4096", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lsg_legal_base_uncased_4096| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|417.6 MB| + +## References + +https://huggingface.co/ccdv/lsg-legal-base-uncased-4096 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-lsg_legal_small_uncased_4096_en.md b/docs/_posts/ahmedlone127/2023-09-13-lsg_legal_small_uncased_4096_en.md new file mode 100644 index 00000000000000..269581411ea0dc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-lsg_legal_small_uncased_4096_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English lsg_legal_small_uncased_4096 BertEmbeddings from ccdv +author: John Snow Labs +name: lsg_legal_small_uncased_4096 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lsg_legal_small_uncased_4096` is a English model originally trained by ccdv. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lsg_legal_small_uncased_4096_en_5.1.1_3.0_1694591070073.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lsg_legal_small_uncased_4096_en_5.1.1_3.0_1694591070073.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("lsg_legal_small_uncased_4096","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("lsg_legal_small_uncased_4096", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lsg_legal_small_uncased_4096| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|137.4 MB| + +## References + +https://huggingface.co/ccdv/lsg-legal-small-uncased-4096 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-marathi_bert_small_mr.md b/docs/_posts/ahmedlone127/2023-09-13-marathi_bert_small_mr.md new file mode 100644 index 00000000000000..b9381b5b95cfc3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-marathi_bert_small_mr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Marathi marathi_bert_small BertEmbeddings from l3cube-pune +author: John Snow Labs +name: marathi_bert_small +date: 2023-09-13 +tags: [bert, mr, open_source, fill_mask, onnx] +task: Embeddings +language: mr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`marathi_bert_small` is a Marathi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/marathi_bert_small_mr_5.1.1_3.0_1694584448262.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/marathi_bert_small_mr_5.1.1_3.0_1694584448262.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("marathi_bert_small","mr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("marathi_bert_small", "mr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|marathi_bert_small| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|mr| +|Size:|311.1 MB| + +## References + +https://huggingface.co/l3cube-pune/marathi-bert-small \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-marathi_bert_smaller_mr.md b/docs/_posts/ahmedlone127/2023-09-13-marathi_bert_smaller_mr.md new file mode 100644 index 00000000000000..e3fad431f278b4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-marathi_bert_smaller_mr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Marathi marathi_bert_smaller BertEmbeddings from l3cube-pune +author: John Snow Labs +name: marathi_bert_smaller +date: 2023-09-13 +tags: [bert, mr, open_source, fill_mask, onnx] +task: Embeddings +language: mr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`marathi_bert_smaller` is a Marathi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/marathi_bert_smaller_mr_5.1.1_3.0_1694584572735.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/marathi_bert_smaller_mr_5.1.1_3.0_1694584572735.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("marathi_bert_smaller","mr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("marathi_bert_smaller", "mr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|marathi_bert_smaller| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|mr| +|Size:|204.9 MB| + +## References + +https://huggingface.co/l3cube-pune/marathi-bert-smaller \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-master_base_pretrained_msmarco_en.md b/docs/_posts/ahmedlone127/2023-09-13-master_base_pretrained_msmarco_en.md new file mode 100644 index 00000000000000..80367d6897e501 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-master_base_pretrained_msmarco_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English master_base_pretrained_msmarco BertEmbeddings from lx865712528 +author: John Snow Labs +name: master_base_pretrained_msmarco +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`master_base_pretrained_msmarco` is a English model originally trained by lx865712528. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/master_base_pretrained_msmarco_en_5.1.1_3.0_1694593327779.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/master_base_pretrained_msmarco_en_5.1.1_3.0_1694593327779.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("master_base_pretrained_msmarco","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("master_base_pretrained_msmarco", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|master_base_pretrained_msmarco| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.9 MB| + +## References + +https://huggingface.co/lx865712528/master-base-pretrained-msmarco \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-master_base_pretrained_wiki_en.md b/docs/_posts/ahmedlone127/2023-09-13-master_base_pretrained_wiki_en.md new file mode 100644 index 00000000000000..e329a7007d0685 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-master_base_pretrained_wiki_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English master_base_pretrained_wiki BertEmbeddings from lx865712528 +author: John Snow Labs +name: master_base_pretrained_wiki +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`master_base_pretrained_wiki` is a English model originally trained by lx865712528. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/master_base_pretrained_wiki_en_5.1.1_3.0_1694593477244.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/master_base_pretrained_wiki_en_5.1.1_3.0_1694593477244.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("master_base_pretrained_wiki","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("master_base_pretrained_wiki", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|master_base_pretrained_wiki| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.9 MB| + +## References + +https://huggingface.co/lx865712528/master-base-pretrained-wiki \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mathbert_croatian_en.md b/docs/_posts/ahmedlone127/2023-09-13-mathbert_croatian_en.md new file mode 100644 index 00000000000000..5ec63e8a94fc1f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mathbert_croatian_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mathbert_croatian BertEmbeddings from mpajas +author: John Snow Labs +name: mathbert_croatian +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mathbert_croatian` is a English model originally trained by mpajas. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mathbert_croatian_en_5.1.1_3.0_1694593152942.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mathbert_croatian_en_5.1.1_3.0_1694593152942.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mathbert_croatian","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mathbert_croatian", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mathbert_croatian| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/mpajas/MathBERT_hr \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mattpuscibert_en.md b/docs/_posts/ahmedlone127/2023-09-13-mattpuscibert_en.md new file mode 100644 index 00000000000000..cb88cc1b9c9b72 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mattpuscibert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mattpuscibert BertEmbeddings from lfoppiano +author: John Snow Labs +name: mattpuscibert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mattpuscibert` is a English model originally trained by lfoppiano. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mattpuscibert_en_5.1.1_3.0_1694586067878.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mattpuscibert_en_5.1.1_3.0_1694586067878.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mattpuscibert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mattpuscibert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mattpuscibert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/lfoppiano/MatTPUSciBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mbert_rom_arabic_en.md b/docs/_posts/ahmedlone127/2023-09-13-mbert_rom_arabic_en.md new file mode 100644 index 00000000000000..7b8139e8801249 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mbert_rom_arabic_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mbert_rom_arabic BertEmbeddings from Zappandy +author: John Snow Labs +name: mbert_rom_arabic +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbert_rom_arabic` is a English model originally trained by Zappandy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbert_rom_arabic_en_5.1.1_3.0_1694576557333.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbert_rom_arabic_en_5.1.1_3.0_1694576557333.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbert_rom_arabic","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbert_rom_arabic", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbert_rom_arabic| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/Zappandy/mBERT-rom-arabic \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mbertv2.0_en.md b/docs/_posts/ahmedlone127/2023-09-13-mbertv2.0_en.md new file mode 100644 index 00000000000000..3086268a0d014c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mbertv2.0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mbertv2.0 BertEmbeddings from bongsoo +author: John Snow Labs +name: mbertv2.0 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbertv2.0` is a English model originally trained by bongsoo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbertv2.0_en_5.1.1_3.0_1694583366759.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbertv2.0_en_5.1.1_3.0_1694583366759.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbertv2.0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbertv2.0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbertv2.0| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|753.1 MB| + +## References + +https://huggingface.co/bongsoo/mbertV2.0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-me_bert_mixed_v2_mr.md b/docs/_posts/ahmedlone127/2023-09-13-me_bert_mixed_v2_mr.md new file mode 100644 index 00000000000000..210148a91de4eb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-me_bert_mixed_v2_mr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Marathi me_bert_mixed_v2 BertEmbeddings from l3cube-pune +author: John Snow Labs +name: me_bert_mixed_v2 +date: 2023-09-13 +tags: [bert, mr, open_source, fill_mask, onnx] +task: Embeddings +language: mr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`me_bert_mixed_v2` is a Marathi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/me_bert_mixed_v2_mr_5.1.1_3.0_1694591075469.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/me_bert_mixed_v2_mr_5.1.1_3.0_1694591075469.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("me_bert_mixed_v2","mr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("me_bert_mixed_v2", "mr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|me_bert_mixed_v2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|mr| +|Size:|890.6 MB| + +## References + +https://huggingface.co/l3cube-pune/me-bert-mixed-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-meda_bert_da.md b/docs/_posts/ahmedlone127/2023-09-13-meda_bert_da.md new file mode 100644 index 00000000000000..1e0be1d1404606 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-meda_bert_da.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Danish meda_bert BertEmbeddings from jannikskytt +author: John Snow Labs +name: meda_bert +date: 2023-09-13 +tags: [bert, da, open_source, fill_mask, onnx] +task: Embeddings +language: da +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`meda_bert` is a Danish model originally trained by jannikskytt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/meda_bert_da_5.1.1_3.0_1694592472319.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/meda_bert_da_5.1.1_3.0_1694592472319.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("meda_bert","da") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("meda_bert", "da") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|meda_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|da| +|Size:|412.3 MB| + +## References + +https://huggingface.co/jannikskytt/MeDa-Bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-medbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-medbert_en.md new file mode 100644 index 00000000000000..1e5cd957cfa4a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-medbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English medbert BertEmbeddings from Charangan +author: John Snow Labs +name: medbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`medbert` is a English model originally trained by Charangan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/medbert_en_5.1.1_3.0_1694585151622.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/medbert_en_5.1.1_3.0_1694585151622.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("medbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("medbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|medbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.1 MB| + +## References + +https://huggingface.co/Charangan/MedBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-melayubert_ms.md b/docs/_posts/ahmedlone127/2023-09-13-melayubert_ms.md new file mode 100644 index 00000000000000..482af23699300b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-melayubert_ms.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Malay (macrolanguage) melayubert BertEmbeddings from StevenLimcorn +author: John Snow Labs +name: melayubert +date: 2023-09-13 +tags: [bert, ms, open_source, fill_mask, onnx] +task: Embeddings +language: ms +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`melayubert` is a Malay (macrolanguage) model originally trained by StevenLimcorn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/melayubert_ms_5.1.1_3.0_1694571651129.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/melayubert_ms_5.1.1_3.0_1694571651129.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("melayubert","ms") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("melayubert", "ms") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|melayubert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ms| +|Size:|408.1 MB| + +## References + +https://huggingface.co/StevenLimcorn/MelayuBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-minilm_l6_h384_uncased_eli5_en.md b/docs/_posts/ahmedlone127/2023-09-13-minilm_l6_h384_uncased_eli5_en.md new file mode 100644 index 00000000000000..b69ab1d7fbadcb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-minilm_l6_h384_uncased_eli5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English minilm_l6_h384_uncased_eli5 BertEmbeddings from JackWolfard +author: John Snow Labs +name: minilm_l6_h384_uncased_eli5 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`minilm_l6_h384_uncased_eli5` is a English model originally trained by JackWolfard. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/minilm_l6_h384_uncased_eli5_en_5.1.1_3.0_1694583345340.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/minilm_l6_h384_uncased_eli5_en_5.1.1_3.0_1694583345340.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("minilm_l6_h384_uncased_eli5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("minilm_l6_h384_uncased_eli5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|minilm_l6_h384_uncased_eli5| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|84.6 MB| + +## References + +https://huggingface.co/JackWolfard/minilm-l6-h384-uncased-eli5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mlm_20230403_001_1_en.md b/docs/_posts/ahmedlone127/2023-09-13-mlm_20230403_001_1_en.md new file mode 100644 index 00000000000000..1dda6b2a55d6e5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mlm_20230403_001_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230403_001_1 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230403_001_1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230403_001_1` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230403_001_1_en_5.1.1_3.0_1694605119094.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230403_001_1_en_5.1.1_3.0_1694605119094.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230403_001_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230403_001_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230403_001_1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.2 MB| + +## References + +https://huggingface.co/intanm/mlm-20230403-001-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mlm_20230403_002_1_en.md b/docs/_posts/ahmedlone127/2023-09-13-mlm_20230403_002_1_en.md new file mode 100644 index 00000000000000..122eade6c26d81 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mlm_20230403_002_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230403_002_1 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230403_002_1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230403_002_1` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230403_002_1_en_5.1.1_3.0_1694604236712.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230403_002_1_en_5.1.1_3.0_1694604236712.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230403_002_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230403_002_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230403_002_1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.2 MB| + +## References + +https://huggingface.co/intanm/mlm-20230403-002-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mlm_20230404_001_2_en.md b/docs/_posts/ahmedlone127/2023-09-13-mlm_20230404_001_2_en.md new file mode 100644 index 00000000000000..a29c0705353232 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mlm_20230404_001_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230404_001_2 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230404_001_2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230404_001_2` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230404_001_2_en_5.1.1_3.0_1694608777175.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230404_001_2_en_5.1.1_3.0_1694608777175.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230404_001_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230404_001_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230404_001_2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.2 MB| + +## References + +https://huggingface.co/intanm/mlm-20230404-001-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-models_en.md b/docs/_posts/ahmedlone127/2023-09-13-models_en.md new file mode 100644 index 00000000000000..1d5b8802c96303 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-models_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English models BertEmbeddings from Dinithi +author: John Snow Labs +name: models +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`models` is a English model originally trained by Dinithi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/models_en_5.1.1_3.0_1694589815357.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/models_en_5.1.1_3.0_1694589815357.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("models","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("models", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|models| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.3 MB| + +## References + +https://huggingface.co/Dinithi/models \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-morrbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-morrbert_en.md new file mode 100644 index 00000000000000..d753991de415db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-morrbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English morrbert BertEmbeddings from otmangi +author: John Snow Labs +name: morrbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`morrbert` is a English model originally trained by otmangi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/morrbert_en_5.1.1_3.0_1694580636063.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/morrbert_en_5.1.1_3.0_1694580636063.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("morrbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("morrbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|morrbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|469.9 MB| + +## References + +https://huggingface.co/otmangi/MorrBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mrf_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-mrf_bert_en.md new file mode 100644 index 00000000000000..fc040d7feadd6e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mrf_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mrf_bert BertEmbeddings from petrichorRainbow +author: John Snow Labs +name: mrf_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mrf_bert` is a English model originally trained by petrichorRainbow. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mrf_bert_en_5.1.1_3.0_1694608052096.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mrf_bert_en_5.1.1_3.0_1694608052096.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mrf_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mrf_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mrf_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/petrichorRainbow/mrf-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-multi_dialect_bert_base_arabic_ar.md b/docs/_posts/ahmedlone127/2023-09-13-multi_dialect_bert_base_arabic_ar.md new file mode 100644 index 00000000000000..135eaa9b58672b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-multi_dialect_bert_base_arabic_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic multi_dialect_bert_base_arabic BertEmbeddings from bashar-talafha +author: John Snow Labs +name: multi_dialect_bert_base_arabic +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multi_dialect_bert_base_arabic` is a Arabic model originally trained by bashar-talafha. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/multi_dialect_bert_base_arabic_ar_5.1.1_3.0_1694585102562.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multi_dialect_bert_base_arabic_ar_5.1.1_3.0_1694585102562.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("multi_dialect_bert_base_arabic","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("multi_dialect_bert_base_arabic", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|multi_dialect_bert_base_arabic| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|411.5 MB| + +## References + +https://huggingface.co/bashar-talafha/multi-dialect-bert-base-arabic \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mymodel001_en.md b/docs/_posts/ahmedlone127/2023-09-13-mymodel001_en.md new file mode 100644 index 00000000000000..4af3b6e3af1de1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mymodel001_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mymodel001 BertEmbeddings from coiour +author: John Snow Labs +name: mymodel001 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mymodel001` is a English model originally trained by coiour. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mymodel001_en_5.1.1_3.0_1694593944523.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mymodel001_en_5.1.1_3.0_1694593944523.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mymodel001","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mymodel001", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mymodel001| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/coiour/mymodel001 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mymodel04_wbmitcast_en.md b/docs/_posts/ahmedlone127/2023-09-13-mymodel04_wbmitcast_en.md new file mode 100644 index 00000000000000..8eefda37951adf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mymodel04_wbmitcast_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mymodel04_wbmitcast BertEmbeddings from wbmitcast +author: John Snow Labs +name: mymodel04_wbmitcast +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mymodel04_wbmitcast` is a English model originally trained by wbmitcast. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mymodel04_wbmitcast_en_5.1.1_3.0_1694583725964.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mymodel04_wbmitcast_en_5.1.1_3.0_1694583725964.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mymodel04_wbmitcast","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mymodel04_wbmitcast", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mymodel04_wbmitcast| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/wbmitcast/mymodel04 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mymodel1001_en.md b/docs/_posts/ahmedlone127/2023-09-13-mymodel1001_en.md new file mode 100644 index 00000000000000..4f3352e6c9268a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mymodel1001_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mymodel1001 BertEmbeddings from cwitcate +author: John Snow Labs +name: mymodel1001 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mymodel1001` is a English model originally trained by cwitcate. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mymodel1001_en_5.1.1_3.0_1694594564846.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mymodel1001_en_5.1.1_3.0_1694594564846.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mymodel1001","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mymodel1001", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mymodel1001| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/cwitcate/mymodel1001 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-naval_ko.md b/docs/_posts/ahmedlone127/2023-09-13-naval_ko.md new file mode 100644 index 00000000000000..bf8f8e20723e91 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-naval_ko.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Korean naval BertEmbeddings from rlatmddus159 +author: John Snow Labs +name: naval +date: 2023-09-13 +tags: [bert, ko, open_source, fill_mask, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`naval` is a Korean model originally trained by rlatmddus159. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/naval_ko_5.1.1_3.0_1694592395863.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/naval_ko_5.1.1_3.0_1694592395863.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("naval","ko") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("naval", "ko") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|naval| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ko| +|Size:|412.4 MB| + +## References + +https://huggingface.co/rlatmddus159/naval \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-ncert_bio_en.md b/docs/_posts/ahmedlone127/2023-09-13-ncert_bio_en.md new file mode 100644 index 00000000000000..e028ea29f2e04d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-ncert_bio_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ncert_bio BertEmbeddings from S1d-dha-nth3 +author: John Snow Labs +name: ncert_bio +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ncert_bio` is a English model originally trained by S1d-dha-nth3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ncert_bio_en_5.1.1_3.0_1694585454986.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ncert_bio_en_5.1.1_3.0_1694585454986.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ncert_bio","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ncert_bio", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ncert_bio| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/S1d-dha-nth3/ncert_bio \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-nepal_bhasa_model_en.md b/docs/_posts/ahmedlone127/2023-09-13-nepal_bhasa_model_en.md new file mode 100644 index 00000000000000..c8a926d5013ba4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-nepal_bhasa_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English nepal_bhasa_model BertEmbeddings from swadesh7 +author: John Snow Labs +name: nepal_bhasa_model +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nepal_bhasa_model` is a English model originally trained by swadesh7. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nepal_bhasa_model_en_5.1.1_3.0_1694597351777.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nepal_bhasa_model_en_5.1.1_3.0_1694597351777.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("nepal_bhasa_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("nepal_bhasa_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nepal_bhasa_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.1 MB| + +## References + +https://huggingface.co/swadesh7/new_model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-nepnewsbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-nepnewsbert_en.md new file mode 100644 index 00000000000000..594d327855c6c3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-nepnewsbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English nepnewsbert BertEmbeddings from Shushant +author: John Snow Labs +name: nepnewsbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nepnewsbert` is a English model originally trained by Shushant. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nepnewsbert_en_5.1.1_3.0_1694571258663.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nepnewsbert_en_5.1.1_3.0_1694571258663.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("nepnewsbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("nepnewsbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nepnewsbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.7 MB| + +## References + +https://huggingface.co/Shushant/NepNewsBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-netbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-netbert_en.md new file mode 100644 index 00000000000000..dd6bdc66980765 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-netbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English netbert BertEmbeddings from antoinelouis +author: John Snow Labs +name: netbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`netbert` is a English model originally trained by antoinelouis. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/netbert_en_5.1.1_3.0_1694580370077.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/netbert_en_5.1.1_3.0_1694580370077.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("netbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("netbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|netbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.1 MB| + +## References + +https://huggingface.co/antoinelouis/netbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-newmodel_en.md b/docs/_posts/ahmedlone127/2023-09-13-newmodel_en.md new file mode 100644 index 00000000000000..ba37aa9589d2a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-newmodel_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English newmodel BertEmbeddings from Dinithi +author: John Snow Labs +name: newmodel +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`newmodel` is a English model originally trained by Dinithi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/newmodel_en_5.1.1_3.0_1694590192126.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/newmodel_en_5.1.1_3.0_1694590192126.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("newmodel","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("newmodel", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|newmodel| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.3 MB| + +## References + +https://huggingface.co/Dinithi/NewModel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-nl2_en.md b/docs/_posts/ahmedlone127/2023-09-13-nl2_en.md new file mode 100644 index 00000000000000..4d58ee322f8dd9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-nl2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English nl2 BertEmbeddings from willemjan +author: John Snow Labs +name: nl2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nl2` is a English model originally trained by willemjan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nl2_en_5.1.1_3.0_1694585097782.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nl2_en_5.1.1_3.0_1694585097782.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("nl2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("nl2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nl2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/willemjan/nl2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-nlpload_en.md b/docs/_posts/ahmedlone127/2023-09-13-nlpload_en.md new file mode 100644 index 00000000000000..059972c587a9df --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-nlpload_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English nlpload BertEmbeddings from beiluo +author: John Snow Labs +name: nlpload +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nlpload` is a English model originally trained by beiluo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nlpload_en_5.1.1_3.0_1694586255849.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nlpload_en_5.1.1_3.0_1694586255849.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("nlpload","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("nlpload", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nlpload| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/beiluo/nlpload \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-nuclear_medicine_dabert_en.md b/docs/_posts/ahmedlone127/2023-09-13-nuclear_medicine_dabert_en.md new file mode 100644 index 00000000000000..a03e803d152e11 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-nuclear_medicine_dabert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English nuclear_medicine_dabert BertEmbeddings from Zach88 +author: John Snow Labs +name: nuclear_medicine_dabert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nuclear_medicine_dabert` is a English model originally trained by Zach88. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nuclear_medicine_dabert_en_5.1.1_3.0_1694593201567.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nuclear_medicine_dabert_en_5.1.1_3.0_1694593201567.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("nuclear_medicine_dabert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("nuclear_medicine_dabert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nuclear_medicine_dabert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Zach88/nuclear_medicine_DABERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-nuclear_medicine_dabioclincialbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-nuclear_medicine_dabioclincialbert_en.md new file mode 100644 index 00000000000000..90c8d759c35bd2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-nuclear_medicine_dabioclincialbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English nuclear_medicine_dabioclincialbert BertEmbeddings from Zach88 +author: John Snow Labs +name: nuclear_medicine_dabioclincialbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nuclear_medicine_dabioclincialbert` is a English model originally trained by Zach88. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nuclear_medicine_dabioclincialbert_en_5.1.1_3.0_1694593343797.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nuclear_medicine_dabioclincialbert_en_5.1.1_3.0_1694593343797.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("nuclear_medicine_dabioclincialbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("nuclear_medicine_dabioclincialbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nuclear_medicine_dabioclincialbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.3 MB| + +## References + +https://huggingface.co/Zach88/nuclear_medicine_DAbioClincialBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-op_bert_mathqa_en.md b/docs/_posts/ahmedlone127/2023-09-13-op_bert_mathqa_en.md new file mode 100644 index 00000000000000..707ccf8da66b92 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-op_bert_mathqa_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English op_bert_mathqa BertEmbeddings from Gxg +author: John Snow Labs +name: op_bert_mathqa +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`op_bert_mathqa` is a English model originally trained by Gxg. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/op_bert_mathqa_en_5.1.1_3.0_1694589793352.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/op_bert_mathqa_en_5.1.1_3.0_1694589793352.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("op_bert_mathqa","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("op_bert_mathqa", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|op_bert_mathqa| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Gxg/Op_Bert_MathQA \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-output_bert_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-output_bert_uncased_en.md new file mode 100644 index 00000000000000..3ddbaf885a7ca8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-output_bert_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English output_bert_uncased BertEmbeddings from btk +author: John Snow Labs +name: output_bert_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`output_bert_uncased` is a English model originally trained by btk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/output_bert_uncased_en_5.1.1_3.0_1694589596652.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/output_bert_uncased_en_5.1.1_3.0_1694589596652.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("output_bert_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("output_bert_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|output_bert_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/btk/output_bert_uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-pak_legal_bert_small_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-pak_legal_bert_small_uncased_en.md new file mode 100644 index 00000000000000..68b62e03dd9250 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-pak_legal_bert_small_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pak_legal_bert_small_uncased BertEmbeddings from AISystems +author: John Snow Labs +name: pak_legal_bert_small_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pak_legal_bert_small_uncased` is a English model originally trained by AISystems. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pak_legal_bert_small_uncased_en_5.1.1_3.0_1694596392979.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pak_legal_bert_small_uncased_en_5.1.1_3.0_1694596392979.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("pak_legal_bert_small_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("pak_legal_bert_small_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pak_legal_bert_small_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|130.5 MB| + +## References + +https://huggingface.co/AISystems/PAK-LEGAL-BERT-small-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-paperly_imply_plus_mlm_3rd_time_en.md b/docs/_posts/ahmedlone127/2023-09-13-paperly_imply_plus_mlm_3rd_time_en.md new file mode 100644 index 00000000000000..0a22e61f319993 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-paperly_imply_plus_mlm_3rd_time_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English paperly_imply_plus_mlm_3rd_time BertEmbeddings from ashwathjadhav23 +author: John Snow Labs +name: paperly_imply_plus_mlm_3rd_time +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`paperly_imply_plus_mlm_3rd_time` is a English model originally trained by ashwathjadhav23. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/paperly_imply_plus_mlm_3rd_time_en_5.1.1_3.0_1694586225464.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/paperly_imply_plus_mlm_3rd_time_en_5.1.1_3.0_1694586225464.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("paperly_imply_plus_mlm_3rd_time","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("paperly_imply_plus_mlm_3rd_time", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|paperly_imply_plus_mlm_3rd_time| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/ashwathjadhav23/paperly_imply_plus_MLM_3rd_time \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-parlbert_german_v2_de.md b/docs/_posts/ahmedlone127/2023-09-13-parlbert_german_v2_de.md new file mode 100644 index 00000000000000..bf78662f3e709b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-parlbert_german_v2_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German parlbert_german_v2 BertEmbeddings from chkla +author: John Snow Labs +name: parlbert_german_v2 +date: 2023-09-13 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`parlbert_german_v2` is a German model originally trained by chkla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/parlbert_german_v2_de_5.1.1_3.0_1694595611001.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/parlbert_german_v2_de_5.1.1_3.0_1694595611001.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("parlbert_german_v2","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("parlbert_german_v2", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|parlbert_german_v2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|409.7 MB| + +## References + +https://huggingface.co/chkla/parlbert-german-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-pcscibert_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-pcscibert_uncased_en.md new file mode 100644 index 00000000000000..95a8bc32aa1ced --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-pcscibert_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pcscibert_uncased BertEmbeddings from jmzk96 +author: John Snow Labs +name: pcscibert_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pcscibert_uncased` is a English model originally trained by jmzk96. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pcscibert_uncased_en_5.1.1_3.0_1694592569814.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pcscibert_uncased_en_5.1.1_3.0_1694592569814.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("pcscibert_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("pcscibert_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pcscibert_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/jmzk96/PCSciBERT_uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-protaugment_lm_clinic150_en.md b/docs/_posts/ahmedlone127/2023-09-13-protaugment_lm_clinic150_en.md new file mode 100644 index 00000000000000..8be2edd33ee6d8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-protaugment_lm_clinic150_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English protaugment_lm_clinic150 BertEmbeddings from tdopierre +author: John Snow Labs +name: protaugment_lm_clinic150 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`protaugment_lm_clinic150` is a English model originally trained by tdopierre. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/protaugment_lm_clinic150_en_5.1.1_3.0_1694577083272.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/protaugment_lm_clinic150_en_5.1.1_3.0_1694577083272.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("protaugment_lm_clinic150","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("protaugment_lm_clinic150", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|protaugment_lm_clinic150| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.4 MB| + +## References + +https://huggingface.co/tdopierre/ProtAugment-LM-Clinic150 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-protaugment_lm_hwu64_en.md b/docs/_posts/ahmedlone127/2023-09-13-protaugment_lm_hwu64_en.md new file mode 100644 index 00000000000000..19fb1698d6cdef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-protaugment_lm_hwu64_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English protaugment_lm_hwu64 BertEmbeddings from tdopierre +author: John Snow Labs +name: protaugment_lm_hwu64 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`protaugment_lm_hwu64` is a English model originally trained by tdopierre. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/protaugment_lm_hwu64_en_5.1.1_3.0_1694577269187.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/protaugment_lm_hwu64_en_5.1.1_3.0_1694577269187.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("protaugment_lm_hwu64","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("protaugment_lm_hwu64", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|protaugment_lm_hwu64| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.5 MB| + +## References + +https://huggingface.co/tdopierre/ProtAugment-LM-HWU64 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-protaugment_lm_liu_en.md b/docs/_posts/ahmedlone127/2023-09-13-protaugment_lm_liu_en.md new file mode 100644 index 00000000000000..9fef29f88b1833 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-protaugment_lm_liu_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English protaugment_lm_liu BertEmbeddings from tdopierre +author: John Snow Labs +name: protaugment_lm_liu +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`protaugment_lm_liu` is a English model originally trained by tdopierre. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/protaugment_lm_liu_en_5.1.1_3.0_1694577437431.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/protaugment_lm_liu_en_5.1.1_3.0_1694577437431.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("protaugment_lm_liu","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("protaugment_lm_liu", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|protaugment_lm_liu| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.4 MB| + +## References + +https://huggingface.co/tdopierre/ProtAugment-LM-Liu \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-quant_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-quant_bert_en.md new file mode 100644 index 00000000000000..33fd18fdd3cacb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-quant_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English quant_bert BertEmbeddings from dani0f +author: John Snow Labs +name: quant_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`quant_bert` is a English model originally trained by dani0f. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/quant_bert_en_5.1.1_3.0_1694580896756.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/quant_bert_en_5.1.1_3.0_1694580896756.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("quant_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("quant_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|quant_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.7 MB| + +## References + +https://huggingface.co/dani0f/quant_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-radbert_2m_en.md b/docs/_posts/ahmedlone127/2023-09-13-radbert_2m_en.md new file mode 100644 index 00000000000000..c05848d4764dee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-radbert_2m_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English radbert_2m BertEmbeddings from UCSD-VA-health +author: John Snow Labs +name: radbert_2m +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`radbert_2m` is a English model originally trained by UCSD-VA-health. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/radbert_2m_en_5.1.1_3.0_1694571151694.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/radbert_2m_en_5.1.1_3.0_1694571151694.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("radbert_2m","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("radbert_2m", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|radbert_2m| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/UCSD-VA-health/RadBERT-2m \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-retromae_beir_en.md b/docs/_posts/ahmedlone127/2023-09-13-retromae_beir_en.md new file mode 100644 index 00000000000000..5339be5c9bc0fc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-retromae_beir_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English retromae_beir BertEmbeddings from Shitao +author: John Snow Labs +name: retromae_beir +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`retromae_beir` is a English model originally trained by Shitao. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/retromae_beir_en_5.1.1_3.0_1694594494255.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/retromae_beir_en_5.1.1_3.0_1694594494255.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("retromae_beir","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("retromae_beir", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|retromae_beir| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.6 MB| + +## References + +https://huggingface.co/Shitao/RetroMAE_BEIR \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-retromae_en.md b/docs/_posts/ahmedlone127/2023-09-13-retromae_en.md new file mode 100644 index 00000000000000..8da9f2925fb0d1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-retromae_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English retromae BertEmbeddings from Shitao +author: John Snow Labs +name: retromae +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`retromae` is a English model originally trained by Shitao. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/retromae_en_5.1.1_3.0_1694594030983.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/retromae_en_5.1.1_3.0_1694594030983.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("retromae","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("retromae", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|retromae| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.6 MB| + +## References + +https://huggingface.co/Shitao/RetroMAE \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-retromae_msmarco_en.md b/docs/_posts/ahmedlone127/2023-09-13-retromae_msmarco_en.md new file mode 100644 index 00000000000000..4b81a14b8c23ec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-retromae_msmarco_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English retromae_msmarco BertEmbeddings from Shitao +author: John Snow Labs +name: retromae_msmarco +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`retromae_msmarco` is a English model originally trained by Shitao. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/retromae_msmarco_en_5.1.1_3.0_1694594205951.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/retromae_msmarco_en_5.1.1_3.0_1694594205951.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("retromae_msmarco","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("retromae_msmarco", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|retromae_msmarco| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.7 MB| + +## References + +https://huggingface.co/Shitao/RetroMAE_MSMARCO \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-retromae_msmarco_finetune_en.md b/docs/_posts/ahmedlone127/2023-09-13-retromae_msmarco_finetune_en.md new file mode 100644 index 00000000000000..fe4dd6eefedd88 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-retromae_msmarco_finetune_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English retromae_msmarco_finetune BertEmbeddings from Shitao +author: John Snow Labs +name: retromae_msmarco_finetune +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`retromae_msmarco_finetune` is a English model originally trained by Shitao. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/retromae_msmarco_finetune_en_5.1.1_3.0_1694594334803.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/retromae_msmarco_finetune_en_5.1.1_3.0_1694594334803.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("retromae_msmarco_finetune","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("retromae_msmarco_finetune", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|retromae_msmarco_finetune| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.7 MB| + +## References + +https://huggingface.co/Shitao/RetroMAE_MSMARCO_finetune \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-rubert_base_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-13-rubert_base_finetuned_en.md new file mode 100644 index 00000000000000..2355d04032b6e4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-rubert_base_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English rubert_base_finetuned BertEmbeddings from rugo +author: John Snow Labs +name: rubert_base_finetuned +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`rubert_base_finetuned` is a English model originally trained by rugo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/rubert_base_finetuned_en_5.1.1_3.0_1694575565545.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/rubert_base_finetuned_en_5.1.1_3.0_1694575565545.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("rubert_base_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("rubert_base_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rubert_base_finetuned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|667.0 MB| + +## References + +https://huggingface.co/rugo/ruBert-base-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-rubert_base_finetuned_russian_moshkov_child_corpus_pro_en.md b/docs/_posts/ahmedlone127/2023-09-13-rubert_base_finetuned_russian_moshkov_child_corpus_pro_en.md new file mode 100644 index 00000000000000..03340c4ee972e4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-rubert_base_finetuned_russian_moshkov_child_corpus_pro_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English rubert_base_finetuned_russian_moshkov_child_corpus_pro BertEmbeddings from a-v-bely +author: John Snow Labs +name: rubert_base_finetuned_russian_moshkov_child_corpus_pro +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`rubert_base_finetuned_russian_moshkov_child_corpus_pro` is a English model originally trained by a-v-bely. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/rubert_base_finetuned_russian_moshkov_child_corpus_pro_en_5.1.1_3.0_1694593004356.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/rubert_base_finetuned_russian_moshkov_child_corpus_pro_en_5.1.1_3.0_1694593004356.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("rubert_base_finetuned_russian_moshkov_child_corpus_pro","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("rubert_base_finetuned_russian_moshkov_child_corpus_pro", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rubert_base_finetuned_russian_moshkov_child_corpus_pro| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|666.9 MB| + +## References + +https://huggingface.co/a-v-bely/ruBert-base-finetuned-russian-moshkov-child-corpus-pro \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-rubert_tiny2_ru.md b/docs/_posts/ahmedlone127/2023-09-13-rubert_tiny2_ru.md new file mode 100644 index 00000000000000..aed9ae75b7de5d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-rubert_tiny2_ru.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Russian rubert_tiny2 BertEmbeddings from cointegrated +author: John Snow Labs +name: rubert_tiny2 +date: 2023-09-13 +tags: [bert, ru, open_source, fill_mask, onnx] +task: Embeddings +language: ru +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`rubert_tiny2` is a Russian model originally trained by cointegrated. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/rubert_tiny2_ru_5.1.1_3.0_1694593810298.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/rubert_tiny2_ru_5.1.1_3.0_1694593810298.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("rubert_tiny2","ru") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("rubert_tiny2", "ru") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rubert_tiny2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ru| +|Size:|109.1 MB| + +## References + +https://huggingface.co/cointegrated/rubert-tiny2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-rubert_tiny_ru.md b/docs/_posts/ahmedlone127/2023-09-13-rubert_tiny_ru.md new file mode 100644 index 00000000000000..ec331b08510361 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-rubert_tiny_ru.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Russian rubert_tiny BertEmbeddings from cointegrated +author: John Snow Labs +name: rubert_tiny +date: 2023-09-13 +tags: [bert, ru, open_source, fill_mask, onnx] +task: Embeddings +language: ru +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`rubert_tiny` is a Russian model originally trained by cointegrated. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/rubert_tiny_ru_5.1.1_3.0_1694593728672.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/rubert_tiny_ru_5.1.1_3.0_1694593728672.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("rubert_tiny","ru") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("rubert_tiny", "ru") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rubert_tiny| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ru| +|Size:|43.8 MB| + +## References + +https://huggingface.co/cointegrated/rubert-tiny \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-sanay_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-sanay_bert_en.md new file mode 100644 index 00000000000000..cc972bad977218 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-sanay_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sanay_bert BertEmbeddings from miladfa7 +author: John Snow Labs +name: sanay_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sanay_bert` is a English model originally trained by miladfa7. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sanay_bert_en_5.1.1_3.0_1694570248333.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sanay_bert_en_5.1.1_3.0_1694570248333.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sanay_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sanay_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sanay_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|602.3 MB| + +## References + +https://huggingface.co/miladfa7/sanay-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-sanaybert_model_v1_en.md b/docs/_posts/ahmedlone127/2023-09-13-sanaybert_model_v1_en.md new file mode 100644 index 00000000000000..adf2f14f361d8e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-sanaybert_model_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sanaybert_model_v1 BertEmbeddings from miladfa7 +author: John Snow Labs +name: sanaybert_model_v1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sanaybert_model_v1` is a English model originally trained by miladfa7. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sanaybert_model_v1_en_5.1.1_3.0_1694570593906.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sanaybert_model_v1_en_5.1.1_3.0_1694570593906.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sanaybert_model_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sanaybert_model_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sanaybert_model_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|606.3 MB| + +## References + +https://huggingface.co/miladfa7/sanayBERT_model_V1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-sci_summary_2_en.md b/docs/_posts/ahmedlone127/2023-09-13-sci_summary_2_en.md new file mode 100644 index 00000000000000..a474d3f7900764 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-sci_summary_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sci_summary_2 BertEmbeddings from PrathameshPawar +author: John Snow Labs +name: sci_summary_2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sci_summary_2` is a English model originally trained by PrathameshPawar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sci_summary_2_en_5.1.1_3.0_1694605799787.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sci_summary_2_en_5.1.1_3.0_1694605799787.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sci_summary_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sci_summary_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sci_summary_2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.7 MB| + +## References + +https://huggingface.co/PrathameshPawar/sci_summary_2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-sci_summary_3_en.md b/docs/_posts/ahmedlone127/2023-09-13-sci_summary_3_en.md new file mode 100644 index 00000000000000..2aff2c17e6f193 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-sci_summary_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sci_summary_3 BertEmbeddings from PrathameshPawar +author: John Snow Labs +name: sci_summary_3 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sci_summary_3` is a English model originally trained by PrathameshPawar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sci_summary_3_en_5.1.1_3.0_1694610884788.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sci_summary_3_en_5.1.1_3.0_1694610884788.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sci_summary_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sci_summary_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sci_summary_3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.7 MB| + +## References + +https://huggingface.co/PrathameshPawar/sci_summary_3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-scibert_scivocab_cased_finetuned_scc_en.md b/docs/_posts/ahmedlone127/2023-09-13-scibert_scivocab_cased_finetuned_scc_en.md new file mode 100644 index 00000000000000..67559b1f94102b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-scibert_scivocab_cased_finetuned_scc_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English scibert_scivocab_cased_finetuned_scc BertEmbeddings from phd2023 +author: John Snow Labs +name: scibert_scivocab_cased_finetuned_scc +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scibert_scivocab_cased_finetuned_scc` is a English model originally trained by phd2023. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scibert_scivocab_cased_finetuned_scc_en_5.1.1_3.0_1694589613830.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scibert_scivocab_cased_finetuned_scc_en_5.1.1_3.0_1694589613830.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("scibert_scivocab_cased_finetuned_scc","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("scibert_scivocab_cased_finetuned_scc", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scibert_scivocab_cased_finetuned_scc| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/phd2023/scibert_scivocab_cased-finetuned-scc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-scibert_scivocab_uncased_malayalam_finetuned_imdb_en.md b/docs/_posts/ahmedlone127/2023-09-13-scibert_scivocab_uncased_malayalam_finetuned_imdb_en.md new file mode 100644 index 00000000000000..4f1772f77ae8bf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-scibert_scivocab_uncased_malayalam_finetuned_imdb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English scibert_scivocab_uncased_malayalam_finetuned_imdb BertEmbeddings from Sarmila +author: John Snow Labs +name: scibert_scivocab_uncased_malayalam_finetuned_imdb +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scibert_scivocab_uncased_malayalam_finetuned_imdb` is a English model originally trained by Sarmila. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scibert_scivocab_uncased_malayalam_finetuned_imdb_en_5.1.1_3.0_1694594325370.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scibert_scivocab_uncased_malayalam_finetuned_imdb_en_5.1.1_3.0_1694594325370.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("scibert_scivocab_uncased_malayalam_finetuned_imdb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("scibert_scivocab_uncased_malayalam_finetuned_imdb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scibert_scivocab_uncased_malayalam_finetuned_imdb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.0 MB| + +## References + +https://huggingface.co/Sarmila/scibert_scivocab_uncased_ml-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-sentmae_msmarco_domain_en.md b/docs/_posts/ahmedlone127/2023-09-13-sentmae_msmarco_domain_en.md new file mode 100644 index 00000000000000..19be8159194bcb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-sentmae_msmarco_domain_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sentmae_msmarco_domain BertEmbeddings from SamuelYang +author: John Snow Labs +name: sentmae_msmarco_domain +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sentmae_msmarco_domain` is a English model originally trained by SamuelYang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sentmae_msmarco_domain_en_5.1.1_3.0_1694592225516.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sentmae_msmarco_domain_en_5.1.1_3.0_1694592225516.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sentmae_msmarco_domain","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sentmae_msmarco_domain", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sentmae_msmarco_domain| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.7 MB| + +## References + +https://huggingface.co/SamuelYang/SentMAE_MSMARCO_Domain \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-sikuroberta_zh.md b/docs/_posts/ahmedlone127/2023-09-13-sikuroberta_zh.md new file mode 100644 index 00000000000000..e2a866b6c6ab3f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-sikuroberta_zh.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Chinese sikuroberta BertEmbeddings from SIKU-BERT +author: John Snow Labs +name: sikuroberta +date: 2023-09-13 +tags: [bert, zh, open_source, fill_mask, onnx] +task: Embeddings +language: zh +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sikuroberta` is a Chinese model originally trained by SIKU-BERT. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sikuroberta_zh_5.1.1_3.0_1694570428058.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sikuroberta_zh_5.1.1_3.0_1694570428058.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sikuroberta","zh") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sikuroberta", "zh") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sikuroberta| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|zh| +|Size:|405.9 MB| + +## References + +https://huggingface.co/SIKU-BERT/sikuroberta \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-slimr_pp_msmarco_passage_en.md b/docs/_posts/ahmedlone127/2023-09-13-slimr_pp_msmarco_passage_en.md new file mode 100644 index 00000000000000..1c3c4d707f7af6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-slimr_pp_msmarco_passage_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English slimr_pp_msmarco_passage BertEmbeddings from castorini +author: John Snow Labs +name: slimr_pp_msmarco_passage +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`slimr_pp_msmarco_passage` is a English model originally trained by castorini. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/slimr_pp_msmarco_passage_en_5.1.1_3.0_1694577420042.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/slimr_pp_msmarco_passage_en_5.1.1_3.0_1694577420042.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("slimr_pp_msmarco_passage","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("slimr_pp_msmarco_passage", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|slimr_pp_msmarco_passage| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.4 MB| + +## References + +https://huggingface.co/castorini/slimr-pp-msmarco-passage \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_qqp_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_qqp_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..32cd6e16c56c25 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_qqp_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_qqp_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_qqp_custom_tokenizer_expand_vocab +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_qqp_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_qqp_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563423736.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_qqp_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694563423736.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_qqp_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_qqp_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_qqp_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|122.8 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-qqp-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-spa_en.md b/docs/_posts/ahmedlone127/2023-09-13-spa_en.md new file mode 100644 index 00000000000000..709b72b2b9a23b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-spa_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English spa BertEmbeddings from willemjan +author: John Snow Labs +name: spa +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`spa` is a English model originally trained by willemjan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/spa_en_5.1.1_3.0_1694585317302.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/spa_en_5.1.1_3.0_1694585317302.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("spa","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("spa", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|spa| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/willemjan/spa \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-spanish_bert_base_spanish_wwm_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-spanish_bert_base_spanish_wwm_cased_en.md new file mode 100644 index 00000000000000..4bb20b5f13cff8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-spanish_bert_base_spanish_wwm_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English spanish_bert_base_spanish_wwm_cased BertEmbeddings from jorgeortizfuentes +author: John Snow Labs +name: spanish_bert_base_spanish_wwm_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`spanish_bert_base_spanish_wwm_cased` is a English model originally trained by jorgeortizfuentes. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/spanish_bert_base_spanish_wwm_cased_en_5.1.1_3.0_1694605536375.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/spanish_bert_base_spanish_wwm_cased_en_5.1.1_3.0_1694605536375.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("spanish_bert_base_spanish_wwm_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("spanish_bert_base_spanish_wwm_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|spanish_bert_base_spanish_wwm_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/jorgeortizfuentes/spanish-bert-base-spanish-wwm-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-spanish_hate_speech_en.md b/docs/_posts/ahmedlone127/2023-09-13-spanish_hate_speech_en.md new file mode 100644 index 00000000000000..e6a6a6dcba7de3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-spanish_hate_speech_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English spanish_hate_speech BertEmbeddings from jorgeortizfuentes +author: John Snow Labs +name: spanish_hate_speech +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`spanish_hate_speech` is a English model originally trained by jorgeortizfuentes. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/spanish_hate_speech_en_5.1.1_3.0_1694582414893.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/spanish_hate_speech_en_5.1.1_3.0_1694582414893.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("spanish_hate_speech","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("spanish_hate_speech", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|spanish_hate_speech| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.2 MB| + +## References + +https://huggingface.co/jorgeortizfuentes/spanish_hate_speech \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-sparse_model_en.md b/docs/_posts/ahmedlone127/2023-09-13-sparse_model_en.md new file mode 100644 index 00000000000000..0b52241d4baeeb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-sparse_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sparse_model BertEmbeddings from gubartz +author: John Snow Labs +name: sparse_model +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sparse_model` is a English model originally trained by gubartz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sparse_model_en_5.1.1_3.0_1694594774734.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sparse_model_en_5.1.1_3.0_1694594774734.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sparse_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sparse_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sparse_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.0 MB| + +## References + +https://huggingface.co/gubartz/sparse_model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-structbert_large_en.md b/docs/_posts/ahmedlone127/2023-09-13-structbert_large_en.md new file mode 100644 index 00000000000000..14db2423484389 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-structbert_large_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English structbert_large BertEmbeddings from bayartsogt +author: John Snow Labs +name: structbert_large +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`structbert_large` is a English model originally trained by bayartsogt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/structbert_large_en_5.1.1_3.0_1694586021635.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/structbert_large_en_5.1.1_3.0_1694586021635.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("structbert_large","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("structbert_large", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|structbert_large| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/bayartsogt/structbert-large \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-test1_stormzj_en.md b/docs/_posts/ahmedlone127/2023-09-13-test1_stormzj_en.md new file mode 100644 index 00000000000000..cf0ce36396b584 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-test1_stormzj_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English test1_stormzj BertEmbeddings from StormZJ +author: John Snow Labs +name: test1_stormzj +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`test1_stormzj` is a English model originally trained by StormZJ. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/test1_stormzj_en_5.1.1_3.0_1694571808785.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/test1_stormzj_en_5.1.1_3.0_1694571808785.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("test1_stormzj","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("test1_stormzj", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|test1_stormzj| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/StormZJ/test1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-test_itamarl_en.md b/docs/_posts/ahmedlone127/2023-09-13-test_itamarl_en.md new file mode 100644 index 00000000000000..63efb16550a384 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-test_itamarl_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English test_itamarl BertEmbeddings from Itamarl +author: John Snow Labs +name: test_itamarl +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`test_itamarl` is a English model originally trained by Itamarl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/test_itamarl_en_5.1.1_3.0_1694579297569.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/test_itamarl_en_5.1.1_3.0_1694579297569.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("test_itamarl","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("test_itamarl", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|test_itamarl| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/Itamarl/test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tfhbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-tfhbert_en.md new file mode 100644 index 00000000000000..0ee4ae7d344486 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tfhbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tfhbert BertEmbeddings from hsc748NLP +author: John Snow Labs +name: tfhbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tfhbert` is a English model originally trained by hsc748NLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tfhbert_en_5.1.1_3.0_1694599490702.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tfhbert_en_5.1.1_3.0_1694599490702.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tfhbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tfhbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tfhbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/hsc748NLP/TfhBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tiny_biobert_en.md b/docs/_posts/ahmedlone127/2023-09-13-tiny_biobert_en.md new file mode 100644 index 00000000000000..a8f073ec4d30d3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tiny_biobert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_biobert BertEmbeddings from nlpie +author: John Snow Labs +name: tiny_biobert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_biobert` is a English model originally trained by nlpie. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_biobert_en_5.1.1_3.0_1694576110775.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_biobert_en_5.1.1_3.0_1694576110775.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_biobert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_biobert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_biobert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|51.9 MB| + +## References + +https://huggingface.co/nlpie/tiny-biobert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_ag_large_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_ag_large_scale_en.md new file mode 100644 index 00000000000000..de0465f4fac5e2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_ag_large_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_ag_large_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_ag_large_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_ag_large_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_ag_large_scale_en_5.1.1_3.0_1694588153957.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_ag_large_scale_en_5.1.1_3.0_1694588153957.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_ag_large_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_ag_large_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_ag_large_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/yxchar/tlm-ag-large-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_ag_small_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_ag_small_scale_en.md new file mode 100644 index 00000000000000..93318a8794221f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_ag_small_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_ag_small_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_ag_small_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_ag_small_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_ag_small_scale_en_5.1.1_3.0_1694588495778.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_ag_small_scale_en_5.1.1_3.0_1694588495778.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_ag_small_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_ag_small_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_ag_small_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.4 MB| + +## References + +https://huggingface.co/yxchar/tlm-ag-small-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_amazon_medium_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_amazon_medium_scale_en.md new file mode 100644 index 00000000000000..8ed4a8d6bff3a0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_amazon_medium_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_amazon_medium_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_amazon_medium_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_amazon_medium_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_amazon_medium_scale_en_5.1.1_3.0_1694588986337.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_amazon_medium_scale_en_5.1.1_3.0_1694588986337.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_amazon_medium_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_amazon_medium_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_amazon_medium_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.3 MB| + +## References + +https://huggingface.co/yxchar/tlm-amazon-medium-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_amazon_small_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_amazon_small_scale_en.md new file mode 100644 index 00000000000000..91e7a56ab6523d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_amazon_small_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_amazon_small_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_amazon_small_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_amazon_small_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_amazon_small_scale_en_5.1.1_3.0_1694589149911.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_amazon_small_scale_en_5.1.1_3.0_1694589149911.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_amazon_small_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_amazon_small_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_amazon_small_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.4 MB| + +## References + +https://huggingface.co/yxchar/tlm-amazon-small-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_chemprot_large_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_chemprot_large_scale_en.md new file mode 100644 index 00000000000000..81e55ae4a9ab29 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_chemprot_large_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_chemprot_large_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_chemprot_large_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_chemprot_large_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_chemprot_large_scale_en_5.1.1_3.0_1694589506049.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_chemprot_large_scale_en_5.1.1_3.0_1694589506049.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_chemprot_large_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_chemprot_large_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_chemprot_large_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/yxchar/tlm-chemprot-large-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_chemprot_medium_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_chemprot_medium_scale_en.md new file mode 100644 index 00000000000000..980cb100621e97 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_chemprot_medium_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_chemprot_medium_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_chemprot_medium_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_chemprot_medium_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_chemprot_medium_scale_en_5.1.1_3.0_1694589697972.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_chemprot_medium_scale_en_5.1.1_3.0_1694589697972.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_chemprot_medium_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_chemprot_medium_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_chemprot_medium_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.4 MB| + +## References + +https://huggingface.co/yxchar/tlm-chemprot-medium-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_chemprot_small_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_chemprot_small_scale_en.md new file mode 100644 index 00000000000000..c414caa64d0b8c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_chemprot_small_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_chemprot_small_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_chemprot_small_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_chemprot_small_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_chemprot_small_scale_en_5.1.1_3.0_1694589852523.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_chemprot_small_scale_en_5.1.1_3.0_1694589852523.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_chemprot_small_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_chemprot_small_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_chemprot_small_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.4 MB| + +## References + +https://huggingface.co/yxchar/tlm-chemprot-small-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_citation_intent_large_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_citation_intent_large_scale_en.md new file mode 100644 index 00000000000000..7a984aea63d88a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_citation_intent_large_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_citation_intent_large_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_citation_intent_large_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_citation_intent_large_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_citation_intent_large_scale_en_5.1.1_3.0_1694590161911.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_citation_intent_large_scale_en_5.1.1_3.0_1694590161911.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_citation_intent_large_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_citation_intent_large_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_citation_intent_large_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/yxchar/tlm-citation_intent-large-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_citation_intent_medium_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_citation_intent_medium_scale_en.md new file mode 100644 index 00000000000000..a4fb9d1f03d770 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_citation_intent_medium_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_citation_intent_medium_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_citation_intent_medium_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_citation_intent_medium_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_citation_intent_medium_scale_en_5.1.1_3.0_1694590345221.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_citation_intent_medium_scale_en_5.1.1_3.0_1694590345221.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_citation_intent_medium_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_citation_intent_medium_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_citation_intent_medium_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.4 MB| + +## References + +https://huggingface.co/yxchar/tlm-citation_intent-medium-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_citation_intent_small_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_citation_intent_small_scale_en.md new file mode 100644 index 00000000000000..f7df55ee82308c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_citation_intent_small_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_citation_intent_small_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_citation_intent_small_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_citation_intent_small_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_citation_intent_small_scale_en_5.1.1_3.0_1694590503788.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_citation_intent_small_scale_en_5.1.1_3.0_1694590503788.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_citation_intent_small_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_citation_intent_small_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_citation_intent_small_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.4 MB| + +## References + +https://huggingface.co/yxchar/tlm-citation_intent-small-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_hyp_large_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_hyp_large_scale_en.md new file mode 100644 index 00000000000000..4855486648c0c5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_hyp_large_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_hyp_large_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_hyp_large_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_hyp_large_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_hyp_large_scale_en_5.1.1_3.0_1694590870329.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_hyp_large_scale_en_5.1.1_3.0_1694590870329.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_hyp_large_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_hyp_large_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_hyp_large_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/yxchar/tlm-hyp-large-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_hyp_small_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_hyp_small_scale_en.md new file mode 100644 index 00000000000000..08288300a0a9ba --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_hyp_small_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_hyp_small_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_hyp_small_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_hyp_small_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_hyp_small_scale_en_5.1.1_3.0_1694591197892.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_hyp_small_scale_en_5.1.1_3.0_1694591197892.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_hyp_small_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_hyp_small_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_hyp_small_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.3 MB| + +## References + +https://huggingface.co/yxchar/tlm-hyp-small-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_imdb_large_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_imdb_large_scale_en.md new file mode 100644 index 00000000000000..8e904c407fc1fe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_imdb_large_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_imdb_large_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_imdb_large_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_imdb_large_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_imdb_large_scale_en_5.1.1_3.0_1694591528950.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_imdb_large_scale_en_5.1.1_3.0_1694591528950.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_imdb_large_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_imdb_large_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_imdb_large_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/yxchar/tlm-imdb-large-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_imdb_medium_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_imdb_medium_scale_en.md new file mode 100644 index 00000000000000..04fc43ae5abd4c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_imdb_medium_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_imdb_medium_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_imdb_medium_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_imdb_medium_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_imdb_medium_scale_en_5.1.1_3.0_1694591680411.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_imdb_medium_scale_en_5.1.1_3.0_1694591680411.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_imdb_medium_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_imdb_medium_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_imdb_medium_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.4 MB| + +## References + +https://huggingface.co/yxchar/tlm-imdb-medium-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_imdb_small_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_imdb_small_scale_en.md new file mode 100644 index 00000000000000..efd07eeeb510bb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_imdb_small_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_imdb_small_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_imdb_small_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_imdb_small_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_imdb_small_scale_en_5.1.1_3.0_1694591852708.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_imdb_small_scale_en_5.1.1_3.0_1694591852708.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_imdb_small_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_imdb_small_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_imdb_small_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.4 MB| + +## References + +https://huggingface.co/yxchar/tlm-imdb-small-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_rct_20k_large_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_rct_20k_large_scale_en.md new file mode 100644 index 00000000000000..407b317bf5fa2e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_rct_20k_large_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_rct_20k_large_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_rct_20k_large_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_rct_20k_large_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_rct_20k_large_scale_en_5.1.1_3.0_1694592138039.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_rct_20k_large_scale_en_5.1.1_3.0_1694592138039.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_rct_20k_large_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_rct_20k_large_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_rct_20k_large_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/yxchar/tlm-rct-20k-large-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_rct_20k_medium_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_rct_20k_medium_scale_en.md new file mode 100644 index 00000000000000..3f50500679a8ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_rct_20k_medium_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_rct_20k_medium_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_rct_20k_medium_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_rct_20k_medium_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_rct_20k_medium_scale_en_5.1.1_3.0_1694592289324.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_rct_20k_medium_scale_en_5.1.1_3.0_1694592289324.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_rct_20k_medium_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_rct_20k_medium_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_rct_20k_medium_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.4 MB| + +## References + +https://huggingface.co/yxchar/tlm-rct-20k-medium-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_rct_20k_small_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_rct_20k_small_scale_en.md new file mode 100644 index 00000000000000..cb90aa270ac363 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_rct_20k_small_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_rct_20k_small_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_rct_20k_small_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_rct_20k_small_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_rct_20k_small_scale_en_5.1.1_3.0_1694592467272.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_rct_20k_small_scale_en_5.1.1_3.0_1694592467272.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_rct_20k_small_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_rct_20k_small_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_rct_20k_small_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.4 MB| + +## References + +https://huggingface.co/yxchar/tlm-rct-20k-small-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_sciie_large_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_sciie_large_scale_en.md new file mode 100644 index 00000000000000..226b063661ca9b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_sciie_large_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_sciie_large_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_sciie_large_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_sciie_large_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_sciie_large_scale_en_5.1.1_3.0_1694592765586.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_sciie_large_scale_en_5.1.1_3.0_1694592765586.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_sciie_large_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_sciie_large_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_sciie_large_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/yxchar/tlm-sciie-large-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_sciie_medium_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_sciie_medium_scale_en.md new file mode 100644 index 00000000000000..c48b9d11323049 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_sciie_medium_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_sciie_medium_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_sciie_medium_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_sciie_medium_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_sciie_medium_scale_en_5.1.1_3.0_1694592917809.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_sciie_medium_scale_en_5.1.1_3.0_1694592917809.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_sciie_medium_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_sciie_medium_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_sciie_medium_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.4 MB| + +## References + +https://huggingface.co/yxchar/tlm-sciie-medium-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_sciie_small_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_sciie_small_scale_en.md new file mode 100644 index 00000000000000..33ce8441f75e35 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_sciie_small_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_sciie_small_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_sciie_small_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_sciie_small_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_sciie_small_scale_en_5.1.1_3.0_1694593064743.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_sciie_small_scale_en_5.1.1_3.0_1694593064743.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_sciie_small_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_sciie_small_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_sciie_small_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.4 MB| + +## References + +https://huggingface.co/yxchar/tlm-sciie-small-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tod_bert_jnt_v1_en.md b/docs/_posts/ahmedlone127/2023-09-13-tod_bert_jnt_v1_en.md new file mode 100644 index 00000000000000..69ed4ebf88f200 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tod_bert_jnt_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tod_bert_jnt_v1 BertEmbeddings from TODBERT +author: John Snow Labs +name: tod_bert_jnt_v1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tod_bert_jnt_v1` is a English model originally trained by TODBERT. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tod_bert_jnt_v1_en_5.1.1_3.0_1694572063887.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tod_bert_jnt_v1_en_5.1.1_3.0_1694572063887.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tod_bert_jnt_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tod_bert_jnt_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tod_bert_jnt_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/TODBERT/TOD-BERT-JNT-V1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tsonga_test2_en.md b/docs/_posts/ahmedlone127/2023-09-13-tsonga_test2_en.md new file mode 100644 index 00000000000000..a9f04732a4b352 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tsonga_test2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tsonga_test2 BertEmbeddings from taesu +author: John Snow Labs +name: tsonga_test2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tsonga_test2` is a English model originally trained by taesu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tsonga_test2_en_5.1.1_3.0_1694576212078.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tsonga_test2_en_5.1.1_3.0_1694576212078.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tsonga_test2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tsonga_test2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tsonga_test2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.8 MB| + +## References + +https://huggingface.co/taesu/ts-test2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tulio_chilean_spanish_bert_es.md b/docs/_posts/ahmedlone127/2023-09-13-tulio_chilean_spanish_bert_es.md new file mode 100644 index 00000000000000..66972e202146f3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tulio_chilean_spanish_bert_es.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Castilian, Spanish tulio_chilean_spanish_bert BertEmbeddings from dccuchile +author: John Snow Labs +name: tulio_chilean_spanish_bert +date: 2023-09-13 +tags: [bert, es, open_source, fill_mask, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tulio_chilean_spanish_bert` is a Castilian, Spanish model originally trained by dccuchile. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tulio_chilean_spanish_bert_es_5.1.1_3.0_1694608432981.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tulio_chilean_spanish_bert_es_5.1.1_3.0_1694608432981.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tulio_chilean_spanish_bert","es") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tulio_chilean_spanish_bert", "es") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tulio_chilean_spanish_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|es| +|Size:|409.3 MB| + +## References + +https://huggingface.co/dccuchile/tulio-chilean-spanish-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-twteval_pretrained_en.md b/docs/_posts/ahmedlone127/2023-09-13-twteval_pretrained_en.md new file mode 100644 index 00000000000000..1f668566545b48 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-twteval_pretrained_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English twteval_pretrained BertEmbeddings from ArnavL +author: John Snow Labs +name: twteval_pretrained +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`twteval_pretrained` is a English model originally trained by ArnavL. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/twteval_pretrained_en_5.1.1_3.0_1694608777119.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/twteval_pretrained_en_5.1.1_3.0_1694608777119.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("twteval_pretrained","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("twteval_pretrained", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|twteval_pretrained| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/ArnavL/twteval-pretrained \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-uzbert_base_uncased_uz.md b/docs/_posts/ahmedlone127/2023-09-13-uzbert_base_uncased_uz.md new file mode 100644 index 00000000000000..61739a056f0a81 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-uzbert_base_uncased_uz.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Uzbek uzbert_base_uncased BertEmbeddings from coppercitylabs +author: John Snow Labs +name: uzbert_base_uncased +date: 2023-09-13 +tags: [bert, uz, open_source, fill_mask, onnx] +task: Embeddings +language: uz +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`uzbert_base_uncased` is a Uzbek model originally trained by coppercitylabs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/uzbert_base_uncased_uz_5.1.1_3.0_1694594401573.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/uzbert_base_uncased_uz_5.1.1_3.0_1694594401573.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("uzbert_base_uncased","uz") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("uzbert_base_uncased", "uz") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|uzbert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|uz| +|Size:|406.8 MB| + +## References + +https://huggingface.co/coppercitylabs/uzbert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-wiki_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-wiki_bert_en.md new file mode 100644 index 00000000000000..0489731b258f2f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-wiki_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English wiki_bert BertEmbeddings from dhimskyy +author: John Snow Labs +name: wiki_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`wiki_bert` is a English model originally trained by dhimskyy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/wiki_bert_en_5.1.1_3.0_1694602328160.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/wiki_bert_en_5.1.1_3.0_1694602328160.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("wiki_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("wiki_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|wiki_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|847.5 MB| + +## References + +https://huggingface.co/dhimskyy/wiki-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-wisdomify_en.md b/docs/_posts/ahmedlone127/2023-09-13-wisdomify_en.md new file mode 100644 index 00000000000000..a57d30e1af3118 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-wisdomify_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English wisdomify BertEmbeddings from wisdomify +author: John Snow Labs +name: wisdomify +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`wisdomify` is a English model originally trained by wisdomify. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/wisdomify_en_5.1.1_3.0_1694585645519.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/wisdomify_en_5.1.1_3.0_1694585645519.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("wisdomify","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("wisdomify", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|wisdomify| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.2 MB| + +## References + +https://huggingface.co/wisdomify/wisdomify \ No newline at end of file From d6f3fe5267e1fad0df5db106525a91a62a877f4d Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Thu, 14 Sep 2023 13:28:41 +0700 Subject: [PATCH 43/55] 2023-09-13-bert_base_uncased_issues_128_juandeun_en (#13981) * Add model 2023-09-13-b_fb_sms_lm_en * Add model 2023-09-13-kannada_bert_kn * Add model 2023-09-13-bert_base_uncased_finetuned_bert_auto7_en * Add model 2023-09-13-betonews_tweetcontext_en * Add model 2023-09-13-bert_twitter_hashtag_en * Add model 2023-09-13-bert_base_cased_finetuned_bert_auto7_en * Add model 2023-09-13-telugu_bert_te * Add model 2023-09-13-arab_bert_en * 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2023-09-13-bert_large_cased_finetuned_hkdse_english_paper4_en * Add model 2023-09-13-dabert_multi_en * Add model 2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_14_en * Add model 2023-09-13-bert_base_spanish_amvv_uncased_en * Add model 2023-09-13-manubert_en * Add model 2023-09-13-greeksocialbert_base_greek_uncased_v1_el * Add model 2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_15_en * Add model 2023-09-13-bert_base_pashto_v1_ps * Add model 2023-09-13-parlbert_german_v1_de * Add model 2023-09-13-bert_pretraining_gaudi_2_batch_size_64_en * Add model 2023-09-13-bert_base_cased_portuguese_c_corpus_en * Add model 2023-09-13-testc8_1_en * Add model 2023-09-13-klue_bert_epoch3_en * Add model 2023-09-13-bert_base_stackoverflow_comments_1m_en * Add model 2023-09-13-testc8_2_en * Add model 2023-09-13-bert_base_stackoverflow_comments_2m_en * Add model 2023-09-13-kcbert_base_finetuned_en * Add model 2023-09-13-bert_base_arabic_miner_en * Add model 2023-09-14-bert_base_greek_uncased_v5_finetuned_polylex_malagasy_en * Add model 2023-09-14-dummy_model_linbo_en * Add model 2023-09-14-bert_base_code_comments_en * Add model 2023-09-14-bert_base_greek_uncased_v6_finetuned_polylex_malagasy_en * Add model 2023-09-14-bert_base_uncased_narsil_en * Add model 2023-09-14-bertugues_base_portuguese_cased_pt * Add model 2023-09-14-bert_large_stackoverflow_comments_1m_en * Add model 2023-09-14-retromae_msmarco_distill_en * Add model 2023-09-14-archaeobert_en * Add model 2023-09-14-klue_bert_mlm_en * Add model 2023-09-14-bert_base_uncased_issues_128_mabrouk_en * Add model 2023-09-14-legalbert_large_1.7m_1_en * Add model 2023-09-14-telugu_bert_scratch_te * Add model 2023-09-14-muril_base_cased_en * Add model 2023-09-14-malayalam_bert_scratch_ml * Add model 2023-09-14-weights_bert_mlm_epoch50_en * Add model 2023-09-14-bert_base_cased_conversational_finetuned_wallisian_en * Add model 2023-09-14-mbert_squad_en * Add model 2023-09-14-gujarati_bert_scratch_gu * Add model 2023-09-14-9.4aistudy_en * Add model 2023-09-14-bert_base_uncased_issues_128_veeps_en * Add model 2023-09-14-bert_base_german_europeana_td_cased_en * Add model 2023-09-14-kannada_bert_scratch_kn * Add model 2023-09-14-bert_base_uncased_issues_128_bh8648_en * Add model 2023-09-14-awesome_align_with_corsican_xx * Add model 2023-09-14-bert_base_kor_v1_en * Add model 2023-09-14-bert_base_uncased_finetuned_wallisian_en * Add model 2023-09-14-test_dushen_en * Add model 2023-09-14-bert_base_uncased_finetuned_wallisian_lower_en * Add model 2023-09-14-legalbert_large_1.7m_2_en * Add model 2023-09-14-domain_adapted_arbert_goudma_bert_en * Add model 2023-09-14-medbert_512_norwegian_duplicates_de * Add model 2023-09-14-closure_system_door_inne_bert_base_uncased_en * Add model 2023-09-14-gepabert_de * Add model 2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_5_en * Add model 2023-09-14-bert_system_en * Add model 2023-09-14-mergedistill_base_cased_anneal_en * Add model 2023-09-14-aligner_english_vietnamese_en * Add model 2023-09-14-medbit_r3_plus_it * Add model 2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_6_en * Add model 2023-09-14-mymodel_en * Add model 2023-09-14-door_inner_with_sa_bert_base_uncased_en * Add model 2023-09-14-public_models_en * Add model 2023-09-14-frpile_mlm_en * Add model 2023-09-14-radbert_en * Add model 2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_7_en * Add model 2023-09-14-bert_application_en * Add model 2023-09-14-legal_hebert_ft_en * Add model 2023-09-14-mlm_20230416_003_1_en * Add model 2023-09-14-vatestnew_en * Add model 2023-09-14-dzarabert_ar * Add model 2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_10_en * Add model 2023-09-14-alephbertgimmel_base_512_he * Add model 2023-09-14-mergedistill_base_cased_anneal_v4_en * Add model 2023-09-14-mvr_squad_bert_base_multilingual_cased_xx * Add model 2023-09-14-mlm_20230416_003_2_en * Add model 2023-09-14-medbit_it * Add model 2023-09-14-bert_base_uncased_mlm_scirepeval_fos_chemistry_en * Add model 2023-09-14-medruberttiny2_ru * Add model 2023-09-14-bert_base_uncased_issues_128_abhilashawasthi_en * Add model 2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_11_en * Add model 2023-09-14-sagorbert_nwp_finetuning_test2_en * Add model 2023-09-14-bert_base_uncased_reviews_128_en * Add model 2023-09-14-biobit_it * Add model 2023-09-14-bert_base_uncased_issues_128_reaverlee_en * Add model 2023-09-14-bert_nlp_project_imdb_en * Add model 2023-09-14-biomedvlp_cxr_bert_general_en * Add model 2023-09-14-bert_base_uncased_finetuned_char_hangman_en * Add model 2023-09-14-clinicaltrialbiobert_en * Add model 2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_12_en * Add model 2023-09-14-mlperf_inference_bert_pytorch_fp32_squad_v1.1_en * Add model 2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_13_en * Add model 2023-09-14-bert_base_bookcorpus_en * Add model 2023-09-14-autotrain_acc_keys_2347073860_en * Add model 2023-09-14-ucb_bert_finetunned_en * Add model 2023-09-14-bert_nlp_project_google_en * Add model 2023-09-14-bert_base_wikitext_en * Add model 2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_20_en * Add model 2023-09-14-splade_cocondenser_selfdistil_naver_en * Add model 2023-09-14-jobs_pretraining_model_en * Add model 2023-09-14-logion_50k_wordpiece_en * Add model 2023-09-14-splade_cocondenser_ensembledistil_en * Add model 2023-09-14-bert_based_ner_models_en * Add model 2023-09-14-model_imdb_finetuned_en * Add model 2023-09-14-bnlp_tokenizer_paraphrase_mlm_bert_900001_en * Add model 2023-09-14-gbert_large_finetuned_cust_en * Add model 2023-09-14-project3_model_en * Add model 2023-09-14-bert_base_cased_finetuned_chemistry_en * Add model 2023-09-14-sagorbert_nwp_finetuning_test4_en * Add model 2023-09-14-bert_base_uncased_mlp_scirepeval_chemistry_large_en * Add model 2023-09-14-skc_mlm_german_torch_de * Add model 2023-09-14-kw_pubmed_1000_0.0003_en * Add model 2023-09-14-test_bert_base_uncased_en * Add model 2023-09-14-test_bert_base_spanish_wwm_cased_finetuned_ultrasounds_en * Add model 2023-09-14-akkbert_en * Add model 2023-09-14-kw_pubmed_1000_0.00006_en * Add model 2023-09-14-tiny_mlm_imdb_en * Add model 2023-09-14-tiny_mlm_tweet_en * Add model 2023-09-14-kw_pubmed_1000_0.000006_en * Add model 2023-09-14-oyo_bert_base_yo * Add model 2023-09-14-mini_mlm_tweet_en * Add model 2023-09-14-small_mlm_tweet_en * Add model 2023-09-14-gbert_large_finetuned_cust18_en * Add model 2023-09-14-bert_ucb_v1_en * Add model 2023-09-14-mini_mlm_imdb_en * Add model 2023-09-14-louribert_en * Add model 2023-09-14-medium_mlm_tweet_en * Add model 2023-09-14-applicationbert_en * Add model 2023-09-14-base_mlm_tweet_en * Add model 2023-09-14-bertimbau_pt * Add model 2023-09-14-small_mlm_imdb_en * Add model 2023-09-14-vbert_2021_base_en * Add model 2023-09-14-louribert_more_tokens_saeid7776_en * Add model 2023-09-14-model_saeid7776_en * Add model 2023-09-14-model_v02_en * Add model 2023-09-14-bert_base_uncased_duplicate_en * Add model 2023-09-14-bert_base_minipile_128_en * Add model 2023-09-14-gbert_base_finetuned_twitter_janst_en * Add model 2023-09-14-bert_large_nordic_pile_1m_steps_en * Add model 2023-09-14-bert_large_nordic_pile_1m_steps_sv * Add model 2023-09-14-bibert_v0.1_en * Add model 2023-09-14-bert_base_bangla_finetuned_summarization_dataset_en * Add model 2023-09-14-incorporation_of_company_related_factual_knowledge_into_pre_trained_language_models_en * Add model 2023-09-14-bert_multilang_finetune_bangla_summarization_dataset_en * Add model 2023-09-14-bert_base_uncased_finetuned_wikitext_en * Add model 2023-09-14-antismetisim1_finetuned_mlm_en * Add model 2023-09-14-parlbert_german_law_de * Add model 2023-09-14-dictabert_seg_he * Add model 2023-09-14-dictabert_he * Add model 2023-09-14-dictabert_morph_he * Add model 2023-09-14-scholarbert_100_64bit_en * Add model 2023-09-14-coronasentana_en * Add model 2023-09-14-gbert_large_autopart_en * Add model 2023-09-14-itd_bert_en * Add model 2023-09-14-itd_longformer_en * Add model 2023-09-14-lumbarradiologyreports_en * Add model 2023-09-14-bert_base_german_cased_mlm_basque_chemistry_regulation_en * Add model 2023-09-14-bert_base_spanish_wwm_cased_finetuned_peppa_pig_en * Add model 2023-09-14-bert_base_spanish_wwm_cased_finetuned_wine_reviews_spanish_en * Add model 2023-09-14-antismetisimlargedata_finetuned_mlm_en * Add model 2023-09-14-word_ethical_ko * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_whisper_1ep_en * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_whisper_2ep_en * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_whisper_3ep_en * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_whisper_4ep_en * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_whisper_5ep_en * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_whisper_6ep_en * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_whisper_7ep_en * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_whisper_8ep_en * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_whisper_9ep_en * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_whisper_10ep_en * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_manual_1ep_en * Add model 2023-09-14-bert_base_uncased_finetuned_wallisian_manual_1ep_lower_en * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_manual_2ep_en * Add model 2023-09-14-bert_base_uncased_finetuned_wallisian_manual_2ep_lower_en * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_manual_3ep_en * Add model 2023-09-14-bert_base_uncased_finetuned_wallisian_manual_3ep_lower_en * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_manual_4ep_en * Add model 2023-09-14-bert_base_uncased_finetuned_wallisian_manual_4ep_lower_en * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_manual_5ep_en * Add model 2023-09-14-bert_base_uncased_finetuned_wallisian_manual_5ep_lower_en * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_manual_6ep_en * Add model 2023-09-14-bert_base_uncased_finetuned_wallisian_manual_6ep_lower_en * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_manual_7ep_en * Add model 2023-09-14-bert_base_uncased_finetuned_wallisian_manual_7ep_lower_en * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_manual_8ep_en * Add model 2023-09-14-bert_base_uncased_finetuned_wallisian_manual_8ep_lower_en --------- Co-authored-by: ahmedlone127 --- ...bert_base_dutch_cased_finetuned_mark_en.md | 93 +++++++++++++++++++ .../2023-09-12-legal_bert_small_uncased_en.md | 93 +++++++++++++++++++ .../2023-09-13-adopted_bert_base_cased_en.md | 93 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100644 docs/_posts/ahmedlone127/2023-09-14-weights_bert_mlm_epoch50_en.md create mode 100644 docs/_posts/ahmedlone127/2023-09-14-word_ethical_ko.md diff --git a/docs/_posts/ahmedlone127/2023-09-12-bert_base_dutch_cased_finetuned_mark_en.md b/docs/_posts/ahmedlone127/2023-09-12-bert_base_dutch_cased_finetuned_mark_en.md new file mode 100644 index 00000000000000..1ea2c73664ac48 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-bert_base_dutch_cased_finetuned_mark_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_dutch_cased_finetuned_mark BertEmbeddings from markverschuren +author: John Snow Labs +name: bert_base_dutch_cased_finetuned_mark +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_dutch_cased_finetuned_mark` is a English model originally trained by markverschuren. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_dutch_cased_finetuned_mark_en_5.1.1_3.0_1694551719944.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_dutch_cased_finetuned_mark_en_5.1.1_3.0_1694551719944.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_dutch_cased_finetuned_mark","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_dutch_cased_finetuned_mark", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_dutch_cased_finetuned_mark| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/markverschuren/bert-base-dutch-cased-finetuned-mark \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-12-legal_bert_small_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-12-legal_bert_small_uncased_en.md new file mode 100644 index 00000000000000..433d1f39c8066a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-12-legal_bert_small_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English legal_bert_small_uncased BertEmbeddings from nlpaueb +author: John Snow Labs +name: legal_bert_small_uncased +date: 2023-09-12 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legal_bert_small_uncased` is a English model originally trained by nlpaueb. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legal_bert_small_uncased_en_5.1.1_3.0_1694561644609.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legal_bert_small_uncased_en_5.1.1_3.0_1694561644609.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legal_bert_small_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legal_bert_small_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legal_bert_small_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|130.6 MB| + +## References + +https://huggingface.co/nlpaueb/legal-bert-small-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-adopted_bert_base_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-adopted_bert_base_cased_en.md new file mode 100644 index 00000000000000..5a74a536c75295 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-adopted_bert_base_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English adopted_bert_base_cased BertEmbeddings from sivanravid +author: John Snow Labs +name: adopted_bert_base_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`adopted_bert_base_cased` is a English model originally trained by sivanravid. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/adopted_bert_base_cased_en_5.1.1_3.0_1694617850169.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/adopted_bert_base_cased_en_5.1.1_3.0_1694617850169.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("adopted_bert_base_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("adopted_bert_base_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|adopted_bert_base_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/sivanravid/adopted-bert-base-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-aivengers_bert_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-13-aivengers_bert_finetuned_en.md new file mode 100644 index 00000000000000..211ca83e5c0862 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-aivengers_bert_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English aivengers_bert_finetuned BertEmbeddings from dkqp +author: John Snow Labs +name: aivengers_bert_finetuned +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`aivengers_bert_finetuned` is a English model originally trained by dkqp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/aivengers_bert_finetuned_en_5.1.1_3.0_1694620043636.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/aivengers_bert_finetuned_en_5.1.1_3.0_1694620043636.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("aivengers_bert_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("aivengers_bert_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|aivengers_bert_finetuned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/dkqp/AiVENGERS_BERT_FineTuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-aivengers_multilingual_base_xx.md b/docs/_posts/ahmedlone127/2023-09-13-aivengers_multilingual_base_xx.md new file mode 100644 index 00000000000000..5d063928b4a497 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-aivengers_multilingual_base_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual aivengers_multilingual_base BertEmbeddings from kimjae +author: John Snow Labs +name: aivengers_multilingual_base +date: 2023-09-13 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`aivengers_multilingual_base` is a Multilingual model originally trained by kimjae. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/aivengers_multilingual_base_xx_5.1.1_3.0_1694632030712.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/aivengers_multilingual_base_xx_5.1.1_3.0_1694632030712.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("aivengers_multilingual_base","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("aivengers_multilingual_base", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|aivengers_multilingual_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|665.0 MB| + +## References + +https://huggingface.co/kimjae/aivengers_multilingual_base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-alberti_bert_base_multilingual_cased_flax_community_xx.md b/docs/_posts/ahmedlone127/2023-09-13-alberti_bert_base_multilingual_cased_flax_community_xx.md new file mode 100644 index 00000000000000..dc42e3991efcd5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-alberti_bert_base_multilingual_cased_flax_community_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual alberti_bert_base_multilingual_cased_flax_community BertEmbeddings from flax-community +author: John Snow Labs +name: alberti_bert_base_multilingual_cased_flax_community +date: 2023-09-13 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`alberti_bert_base_multilingual_cased_flax_community` is a Multilingual model originally trained by flax-community. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/alberti_bert_base_multilingual_cased_flax_community_xx_5.1.1_3.0_1694642221069.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/alberti_bert_base_multilingual_cased_flax_community_xx_5.1.1_3.0_1694642221069.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("alberti_bert_base_multilingual_cased_flax_community","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("alberti_bert_base_multilingual_cased_flax_community", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|alberti_bert_base_multilingual_cased_flax_community| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|664.4 MB| + +## References + +https://huggingface.co/flax-community/alberti-bert-base-multilingual-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-alephbertgimmel_small_128_he.md b/docs/_posts/ahmedlone127/2023-09-13-alephbertgimmel_small_128_he.md new file mode 100644 index 00000000000000..9a91bffe8d49c9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-alephbertgimmel_small_128_he.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hebrew alephbertgimmel_small_128 BertEmbeddings from imvladikon +author: John Snow Labs +name: alephbertgimmel_small_128 +date: 2023-09-13 +tags: [bert, he, open_source, fill_mask, onnx] +task: Embeddings +language: he +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`alephbertgimmel_small_128` is a Hebrew model originally trained by imvladikon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/alephbertgimmel_small_128_he_5.1.1_3.0_1694642025855.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/alephbertgimmel_small_128_he_5.1.1_3.0_1694642025855.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("alephbertgimmel_small_128","he") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("alephbertgimmel_small_128", "he") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|alephbertgimmel_small_128| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|he| +|Size:|295.5 MB| + +## References + +https://huggingface.co/imvladikon/alephbertgimmel-small-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-answer_model_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-answer_model_bert_base_uncased_en.md new file mode 100644 index 00000000000000..65cf8254efadbd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-answer_model_bert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English answer_model_bert_base_uncased BertEmbeddings from Mayank393 +author: John Snow Labs +name: answer_model_bert_base_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`answer_model_bert_base_uncased` is a English model originally trained by Mayank393. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/answer_model_bert_base_uncased_en_5.1.1_3.0_1694619524376.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/answer_model_bert_base_uncased_en_5.1.1_3.0_1694619524376.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("answer_model_bert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("answer_model_bert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|answer_model_bert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Mayank393/Answer_Model_Bert_Base_uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-arab_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-arab_bert_en.md new file mode 100644 index 00000000000000..551c27abf4ba4f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-arab_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English arab_bert BertEmbeddings from MutazYoune +author: John Snow Labs +name: arab_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arab_bert` is a English model originally trained by MutazYoune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arab_bert_en_5.1.1_3.0_1694639723986.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arab_bert_en_5.1.1_3.0_1694639723986.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arab_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arab_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arab_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.4 MB| + +## References + +https://huggingface.co/MutazYoune/ARAB_BERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-arabertautomodelformaskedlm_en.md b/docs/_posts/ahmedlone127/2023-09-13-arabertautomodelformaskedlm_en.md new file mode 100644 index 00000000000000..38570a5356f300 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-arabertautomodelformaskedlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English arabertautomodelformaskedlm BertEmbeddings from oknashar +author: John Snow Labs +name: arabertautomodelformaskedlm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`arabertautomodelformaskedlm` is a English model originally trained by oknashar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/arabertautomodelformaskedlm_en_5.1.1_3.0_1694615119192.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/arabertautomodelformaskedlm_en_5.1.1_3.0_1694615119192.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("arabertautomodelformaskedlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("arabertautomodelformaskedlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|arabertautomodelformaskedlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|504.6 MB| + +## References + +https://huggingface.co/oknashar/arabertAutoModelForMaskedLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-assamese_bert_as.md b/docs/_posts/ahmedlone127/2023-09-13-assamese_bert_as.md new file mode 100644 index 00000000000000..e63db8e5e431b3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-assamese_bert_as.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Assamese assamese_bert BertEmbeddings from l3cube-pune +author: John Snow Labs +name: assamese_bert +date: 2023-09-13 +tags: [bert, as, open_source, fill_mask, onnx] +task: Embeddings +language: as +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`assamese_bert` is a Assamese model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/assamese_bert_as_5.1.1_3.0_1694642657748.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/assamese_bert_as_5.1.1_3.0_1694642657748.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("assamese_bert","as") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("assamese_bert", "as") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|assamese_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|as| +|Size:|890.4 MB| + +## References + +https://huggingface.co/l3cube-pune/assamese-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-b_fb_sms_lm_en.md b/docs/_posts/ahmedlone127/2023-09-13-b_fb_sms_lm_en.md new file mode 100644 index 00000000000000..bb4d7f3bdcbf69 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-b_fb_sms_lm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English b_fb_sms_lm BertEmbeddings from adnankhawaja +author: John Snow Labs +name: b_fb_sms_lm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`b_fb_sms_lm` is a English model originally trained by adnankhawaja. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/b_fb_sms_lm_en_5.1.1_3.0_1694638757732.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/b_fb_sms_lm_en_5.1.1_3.0_1694638757732.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("b_fb_sms_lm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("b_fb_sms_lm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|b_fb_sms_lm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/adnankhawaja/B_FB_SMS_LM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-b_t_fb_lm_en.md b/docs/_posts/ahmedlone127/2023-09-13-b_t_fb_lm_en.md new file mode 100644 index 00000000000000..2f4710361289f9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-b_t_fb_lm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English b_t_fb_lm BertEmbeddings from adnankhawaja +author: John Snow Labs +name: b_t_fb_lm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`b_t_fb_lm` is a English model originally trained by adnankhawaja. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/b_t_fb_lm_en_5.1.1_3.0_1694637241499.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/b_t_fb_lm_en_5.1.1_3.0_1694637241499.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("b_t_fb_lm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("b_t_fb_lm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|b_t_fb_lm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/adnankhawaja/B_T_FB_LM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-b_t_sms_lm_en.md b/docs/_posts/ahmedlone127/2023-09-13-b_t_sms_lm_en.md new file mode 100644 index 00000000000000..eb825775e44d97 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-b_t_sms_lm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English b_t_sms_lm BertEmbeddings from adnankhawaja +author: John Snow Labs +name: b_t_sms_lm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`b_t_sms_lm` is a English model originally trained by adnankhawaja. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/b_t_sms_lm_en_5.1.1_3.0_1694638241378.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/b_t_sms_lm_en_5.1.1_3.0_1694638241378.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("b_t_sms_lm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("b_t_sms_lm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|b_t_sms_lm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/adnankhawaja/B_T_SMS_LM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-backbone_bertnsp_600_en.md b/docs/_posts/ahmedlone127/2023-09-13-backbone_bertnsp_600_en.md new file mode 100644 index 00000000000000..246af4e1869171 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-backbone_bertnsp_600_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English backbone_bertnsp_600 BertEmbeddings from approach0 +author: John Snow Labs +name: backbone_bertnsp_600 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`backbone_bertnsp_600` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/backbone_bertnsp_600_en_5.1.1_3.0_1694618178947.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/backbone_bertnsp_600_en_5.1.1_3.0_1694618178947.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("backbone_bertnsp_600","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("backbone_bertnsp_600", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|backbone_bertnsp_600| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/backbone-bertnsp-600 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-backbone_cocomae_600_en.md b/docs/_posts/ahmedlone127/2023-09-13-backbone_cocomae_600_en.md new file mode 100644 index 00000000000000..ab45a99b6dd697 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-backbone_cocomae_600_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English backbone_cocomae_600 BertEmbeddings from approach0 +author: John Snow Labs +name: backbone_cocomae_600 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`backbone_cocomae_600` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/backbone_cocomae_600_en_5.1.1_3.0_1694617810215.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/backbone_cocomae_600_en_5.1.1_3.0_1694617810215.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("backbone_cocomae_600","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("backbone_cocomae_600", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|backbone_cocomae_600| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/approach0/backbone-cocomae-600 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-backbone_cocondenser_600_en.md b/docs/_posts/ahmedlone127/2023-09-13-backbone_cocondenser_600_en.md new file mode 100644 index 00000000000000..9756e971664562 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-backbone_cocondenser_600_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English backbone_cocondenser_600 BertEmbeddings from approach0 +author: John Snow Labs +name: backbone_cocondenser_600 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`backbone_cocondenser_600` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/backbone_cocondenser_600_en_5.1.1_3.0_1694618588688.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/backbone_cocondenser_600_en_5.1.1_3.0_1694618588688.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("backbone_cocondenser_600","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("backbone_cocondenser_600", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|backbone_cocondenser_600| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/approach0/backbone-cocondenser-600 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-backbone_cotmae_600_en.md b/docs/_posts/ahmedlone127/2023-09-13-backbone_cotmae_600_en.md new file mode 100644 index 00000000000000..4dfcb19b4d69c4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-backbone_cotmae_600_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English backbone_cotmae_600 BertEmbeddings from approach0 +author: John Snow Labs +name: backbone_cotmae_600 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`backbone_cotmae_600` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/backbone_cotmae_600_en_5.1.1_3.0_1694619025139.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/backbone_cotmae_600_en_5.1.1_3.0_1694619025139.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("backbone_cotmae_600","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("backbone_cotmae_600", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|backbone_cotmae_600| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/backbone-cotmae-600 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-batteryscibert_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-batteryscibert_cased_en.md new file mode 100644 index 00000000000000..f08dbd892aff21 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-batteryscibert_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English batteryscibert_cased BertEmbeddings from batterydata +author: John Snow Labs +name: batteryscibert_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`batteryscibert_cased` is a English model originally trained by batterydata. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/batteryscibert_cased_en_5.1.1_3.0_1694585574709.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/batteryscibert_cased_en_5.1.1_3.0_1694585574709.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("batteryscibert_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("batteryscibert_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|batteryscibert_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/batterydata/batteryscibert-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bengali_bert_bn.md b/docs/_posts/ahmedlone127/2023-09-13-bengali_bert_bn.md new file mode 100644 index 00000000000000..0ae5fbf956517b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bengali_bert_bn.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Bengali bengali_bert BertEmbeddings from l3cube-pune +author: John Snow Labs +name: bengali_bert +date: 2023-09-13 +tags: [bert, bn, open_source, fill_mask, onnx] +task: Embeddings +language: bn +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bengali_bert` is a Bengali model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bengali_bert_bn_5.1.1_3.0_1694644522311.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bengali_bert_bn_5.1.1_3.0_1694644522311.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bengali_bert","bn") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bengali_bert", "bn") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bengali_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|bn| +|Size:|890.5 MB| + +## References + +https://huggingface.co/l3cube-pune/bengali-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-berdou_200k_en.md b/docs/_posts/ahmedlone127/2023-09-13-berdou_200k_en.md new file mode 100644 index 00000000000000..6dcb474ef98322 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-berdou_200k_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English berdou_200k BertEmbeddings from flavio-nakasato +author: John Snow Labs +name: berdou_200k +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`berdou_200k` is a English model originally trained by flavio-nakasato. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/berdou_200k_en_5.1.1_3.0_1694641173323.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/berdou_200k_en_5.1.1_3.0_1694641173323.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("berdou_200k","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("berdou_200k", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|berdou_200k| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/flavio-nakasato/berdou_200k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-berdou_500k_en.md b/docs/_posts/ahmedlone127/2023-09-13-berdou_500k_en.md new file mode 100644 index 00000000000000..5e89f0cbc0081d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-berdou_500k_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English berdou_500k BertEmbeddings from flavio-nakasato +author: John Snow Labs +name: berdou_500k +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`berdou_500k` is a English model originally trained by flavio-nakasato. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/berdou_500k_en_5.1.1_3.0_1694641766005.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/berdou_500k_en_5.1.1_3.0_1694641766005.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("berdou_500k","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("berdou_500k", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|berdou_500k| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/flavio-nakasato/berdou_500k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabert_ar.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabert_ar.md new file mode 100644 index 00000000000000..5c99d91275cbaa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabert_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic bert_base_arabert BertEmbeddings from aubmindlab +author: John Snow Labs +name: bert_base_arabert +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabert` is a Arabic model originally trained by aubmindlab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabert_ar_5.1.1_3.0_1694582655825.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabert_ar_5.1.1_3.0_1694582655825.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_arabert","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_arabert", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|504.6 MB| + +## References + +https://huggingface.co/aubmindlab/bert-base-arabert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabic_miner_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabic_miner_en.md new file mode 100644 index 00000000000000..cae8a1248a1c4f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_arabic_miner_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_arabic_miner BertEmbeddings from giganticode +author: John Snow Labs +name: bert_base_arabic_miner +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabic_miner` is a English model originally trained by giganticode. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabic_miner_en_5.1.1_3.0_1694649546700.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabic_miner_en_5.1.1_3.0_1694649546700.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_arabic_miner","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_arabic_miner", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabic_miner| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/giganticode/bert-base-ar_miner \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_arapoembert_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_arapoembert_en.md new file mode 100644 index 00000000000000..c4614ae96da625 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_arapoembert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_arapoembert BertEmbeddings from faisalq +author: John Snow Labs +name: bert_base_arapoembert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arapoembert` is a English model originally trained by faisalq. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arapoembert_en_5.1.1_3.0_1694614180429.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arapoembert_en_5.1.1_3.0_1694614180429.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_arapoembert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_arapoembert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arapoembert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/faisalq/bert-base-arapoembert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_10_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_10_mlm_en.md new file mode 100644 index 00000000000000..bf002b47398ea9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_10_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_10_mlm BertEmbeddings from rithwik-db +author: John Snow Labs +name: bert_base_cased_10_mlm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_10_mlm` is a English model originally trained by rithwik-db. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_10_mlm_en_5.1.1_3.0_1694615520000.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_10_mlm_en_5.1.1_3.0_1694615520000.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_10_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_10_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_10_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/rithwik-db/bert-base-cased-10-MLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_500_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_500_mlm_en.md new file mode 100644 index 00000000000000..aaefbd47534a47 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_500_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_500_mlm BertEmbeddings from rithwik-db +author: John Snow Labs +name: bert_base_cased_500_mlm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_500_mlm` is a English model originally trained by rithwik-db. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_500_mlm_en_5.1.1_3.0_1694616384008.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_500_mlm_en_5.1.1_3.0_1694616384008.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_500_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_500_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_500_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.5 MB| + +## References + +https://huggingface.co/rithwik-db/bert-base-cased-500-MLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_50_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_50_mlm_en.md new file mode 100644 index 00000000000000..5987f965b1449e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_50_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_50_mlm BertEmbeddings from rithwik-db +author: John Snow Labs +name: bert_base_cased_50_mlm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_50_mlm` is a English model originally trained by rithwik-db. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_50_mlm_en_5.1.1_3.0_1694616027167.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_50_mlm_en_5.1.1_3.0_1694616027167.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_50_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_50_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_50_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/rithwik-db/bert-base-cased-50-MLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_b4h7_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_b4h7_en.md new file mode 100644 index 00000000000000..aaae0d23b50f37 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_b4h7_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_b4h7 BertEmbeddings from mdroth +author: John Snow Labs +name: bert_base_cased_b4h7 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_b4h7` is a English model originally trained by mdroth. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_b4h7_en_5.1.1_3.0_1694626233588.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_b4h7_en_5.1.1_3.0_1694626233588.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_b4h7","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_b4h7", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_b4h7| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/mdroth/bert-base-cased_B4H7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_finetuned_bert_auto7_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_finetuned_bert_auto7_en.md new file mode 100644 index 00000000000000..348063868c202e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_finetuned_bert_auto7_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_bert_auto7 BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_base_cased_finetuned_bert_auto7 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_bert_auto7` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_bert_auto7_en_5.1.1_3.0_1694639557369.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_bert_auto7_en_5.1.1_3.0_1694639557369.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_bert_auto7","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_bert_auto7", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_bert_auto7| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-base-cased-finetuned-bert-auto7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_finetuned_bert_mlm5_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_finetuned_bert_mlm5_en.md new file mode 100644 index 00000000000000..adbe9eb0786dbb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_finetuned_bert_mlm5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_bert_mlm5 BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_base_cased_finetuned_bert_mlm5 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_bert_mlm5` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_bert_mlm5_en_5.1.1_3.0_1694642519725.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_bert_mlm5_en_5.1.1_3.0_1694642519725.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_bert_mlm5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_bert_mlm5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_bert_mlm5| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-base-cased-finetuned-BERT-mlm5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_googlere_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_googlere_en.md new file mode 100644 index 00000000000000..f8d9e5f2f87878 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_googlere_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_googlere BertEmbeddings from triet1102 +author: John Snow Labs +name: bert_base_cased_googlere +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_googlere` is a English model originally trained by triet1102. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_googlere_en_5.1.1_3.0_1694614086575.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_googlere_en_5.1.1_3.0_1694614086575.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_googlere","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_googlere", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_googlere| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/triet1102/bert-base-cased-GoogleRE \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_googlere_masked_subj_obj_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_googlere_masked_subj_obj_en.md new file mode 100644 index 00000000000000..a2a1a54c42a41e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_googlere_masked_subj_obj_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_googlere_masked_subj_obj BertEmbeddings from triet1102 +author: John Snow Labs +name: bert_base_cased_googlere_masked_subj_obj +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_googlere_masked_subj_obj` is a English model originally trained by triet1102. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_googlere_masked_subj_obj_en_5.1.1_3.0_1694615522376.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_googlere_masked_subj_obj_en_5.1.1_3.0_1694615522376.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_googlere_masked_subj_obj","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_googlere_masked_subj_obj", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_googlere_masked_subj_obj| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/triet1102/bert-base-cased-GoogleRE-masked-subj-obj \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_model_attribution_challenge_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_model_attribution_challenge_en.md new file mode 100644 index 00000000000000..c80b08154ec392 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_model_attribution_challenge_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_model_attribution_challenge BertEmbeddings from model-attribution-challenge +author: John Snow Labs +name: bert_base_cased_model_attribution_challenge +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_model_attribution_challenge` is a English model originally trained by model-attribution-challenge. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_model_attribution_challenge_en_5.1.1_3.0_1694627672776.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_model_attribution_challenge_en_5.1.1_3.0_1694627672776.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_model_attribution_challenge","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_model_attribution_challenge", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_model_attribution_challenge| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/model-attribution-challenge/bert-base-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_portuguese_c_corpus_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_portuguese_c_corpus_en.md new file mode 100644 index 00000000000000..deb118ac14cd10 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_portuguese_c_corpus_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_portuguese_c_corpus BertEmbeddings from rosimeirecosta +author: John Snow Labs +name: bert_base_cased_portuguese_c_corpus +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_portuguese_c_corpus` is a English model originally trained by rosimeirecosta. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_portuguese_c_corpus_en_5.1.1_3.0_1694648701939.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_portuguese_c_corpus_en_5.1.1_3.0_1694648701939.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_portuguese_c_corpus","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_portuguese_c_corpus", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_portuguese_c_corpus| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/rosimeirecosta/bert-base-cased-pt-c-corpus \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_portuguese_lenerbr_vittorio_girardi_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_portuguese_lenerbr_vittorio_girardi_en.md new file mode 100644 index 00000000000000..bc36d06fbffd5e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_cased_portuguese_lenerbr_vittorio_girardi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_portuguese_lenerbr_vittorio_girardi BertEmbeddings from vittorio-girardi +author: John Snow Labs +name: bert_base_cased_portuguese_lenerbr_vittorio_girardi +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_portuguese_lenerbr_vittorio_girardi` is a English model originally trained by vittorio-girardi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_portuguese_lenerbr_vittorio_girardi_en_5.1.1_3.0_1694634389905.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_portuguese_lenerbr_vittorio_girardi_en_5.1.1_3.0_1694634389905.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_portuguese_lenerbr_vittorio_girardi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_portuguese_lenerbr_vittorio_girardi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_portuguese_lenerbr_vittorio_girardi| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/vittorio-girardi/bert-base-cased-pt-lenerbr \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_chinese_complaint_128_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_chinese_complaint_128_en.md new file mode 100644 index 00000000000000..c6536d7c5700a4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_chinese_complaint_128_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_chinese_complaint_128 BertEmbeddings from xxr +author: John Snow Labs +name: bert_base_chinese_complaint_128 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_complaint_128` is a English model originally trained by xxr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_complaint_128_en_5.1.1_3.0_1694627957165.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_complaint_128_en_5.1.1_3.0_1694627957165.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_chinese_complaint_128","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_chinese_complaint_128", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_complaint_128| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/xxr/bert-base-chinese-complaint-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_dewiki_v1_de.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_dewiki_v1_de.md new file mode 100644 index 00000000000000..ce40740b60e807 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_dewiki_v1_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German bert_base_dewiki_v1 BertEmbeddings from gwlms +author: John Snow Labs +name: bert_base_dewiki_v1 +date: 2023-09-13 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_dewiki_v1` is a German model originally trained by gwlms. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_dewiki_v1_de_5.1.1_3.0_1694623056420.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_dewiki_v1_de_5.1.1_3.0_1694623056420.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_dewiki_v1","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_dewiki_v1", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_dewiki_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|412.4 MB| + +## References + +https://huggingface.co/gwlms/bert-base-dewiki-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_generics_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_generics_mlm_en.md new file mode 100644 index 00000000000000..58af567f01aee7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_generics_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_generics_mlm BertEmbeddings from sello-ralethe +author: John Snow Labs +name: bert_base_generics_mlm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_generics_mlm` is a English model originally trained by sello-ralethe. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_generics_mlm_en_5.1.1_3.0_1694572846320.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_generics_mlm_en_5.1.1_3.0_1694572846320.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_generics_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_generics_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_generics_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/sello-ralethe/bert-base-generics-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_german_cased_finetuned_swiss_de.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_german_cased_finetuned_swiss_de.md new file mode 100644 index 00000000000000..568ac4a96b1c4d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_german_cased_finetuned_swiss_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German bert_base_german_cased_finetuned_swiss BertEmbeddings from statworx +author: John Snow Labs +name: bert_base_german_cased_finetuned_swiss +date: 2023-09-13 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_german_cased_finetuned_swiss` is a German model originally trained by statworx. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_german_cased_finetuned_swiss_de_5.1.1_3.0_1694635969690.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_german_cased_finetuned_swiss_de_5.1.1_3.0_1694635969690.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_german_cased_finetuned_swiss","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_german_cased_finetuned_swiss", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_german_cased_finetuned_swiss| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|406.9 MB| + +## References + +https://huggingface.co/statworx/bert-base-german-cased-finetuned-swiss \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_historic_multilingual_64k_td_cased_xx.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_historic_multilingual_64k_td_cased_xx.md new file mode 100644 index 00000000000000..8928e9d251c364 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_historic_multilingual_64k_td_cased_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_historic_multilingual_64k_td_cased BertEmbeddings from dbmdz +author: John Snow Labs +name: bert_base_historic_multilingual_64k_td_cased +date: 2023-09-13 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_historic_multilingual_64k_td_cased` is a Multilingual model originally trained by dbmdz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_historic_multilingual_64k_td_cased_xx_5.1.1_3.0_1694618977228.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_historic_multilingual_64k_td_cased_xx_5.1.1_3.0_1694618977228.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_historic_multilingual_64k_td_cased","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_historic_multilingual_64k_td_cased", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_historic_multilingual_64k_td_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|504.6 MB| + +## References + +https://huggingface.co/dbmdz/bert-base-historic-multilingual-64k-td-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_italian_uncased_osiria_it.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_italian_uncased_osiria_it.md new file mode 100644 index 00000000000000..96dfd316df2714 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_italian_uncased_osiria_it.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Italian bert_base_italian_uncased_osiria BertEmbeddings from osiria +author: John Snow Labs +name: bert_base_italian_uncased_osiria +date: 2023-09-13 +tags: [bert, it, open_source, fill_mask, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_italian_uncased_osiria` is a Italian model originally trained by osiria. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_italian_uncased_osiria_it_5.1.1_3.0_1694576263884.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_italian_uncased_osiria_it_5.1.1_3.0_1694576263884.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_italian_uncased_osiria","it") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_italian_uncased_osiria", "it") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_italian_uncased_osiria| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|it| +|Size:|407.1 MB| + +## References + +https://huggingface.co/osiria/bert-base-italian-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_japanese_ssuw_ja.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_japanese_ssuw_ja.md new file mode 100644 index 00000000000000..a423f277841279 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_japanese_ssuw_ja.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Japanese bert_base_japanese_ssuw BertEmbeddings from ku-accms +author: John Snow Labs +name: bert_base_japanese_ssuw +date: 2023-09-13 +tags: [bert, ja, open_source, fill_mask, onnx] +task: Embeddings +language: ja +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_japanese_ssuw` is a Japanese model originally trained by ku-accms. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_japanese_ssuw_ja_5.1.1_3.0_1694642519721.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_japanese_ssuw_ja_5.1.1_3.0_1694642519721.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_japanese_ssuw","ja") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_japanese_ssuw", "ja") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_japanese_ssuw| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ja| +|Size:|412.0 MB| + +## References + +https://huggingface.co/ku-accms/bert-base-japanese-ssuw \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_multilingual_cased_finetuned_lener_breton_xx.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_multilingual_cased_finetuned_lener_breton_xx.md new file mode 100644 index 00000000000000..40d3a6bee214e8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_multilingual_cased_finetuned_lener_breton_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_cased_finetuned_lener_breton BertEmbeddings from Luciano +author: John Snow Labs +name: bert_base_multilingual_cased_finetuned_lener_breton +date: 2023-09-13 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_cased_finetuned_lener_breton` is a Multilingual model originally trained by Luciano. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_lener_breton_xx_5.1.1_3.0_1694617850537.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_lener_breton_xx_5.1.1_3.0_1694617850537.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_multilingual_cased_finetuned_lener_breton","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_multilingual_cased_finetuned_lener_breton", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_cased_finetuned_lener_breton| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|665.1 MB| + +## References + +https://huggingface.co/Luciano/bert-base-multilingual-cased-finetuned-lener_br \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_multilingual_cased_iwslt14deen_xx.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_multilingual_cased_iwslt14deen_xx.md new file mode 100644 index 00000000000000..5609e17478e5a3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_multilingual_cased_iwslt14deen_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_cased_iwslt14deen BertEmbeddings from miugod +author: John Snow Labs +name: bert_base_multilingual_cased_iwslt14deen +date: 2023-09-13 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_cased_iwslt14deen` is a Multilingual model originally trained by miugod. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_iwslt14deen_xx_5.1.1_3.0_1694598191715.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_iwslt14deen_xx_5.1.1_3.0_1694598191715.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_multilingual_cased_iwslt14deen","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_multilingual_cased_iwslt14deen", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_cased_iwslt14deen| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|664.2 MB| + +## References + +https://huggingface.co/miugod/bert-base-multilingual-cased-iwslt14deen \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_multilingual_uncased_pretrained_xx.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_multilingual_uncased_pretrained_xx.md new file mode 100644 index 00000000000000..925cbcc12df313 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_multilingual_uncased_pretrained_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_uncased_pretrained BertEmbeddings from am-shb +author: John Snow Labs +name: bert_base_multilingual_uncased_pretrained +date: 2023-09-13 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_uncased_pretrained` is a Multilingual model originally trained by am-shb. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_uncased_pretrained_xx_5.1.1_3.0_1694579516141.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_uncased_pretrained_xx_5.1.1_3.0_1694579516141.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_multilingual_uncased_pretrained","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_multilingual_uncased_pretrained", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_uncased_pretrained| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|625.5 MB| + +## References + +https://huggingface.co/am-shb/bert-base-multilingual-uncased-pretrained \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_pashto_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_pashto_en.md new file mode 100644 index 00000000000000..79e6809ee1b7b0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_pashto_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_pashto BertEmbeddings from ijazulhaq +author: John Snow Labs +name: bert_base_pashto +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_pashto` is a English model originally trained by ijazulhaq. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_pashto_en_5.1.1_3.0_1694628431394.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_pashto_en_5.1.1_3.0_1694628431394.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_pashto","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_pashto", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_pashto| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.4 MB| + +## References + +https://huggingface.co/ijazulhaq/bert-base-pashto \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_pashto_v1_ps.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_pashto_v1_ps.md new file mode 100644 index 00000000000000..b618258ebd17d2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_pashto_v1_ps.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Pashto, Pushto bert_base_pashto_v1 BertEmbeddings from ijazulhaq +author: John Snow Labs +name: bert_base_pashto_v1 +date: 2023-09-13 +tags: [bert, ps, open_source, fill_mask, onnx] +task: Embeddings +language: ps +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_pashto_v1` is a Pashto, Pushto model originally trained by ijazulhaq. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_pashto_v1_ps_5.1.1_3.0_1694648224326.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_pashto_v1_ps_5.1.1_3.0_1694648224326.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_pashto_v1","ps") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_pashto_v1", "ps") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_pashto_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ps| +|Size:|406.3 MB| + +## References + +https://huggingface.co/ijazulhaq/bert-base-pashto-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_polish_uncased_v1_pl.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_polish_uncased_v1_pl.md new file mode 100644 index 00000000000000..8f0551a218592c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_polish_uncased_v1_pl.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Polish bert_base_polish_uncased_v1 BertEmbeddings from dkleczek +author: John Snow Labs +name: bert_base_polish_uncased_v1 +date: 2023-09-13 +tags: [bert, pl, open_source, fill_mask, onnx] +task: Embeddings +language: pl +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_polish_uncased_v1` is a Polish model originally trained by dkleczek. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_polish_uncased_v1_pl_5.1.1_3.0_1694625516398.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_polish_uncased_v1_pl_5.1.1_3.0_1694625516398.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_polish_uncased_v1","pl") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_polish_uncased_v1", "pl") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_polish_uncased_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pl| +|Size:|493.5 MB| + +## References + +https://huggingface.co/dkleczek/bert-base-polish-uncased-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_portuguese_cased_finetuned_enjoei_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_portuguese_cased_finetuned_enjoei_en.md new file mode 100644 index 00000000000000..2fe536e596bd4d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_portuguese_cased_finetuned_enjoei_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_portuguese_cased_finetuned_enjoei BertEmbeddings from gabrielgmendonca +author: John Snow Labs +name: bert_base_portuguese_cased_finetuned_enjoei +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_portuguese_cased_finetuned_enjoei` is a English model originally trained by gabrielgmendonca. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_portuguese_cased_finetuned_enjoei_en_5.1.1_3.0_1694618398309.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_portuguese_cased_finetuned_enjoei_en_5.1.1_3.0_1694618398309.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_portuguese_cased_finetuned_enjoei","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_portuguese_cased_finetuned_enjoei", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_portuguese_cased_finetuned_enjoei| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/gabrielgmendonca/bert-base-portuguese-cased-finetuned-enjoei \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_romanian_cased_v1_ro.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_romanian_cased_v1_ro.md new file mode 100644 index 00000000000000..e92c70cd72616d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_romanian_cased_v1_ro.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Moldavian, Moldovan, Romanian bert_base_romanian_cased_v1 BertEmbeddings from dumitrescustefan +author: John Snow Labs +name: bert_base_romanian_cased_v1 +date: 2023-09-13 +tags: [bert, ro, open_source, fill_mask, onnx] +task: Embeddings +language: ro +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_romanian_cased_v1` is a Moldavian, Moldovan, Romanian model originally trained by dumitrescustefan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_romanian_cased_v1_ro_5.1.1_3.0_1694627761142.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_romanian_cased_v1_ro_5.1.1_3.0_1694627761142.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_romanian_cased_v1","ro") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_romanian_cased_v1", "ro") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_romanian_cased_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ro| +|Size:|464.0 MB| + +## References + +https://huggingface.co/dumitrescustefan/bert-base-romanian-cased-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_romanian_uncased_v1_ro.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_romanian_uncased_v1_ro.md new file mode 100644 index 00000000000000..342db426389a75 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_romanian_uncased_v1_ro.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Moldavian, Moldovan, Romanian bert_base_romanian_uncased_v1 BertEmbeddings from dumitrescustefan +author: John Snow Labs +name: bert_base_romanian_uncased_v1 +date: 2023-09-13 +tags: [bert, ro, open_source, fill_mask, onnx] +task: Embeddings +language: ro +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_romanian_uncased_v1` is a Moldavian, Moldovan, Romanian model originally trained by dumitrescustefan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_romanian_uncased_v1_ro_5.1.1_3.0_1694628307563.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_romanian_uncased_v1_ro_5.1.1_3.0_1694628307563.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_romanian_uncased_v1","ro") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_romanian_uncased_v1", "ro") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_romanian_uncased_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ro| +|Size:|464.4 MB| + +## References + +https://huggingface.co/dumitrescustefan/bert-base-romanian-uncased-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_amvv_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_amvv_uncased_en.md new file mode 100644 index 00000000000000..da6672cd276449 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_spanish_amvv_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_spanish_amvv_uncased BertEmbeddings from amvargasv +author: John Snow Labs +name: bert_base_spanish_amvv_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_spanish_amvv_uncased` is a English model originally trained by amvargasv. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_spanish_amvv_uncased_en_5.1.1_3.0_1694647970479.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_spanish_amvv_uncased_en_5.1.1_3.0_1694647970479.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_spanish_amvv_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_spanish_amvv_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_spanish_amvv_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.6 MB| + +## References + +https://huggingface.co/amvargasv/bert-base-spanish-amvv-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_stackoverflow_comments_1m_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_stackoverflow_comments_1m_en.md new file mode 100644 index 00000000000000..67b2321f93e974 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_stackoverflow_comments_1m_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_stackoverflow_comments_1m BertEmbeddings from giganticode +author: John Snow Labs +name: bert_base_stackoverflow_comments_1m +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_stackoverflow_comments_1m` is a English model originally trained by giganticode. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_stackoverflow_comments_1m_en_5.1.1_3.0_1694648985744.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_stackoverflow_comments_1m_en_5.1.1_3.0_1694648985744.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_stackoverflow_comments_1m","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_stackoverflow_comments_1m", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_stackoverflow_comments_1m| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/giganticode/bert-base-StackOverflow-comments_1M \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_stackoverflow_comments_2m_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_stackoverflow_comments_2m_en.md new file mode 100644 index 00000000000000..083c6b57462ac3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_stackoverflow_comments_2m_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_stackoverflow_comments_2m BertEmbeddings from giganticode +author: John Snow Labs +name: bert_base_stackoverflow_comments_2m +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_stackoverflow_comments_2m` is a English model originally trained by giganticode. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_stackoverflow_comments_2m_en_5.1.1_3.0_1694649245162.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_stackoverflow_comments_2m_en_5.1.1_3.0_1694649245162.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_stackoverflow_comments_2m","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_stackoverflow_comments_2m", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_stackoverflow_comments_2m| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/giganticode/bert-base-StackOverflow-comments_2M \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_swedish_cased_alpha_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_swedish_cased_alpha_en.md new file mode 100644 index 00000000000000..ab636f8c757459 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_swedish_cased_alpha_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_swedish_cased_alpha BertEmbeddings from KBLab +author: John Snow Labs +name: bert_base_swedish_cased_alpha +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_swedish_cased_alpha` is a English model originally trained by KBLab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_swedish_cased_alpha_en_5.1.1_3.0_1694563762856.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_swedish_cased_alpha_en_5.1.1_3.0_1694563762856.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_swedish_cased_alpha","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_swedish_cased_alpha", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_swedish_cased_alpha| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.2 MB| + +## References + +https://huggingface.co/KBLab/bert-base-swedish-cased-alpha \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_token_dropping_dewiki_v1_de.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_token_dropping_dewiki_v1_de.md new file mode 100644 index 00000000000000..d598a22adbfa34 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_token_dropping_dewiki_v1_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German bert_base_token_dropping_dewiki_v1 BertEmbeddings from gwlms +author: John Snow Labs +name: bert_base_token_dropping_dewiki_v1 +date: 2023-09-13 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_token_dropping_dewiki_v1` is a German model originally trained by gwlms. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_token_dropping_dewiki_v1_de_5.1.1_3.0_1694624494518.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_token_dropping_dewiki_v1_de_5.1.1_3.0_1694624494518.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_token_dropping_dewiki_v1","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_token_dropping_dewiki_v1", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_token_dropping_dewiki_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|412.4 MB| + +## References + +https://huggingface.co/gwlms/bert-base-token-dropping-dewiki-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_turkish_cased_offensive_mlm_tr.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_turkish_cased_offensive_mlm_tr.md new file mode 100644 index 00000000000000..c0763e2b347050 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_turkish_cased_offensive_mlm_tr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Turkish bert_base_turkish_cased_offensive_mlm BertEmbeddings from Overfit-GM +author: John Snow Labs +name: bert_base_turkish_cased_offensive_mlm +date: 2023-09-13 +tags: [bert, tr, open_source, fill_mask, onnx] +task: Embeddings +language: tr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_turkish_cased_offensive_mlm` is a Turkish model originally trained by Overfit-GM. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_turkish_cased_offensive_mlm_tr_5.1.1_3.0_1694608252958.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_turkish_cased_offensive_mlm_tr_5.1.1_3.0_1694608252958.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_turkish_cased_offensive_mlm","tr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_turkish_cased_offensive_mlm", "tr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_turkish_cased_offensive_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|tr| +|Size:|412.3 MB| + +## References + +https://huggingface.co/Overfit-GM/bert-base-turkish-cased-offensive-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2_finetuned_rramicus_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2_finetuned_rramicus_en.md new file mode 100644 index 00000000000000..de88314f4642ea --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_2_finetuned_rramicus_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_2_finetuned_rramicus BertEmbeddings from repro-rights-amicus-briefs +author: John Snow Labs +name: bert_base_uncased_2_finetuned_rramicus +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_2_finetuned_rramicus` is a English model originally trained by repro-rights-amicus-briefs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_2_finetuned_rramicus_en_5.1.1_3.0_1694636800409.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_2_finetuned_rramicus_en_5.1.1_3.0_1694636800409.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_2_finetuned_rramicus","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_2_finetuned_rramicus", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_2_finetuned_rramicus| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/repro-rights-amicus-briefs/bert-base-uncased-2-finetuned-RRamicus \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_ancient_greek_v1_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_ancient_greek_v1_en.md new file mode 100644 index 00000000000000..89129e45c8ba2b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_ancient_greek_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_ancient_greek_v1 BertEmbeddings from Sonnenblume +author: John Snow Labs +name: bert_base_uncased_ancient_greek_v1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_ancient_greek_v1` is a English model originally trained by Sonnenblume. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_ancient_greek_v1_en_5.1.1_3.0_1694616781198.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_ancient_greek_v1_en_5.1.1_3.0_1694616781198.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_ancient_greek_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_ancient_greek_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_ancient_greek_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|421.0 MB| + +## References + +https://huggingface.co/Sonnenblume/bert-base-uncased-ancient-greek-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_ancient_greek_v3_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_ancient_greek_v3_en.md new file mode 100644 index 00000000000000..19179b1becd45f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_ancient_greek_v3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_ancient_greek_v3 BertEmbeddings from Sonnenblume +author: John Snow Labs +name: bert_base_uncased_ancient_greek_v3 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_ancient_greek_v3` is a English model originally trained by Sonnenblume. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_ancient_greek_v3_en_5.1.1_3.0_1694617164732.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_ancient_greek_v3_en_5.1.1_3.0_1694617164732.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_ancient_greek_v3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_ancient_greek_v3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_ancient_greek_v3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|421.0 MB| + +## References + +https://huggingface.co/Sonnenblume/bert-base-uncased-ancient-greek-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_ancient_greek_v4_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_ancient_greek_v4_en.md new file mode 100644 index 00000000000000..01260c6d80d789 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_ancient_greek_v4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_ancient_greek_v4 BertEmbeddings from Sonnenblume +author: John Snow Labs +name: bert_base_uncased_ancient_greek_v4 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_ancient_greek_v4` is a English model originally trained by Sonnenblume. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_ancient_greek_v4_en_5.1.1_3.0_1694636309967.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_ancient_greek_v4_en_5.1.1_3.0_1694636309967.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_ancient_greek_v4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_ancient_greek_v4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_ancient_greek_v4| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|421.0 MB| + +## References + +https://huggingface.co/Sonnenblume/bert-base-uncased-ancient-greek-v4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bert_auto5_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bert_auto5_en.md new file mode 100644 index 00000000000000..ed245ba4391507 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bert_auto5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_bert_auto5 BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_base_uncased_finetuned_bert_auto5 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_bert_auto5` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_bert_auto5_en_5.1.1_3.0_1694638038292.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_bert_auto5_en_5.1.1_3.0_1694638038292.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_bert_auto5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_bert_auto5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_bert_auto5| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-base-uncased-finetuned-bert-auto5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bert_auto6_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bert_auto6_en.md new file mode 100644 index 00000000000000..7fab8b7bb60418 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bert_auto6_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_bert_auto6 BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_base_uncased_finetuned_bert_auto6 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_bert_auto6` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_bert_auto6_en_5.1.1_3.0_1694638484821.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_bert_auto6_en_5.1.1_3.0_1694638484821.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_bert_auto6","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_bert_auto6", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_bert_auto6| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-base-uncased-finetuned-bert-auto6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bert_auto7_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bert_auto7_en.md new file mode 100644 index 00000000000000..cee0b7df827814 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bert_auto7_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_bert_auto7 BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_base_uncased_finetuned_bert_auto7 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_bert_auto7` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_bert_auto7_en_5.1.1_3.0_1694638942003.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_bert_auto7_en_5.1.1_3.0_1694638942003.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_bert_auto7","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_bert_auto7", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_bert_auto7| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-base-uncased-finetuned-bert-auto7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bert_auto_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bert_auto_en.md new file mode 100644 index 00000000000000..93b436cea9e9b4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bert_auto_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_bert_auto BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_base_uncased_finetuned_bert_auto +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_bert_auto` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_bert_auto_en_5.1.1_3.0_1694637609034.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_bert_auto_en_5.1.1_3.0_1694637609034.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_bert_auto","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_bert_auto", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_bert_auto| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-base-uncased-finetuned-bert-auto \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bert_mlm9_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bert_mlm9_en.md new file mode 100644 index 00000000000000..d8c678257f171b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bert_mlm9_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_bert_mlm9 BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_base_uncased_finetuned_bert_mlm9 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_bert_mlm9` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_bert_mlm9_en_5.1.1_3.0_1694643632914.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_bert_mlm9_en_5.1.1_3.0_1694643632914.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_bert_mlm9","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_bert_mlm9", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_bert_mlm9| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-base-uncased-finetuned-BERT-mlm9 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bert_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bert_mlm_en.md new file mode 100644 index 00000000000000..e238a28cfb0d90 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_bert_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_bert_mlm BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_base_uncased_finetuned_bert_mlm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_bert_mlm` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_bert_mlm_en_5.1.1_3.0_1694640077705.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_bert_mlm_en_5.1.1_3.0_1694640077705.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_bert_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_bert_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_bert_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-base-uncased-finetuned-bert-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_eva_accelerate_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_eva_accelerate_en.md new file mode 100644 index 00000000000000..ad0bc6388cdfad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_eva_accelerate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_eva_accelerate BertEmbeddings from CesarLeblanc +author: John Snow Labs +name: bert_base_uncased_finetuned_eva_accelerate +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_eva_accelerate` is a English model originally trained by CesarLeblanc. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_eva_accelerate_en_5.1.1_3.0_1694616995090.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_eva_accelerate_en_5.1.1_3.0_1694616995090.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_eva_accelerate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_eva_accelerate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_eva_accelerate| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|440.6 MB| + +## References + +https://huggingface.co/CesarLeblanc/bert-base-uncased-finetuned-eva-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_eva_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_eva_en.md new file mode 100644 index 00000000000000..7f40930725eb5a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_eva_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_eva BertEmbeddings from CesarLeblanc +author: John Snow Labs +name: bert_base_uncased_finetuned_eva +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_eva` is a English model originally trained by CesarLeblanc. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_eva_en_5.1.1_3.0_1694616645644.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_eva_en_5.1.1_3.0_1694616645644.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_eva","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_eva", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_eva| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|440.7 MB| + +## References + +https://huggingface.co/CesarLeblanc/bert-base-uncased-finetuned-eva \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_auto3_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_auto3_en.md new file mode 100644 index 00000000000000..1f5ca3901ccdd8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_auto3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_himani_auto3 BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_base_uncased_finetuned_himani_auto3 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_himani_auto3` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_himani_auto3_en_5.1.1_3.0_1694637243769.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_himani_auto3_en_5.1.1_3.0_1694637243769.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_himani_auto3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_himani_auto3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_himani_auto3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-base-uncased-finetuned-himani-auto3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_12_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_12_en.md new file mode 100644 index 00000000000000..30a52eb8ef2ce8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_12_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_himani_gen_mlm_12 BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_base_uncased_finetuned_himani_gen_mlm_12 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_himani_gen_mlm_12` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_himani_gen_mlm_12_en_5.1.1_3.0_1694646419148.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_himani_gen_mlm_12_en_5.1.1_3.0_1694646419148.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_himani_gen_mlm_12","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_himani_gen_mlm_12", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_himani_gen_mlm_12| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-base-uncased-finetuned-himani-gen-MLM-12 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_13_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_13_en.md new file mode 100644 index 00000000000000..025b90f82be72c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_13_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_himani_gen_mlm_13 BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_base_uncased_finetuned_himani_gen_mlm_13 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_himani_gen_mlm_13` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_himani_gen_mlm_13_en_5.1.1_3.0_1694647435616.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_himani_gen_mlm_13_en_5.1.1_3.0_1694647435616.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_himani_gen_mlm_13","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_himani_gen_mlm_13", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_himani_gen_mlm_13| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-base-uncased-finetuned-himani-gen-MLM-13 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_14_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_14_en.md new file mode 100644 index 00000000000000..1f93fa43a398d7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_14_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_himani_gen_mlm_14 BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_base_uncased_finetuned_himani_gen_mlm_14 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_himani_gen_mlm_14` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_himani_gen_mlm_14_en_5.1.1_3.0_1694647735738.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_himani_gen_mlm_14_en_5.1.1_3.0_1694647735738.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_himani_gen_mlm_14","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_himani_gen_mlm_14", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_himani_gen_mlm_14| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-base-uncased-finetuned-himani-gen-MLM-14 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_15_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_15_en.md new file mode 100644 index 00000000000000..5d94900e2a8e98 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_15_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_himani_gen_mlm_15 BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_base_uncased_finetuned_himani_gen_mlm_15 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_himani_gen_mlm_15` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_himani_gen_mlm_15_en_5.1.1_3.0_1694648220318.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_himani_gen_mlm_15_en_5.1.1_3.0_1694648220318.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_himani_gen_mlm_15","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_himani_gen_mlm_15", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_himani_gen_mlm_15| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-base-uncased-finetuned-himani-gen-MLM-15 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_1_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_1_en.md new file mode 100644 index 00000000000000..eec8122a404e29 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_himani_gen_mlm_1 BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_base_uncased_finetuned_himani_gen_mlm_1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_himani_gen_mlm_1` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_himani_gen_mlm_1_en_5.1.1_3.0_1694645767215.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_himani_gen_mlm_1_en_5.1.1_3.0_1694645767215.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_himani_gen_mlm_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_himani_gen_mlm_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_himani_gen_mlm_1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-base-uncased-finetuned-himani-gen-MLM-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_en.md new file mode 100644 index 00000000000000..cb4afbfc9e3672 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_gen_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_himani_gen_mlm BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_base_uncased_finetuned_himani_gen_mlm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_himani_gen_mlm` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_himani_gen_mlm_en_5.1.1_3.0_1694645433865.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_himani_gen_mlm_en_5.1.1_3.0_1694645433865.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_himani_gen_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_himani_gen_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_himani_gen_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-base-uncased-finetuned-himani-gen-MLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_n_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_n_en.md new file mode 100644 index 00000000000000..c7511dadb718eb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_himani_n_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_himani_n BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_base_uncased_finetuned_himani_n +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_himani_n` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_himani_n_en_5.1.1_3.0_1694636833361.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_himani_n_en_5.1.1_3.0_1694636833361.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_himani_n","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_himani_n", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_himani_n| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-base-uncased-finetuned-himani-n \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_imdb_jasheu_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_imdb_jasheu_en.md new file mode 100644 index 00000000000000..847243b5266b65 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_imdb_jasheu_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_imdb_jasheu BertEmbeddings from jasheu +author: John Snow Labs +name: bert_base_uncased_finetuned_imdb_jasheu +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_imdb_jasheu` is a English model originally trained by jasheu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_imdb_jasheu_en_5.1.1_3.0_1694624085789.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_imdb_jasheu_en_5.1.1_3.0_1694624085789.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_imdb_jasheu","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_imdb_jasheu", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_imdb_jasheu| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/jasheu/bert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_imdb_medhabi_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_imdb_medhabi_en.md new file mode 100644 index 00000000000000..9c8c1789981c46 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_imdb_medhabi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_imdb_medhabi BertEmbeddings from medhabi +author: John Snow Labs +name: bert_base_uncased_finetuned_imdb_medhabi +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_imdb_medhabi` is a English model originally trained by medhabi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_imdb_medhabi_en_5.1.1_3.0_1694626974894.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_imdb_medhabi_en_5.1.1_3.0_1694626974894.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_imdb_medhabi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_imdb_medhabi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_imdb_medhabi| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/medhabi/bert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_imdb_sarmila_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_imdb_sarmila_en.md new file mode 100644 index 00000000000000..0451f1a577d372 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_finetuned_imdb_sarmila_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_imdb_sarmila BertEmbeddings from Sarmila +author: John Snow Labs +name: bert_base_uncased_finetuned_imdb_sarmila +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_imdb_sarmila` is a English model originally trained by Sarmila. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_imdb_sarmila_en_5.1.1_3.0_1694641541584.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_imdb_sarmila_en_5.1.1_3.0_1694641541584.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_imdb_sarmila","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_imdb_sarmila", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_imdb_sarmila| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Sarmila/bert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_antoinev17_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_antoinev17_en.md new file mode 100644 index 00000000000000..6c6943aaa25509 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_antoinev17_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_antoinev17 BertEmbeddings from antoinev17 +author: John Snow Labs +name: bert_base_uncased_issues_128_antoinev17 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_antoinev17` is a English model originally trained by antoinev17. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_antoinev17_en_5.1.1_3.0_1694634951894.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_antoinev17_en_5.1.1_3.0_1694634951894.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_antoinev17","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_antoinev17", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_antoinev17| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/antoinev17/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_betelgeux_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_betelgeux_en.md new file mode 100644 index 00000000000000..8cc07f931d8b7d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_betelgeux_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_betelgeux BertEmbeddings from betelgeux +author: John Snow Labs +name: bert_base_uncased_issues_128_betelgeux +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_betelgeux` is a English model originally trained by betelgeux. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_betelgeux_en_5.1.1_3.0_1694627127578.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_betelgeux_en_5.1.1_3.0_1694627127578.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_betelgeux","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_betelgeux", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_betelgeux| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/betelgeux/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_cj_mills_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_cj_mills_en.md new file mode 100644 index 00000000000000..bed15b90576773 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_cj_mills_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_cj_mills BertEmbeddings from cj-mills +author: John Snow Labs +name: bert_base_uncased_issues_128_cj_mills +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_cj_mills` is a English model originally trained by cj-mills. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_cj_mills_en_5.1.1_3.0_1694645354456.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_cj_mills_en_5.1.1_3.0_1694645354456.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_cj_mills","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_cj_mills", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_cj_mills| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/cj-mills/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_frahman_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_frahman_en.md new file mode 100644 index 00000000000000..ffb41eba22f342 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_frahman_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_frahman BertEmbeddings from frahman +author: John Snow Labs +name: bert_base_uncased_issues_128_frahman +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_frahman` is a English model originally trained by frahman. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_frahman_en_5.1.1_3.0_1694627458573.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_frahman_en_5.1.1_3.0_1694627458573.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_frahman","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_frahman", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_frahman| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/frahman/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_hanwoon_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_hanwoon_en.md new file mode 100644 index 00000000000000..45709508e28952 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_hanwoon_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_hanwoon BertEmbeddings from Hanwoon +author: John Snow Labs +name: bert_base_uncased_issues_128_hanwoon +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_hanwoon` is a English model originally trained by Hanwoon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_hanwoon_en_5.1.1_3.0_1694619277361.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_hanwoon_en_5.1.1_3.0_1694619277361.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_hanwoon","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_hanwoon", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_hanwoon| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Hanwoon/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_hudee_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_hudee_en.md new file mode 100644 index 00000000000000..f87d854ac2f1fc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_hudee_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_hudee BertEmbeddings from Hudee +author: John Snow Labs +name: bert_base_uncased_issues_128_hudee +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_hudee` is a English model originally trained by Hudee. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_hudee_en_5.1.1_3.0_1694637838114.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_hudee_en_5.1.1_3.0_1694637838114.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_hudee","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_hudee", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_hudee| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Hudee/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_juandeun_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_juandeun_en.md new file mode 100644 index 00000000000000..f6843fa12ad757 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_juandeun_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_juandeun BertEmbeddings from juandeun +author: John Snow Labs +name: bert_base_uncased_issues_128_juandeun +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_juandeun` is a English model originally trained by juandeun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_juandeun_en_5.1.1_3.0_1694600405699.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_juandeun_en_5.1.1_3.0_1694600405699.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_juandeun","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_juandeun", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_juandeun| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/juandeun/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_kiki2013_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_kiki2013_en.md new file mode 100644 index 00000000000000..7858ab760fd57f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_kiki2013_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_kiki2013 BertEmbeddings from kiki2013 +author: John Snow Labs +name: bert_base_uncased_issues_128_kiki2013 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_kiki2013` is a English model originally trained by kiki2013. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_kiki2013_en_5.1.1_3.0_1694609694156.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_kiki2013_en_5.1.1_3.0_1694609694156.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_kiki2013","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_kiki2013", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_kiki2013| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/kiki2013/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_lijingxin_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_lijingxin_en.md new file mode 100644 index 00000000000000..0a05f166fad5ba --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_lijingxin_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_lijingxin BertEmbeddings from lijingxin +author: John Snow Labs +name: bert_base_uncased_issues_128_lijingxin +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_lijingxin` is a English model originally trained by lijingxin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_lijingxin_en_5.1.1_3.0_1694611963447.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_lijingxin_en_5.1.1_3.0_1694611963447.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_lijingxin","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_lijingxin", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_lijingxin| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/lijingxin/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_martinwunderlich_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_martinwunderlich_en.md new file mode 100644 index 00000000000000..d2b2cd560a2ea8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_martinwunderlich_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_martinwunderlich BertEmbeddings from martinwunderlich +author: John Snow Labs +name: bert_base_uncased_issues_128_martinwunderlich +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_martinwunderlich` is a English model originally trained by martinwunderlich. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_martinwunderlich_en_5.1.1_3.0_1694633945364.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_martinwunderlich_en_5.1.1_3.0_1694633945364.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_martinwunderlich","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_martinwunderlich", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_martinwunderlich| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/martinwunderlich/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_olpa_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_olpa_en.md new file mode 100644 index 00000000000000..33466e6cf42ab6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_issues_128_olpa_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_olpa BertEmbeddings from olpa +author: John Snow Labs +name: bert_base_uncased_issues_128_olpa +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_olpa` is a English model originally trained by olpa. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_olpa_en_5.1.1_3.0_1694631357838.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_olpa_en_5.1.1_3.0_1694631357838.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_olpa","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_olpa", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_olpa| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/olpa/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_mlm_en.md new file mode 100644 index 00000000000000..63f27883f83cc0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_mlm BertEmbeddings from wypoon +author: John Snow Labs +name: bert_base_uncased_mlm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_mlm` is a English model originally trained by wypoon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mlm_en_5.1.1_3.0_1694614779984.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mlm_en_5.1.1_3.0_1694614779984.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/wypoon/bert-base-uncased-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_model_attribution_challenge_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_model_attribution_challenge_en.md new file mode 100644 index 00000000000000..aea6646fb8eb77 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_model_attribution_challenge_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_model_attribution_challenge BertEmbeddings from model-attribution-challenge +author: John Snow Labs +name: bert_base_uncased_model_attribution_challenge +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_model_attribution_challenge` is a English model originally trained by model-attribution-challenge. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_model_attribution_challenge_en_5.1.1_3.0_1694628303662.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_model_attribution_challenge_en_5.1.1_3.0_1694628303662.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_model_attribution_challenge","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_model_attribution_challenge", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_model_attribution_challenge| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/model-attribution-challenge/bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_multi_128_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_multi_128_en.md new file mode 100644 index 00000000000000..21d3f1a31b8aef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_multi_128_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_multi_128 BertEmbeddings from xxr +author: John Snow Labs +name: bert_base_uncased_multi_128 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_multi_128` is a English model originally trained by xxr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_multi_128_en_5.1.1_3.0_1694625544033.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_multi_128_en_5.1.1_3.0_1694625544033.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_multi_128","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_multi_128", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_multi_128| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/xxr/bert-base-uncased-multi-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_sparse_80_1x4_block_pruneofa_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_sparse_80_1x4_block_pruneofa_en.md new file mode 100644 index 00000000000000..498a7d19a4b591 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_sparse_80_1x4_block_pruneofa_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_sparse_80_1x4_block_pruneofa BertEmbeddings from Intel +author: John Snow Labs +name: bert_base_uncased_sparse_80_1x4_block_pruneofa +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sparse_80_1x4_block_pruneofa` is a English model originally trained by Intel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sparse_80_1x4_block_pruneofa_en_5.1.1_3.0_1694621945507.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sparse_80_1x4_block_pruneofa_en_5.1.1_3.0_1694621945507.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_sparse_80_1x4_block_pruneofa","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_sparse_80_1x4_block_pruneofa", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sparse_80_1x4_block_pruneofa| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|195.0 MB| + +## References + +https://huggingface.co/Intel/bert-base-uncased-sparse-80-1x4-block-pruneofa \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_swahili_sw.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_swahili_sw.md new file mode 100644 index 00000000000000..38c58fe8d9941e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_uncased_swahili_sw.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Swahili (macrolanguage) bert_base_uncased_swahili BertEmbeddings from flax-community +author: John Snow Labs +name: bert_base_uncased_swahili +date: 2023-09-13 +tags: [bert, sw, open_source, fill_mask, onnx] +task: Embeddings +language: sw +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_swahili` is a Swahili (macrolanguage) model originally trained by flax-community. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_swahili_sw_5.1.1_3.0_1694642650442.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_swahili_sw_5.1.1_3.0_1694642650442.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_swahili","sw") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_swahili", "sw") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_swahili| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|sw| +|Size:|408.0 MB| + +## References + +https://huggingface.co/flax-community/bert-base-uncased-swahili \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_base_vn_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_base_vn_en.md new file mode 100644 index 00000000000000..4397c42a1ae458 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_base_vn_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_vn BertEmbeddings from NlpHUST +author: John Snow Labs +name: bert_base_vn +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_vn` is a English model originally trained by NlpHUST. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_vn_en_5.1.1_3.0_1694624251745.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_vn_en_5.1.1_3.0_1694624251745.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_vn","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_vn", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_vn| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|498.8 MB| + +## References + +https://huggingface.co/NlpHUST/bert-base-vn \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_based_answer_model_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_based_answer_model_en.md new file mode 100644 index 00000000000000..46cfd40d698bc4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_based_answer_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_based_answer_model BertEmbeddings from Kunjesh07 +author: John Snow Labs +name: bert_based_answer_model +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_based_answer_model` is a English model originally trained by Kunjesh07. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_based_answer_model_en_5.1.1_3.0_1694616027282.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_based_answer_model_en_5.1.1_3.0_1694616027282.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_based_answer_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_based_answer_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_based_answer_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Kunjesh07/Bert-Based-Answer-Model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_cluster_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_cluster_en.md new file mode 100644 index 00000000000000..29a284c498eb04 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_cluster_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_cluster BertEmbeddings from mipatov +author: John Snow Labs +name: bert_cluster +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_cluster` is a English model originally trained by mipatov. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_cluster_en_5.1.1_3.0_1694647045460.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_cluster_en_5.1.1_3.0_1694647045460.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_cluster","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_cluster", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_cluster| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|664.9 MB| + +## References + +https://huggingface.co/mipatov/bert_cluster \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_fintuning_test1_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_fintuning_test1_en.md new file mode 100644 index 00000000000000..421adfc07a9f4f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_fintuning_test1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_fintuning_test1 BertEmbeddings from ZhaoyiGUAN +author: John Snow Labs +name: bert_fintuning_test1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_fintuning_test1` is a English model originally trained by ZhaoyiGUAN. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_fintuning_test1_en_5.1.1_3.0_1694574944830.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_fintuning_test1_en_5.1.1_3.0_1694574944830.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_fintuning_test1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_fintuning_test1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_fintuning_test1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/ZhaoyiGUAN/Bert_Fintuning_Test1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_kor_base_pz_language_test_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_kor_base_pz_language_test_en.md new file mode 100644 index 00000000000000..ec736e433c0d7f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_kor_base_pz_language_test_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_kor_base_pz_language_test BertEmbeddings from Hanwoon +author: John Snow Labs +name: bert_kor_base_pz_language_test +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_kor_base_pz_language_test` is a English model originally trained by Hanwoon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_kor_base_pz_language_test_en_5.1.1_3.0_1694620643379.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_kor_base_pz_language_test_en_5.1.1_3.0_1694620643379.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_kor_base_pz_language_test","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_kor_base_pz_language_test", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_kor_base_pz_language_test| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|441.1 MB| + +## References + +https://huggingface.co/Hanwoon/bert-kor-base-pz-language-test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_l12_h240_a12_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_l12_h240_a12_en.md new file mode 100644 index 00000000000000..17ce53d63b13f2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_l12_h240_a12_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_l12_h240_a12 BertEmbeddings from eli4s +author: John Snow Labs +name: bert_l12_h240_a12 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_l12_h240_a12` is a English model originally trained by eli4s. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_l12_h240_a12_en_5.1.1_3.0_1694629467102.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_l12_h240_a12_en_5.1.1_3.0_1694629467102.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_l12_h240_a12","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_l12_h240_a12", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_l12_h240_a12| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|105.1 MB| + +## References + +https://huggingface.co/eli4s/Bert-L12-h240-A12 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_l12_h256_a4_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_l12_h256_a4_en.md new file mode 100644 index 00000000000000..63e2963fe9b7ba --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_l12_h256_a4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_l12_h256_a4 BertEmbeddings from eli4s +author: John Snow Labs +name: bert_l12_h256_a4 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_l12_h256_a4` is a English model originally trained by eli4s. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_l12_h256_a4_en_5.1.1_3.0_1694629695737.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_l12_h256_a4_en_5.1.1_3.0_1694629695737.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_l12_h256_a4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_l12_h256_a4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_l12_h256_a4| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|112.8 MB| + +## References + +https://huggingface.co/eli4s/Bert-L12-h256-A4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_l12_h384_a6_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_l12_h384_a6_en.md new file mode 100644 index 00000000000000..2bda8fd14d7977 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_l12_h384_a6_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_l12_h384_a6 BertEmbeddings from eli4s +author: John Snow Labs +name: bert_l12_h384_a6 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_l12_h384_a6` is a English model originally trained by eli4s. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_l12_h384_a6_en_5.1.1_3.0_1694629972509.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_l12_h384_a6_en_5.1.1_3.0_1694629972509.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_l12_h384_a6","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_l12_h384_a6", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_l12_h384_a6| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|177.8 MB| + +## References + +https://huggingface.co/eli4s/Bert-L12-h384-A6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_hkdse_english_paper4_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_hkdse_english_paper4_en.md new file mode 100644 index 00000000000000..1906ba067e6f5b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_finetuned_hkdse_english_paper4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_finetuned_hkdse_english_paper4 BertEmbeddings from Wootang01 +author: John Snow Labs +name: bert_large_cased_finetuned_hkdse_english_paper4 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_finetuned_hkdse_english_paper4` is a English model originally trained by Wootang01. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_hkdse_english_paper4_en_5.1.1_3.0_1694647633988.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_hkdse_english_paper4_en_5.1.1_3.0_1694647633988.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_finetuned_hkdse_english_paper4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_finetuned_hkdse_english_paper4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_finetuned_hkdse_english_paper4| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/Wootang01/bert-large-cased-finetuned-hkdse-english-paper4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_portuguese_lenerbr_vittorio_girardi_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_portuguese_lenerbr_vittorio_girardi_en.md new file mode 100644 index 00000000000000..2d00656cbff7e9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_portuguese_lenerbr_vittorio_girardi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_portuguese_lenerbr_vittorio_girardi BertEmbeddings from vittorio-girardi +author: John Snow Labs +name: bert_large_cased_portuguese_lenerbr_vittorio_girardi +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_portuguese_lenerbr_vittorio_girardi` is a English model originally trained by vittorio-girardi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_portuguese_lenerbr_vittorio_girardi_en_5.1.1_3.0_1694635205835.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_portuguese_lenerbr_vittorio_girardi_en_5.1.1_3.0_1694635205835.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_portuguese_lenerbr_vittorio_girardi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_portuguese_lenerbr_vittorio_girardi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_portuguese_lenerbr_vittorio_girardi| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/vittorio-girardi/bert-large-cased-pt-lenerbr \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_0_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_0_en.md new file mode 100644 index 00000000000000..7c833d1b9264cc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_0 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_0 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_0` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_0_en_5.1.1_3.0_1694645453821.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_0_en_5.1.1_3.0_1694645453821.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_0| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_1_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_1_en.md new file mode 100644 index 00000000000000..6fda8b16781464 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_1 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_1` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_1_en_5.1.1_3.0_1694646476945.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_1_en_5.1.1_3.0_1694646476945.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_24_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_24_en.md new file mode 100644 index 00000000000000..28a34cb128ea8f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_24_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_24 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_24 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_24` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_24_en_5.1.1_3.0_1694570634995.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_24_en_5.1.1_3.0_1694570634995.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_24","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_24", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_fast_24| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-24 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_18_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_18_en.md new file mode 100644 index 00000000000000..32daae69f07e18 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_18_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_18 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_18 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_18` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_18_en_5.1.1_3.0_1694575920293.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_18_en_5.1.1_3.0_1694575920293.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_18","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_18", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_18| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR50-8-fast-18 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_5_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_5_en.md new file mode 100644 index 00000000000000..b555a847265302 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_5 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_5 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_5` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_5_en_5.1.1_3.0_1694571831003.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_5_en_5.1.1_3.0_1694571831003.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_5| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR50-8-fast-5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_7_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_7_en.md new file mode 100644 index 00000000000000..ef92da68f18d56 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_7_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_7 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_7 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_7` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_7_en_5.1.1_3.0_1694572454796.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_7_en_5.1.1_3.0_1694572454796.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_7","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_7", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr50_8_fast_7| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR50-8-fast-7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_whole_word_masking_finetuned_bert_mlm6_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_whole_word_masking_finetuned_bert_mlm6_en.md new file mode 100644 index 00000000000000..50264b05b27605 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_cased_whole_word_masking_finetuned_bert_mlm6_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_whole_word_masking_finetuned_bert_mlm6 BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_large_cased_whole_word_masking_finetuned_bert_mlm6 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_whole_word_masking_finetuned_bert_mlm6` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_whole_word_masking_finetuned_bert_mlm6_en_5.1.1_3.0_1694643215167.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_whole_word_masking_finetuned_bert_mlm6_en_5.1.1_3.0_1694643215167.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_whole_word_masking_finetuned_bert_mlm6","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_whole_word_masking_finetuned_bert_mlm6", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_whole_word_masking_finetuned_bert_mlm6| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-large-cased-whole-word-masking-finetuned-BERT-mlm6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_uncased_finetuned_bert_mlm5_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_uncased_finetuned_bert_mlm5_en.md new file mode 100644 index 00000000000000..0dd46dc6a2b92e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_uncased_finetuned_bert_mlm5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_finetuned_bert_mlm5 BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_large_uncased_finetuned_bert_mlm5 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_finetuned_bert_mlm5` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_finetuned_bert_mlm5_en_5.1.1_3.0_1694642005687.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_finetuned_bert_mlm5_en_5.1.1_3.0_1694642005687.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_finetuned_bert_mlm5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_finetuned_bert_mlm5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_finetuned_bert_mlm5| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-large-uncased-finetuned-bert-mlm5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_uncased_sparse_80_1x4_block_pruneofa_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_uncased_sparse_80_1x4_block_pruneofa_en.md new file mode 100644 index 00000000000000..ed30eb4cde1dd6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_uncased_sparse_80_1x4_block_pruneofa_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_sparse_80_1x4_block_pruneofa BertEmbeddings from Intel +author: John Snow Labs +name: bert_large_uncased_sparse_80_1x4_block_pruneofa +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_sparse_80_1x4_block_pruneofa` is a English model originally trained by Intel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sparse_80_1x4_block_pruneofa_en_5.1.1_3.0_1694621561165.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sparse_80_1x4_block_pruneofa_en_5.1.1_3.0_1694621561165.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_sparse_80_1x4_block_pruneofa","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_sparse_80_1x4_block_pruneofa", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_sparse_80_1x4_block_pruneofa| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|494.9 MB| + +## References + +https://huggingface.co/Intel/bert-large-uncased-sparse-80-1x4-block-pruneofa \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_large_uncased_whole_word_masking_finetuned_bert_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_large_uncased_whole_word_masking_finetuned_bert_mlm_en.md new file mode 100644 index 00000000000000..cd6739e3038b81 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_large_uncased_whole_word_masking_finetuned_bert_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_uncased_whole_word_masking_finetuned_bert_mlm BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_large_uncased_whole_word_masking_finetuned_bert_mlm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_whole_word_masking_finetuned_bert_mlm` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_whole_word_masking_finetuned_bert_mlm_en_5.1.1_3.0_1694641067297.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_whole_word_masking_finetuned_bert_mlm_en_5.1.1_3.0_1694641067297.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_uncased_whole_word_masking_finetuned_bert_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_uncased_whole_word_masking_finetuned_bert_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_whole_word_masking_finetuned_bert_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-large-uncased-whole-word-masking-finetuned-bert-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_medium_arapoembert_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_medium_arapoembert_en.md new file mode 100644 index 00000000000000..0a003e2ed46b26 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_medium_arapoembert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_medium_arapoembert BertEmbeddings from faisalq +author: John Snow Labs +name: bert_medium_arapoembert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_medium_arapoembert` is a English model originally trained by faisalq. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_medium_arapoembert_en_5.1.1_3.0_1694614622989.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_medium_arapoembert_en_5.1.1_3.0_1694614622989.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_medium_arapoembert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_medium_arapoembert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_medium_arapoembert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|356.2 MB| + +## References + +https://huggingface.co/faisalq/bert-medium-arapoembert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_multilingial_geolocation_prediction_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_multilingial_geolocation_prediction_en.md new file mode 100644 index 00000000000000..6a60d1b50a7c64 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_multilingial_geolocation_prediction_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_multilingial_geolocation_prediction BertEmbeddings from k4tel +author: John Snow Labs +name: bert_multilingial_geolocation_prediction +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_multilingial_geolocation_prediction` is a English model originally trained by k4tel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_multilingial_geolocation_prediction_en_5.1.1_3.0_1694601831218.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_multilingial_geolocation_prediction_en_5.1.1_3.0_1694601831218.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_multilingial_geolocation_prediction","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_multilingial_geolocation_prediction", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_multilingial_geolocation_prediction| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|663.2 MB| + +## References + +https://huggingface.co/k4tel/bert-multilingial-geolocation-prediction \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_pretrain_onlydj96_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_pretrain_onlydj96_en.md new file mode 100644 index 00000000000000..71165441c55e35 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_pretrain_onlydj96_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_pretrain_onlydj96 BertEmbeddings from onlydj96 +author: John Snow Labs +name: bert_pretrain_onlydj96 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_pretrain_onlydj96` is a English model originally trained by onlydj96. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_pretrain_onlydj96_en_5.1.1_3.0_1694629039142.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_pretrain_onlydj96_en_5.1.1_3.0_1694629039142.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_pretrain_onlydj96","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_pretrain_onlydj96", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_pretrain_onlydj96| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.6 MB| + +## References + +https://huggingface.co/onlydj96/bert_pretrain \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_pretraining_gaudi_2_batch_size_32_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_pretraining_gaudi_2_batch_size_32_en.md new file mode 100644 index 00000000000000..8936eb5fcbbbe8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_pretraining_gaudi_2_batch_size_32_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_pretraining_gaudi_2_batch_size_32 BertEmbeddings from regisss +author: John Snow Labs +name: bert_pretraining_gaudi_2_batch_size_32 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_pretraining_gaudi_2_batch_size_32` is a English model originally trained by regisss. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_pretraining_gaudi_2_batch_size_32_en_5.1.1_3.0_1694646591739.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_pretraining_gaudi_2_batch_size_32_en_5.1.1_3.0_1694646591739.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_pretraining_gaudi_2_batch_size_32","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_pretraining_gaudi_2_batch_size_32", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_pretraining_gaudi_2_batch_size_32| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.3 MB| + +## References + +https://huggingface.co/regisss/bert-pretraining-gaudi-2-batch-size-32 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_pretraining_gaudi_2_batch_size_64_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_pretraining_gaudi_2_batch_size_64_en.md new file mode 100644 index 00000000000000..ad2d5da98c2987 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_pretraining_gaudi_2_batch_size_64_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_pretraining_gaudi_2_batch_size_64 BertEmbeddings from regisss +author: John Snow Labs +name: bert_pretraining_gaudi_2_batch_size_64 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_pretraining_gaudi_2_batch_size_64` is a English model originally trained by regisss. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_pretraining_gaudi_2_batch_size_64_en_5.1.1_3.0_1694648529845.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_pretraining_gaudi_2_batch_size_64_en_5.1.1_3.0_1694648529845.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_pretraining_gaudi_2_batch_size_64","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_pretraining_gaudi_2_batch_size_64", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_pretraining_gaudi_2_batch_size_64| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.3 MB| + +## References + +https://huggingface.co/regisss/bert-pretraining-gaudi-2-batch-size-64 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_legal_contracts_larger4010_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_legal_contracts_larger4010_en.md new file mode 100644 index 00000000000000..dfd10511e03d99 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_legal_contracts_larger4010_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_small_finetuned_legal_contracts_larger4010 BertEmbeddings from muhtasham +author: John Snow Labs +name: bert_small_finetuned_legal_contracts_larger4010 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_small_finetuned_legal_contracts_larger4010` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_legal_contracts_larger4010_en_5.1.1_3.0_1694572295418.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_legal_contracts_larger4010_en_5.1.1_3.0_1694572295418.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_small_finetuned_legal_contracts_larger4010","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_small_finetuned_legal_contracts_larger4010", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_small_finetuned_legal_contracts_larger4010| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/bert-small-finetuned-legal-contracts-larger4010 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_parsed_longer50_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_parsed_longer50_en.md new file mode 100644 index 00000000000000..5d895c836551c5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_small_finetuned_parsed_longer50_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_small_finetuned_parsed_longer50 BertEmbeddings from muhtasham +author: John Snow Labs +name: bert_small_finetuned_parsed_longer50 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_small_finetuned_parsed_longer50` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_parsed_longer50_en_5.1.1_3.0_1694573838567.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_small_finetuned_parsed_longer50_en_5.1.1_3.0_1694573838567.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_small_finetuned_parsed_longer50","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_small_finetuned_parsed_longer50", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_small_finetuned_parsed_longer50| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/bert-small-finetuned-parsed-longer50 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_tiny_historic_multilingual_cased_xx.md b/docs/_posts/ahmedlone127/2023-09-13-bert_tiny_historic_multilingual_cased_xx.md new file mode 100644 index 00000000000000..4028b6eb87f7bb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_tiny_historic_multilingual_cased_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual bert_tiny_historic_multilingual_cased BertEmbeddings from dbmdz +author: John Snow Labs +name: bert_tiny_historic_multilingual_cased +date: 2023-09-13 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tiny_historic_multilingual_cased` is a Multilingual model originally trained by dbmdz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tiny_historic_multilingual_cased_xx_5.1.1_3.0_1694599550183.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tiny_historic_multilingual_cased_xx_5.1.1_3.0_1694599550183.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_tiny_historic_multilingual_cased","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_tiny_historic_multilingual_cased", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tiny_historic_multilingual_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|17.4 MB| + +## References + +https://huggingface.co/dbmdz/bert-tiny-historic-multilingual-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bert_twitter_hashtag_en.md b/docs/_posts/ahmedlone127/2023-09-13-bert_twitter_hashtag_en.md new file mode 100644 index 00000000000000..8d629b57d1fe87 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bert_twitter_hashtag_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_twitter_hashtag BertEmbeddings from vivianhuang88 +author: John Snow Labs +name: bert_twitter_hashtag +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_twitter_hashtag` is a English model originally trained by vivianhuang88. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_twitter_hashtag_en_5.1.1_3.0_1694639330517.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_twitter_hashtag_en_5.1.1_3.0_1694639330517.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_twitter_hashtag","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_twitter_hashtag", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_twitter_hashtag| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.0 MB| + +## References + +https://huggingface.co/vivianhuang88/bert_twitter_hashtag \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bertcl_en.md b/docs/_posts/ahmedlone127/2023-09-13-bertcl_en.md new file mode 100644 index 00000000000000..2a70f8c56413b6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bertcl_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertcl BertEmbeddings from georgepu1 +author: John Snow Labs +name: bertcl +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertcl` is a English model originally trained by georgepu1. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertcl_en_5.1.1_3.0_1694622288755.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertcl_en_5.1.1_3.0_1694622288755.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertcl","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertcl", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertcl| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/georgepu1/bertcl \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bertinho_galician_base_cased_gl.md b/docs/_posts/ahmedlone127/2023-09-13-bertinho_galician_base_cased_gl.md new file mode 100644 index 00000000000000..2d88a958aad3c9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bertinho_galician_base_cased_gl.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Galician bertinho_galician_base_cased BertEmbeddings from dvilares +author: John Snow Labs +name: bertinho_galician_base_cased +date: 2023-09-13 +tags: [bert, gl, open_source, fill_mask, onnx] +task: Embeddings +language: gl +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertinho_galician_base_cased` is a Galician model originally trained by dvilares. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertinho_galician_base_cased_gl_5.1.1_3.0_1694628723686.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertinho_galician_base_cased_gl_5.1.1_3.0_1694628723686.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertinho_galician_base_cased","gl") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertinho_galician_base_cased", "gl") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertinho_galician_base_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|gl| +|Size:|405.3 MB| + +## References + +https://huggingface.co/dvilares/bertinho-gl-base-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bertinho_galician_small_cased_gl.md b/docs/_posts/ahmedlone127/2023-09-13-bertinho_galician_small_cased_gl.md new file mode 100644 index 00000000000000..db394cb9c2da90 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bertinho_galician_small_cased_gl.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Galician bertinho_galician_small_cased BertEmbeddings from dvilares +author: John Snow Labs +name: bertinho_galician_small_cased +date: 2023-09-13 +tags: [bert, gl, open_source, fill_mask, onnx] +task: Embeddings +language: gl +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertinho_galician_small_cased` is a Galician model originally trained by dvilares. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertinho_galician_small_cased_gl_5.1.1_3.0_1694629051948.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertinho_galician_small_cased_gl_5.1.1_3.0_1694629051948.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertinho_galician_small_cased","gl") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertinho_galician_small_cased", "gl") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertinho_galician_small_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|gl| +|Size:|245.8 MB| + +## References + +https://huggingface.co/dvilares/bertinho-gl-small-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bertu_mt.md b/docs/_posts/ahmedlone127/2023-09-13-bertu_mt.md new file mode 100644 index 00000000000000..31857f561ec391 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bertu_mt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Maltese bertu BertEmbeddings from MLRS +author: John Snow Labs +name: bertu +date: 2023-09-13 +tags: [bert, mt, open_source, fill_mask, onnx] +task: Embeddings +language: mt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertu` is a Maltese model originally trained by MLRS. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertu_mt_5.1.1_3.0_1694635154194.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertu_mt_5.1.1_3.0_1694635154194.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertu","mt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertu", "mt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertu| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|mt| +|Size:|468.7 MB| + +## References + +https://huggingface.co/MLRS/BERTu \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-beto_base_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-beto_base_cased_en.md new file mode 100644 index 00000000000000..f1be7bdb9a449d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-beto_base_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English beto_base_cased BertEmbeddings from espejelomar +author: John Snow Labs +name: beto_base_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`beto_base_cased` is a English model originally trained by espejelomar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/beto_base_cased_en_5.1.1_3.0_1694634951862.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/beto_base_cased_en_5.1.1_3.0_1694634951862.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("beto_base_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("beto_base_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|beto_base_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/espejelomar/beto-base-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-betonews_bodycontext_en.md b/docs/_posts/ahmedlone127/2023-09-13-betonews_bodycontext_en.md new file mode 100644 index 00000000000000..0cde8eb199aa3b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-betonews_bodycontext_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English betonews_bodycontext BertEmbeddings from finiteautomata +author: John Snow Labs +name: betonews_bodycontext +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`betonews_bodycontext` is a English model originally trained by finiteautomata. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/betonews_bodycontext_en_5.1.1_3.0_1694638060106.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/betonews_bodycontext_en_5.1.1_3.0_1694638060106.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("betonews_bodycontext","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("betonews_bodycontext", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|betonews_bodycontext| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.3 MB| + +## References + +https://huggingface.co/finiteautomata/betonews-bodycontext \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-betonews_nonecontext_en.md b/docs/_posts/ahmedlone127/2023-09-13-betonews_nonecontext_en.md new file mode 100644 index 00000000000000..20047403ab7a2d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-betonews_nonecontext_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English betonews_nonecontext BertEmbeddings from finiteautomata +author: John Snow Labs +name: betonews_nonecontext +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`betonews_nonecontext` is a English model originally trained by finiteautomata. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/betonews_nonecontext_en_5.1.1_3.0_1694638603674.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/betonews_nonecontext_en_5.1.1_3.0_1694638603674.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("betonews_nonecontext","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("betonews_nonecontext", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|betonews_nonecontext| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.0 MB| + +## References + +https://huggingface.co/finiteautomata/betonews-nonecontext \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-betonews_tweetcontext_en.md b/docs/_posts/ahmedlone127/2023-09-13-betonews_tweetcontext_en.md new file mode 100644 index 00000000000000..62a20d9dfe8eb2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-betonews_tweetcontext_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English betonews_tweetcontext BertEmbeddings from piuba-bigdata +author: John Snow Labs +name: betonews_tweetcontext +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`betonews_tweetcontext` is a English model originally trained by piuba-bigdata. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/betonews_tweetcontext_en_5.1.1_3.0_1694639163920.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/betonews_tweetcontext_en_5.1.1_3.0_1694639163920.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("betonews_tweetcontext","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("betonews_tweetcontext", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|betonews_tweetcontext| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.1 MB| + +## References + +https://huggingface.co/piuba-bigdata/betonews-tweetcontext \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bio_clinicalbert_emilyalsentzer_en.md b/docs/_posts/ahmedlone127/2023-09-13-bio_clinicalbert_emilyalsentzer_en.md new file mode 100644 index 00000000000000..3de9706bf463b9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bio_clinicalbert_emilyalsentzer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bio_clinicalbert_emilyalsentzer BertEmbeddings from emilyalsentzer +author: John Snow Labs +name: bio_clinicalbert_emilyalsentzer +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bio_clinicalbert_emilyalsentzer` is a English model originally trained by emilyalsentzer. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bio_clinicalbert_emilyalsentzer_en_5.1.1_3.0_1694631294413.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bio_clinicalbert_emilyalsentzer_en_5.1.1_3.0_1694631294413.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bio_clinicalbert_emilyalsentzer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bio_clinicalbert_emilyalsentzer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bio_clinicalbert_emilyalsentzer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.3 MB| + +## References + +https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bio_clinicalbert_surgicalcardiothoracic_en.md b/docs/_posts/ahmedlone127/2023-09-13-bio_clinicalbert_surgicalcardiothoracic_en.md new file mode 100644 index 00000000000000..99e04d422764e8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bio_clinicalbert_surgicalcardiothoracic_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bio_clinicalbert_surgicalcardiothoracic BertEmbeddings from Gaborandi +author: John Snow Labs +name: bio_clinicalbert_surgicalcardiothoracic +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bio_clinicalbert_surgicalcardiothoracic` is a English model originally trained by Gaborandi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bio_clinicalbert_surgicalcardiothoracic_en_5.1.1_3.0_1694620142173.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bio_clinicalbert_surgicalcardiothoracic_en_5.1.1_3.0_1694620142173.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bio_clinicalbert_surgicalcardiothoracic","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bio_clinicalbert_surgicalcardiothoracic", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bio_clinicalbert_surgicalcardiothoracic| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.6 MB| + +## References + +https://huggingface.co/Gaborandi/Bio_ClinicalBERT-SurgicalCardiothoracic \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bio_discharge_summary_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-bio_discharge_summary_bert_en.md new file mode 100644 index 00000000000000..8a984e5c591181 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bio_discharge_summary_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bio_discharge_summary_bert BertEmbeddings from emilyalsentzer +author: John Snow Labs +name: bio_discharge_summary_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bio_discharge_summary_bert` is a English model originally trained by emilyalsentzer. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bio_discharge_summary_bert_en_5.1.1_3.0_1694631653208.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bio_discharge_summary_bert_en_5.1.1_3.0_1694631653208.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bio_discharge_summary_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bio_discharge_summary_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bio_discharge_summary_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.3 MB| + +## References + +https://huggingface.co/emilyalsentzer/Bio_Discharge_Summary_BERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-biobert_base_cased_v1.2_en.md b/docs/_posts/ahmedlone127/2023-09-13-biobert_base_cased_v1.2_en.md new file mode 100644 index 00000000000000..483e34d57ef733 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-biobert_base_cased_v1.2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English biobert_base_cased_v1.2 BertEmbeddings from dmis-lab +author: John Snow Labs +name: biobert_base_cased_v1.2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biobert_base_cased_v1.2` is a English model originally trained by dmis-lab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biobert_base_cased_v1.2_en_5.1.1_3.0_1694625923823.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biobert_base_cased_v1.2_en_5.1.1_3.0_1694625923823.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biobert_base_cased_v1.2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biobert_base_cased_v1.2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biobert_base_cased_v1.2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/dmis-lab/biobert-base-cased-v1.2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-biobert_base_cased_v1.2_finetuned_smpc_en.md b/docs/_posts/ahmedlone127/2023-09-13-biobert_base_cased_v1.2_finetuned_smpc_en.md new file mode 100644 index 00000000000000..c46a71da294d18 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-biobert_base_cased_v1.2_finetuned_smpc_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English biobert_base_cased_v1.2_finetuned_smpc BertEmbeddings from sophy +author: John Snow Labs +name: biobert_base_cased_v1.2_finetuned_smpc +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biobert_base_cased_v1.2_finetuned_smpc` is a English model originally trained by sophy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biobert_base_cased_v1.2_finetuned_smpc_en_5.1.1_3.0_1694635769664.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biobert_base_cased_v1.2_finetuned_smpc_en_5.1.1_3.0_1694635769664.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biobert_base_cased_v1.2_finetuned_smpc","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biobert_base_cased_v1.2_finetuned_smpc", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biobert_base_cased_v1.2_finetuned_smpc| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/sophy/biobert-base-cased-v1.2-finetuned-smpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-biobert_giotto_en.md b/docs/_posts/ahmedlone127/2023-09-13-biobert_giotto_en.md new file mode 100644 index 00000000000000..797b623dfe1836 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-biobert_giotto_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English biobert_giotto BertEmbeddings from dpalominop +author: John Snow Labs +name: biobert_giotto +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biobert_giotto` is a English model originally trained by dpalominop. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biobert_giotto_en_5.1.1_3.0_1694626974873.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biobert_giotto_en_5.1.1_3.0_1694626974873.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biobert_giotto","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biobert_giotto", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biobert_giotto| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.4 MB| + +## References + +https://huggingface.co/dpalominop/biobert-giotto \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-bioformer_litcovid_en.md b/docs/_posts/ahmedlone127/2023-09-13-bioformer_litcovid_en.md new file mode 100644 index 00000000000000..612f8da6ab8354 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-bioformer_litcovid_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bioformer_litcovid BertEmbeddings from bioformers +author: John Snow Labs +name: bioformer_litcovid +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bioformer_litcovid` is a English model originally trained by bioformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bioformer_litcovid_en_5.1.1_3.0_1694626519703.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bioformer_litcovid_en_5.1.1_3.0_1694626519703.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bioformer_litcovid","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bioformer_litcovid", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bioformer_litcovid| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|158.5 MB| + +## References + +https://huggingface.co/bioformers/bioformer-litcovid \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-blade_english_chinese_en.md b/docs/_posts/ahmedlone127/2023-09-13-blade_english_chinese_en.md new file mode 100644 index 00000000000000..a5ca8f67175913 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-blade_english_chinese_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English blade_english_chinese BertEmbeddings from srnair +author: John Snow Labs +name: blade_english_chinese +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`blade_english_chinese` is a English model originally trained by srnair. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/blade_english_chinese_en_5.1.1_3.0_1694615597365.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/blade_english_chinese_en_5.1.1_3.0_1694615597365.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("blade_english_chinese","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("blade_english_chinese", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|blade_english_chinese| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|421.7 MB| + +## References + +https://huggingface.co/srnair/blade-en-zh \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-blade_english_russian_en.md b/docs/_posts/ahmedlone127/2023-09-13-blade_english_russian_en.md new file mode 100644 index 00000000000000..479b5c207277dd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-blade_english_russian_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English blade_english_russian BertEmbeddings from srnair +author: John Snow Labs +name: blade_english_russian +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`blade_english_russian` is a English model originally trained by srnair. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/blade_english_russian_en_5.1.1_3.0_1694620469182.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/blade_english_russian_en_5.1.1_3.0_1694620469182.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("blade_english_russian","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("blade_english_russian", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|blade_english_russian| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|428.2 MB| + +## References + +https://huggingface.co/srnair/blade-en-ru \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-burmese_awesome_mlm_model_en.md b/docs/_posts/ahmedlone127/2023-09-13-burmese_awesome_mlm_model_en.md new file mode 100644 index 00000000000000..dd73175446486a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-burmese_awesome_mlm_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English burmese_awesome_mlm_model BertEmbeddings from wajdii +author: John Snow Labs +name: burmese_awesome_mlm_model +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`burmese_awesome_mlm_model` is a English model originally trained by wajdii. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/burmese_awesome_mlm_model_en_5.1.1_3.0_1694618818921.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/burmese_awesome_mlm_model_en_5.1.1_3.0_1694618818921.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("burmese_awesome_mlm_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("burmese_awesome_mlm_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|burmese_awesome_mlm_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/wajdii/my_awesome_mlm_model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-carlbert_webex_mlm_vignesh95_en.md b/docs/_posts/ahmedlone127/2023-09-13-carlbert_webex_mlm_vignesh95_en.md new file mode 100644 index 00000000000000..a4f09ad74f6873 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-carlbert_webex_mlm_vignesh95_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English carlbert_webex_mlm_vignesh95 BertEmbeddings from Vignesh95 +author: John Snow Labs +name: carlbert_webex_mlm_vignesh95 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`carlbert_webex_mlm_vignesh95` is a English model originally trained by Vignesh95. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/carlbert_webex_mlm_vignesh95_en_5.1.1_3.0_1694616371291.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/carlbert_webex_mlm_vignesh95_en_5.1.1_3.0_1694616371291.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("carlbert_webex_mlm_vignesh95","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("carlbert_webex_mlm_vignesh95", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|carlbert_webex_mlm_vignesh95| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/Vignesh95/carlbert-webex-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-carlbert_webex_mlm_wolof_recipient_en.md b/docs/_posts/ahmedlone127/2023-09-13-carlbert_webex_mlm_wolof_recipient_en.md new file mode 100644 index 00000000000000..68710b1f4dc39a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-carlbert_webex_mlm_wolof_recipient_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English carlbert_webex_mlm_wolof_recipient BertEmbeddings from Vignesh95 +author: John Snow Labs +name: carlbert_webex_mlm_wolof_recipient +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`carlbert_webex_mlm_wolof_recipient` is a English model originally trained by Vignesh95. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/carlbert_webex_mlm_wolof_recipient_en_5.1.1_3.0_1694616716297.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/carlbert_webex_mlm_wolof_recipient_en_5.1.1_3.0_1694616716297.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("carlbert_webex_mlm_wolof_recipient","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("carlbert_webex_mlm_wolof_recipient", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|carlbert_webex_mlm_wolof_recipient| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/Vignesh95/carlbert-webex-mlm-wo-recipient \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cdgp_chilean_sign_language_bert_cloth_en.md b/docs/_posts/ahmedlone127/2023-09-13-cdgp_chilean_sign_language_bert_cloth_en.md new file mode 100644 index 00000000000000..1448868f11e46b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cdgp_chilean_sign_language_bert_cloth_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cdgp_chilean_sign_language_bert_cloth BertEmbeddings from AndyChiang +author: John Snow Labs +name: cdgp_chilean_sign_language_bert_cloth +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cdgp_chilean_sign_language_bert_cloth` is a English model originally trained by AndyChiang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cdgp_chilean_sign_language_bert_cloth_en_5.1.1_3.0_1694592903470.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cdgp_chilean_sign_language_bert_cloth_en_5.1.1_3.0_1694592903470.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cdgp_chilean_sign_language_bert_cloth","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cdgp_chilean_sign_language_bert_cloth", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cdgp_chilean_sign_language_bert_cloth| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/AndyChiang/cdgp-csg-bert-cloth \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cdgp_chilean_sign_language_bert_dgen_en.md b/docs/_posts/ahmedlone127/2023-09-13-cdgp_chilean_sign_language_bert_dgen_en.md new file mode 100644 index 00000000000000..ce5acdfbd83008 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cdgp_chilean_sign_language_bert_dgen_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cdgp_chilean_sign_language_bert_dgen BertEmbeddings from AndyChiang +author: John Snow Labs +name: cdgp_chilean_sign_language_bert_dgen +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cdgp_chilean_sign_language_bert_dgen` is a English model originally trained by AndyChiang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cdgp_chilean_sign_language_bert_dgen_en_5.1.1_3.0_1694593050555.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cdgp_chilean_sign_language_bert_dgen_en_5.1.1_3.0_1694593050555.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cdgp_chilean_sign_language_bert_dgen","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cdgp_chilean_sign_language_bert_dgen", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cdgp_chilean_sign_language_bert_dgen| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/AndyChiang/cdgp-csg-bert-dgen \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-chemical_bert_uncased_finetuned_cust_c1_cust_en.md b/docs/_posts/ahmedlone127/2023-09-13-chemical_bert_uncased_finetuned_cust_c1_cust_en.md new file mode 100644 index 00000000000000..11148203990f4f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-chemical_bert_uncased_finetuned_cust_c1_cust_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English chemical_bert_uncased_finetuned_cust_c1_cust BertEmbeddings from shafin +author: John Snow Labs +name: chemical_bert_uncased_finetuned_cust_c1_cust +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`chemical_bert_uncased_finetuned_cust_c1_cust` is a English model originally trained by shafin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/chemical_bert_uncased_finetuned_cust_c1_cust_en_5.1.1_3.0_1694630083309.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/chemical_bert_uncased_finetuned_cust_c1_cust_en_5.1.1_3.0_1694630083309.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("chemical_bert_uncased_finetuned_cust_c1_cust","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("chemical_bert_uncased_finetuned_cust_c1_cust", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|chemical_bert_uncased_finetuned_cust_c1_cust| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.1 MB| + +## References + +https://huggingface.co/shafin/chemical-bert-uncased-finetuned-cust-c1-cust \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-chemical_bert_uncased_finetuned_cust_c2_en.md b/docs/_posts/ahmedlone127/2023-09-13-chemical_bert_uncased_finetuned_cust_c2_en.md new file mode 100644 index 00000000000000..290f7b4d47d88f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-chemical_bert_uncased_finetuned_cust_c2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English chemical_bert_uncased_finetuned_cust_c2 BertEmbeddings from shafin +author: John Snow Labs +name: chemical_bert_uncased_finetuned_cust_c2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`chemical_bert_uncased_finetuned_cust_c2` is a English model originally trained by shafin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/chemical_bert_uncased_finetuned_cust_c2_en_5.1.1_3.0_1694633093400.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/chemical_bert_uncased_finetuned_cust_c2_en_5.1.1_3.0_1694633093400.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("chemical_bert_uncased_finetuned_cust_c2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("chemical_bert_uncased_finetuned_cust_c2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|chemical_bert_uncased_finetuned_cust_c2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.1 MB| + +## References + +https://huggingface.co/shafin/chemical-bert-uncased-finetuned-cust-c2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-chemical_bert_uncased_finetuned_cust_en.md b/docs/_posts/ahmedlone127/2023-09-13-chemical_bert_uncased_finetuned_cust_en.md new file mode 100644 index 00000000000000..8e12c86e717ffa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-chemical_bert_uncased_finetuned_cust_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English chemical_bert_uncased_finetuned_cust BertEmbeddings from shafin +author: John Snow Labs +name: chemical_bert_uncased_finetuned_cust +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`chemical_bert_uncased_finetuned_cust` is a English model originally trained by shafin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/chemical_bert_uncased_finetuned_cust_en_5.1.1_3.0_1694625924079.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/chemical_bert_uncased_finetuned_cust_en_5.1.1_3.0_1694625924079.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("chemical_bert_uncased_finetuned_cust","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("chemical_bert_uncased_finetuned_cust", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|chemical_bert_uncased_finetuned_cust| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.1 MB| + +## References + +https://huggingface.co/shafin/chemical-bert-uncased-finetuned-cust \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-childes_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-childes_bert_en.md new file mode 100644 index 00000000000000..931e9114a64ad2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-childes_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English childes_bert BertEmbeddings from smeylan +author: John Snow Labs +name: childes_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`childes_bert` is a English model originally trained by smeylan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/childes_bert_en_5.1.1_3.0_1694574765875.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/childes_bert_en_5.1.1_3.0_1694574765875.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("childes_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("childes_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|childes_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/smeylan/childes-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cl_arabertv0.1_base_en.md b/docs/_posts/ahmedlone127/2023-09-13-cl_arabertv0.1_base_en.md new file mode 100644 index 00000000000000..354e265dde3e2f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cl_arabertv0.1_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cl_arabertv0.1_base BertEmbeddings from qahq +author: John Snow Labs +name: cl_arabertv0.1_base +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cl_arabertv0.1_base` is a English model originally trained by qahq. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cl_arabertv0.1_base_en_5.1.1_3.0_1694611150025.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cl_arabertv0.1_base_en_5.1.1_3.0_1694611150025.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cl_arabertv0.1_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cl_arabertv0.1_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cl_arabertv0.1_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|505.0 MB| + +## References + +https://huggingface.co/qahq/CL-AraBERTv0.1-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-clinical_bert_base_128_en.md b/docs/_posts/ahmedlone127/2023-09-13-clinical_bert_base_128_en.md new file mode 100644 index 00000000000000..f18b61939be216 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-clinical_bert_base_128_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English clinical_bert_base_128 BertEmbeddings from Tsubasaz +author: John Snow Labs +name: clinical_bert_base_128 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`clinical_bert_base_128` is a English model originally trained by Tsubasaz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clinical_bert_base_128_en_5.1.1_3.0_1694572855798.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clinical_bert_base_128_en_5.1.1_3.0_1694572855798.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("clinical_bert_base_128","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("clinical_bert_base_128", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinical_bert_base_128| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/Tsubasaz/clinical-bert-base-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cocodr_base_msmarco_en.md b/docs/_posts/ahmedlone127/2023-09-13-cocodr_base_msmarco_en.md new file mode 100644 index 00000000000000..8228637f0d980e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cocodr_base_msmarco_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cocodr_base_msmarco BertEmbeddings from OpenMatch +author: John Snow Labs +name: cocodr_base_msmarco +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cocodr_base_msmarco` is a English model originally trained by OpenMatch. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cocodr_base_msmarco_en_5.1.1_3.0_1694609582266.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cocodr_base_msmarco_en_5.1.1_3.0_1694609582266.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cocodr_base_msmarco","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cocodr_base_msmarco", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cocodr_base_msmarco| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.4 MB| + +## References + +https://huggingface.co/OpenMatch/cocodr-base-msmarco \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cocodr_base_msmarco_warmup_en.md b/docs/_posts/ahmedlone127/2023-09-13-cocodr_base_msmarco_warmup_en.md new file mode 100644 index 00000000000000..160749a93944da --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cocodr_base_msmarco_warmup_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cocodr_base_msmarco_warmup BertEmbeddings from OpenMatch +author: John Snow Labs +name: cocodr_base_msmarco_warmup +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cocodr_base_msmarco_warmup` is a English model originally trained by OpenMatch. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cocodr_base_msmarco_warmup_en_5.1.1_3.0_1694643215149.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cocodr_base_msmarco_warmup_en_5.1.1_3.0_1694643215149.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cocodr_base_msmarco_warmup","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cocodr_base_msmarco_warmup", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cocodr_base_msmarco_warmup| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.4 MB| + +## References + +https://huggingface.co/OpenMatch/cocodr-base-msmarco-warmup \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cocodr_large_msmarco_en.md b/docs/_posts/ahmedlone127/2023-09-13-cocodr_large_msmarco_en.md new file mode 100644 index 00000000000000..c52f7bd32f4645 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cocodr_large_msmarco_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cocodr_large_msmarco BertEmbeddings from OpenMatch +author: John Snow Labs +name: cocodr_large_msmarco +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cocodr_large_msmarco` is a English model originally trained by OpenMatch. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cocodr_large_msmarco_en_5.1.1_3.0_1694610587271.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cocodr_large_msmarco_en_5.1.1_3.0_1694610587271.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cocodr_large_msmarco","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cocodr_large_msmarco", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cocodr_large_msmarco| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/OpenMatch/cocodr-large-msmarco \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cocodr_large_msmarco_idro_only_en.md b/docs/_posts/ahmedlone127/2023-09-13-cocodr_large_msmarco_idro_only_en.md new file mode 100644 index 00000000000000..02dc5ddfc57ef7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cocodr_large_msmarco_idro_only_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cocodr_large_msmarco_idro_only BertEmbeddings from OpenMatch +author: John Snow Labs +name: cocodr_large_msmarco_idro_only +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cocodr_large_msmarco_idro_only` is a English model originally trained by OpenMatch. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cocodr_large_msmarco_idro_only_en_5.1.1_3.0_1694615878283.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cocodr_large_msmarco_idro_only_en_5.1.1_3.0_1694615878283.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cocodr_large_msmarco_idro_only","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cocodr_large_msmarco_idro_only", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cocodr_large_msmarco_idro_only| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/OpenMatch/cocodr-large-msmarco-idro-only \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cocodr_large_msmarco_warmup_en.md b/docs/_posts/ahmedlone127/2023-09-13-cocodr_large_msmarco_warmup_en.md new file mode 100644 index 00000000000000..091ba7798f5740 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cocodr_large_msmarco_warmup_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cocodr_large_msmarco_warmup BertEmbeddings from OpenMatch +author: John Snow Labs +name: cocodr_large_msmarco_warmup +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cocodr_large_msmarco_warmup` is a English model originally trained by OpenMatch. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cocodr_large_msmarco_warmup_en_5.1.1_3.0_1694615098798.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cocodr_large_msmarco_warmup_en_5.1.1_3.0_1694615098798.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cocodr_large_msmarco_warmup","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cocodr_large_msmarco_warmup", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cocodr_large_msmarco_warmup| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/OpenMatch/cocodr-large-msmarco-warmup \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-colbert_bertnsp_220_en.md b/docs/_posts/ahmedlone127/2023-09-13-colbert_bertnsp_220_en.md new file mode 100644 index 00000000000000..46d97738416db0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-colbert_bertnsp_220_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English colbert_bertnsp_220 BertEmbeddings from approach0 +author: John Snow Labs +name: colbert_bertnsp_220 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`colbert_bertnsp_220` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/colbert_bertnsp_220_en_5.1.1_3.0_1694632363032.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/colbert_bertnsp_220_en_5.1.1_3.0_1694632363032.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("colbert_bertnsp_220","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("colbert_bertnsp_220", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|colbert_bertnsp_220| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/colbert-bertnsp-220 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-colbert_bertnsp_600_en.md b/docs/_posts/ahmedlone127/2023-09-13-colbert_bertnsp_600_en.md new file mode 100644 index 00000000000000..b5553e73bcb2ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-colbert_bertnsp_600_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English colbert_bertnsp_600 BertEmbeddings from approach0 +author: John Snow Labs +name: colbert_bertnsp_600 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`colbert_bertnsp_600` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/colbert_bertnsp_600_en_5.1.1_3.0_1694632961846.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/colbert_bertnsp_600_en_5.1.1_3.0_1694632961846.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("colbert_bertnsp_600","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("colbert_bertnsp_600", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|colbert_bertnsp_600| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/colbert-bertnsp-600 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-colbert_cocomae_220_en.md b/docs/_posts/ahmedlone127/2023-09-13-colbert_cocomae_220_en.md new file mode 100644 index 00000000000000..b8c5dd29fd76b8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-colbert_cocomae_220_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English colbert_cocomae_220 BertEmbeddings from approach0 +author: John Snow Labs +name: colbert_cocomae_220 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`colbert_cocomae_220` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/colbert_cocomae_220_en_5.1.1_3.0_1694631433780.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/colbert_cocomae_220_en_5.1.1_3.0_1694631433780.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("colbert_cocomae_220","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("colbert_cocomae_220", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|colbert_cocomae_220| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/approach0/colbert-cocomae-220 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-colbert_cocomae_600_en.md b/docs/_posts/ahmedlone127/2023-09-13-colbert_cocomae_600_en.md new file mode 100644 index 00000000000000..efca6eb167b23c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-colbert_cocomae_600_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English colbert_cocomae_600 BertEmbeddings from approach0 +author: John Snow Labs +name: colbert_cocomae_600 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`colbert_cocomae_600` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/colbert_cocomae_600_en_5.1.1_3.0_1694631908560.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/colbert_cocomae_600_en_5.1.1_3.0_1694631908560.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("colbert_cocomae_600","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("colbert_cocomae_600", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|colbert_cocomae_600| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/colbert-cocomae-600 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-condenser_large_en.md b/docs/_posts/ahmedlone127/2023-09-13-condenser_large_en.md new file mode 100644 index 00000000000000..febd8c7a23d7af --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-condenser_large_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English condenser_large BertEmbeddings from OpenMatch +author: John Snow Labs +name: condenser_large +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`condenser_large` is a English model originally trained by OpenMatch. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/condenser_large_en_5.1.1_3.0_1694612875253.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/condenser_large_en_5.1.1_3.0_1694612875253.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("condenser_large","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("condenser_large", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|condenser_large| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/OpenMatch/condenser-large \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cord_scibert_en.md b/docs/_posts/ahmedlone127/2023-09-13-cord_scibert_en.md new file mode 100644 index 00000000000000..fb2738000a6231 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cord_scibert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cord_scibert BertEmbeddings from athiban2001 +author: John Snow Labs +name: cord_scibert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cord_scibert` is a English model originally trained by athiban2001. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cord_scibert_en_5.1.1_3.0_1694617688873.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cord_scibert_en_5.1.1_3.0_1694617688873.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cord_scibert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cord_scibert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cord_scibert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.7 MB| + +## References + +https://huggingface.co/athiban2001/cord-scibert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-corsican_condenser_large_en.md b/docs/_posts/ahmedlone127/2023-09-13-corsican_condenser_large_en.md new file mode 100644 index 00000000000000..b44932a1fd53f6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-corsican_condenser_large_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English corsican_condenser_large BertEmbeddings from OpenMatch +author: John Snow Labs +name: corsican_condenser_large +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`corsican_condenser_large` is a English model originally trained by OpenMatch. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/corsican_condenser_large_en_5.1.1_3.0_1694613630133.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/corsican_condenser_large_en_5.1.1_3.0_1694613630133.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("corsican_condenser_large","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("corsican_condenser_large", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|corsican_condenser_large| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/OpenMatch/co-condenser-large \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-corsican_condenser_large_msmarco_en.md b/docs/_posts/ahmedlone127/2023-09-13-corsican_condenser_large_msmarco_en.md new file mode 100644 index 00000000000000..82cb24462acbbe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-corsican_condenser_large_msmarco_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English corsican_condenser_large_msmarco BertEmbeddings from OpenMatch +author: John Snow Labs +name: corsican_condenser_large_msmarco +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`corsican_condenser_large_msmarco` is a English model originally trained by OpenMatch. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/corsican_condenser_large_msmarco_en_5.1.1_3.0_1694614396061.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/corsican_condenser_large_msmarco_en_5.1.1_3.0_1694614396061.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("corsican_condenser_large_msmarco","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("corsican_condenser_large_msmarco", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|corsican_condenser_large_msmarco| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/OpenMatch/co-condenser-large-msmarco \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-costa_wiki_en.md b/docs/_posts/ahmedlone127/2023-09-13-costa_wiki_en.md new file mode 100644 index 00000000000000..a345b27c2093bf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-costa_wiki_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English costa_wiki BertEmbeddings from xyma +author: John Snow Labs +name: costa_wiki +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`costa_wiki` is a English model originally trained by xyma. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/costa_wiki_en_5.1.1_3.0_1694624815803.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/costa_wiki_en_5.1.1_3.0_1694624815803.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("costa_wiki","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("costa_wiki", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|costa_wiki| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.3 MB| + +## References + +https://huggingface.co/xyma/COSTA-wiki \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cotmae_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-cotmae_base_uncased_en.md new file mode 100644 index 00000000000000..ec4b67fc38f637 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cotmae_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cotmae_base_uncased BertEmbeddings from caskcsg +author: John Snow Labs +name: cotmae_base_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cotmae_base_uncased` is a English model originally trained by caskcsg. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cotmae_base_uncased_en_5.1.1_3.0_1694617137606.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cotmae_base_uncased_en_5.1.1_3.0_1694617137606.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cotmae_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cotmae_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cotmae_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.7 MB| + +## References + +https://huggingface.co/caskcsg/cotmae_base_uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-ct_pubmedbert_re_en.md b/docs/_posts/ahmedlone127/2023-09-13-ct_pubmedbert_re_en.md new file mode 100644 index 00000000000000..c624a3e3d61279 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-ct_pubmedbert_re_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ct_pubmedbert_re BertEmbeddings from zhangzeyu +author: John Snow Labs +name: ct_pubmedbert_re +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ct_pubmedbert_re` is a English model originally trained by zhangzeyu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ct_pubmedbert_re_en_5.1.1_3.0_1694640469042.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ct_pubmedbert_re_en_5.1.1_3.0_1694640469042.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ct_pubmedbert_re","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ct_pubmedbert_re", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ct_pubmedbert_re| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.0 MB| + +## References + +https://huggingface.co/zhangzeyu/CT-PubMedBERT-RE \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cxr_bioclinicalbert_chunkedv1_en.md b/docs/_posts/ahmedlone127/2023-09-13-cxr_bioclinicalbert_chunkedv1_en.md new file mode 100644 index 00000000000000..be8ae5fc9970d8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cxr_bioclinicalbert_chunkedv1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cxr_bioclinicalbert_chunkedv1 BertEmbeddings from ICLbioengNLP +author: John Snow Labs +name: cxr_bioclinicalbert_chunkedv1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cxr_bioclinicalbert_chunkedv1` is a English model originally trained by ICLbioengNLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cxr_bioclinicalbert_chunkedv1_en_5.1.1_3.0_1694616858394.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cxr_bioclinicalbert_chunkedv1_en_5.1.1_3.0_1694616858394.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cxr_bioclinicalbert_chunkedv1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cxr_bioclinicalbert_chunkedv1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cxr_bioclinicalbert_chunkedv1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.1 MB| + +## References + +https://huggingface.co/ICLbioengNLP/CXR_BioClinicalBERT_chunkedv1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cxr_bioclinicalbert_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-13-cxr_bioclinicalbert_mlm_en.md new file mode 100644 index 00000000000000..86a2d2e4dc25c6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cxr_bioclinicalbert_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cxr_bioclinicalbert_mlm BertEmbeddings from ICLbioengNLP +author: John Snow Labs +name: cxr_bioclinicalbert_mlm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cxr_bioclinicalbert_mlm` is a English model originally trained by ICLbioengNLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cxr_bioclinicalbert_mlm_en_5.1.1_3.0_1694628615018.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cxr_bioclinicalbert_mlm_en_5.1.1_3.0_1694628615018.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cxr_bioclinicalbert_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cxr_bioclinicalbert_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cxr_bioclinicalbert_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.0 MB| + +## References + +https://huggingface.co/ICLbioengNLP/CXR_BioClinicalBERT_MLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-cxr_bioclinicalbert_v1_en.md b/docs/_posts/ahmedlone127/2023-09-13-cxr_bioclinicalbert_v1_en.md new file mode 100644 index 00000000000000..dc32f1b8ccf23b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-cxr_bioclinicalbert_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English cxr_bioclinicalbert_v1 BertEmbeddings from dorltcheng +author: John Snow Labs +name: cxr_bioclinicalbert_v1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cxr_bioclinicalbert_v1` is a English model originally trained by dorltcheng. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cxr_bioclinicalbert_v1_en_5.1.1_3.0_1694613574482.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cxr_bioclinicalbert_v1_en_5.1.1_3.0_1694613574482.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("cxr_bioclinicalbert_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("cxr_bioclinicalbert_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cxr_bioclinicalbert_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.3 MB| + +## References + +https://huggingface.co/dorltcheng/CXR_BioClinicalBERT_v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dabert_multi_en.md b/docs/_posts/ahmedlone127/2023-09-13-dabert_multi_en.md new file mode 100644 index 00000000000000..599fbb123c6dbe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dabert_multi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dabert_multi BertEmbeddings from christofid +author: John Snow Labs +name: dabert_multi +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dabert_multi` is a English model originally trained by christofid. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dabert_multi_en_5.1.1_3.0_1694647641174.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dabert_multi_en_5.1.1_3.0_1694647641174.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dabert_multi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dabert_multi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dabert_multi| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/christofid/dabert-multi \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dajobbert_base_uncased_da.md b/docs/_posts/ahmedlone127/2023-09-13-dajobbert_base_uncased_da.md new file mode 100644 index 00000000000000..a5c41aaf14f5b7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dajobbert_base_uncased_da.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Danish dajobbert_base_uncased BertEmbeddings from jjzha +author: John Snow Labs +name: dajobbert_base_uncased +date: 2023-09-13 +tags: [bert, da, open_source, fill_mask, onnx] +task: Embeddings +language: da +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dajobbert_base_uncased` is a Danish model originally trained by jjzha. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dajobbert_base_uncased_da_5.1.1_3.0_1694632323819.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dajobbert_base_uncased_da_5.1.1_3.0_1694632323819.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dajobbert_base_uncased","da") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dajobbert_base_uncased", "da") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dajobbert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|da| +|Size:|411.3 MB| + +## References + +https://huggingface.co/jjzha/dajobbert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dapbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-dapbert_en.md new file mode 100644 index 00000000000000..c85685f66e7789 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dapbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dapbert BertEmbeddings from christofid +author: John Snow Labs +name: dapbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dapbert` is a English model originally trained by christofid. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dapbert_en_5.1.1_3.0_1694645713628.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dapbert_en_5.1.1_3.0_1694645713628.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dapbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dapbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dapbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/christofid/dapbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dapscibert_en.md b/docs/_posts/ahmedlone127/2023-09-13-dapscibert_en.md new file mode 100644 index 00000000000000..f9ef2c5902ac33 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dapscibert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dapscibert BertEmbeddings from christofid +author: John Snow Labs +name: dapscibert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dapscibert` is a English model originally trained by christofid. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dapscibert_en_5.1.1_3.0_1694646154402.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dapscibert_en_5.1.1_3.0_1694646154402.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dapscibert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dapscibert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dapscibert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.0 MB| + +## References + +https://huggingface.co/christofid/dapscibert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_bertnsp_020_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_bertnsp_020_en.md new file mode 100644 index 00000000000000..38f4bfbc4f4b8d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_bertnsp_020_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_bertnsp_020 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_bertnsp_020 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_bertnsp_020` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_bertnsp_020_en_5.1.1_3.0_1694621826042.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_bertnsp_020_en_5.1.1_3.0_1694621826042.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_bertnsp_020","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_bertnsp_020", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_bertnsp_020| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/dpr-bertnsp-020 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_bertnsp_120_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_bertnsp_120_en.md new file mode 100644 index 00000000000000..62a6ed916cfdca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_bertnsp_120_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_bertnsp_120 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_bertnsp_120 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_bertnsp_120` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_bertnsp_120_en_5.1.1_3.0_1694622137182.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_bertnsp_120_en_5.1.1_3.0_1694622137182.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_bertnsp_120","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_bertnsp_120", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_bertnsp_120| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/dpr-bertnsp-120 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_bertnsp_220_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_bertnsp_220_en.md new file mode 100644 index 00000000000000..cf965b9cc9cf88 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_bertnsp_220_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_bertnsp_220 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_bertnsp_220 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_bertnsp_220` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_bertnsp_220_en_5.1.1_3.0_1694622510938.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_bertnsp_220_en_5.1.1_3.0_1694622510938.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_bertnsp_220","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_bertnsp_220", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_bertnsp_220| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/dpr-bertnsp-220 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_bertnsp_320_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_bertnsp_320_en.md new file mode 100644 index 00000000000000..d4913fff9334d6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_bertnsp_320_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_bertnsp_320 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_bertnsp_320 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_bertnsp_320` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_bertnsp_320_en_5.1.1_3.0_1694622790193.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_bertnsp_320_en_5.1.1_3.0_1694622790193.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_bertnsp_320","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_bertnsp_320", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_bertnsp_320| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/dpr-bertnsp-320 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_bertnsp_520_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_bertnsp_520_en.md new file mode 100644 index 00000000000000..9810c233518a59 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_bertnsp_520_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_bertnsp_520 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_bertnsp_520 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_bertnsp_520` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_bertnsp_520_en_5.1.1_3.0_1694623237282.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_bertnsp_520_en_5.1.1_3.0_1694623237282.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_bertnsp_520","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_bertnsp_520", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_bertnsp_520| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/dpr-bertnsp-520 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cocomae_020_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocomae_020_en.md new file mode 100644 index 00000000000000..ca13cd1b8e3cce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocomae_020_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cocomae_020 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cocomae_020 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cocomae_020` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cocomae_020_en_5.1.1_3.0_1694619413332.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cocomae_020_en_5.1.1_3.0_1694619413332.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cocomae_020","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cocomae_020", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cocomae_020| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/approach0/dpr-cocomae-020 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cocomae_120_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocomae_120_en.md new file mode 100644 index 00000000000000..29e7b1aa1d0bbc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocomae_120_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cocomae_120 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cocomae_120 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cocomae_120` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cocomae_120_en_5.1.1_3.0_1694619848167.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cocomae_120_en_5.1.1_3.0_1694619848167.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cocomae_120","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cocomae_120", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cocomae_120| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/approach0/dpr-cocomae-120 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cocomae_220_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocomae_220_en.md new file mode 100644 index 00000000000000..2ab4db6d7de341 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocomae_220_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cocomae_220 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cocomae_220 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cocomae_220` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cocomae_220_en_5.1.1_3.0_1694620314728.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cocomae_220_en_5.1.1_3.0_1694620314728.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cocomae_220","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cocomae_220", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cocomae_220| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/approach0/dpr-cocomae-220 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cocomae_320_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocomae_320_en.md new file mode 100644 index 00000000000000..3f35572f9173df --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocomae_320_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cocomae_320 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cocomae_320 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cocomae_320` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cocomae_320_en_5.1.1_3.0_1694620794927.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cocomae_320_en_5.1.1_3.0_1694620794927.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cocomae_320","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cocomae_320", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cocomae_320| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/approach0/dpr-cocomae-320 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cocomae_520_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocomae_520_en.md new file mode 100644 index 00000000000000..c0f019de3ba24e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocomae_520_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cocomae_520 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cocomae_520 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cocomae_520` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cocomae_520_en_5.1.1_3.0_1694621336068.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cocomae_520_en_5.1.1_3.0_1694621336068.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cocomae_520","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cocomae_520", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cocomae_520| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/dpr-cocomae-520 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cocondenser_020_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocondenser_020_en.md new file mode 100644 index 00000000000000..3de166049c912d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocondenser_020_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cocondenser_020 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cocondenser_020 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cocondenser_020` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cocondenser_020_en_5.1.1_3.0_1694623728257.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cocondenser_020_en_5.1.1_3.0_1694623728257.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cocondenser_020","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cocondenser_020", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cocondenser_020| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/approach0/dpr-cocondenser-020 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cocondenser_120_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocondenser_120_en.md new file mode 100644 index 00000000000000..b8817b95d90bcb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocondenser_120_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cocondenser_120 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cocondenser_120 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cocondenser_120` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cocondenser_120_en_5.1.1_3.0_1694623975734.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cocondenser_120_en_5.1.1_3.0_1694623975734.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cocondenser_120","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cocondenser_120", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cocondenser_120| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/approach0/dpr-cocondenser-120 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cocondenser_220_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocondenser_220_en.md new file mode 100644 index 00000000000000..8b913747e55857 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocondenser_220_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cocondenser_220 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cocondenser_220 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cocondenser_220` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cocondenser_220_en_5.1.1_3.0_1694624315821.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cocondenser_220_en_5.1.1_3.0_1694624315821.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cocondenser_220","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cocondenser_220", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cocondenser_220| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/approach0/dpr-cocondenser-220 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cocondenser_320_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocondenser_320_en.md new file mode 100644 index 00000000000000..6959bcc77bb353 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocondenser_320_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cocondenser_320 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cocondenser_320 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cocondenser_320` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cocondenser_320_en_5.1.1_3.0_1694624581698.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cocondenser_320_en_5.1.1_3.0_1694624581698.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cocondenser_320","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cocondenser_320", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cocondenser_320| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/approach0/dpr-cocondenser-320 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cocondenser_520_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocondenser_520_en.md new file mode 100644 index 00000000000000..7fb21f76226d8c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cocondenser_520_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cocondenser_520 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cocondenser_520 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cocondenser_520` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cocondenser_520_en_5.1.1_3.0_1694624864486.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cocondenser_520_en_5.1.1_3.0_1694624864486.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cocondenser_520","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cocondenser_520", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cocondenser_520| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/approach0/dpr-cocondenser-520 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cotbert_020_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotbert_020_en.md new file mode 100644 index 00000000000000..84f3f8fa3309d9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotbert_020_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cotbert_020 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cotbert_020 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cotbert_020` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cotbert_020_en_5.1.1_3.0_1694627301790.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cotbert_020_en_5.1.1_3.0_1694627301790.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cotbert_020","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cotbert_020", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cotbert_020| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/dpr-cotbert-020 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cotbert_120_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotbert_120_en.md new file mode 100644 index 00000000000000..75a0082cd419cd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotbert_120_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cotbert_120 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cotbert_120 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cotbert_120` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cotbert_120_en_5.1.1_3.0_1694627761408.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cotbert_120_en_5.1.1_3.0_1694627761408.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cotbert_120","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cotbert_120", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cotbert_120| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/dpr-cotbert-120 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cotbert_220_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotbert_220_en.md new file mode 100644 index 00000000000000..483fa4dc5e631e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotbert_220_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cotbert_220 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cotbert_220 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cotbert_220` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cotbert_220_en_5.1.1_3.0_1694628278286.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cotbert_220_en_5.1.1_3.0_1694628278286.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cotbert_220","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cotbert_220", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cotbert_220| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/dpr-cotbert-220 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cotbert_320_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotbert_320_en.md new file mode 100644 index 00000000000000..1ef444321ec991 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotbert_320_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cotbert_320 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cotbert_320 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cotbert_320` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cotbert_320_en_5.1.1_3.0_1694628675772.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cotbert_320_en_5.1.1_3.0_1694628675772.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cotbert_320","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cotbert_320", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cotbert_320| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/dpr-cotbert-320 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cotbert_520_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotbert_520_en.md new file mode 100644 index 00000000000000..17c9e4e3514ffc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotbert_520_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cotbert_520 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cotbert_520 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cotbert_520` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cotbert_520_en_5.1.1_3.0_1694629038955.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cotbert_520_en_5.1.1_3.0_1694629038955.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cotbert_520","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cotbert_520", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cotbert_520| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/dpr-cotbert-520 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cotmae_020_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotmae_020_en.md new file mode 100644 index 00000000000000..85b51b65aacf58 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotmae_020_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cotmae_020 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cotmae_020 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cotmae_020` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cotmae_020_en_5.1.1_3.0_1694625405708.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cotmae_020_en_5.1.1_3.0_1694625405708.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cotmae_020","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cotmae_020", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cotmae_020| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/dpr-cotmae-020 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cotmae_120_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotmae_120_en.md new file mode 100644 index 00000000000000..d460cfe9823ac2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotmae_120_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cotmae_120 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cotmae_120 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cotmae_120` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cotmae_120_en_5.1.1_3.0_1694625762302.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cotmae_120_en_5.1.1_3.0_1694625762302.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cotmae_120","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cotmae_120", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cotmae_120| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/dpr-cotmae-120 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cotmae_220_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotmae_220_en.md new file mode 100644 index 00000000000000..7ff1f9294f7e0a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotmae_220_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cotmae_220 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cotmae_220 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cotmae_220` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cotmae_220_en_5.1.1_3.0_1694626146098.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cotmae_220_en_5.1.1_3.0_1694626146098.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cotmae_220","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cotmae_220", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cotmae_220| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/dpr-cotmae-220 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cotmae_320_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotmae_320_en.md new file mode 100644 index 00000000000000..60e1e655809b9f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotmae_320_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cotmae_320 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cotmae_320 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cotmae_320` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cotmae_320_en_5.1.1_3.0_1694626464675.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cotmae_320_en_5.1.1_3.0_1694626464675.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cotmae_320","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cotmae_320", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cotmae_320| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/dpr-cotmae-320 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_cotmae_520_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotmae_520_en.md new file mode 100644 index 00000000000000..c6a19128a03475 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_cotmae_520_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_cotmae_520 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_cotmae_520 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_cotmae_520` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_cotmae_520_en_5.1.1_3.0_1694626881352.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_cotmae_520_en_5.1.1_3.0_1694626881352.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_cotmae_520","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_cotmae_520", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_cotmae_520| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/dpr-cotmae-520 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_spanish_passage_encoder_allqa_base_es.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_spanish_passage_encoder_allqa_base_es.md new file mode 100644 index 00000000000000..85c88b6518bbe8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_spanish_passage_encoder_allqa_base_es.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Castilian, Spanish dpr_spanish_passage_encoder_allqa_base BertEmbeddings from IIC +author: John Snow Labs +name: dpr_spanish_passage_encoder_allqa_base +date: 2023-09-13 +tags: [bert, es, open_source, fill_mask, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_spanish_passage_encoder_allqa_base` is a Castilian, Spanish model originally trained by IIC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_spanish_passage_encoder_allqa_base_es_5.1.1_3.0_1694619434308.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_spanish_passage_encoder_allqa_base_es_5.1.1_3.0_1694619434308.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_spanish_passage_encoder_allqa_base","es") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_spanish_passage_encoder_allqa_base", "es") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_spanish_passage_encoder_allqa_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|es| +|Size:|409.5 MB| + +## References + +https://huggingface.co/IIC/dpr-spanish-passage_encoder-allqa-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_spanish_passage_encoder_squades_base_es.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_spanish_passage_encoder_squades_base_es.md new file mode 100644 index 00000000000000..1e089f5458263f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_spanish_passage_encoder_squades_base_es.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Castilian, Spanish dpr_spanish_passage_encoder_squades_base BertEmbeddings from IIC +author: John Snow Labs +name: dpr_spanish_passage_encoder_squades_base +date: 2023-09-13 +tags: [bert, es, open_source, fill_mask, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_spanish_passage_encoder_squades_base` is a Castilian, Spanish model originally trained by IIC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_spanish_passage_encoder_squades_base_es_5.1.1_3.0_1694612516347.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_spanish_passage_encoder_squades_base_es_5.1.1_3.0_1694612516347.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_spanish_passage_encoder_squades_base","es") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_spanish_passage_encoder_squades_base", "es") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_spanish_passage_encoder_squades_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|es| +|Size:|409.5 MB| + +## References + +https://huggingface.co/IIC/dpr-spanish-passage_encoder-squades-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_spanish_question_encoder_allqa_base_es.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_spanish_question_encoder_allqa_base_es.md new file mode 100644 index 00000000000000..3bbe62430dcd6b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_spanish_question_encoder_allqa_base_es.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Castilian, Spanish dpr_spanish_question_encoder_allqa_base BertEmbeddings from IIC +author: John Snow Labs +name: dpr_spanish_question_encoder_allqa_base +date: 2023-09-13 +tags: [bert, es, open_source, fill_mask, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_spanish_question_encoder_allqa_base` is a Castilian, Spanish model originally trained by IIC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_spanish_question_encoder_allqa_base_es_5.1.1_3.0_1694619850497.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_spanish_question_encoder_allqa_base_es_5.1.1_3.0_1694619850497.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_spanish_question_encoder_allqa_base","es") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_spanish_question_encoder_allqa_base", "es") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_spanish_question_encoder_allqa_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|es| +|Size:|409.5 MB| + +## References + +https://huggingface.co/IIC/dpr-spanish-question_encoder-allqa-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_spanish_question_encoder_squades_base_es.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_spanish_question_encoder_squades_base_es.md new file mode 100644 index 00000000000000..51fa558ec481ea --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_spanish_question_encoder_squades_base_es.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Castilian, Spanish dpr_spanish_question_encoder_squades_base BertEmbeddings from IIC +author: John Snow Labs +name: dpr_spanish_question_encoder_squades_base +date: 2023-09-13 +tags: [bert, es, open_source, fill_mask, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_spanish_question_encoder_squades_base` is a Castilian, Spanish model originally trained by IIC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_spanish_question_encoder_squades_base_es_5.1.1_3.0_1694613007694.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_spanish_question_encoder_squades_base_es_5.1.1_3.0_1694613007694.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_spanish_question_encoder_squades_base","es") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_spanish_question_encoder_squades_base", "es") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_spanish_question_encoder_squades_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|es| +|Size:|409.5 MB| + +## References + +https://huggingface.co/IIC/dpr-spanish-question_encoder-squades-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_vanilla_bert_020_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_vanilla_bert_020_en.md new file mode 100644 index 00000000000000..1010fac53bccba --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_vanilla_bert_020_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_vanilla_bert_020 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_vanilla_bert_020 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_vanilla_bert_020` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_vanilla_bert_020_en_5.1.1_3.0_1694629366872.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_vanilla_bert_020_en_5.1.1_3.0_1694629366872.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_vanilla_bert_020","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_vanilla_bert_020", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_vanilla_bert_020| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.0 MB| + +## References + +https://huggingface.co/approach0/dpr-vanilla-bert-020 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_vanilla_bert_120_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_vanilla_bert_120_en.md new file mode 100644 index 00000000000000..1dff432c9b6fd6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_vanilla_bert_120_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_vanilla_bert_120 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_vanilla_bert_120 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_vanilla_bert_120` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_vanilla_bert_120_en_5.1.1_3.0_1694629722071.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_vanilla_bert_120_en_5.1.1_3.0_1694629722071.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_vanilla_bert_120","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_vanilla_bert_120", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_vanilla_bert_120| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.0 MB| + +## References + +https://huggingface.co/approach0/dpr-vanilla-bert-120 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_vanilla_bert_220_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_vanilla_bert_220_en.md new file mode 100644 index 00000000000000..d202ba7dad79ff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_vanilla_bert_220_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_vanilla_bert_220 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_vanilla_bert_220 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_vanilla_bert_220` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_vanilla_bert_220_en_5.1.1_3.0_1694630120901.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_vanilla_bert_220_en_5.1.1_3.0_1694630120901.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_vanilla_bert_220","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_vanilla_bert_220", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_vanilla_bert_220| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.0 MB| + +## References + +https://huggingface.co/approach0/dpr-vanilla-bert-220 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_vanilla_bert_320_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_vanilla_bert_320_en.md new file mode 100644 index 00000000000000..d83e37f7a2dc99 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_vanilla_bert_320_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_vanilla_bert_320 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_vanilla_bert_320 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_vanilla_bert_320` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_vanilla_bert_320_en_5.1.1_3.0_1694630513466.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_vanilla_bert_320_en_5.1.1_3.0_1694630513466.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_vanilla_bert_320","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_vanilla_bert_320", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_vanilla_bert_320| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.0 MB| + +## References + +https://huggingface.co/approach0/dpr-vanilla-bert-320 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-dpr_vanilla_bert_520_en.md b/docs/_posts/ahmedlone127/2023-09-13-dpr_vanilla_bert_520_en.md new file mode 100644 index 00000000000000..9f1cf37b6f1039 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-dpr_vanilla_bert_520_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dpr_vanilla_bert_520 BertEmbeddings from approach0 +author: John Snow Labs +name: dpr_vanilla_bert_520 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dpr_vanilla_bert_520` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dpr_vanilla_bert_520_en_5.1.1_3.0_1694630979516.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dpr_vanilla_bert_520_en_5.1.1_3.0_1694630979516.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dpr_vanilla_bert_520","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dpr_vanilla_bert_520", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dpr_vanilla_bert_520| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.0 MB| + +## References + +https://huggingface.co/approach0/dpr-vanilla-bert-520 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-duck_and_cover_genre_encoder_en.md b/docs/_posts/ahmedlone127/2023-09-13-duck_and_cover_genre_encoder_en.md new file mode 100644 index 00000000000000..7047da12b2b3a5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-duck_and_cover_genre_encoder_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English duck_and_cover_genre_encoder BertEmbeddings from mnne +author: John Snow Labs +name: duck_and_cover_genre_encoder +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`duck_and_cover_genre_encoder` is a English model originally trained by mnne. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/duck_and_cover_genre_encoder_en_5.1.1_3.0_1694625838242.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/duck_and_cover_genre_encoder_en_5.1.1_3.0_1694625838242.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("duck_and_cover_genre_encoder","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("duck_and_cover_genre_encoder", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|duck_and_cover_genre_encoder| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|41.8 MB| + +## References + +https://huggingface.co/mnne/duck-and-cover-genre-encoder \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-econobert_en.md b/docs/_posts/ahmedlone127/2023-09-13-econobert_en.md new file mode 100644 index 00000000000000..200aebd06695e9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-econobert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English econobert BertEmbeddings from samchain +author: John Snow Labs +name: econobert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`econobert` is a English model originally trained by samchain. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/econobert_en_5.1.1_3.0_1694622554131.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/econobert_en_5.1.1_3.0_1694622554131.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("econobert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("econobert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|econobert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/samchain/EconoBert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-fast_food_pure_relation_v0_en.md b/docs/_posts/ahmedlone127/2023-09-13-fast_food_pure_relation_v0_en.md new file mode 100644 index 00000000000000..82b1df2016aac1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-fast_food_pure_relation_v0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English fast_food_pure_relation_v0 BertEmbeddings from yogeshchandrasekharuni +author: John Snow Labs +name: fast_food_pure_relation_v0 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fast_food_pure_relation_v0` is a English model originally trained by yogeshchandrasekharuni. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fast_food_pure_relation_v0_en_5.1.1_3.0_1694618849500.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fast_food_pure_relation_v0_en_5.1.1_3.0_1694618849500.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("fast_food_pure_relation_v0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("fast_food_pure_relation_v0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fast_food_pure_relation_v0| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/yogeshchandrasekharuni/fast-food-pure-relation-v0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-fbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-fbert_en.md new file mode 100644 index 00000000000000..f8b56cc3353814 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-fbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English fbert BertEmbeddings from diptanu +author: John Snow Labs +name: fbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fbert` is a English model originally trained by diptanu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fbert_en_5.1.1_3.0_1694624971451.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fbert_en_5.1.1_3.0_1694624971451.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("fbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("fbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.2 MB| + +## References + +https://huggingface.co/diptanu/fBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-fernet_c5_cs.md b/docs/_posts/ahmedlone127/2023-09-13-fernet_c5_cs.md new file mode 100644 index 00000000000000..f7e3d06fb0d1de --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-fernet_c5_cs.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Czech fernet_c5 BertEmbeddings from fav-kky +author: John Snow Labs +name: fernet_c5 +date: 2023-09-13 +tags: [bert, cs, open_source, fill_mask, onnx] +task: Embeddings +language: cs +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fernet_c5` is a Czech model originally trained by fav-kky. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fernet_c5_cs_5.1.1_3.0_1694636619880.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fernet_c5_cs_5.1.1_3.0_1694636619880.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("fernet_c5","cs") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("fernet_c5", "cs") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fernet_c5| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|cs| +|Size:|609.5 MB| + +## References + +https://huggingface.co/fav-kky/FERNET-C5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-fernet_cc_slovak_sk.md b/docs/_posts/ahmedlone127/2023-09-13-fernet_cc_slovak_sk.md new file mode 100644 index 00000000000000..399e209c6e5596 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-fernet_cc_slovak_sk.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Slovak fernet_cc_slovak BertEmbeddings from fav-kky +author: John Snow Labs +name: fernet_cc_slovak +date: 2023-09-13 +tags: [bert, sk, open_source, fill_mask, onnx] +task: Embeddings +language: sk +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fernet_cc_slovak` is a Slovak model originally trained by fav-kky. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fernet_cc_slovak_sk_5.1.1_3.0_1694637045822.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fernet_cc_slovak_sk_5.1.1_3.0_1694637045822.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("fernet_cc_slovak","sk") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("fernet_cc_slovak", "sk") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fernet_cc_slovak| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|sk| +|Size:|609.4 MB| + +## References + +https://huggingface.co/fav-kky/FERNET-CC_sk \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-film20000bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-film20000bert_base_uncased_en.md new file mode 100644 index 00000000000000..bd4bc9f9a5acfc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-film20000bert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English film20000bert_base_uncased BertEmbeddings from AmaiaSolaun +author: John Snow Labs +name: film20000bert_base_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`film20000bert_base_uncased` is a English model originally trained by AmaiaSolaun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/film20000bert_base_uncased_en_5.1.1_3.0_1694587815260.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/film20000bert_base_uncased_en_5.1.1_3.0_1694587815260.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("film20000bert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("film20000bert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|film20000bert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/AmaiaSolaun/film20000bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-finbert_pretrain_yiyanghkust_en.md b/docs/_posts/ahmedlone127/2023-09-13-finbert_pretrain_yiyanghkust_en.md new file mode 100644 index 00000000000000..7e19262bbe0b91 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-finbert_pretrain_yiyanghkust_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English finbert_pretrain_yiyanghkust BertEmbeddings from philschmid +author: John Snow Labs +name: finbert_pretrain_yiyanghkust +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finbert_pretrain_yiyanghkust` is a English model originally trained by philschmid. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finbert_pretrain_yiyanghkust_en_5.1.1_3.0_1694563205165.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finbert_pretrain_yiyanghkust_en_5.1.1_3.0_1694563205165.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("finbert_pretrain_yiyanghkust","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("finbert_pretrain_yiyanghkust", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finbert_pretrain_yiyanghkust| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/philschmid/finbert-pretrain-yiyanghkust \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-fine_tune_bert_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-13-fine_tune_bert_mlm_en.md new file mode 100644 index 00000000000000..30f540b20bfd4c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-fine_tune_bert_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English fine_tune_bert_mlm BertEmbeddings from mjavadmt +author: John Snow Labs +name: fine_tune_bert_mlm +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tune_bert_mlm` is a English model originally trained by mjavadmt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tune_bert_mlm_en_5.1.1_3.0_1694622010218.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tune_bert_mlm_en_5.1.1_3.0_1694622010218.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("fine_tune_bert_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("fine_tune_bert_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tune_bert_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|606.4 MB| + +## References + +https://huggingface.co/mjavadmt/fine-tune-BERT-MLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-further_train_domain_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-further_train_domain_20_en.md new file mode 100644 index 00000000000000..c7d3db75c2fdcf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-further_train_domain_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English further_train_domain_20 BertEmbeddings from onlydj96 +author: John Snow Labs +name: further_train_domain_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`further_train_domain_20` is a English model originally trained by onlydj96. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/further_train_domain_20_en_5.1.1_3.0_1694588464159.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/further_train_domain_20_en_5.1.1_3.0_1694588464159.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("further_train_domain_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("further_train_domain_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|further_train_domain_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|441.2 MB| + +## References + +https://huggingface.co/onlydj96/further_train_domain_20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-german_medbert_issues_128_de.md b/docs/_posts/ahmedlone127/2023-09-13-german_medbert_issues_128_de.md new file mode 100644 index 00000000000000..94617b60f14ed3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-german_medbert_issues_128_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German german_medbert_issues_128 BertEmbeddings from ogimgio +author: John Snow Labs +name: german_medbert_issues_128 +date: 2023-09-13 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`german_medbert_issues_128` is a German model originally trained by ogimgio. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/german_medbert_issues_128_de_5.1.1_3.0_1694612124790.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/german_medbert_issues_128_de_5.1.1_3.0_1694612124790.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("german_medbert_issues_128","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("german_medbert_issues_128", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|german_medbert_issues_128| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|406.9 MB| + +## References + +https://huggingface.co/ogimgio/German-MedBERT-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-greeksocialbert_base_greek_social_media_v2_el.md b/docs/_posts/ahmedlone127/2023-09-13-greeksocialbert_base_greek_social_media_v2_el.md new file mode 100644 index 00000000000000..2eecd22367db6e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-greeksocialbert_base_greek_social_media_v2_el.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Modern Greek (1453-) greeksocialbert_base_greek_social_media_v2 BertEmbeddings from pchatz +author: John Snow Labs +name: greeksocialbert_base_greek_social_media_v2 +date: 2023-09-13 +tags: [bert, el, open_source, fill_mask, onnx] +task: Embeddings +language: el +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`greeksocialbert_base_greek_social_media_v2` is a Modern Greek (1453-) model originally trained by pchatz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/greeksocialbert_base_greek_social_media_v2_el_5.1.1_3.0_1694613573415.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/greeksocialbert_base_greek_social_media_v2_el_5.1.1_3.0_1694613573415.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("greeksocialbert_base_greek_social_media_v2","el") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("greeksocialbert_base_greek_social_media_v2", "el") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|greeksocialbert_base_greek_social_media_v2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|el| +|Size:|421.3 MB| + +## References + +https://huggingface.co/pchatz/greeksocialbert-base-greek-social-media-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-greeksocialbert_base_greek_uncased_v1_el.md b/docs/_posts/ahmedlone127/2023-09-13-greeksocialbert_base_greek_uncased_v1_el.md new file mode 100644 index 00000000000000..c20e71174595ef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-greeksocialbert_base_greek_uncased_v1_el.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Modern Greek (1453-) greeksocialbert_base_greek_uncased_v1 BertEmbeddings from gealexandri +author: John Snow Labs +name: greeksocialbert_base_greek_uncased_v1 +date: 2023-09-13 +tags: [bert, el, open_source, fill_mask, onnx] +task: Embeddings +language: el +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`greeksocialbert_base_greek_uncased_v1` is a Modern Greek (1453-) model originally trained by gealexandri. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/greeksocialbert_base_greek_uncased_v1_el_5.1.1_3.0_1694648049169.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/greeksocialbert_base_greek_uncased_v1_el_5.1.1_3.0_1694648049169.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("greeksocialbert_base_greek_uncased_v1","el") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("greeksocialbert_base_greek_uncased_v1", "el") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|greeksocialbert_base_greek_uncased_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|el| +|Size:|421.3 MB| + +## References + +https://huggingface.co/gealexandri/greeksocialbert-base-greek-uncased-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-gujarati_bert_gu.md b/docs/_posts/ahmedlone127/2023-09-13-gujarati_bert_gu.md new file mode 100644 index 00000000000000..afebfe448e345c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-gujarati_bert_gu.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Gujarati gujarati_bert BertEmbeddings from l3cube-pune +author: John Snow Labs +name: gujarati_bert +date: 2023-09-13 +tags: [bert, gu, open_source, fill_mask, onnx] +task: Embeddings +language: gu +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`gujarati_bert` is a Gujarati model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gujarati_bert_gu_5.1.1_3.0_1694642031980.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gujarati_bert_gu_5.1.1_3.0_1694642031980.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("gujarati_bert","gu") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("gujarati_bert", "gu") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gujarati_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|gu| +|Size:|890.4 MB| + +## References + +https://huggingface.co/l3cube-pune/gujarati-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-gujiroberta_fan_en.md b/docs/_posts/ahmedlone127/2023-09-13-gujiroberta_fan_en.md new file mode 100644 index 00000000000000..ad0d5548c74829 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-gujiroberta_fan_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English gujiroberta_fan BertEmbeddings from hsc748NLP +author: John Snow Labs +name: gujiroberta_fan +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`gujiroberta_fan` is a English model originally trained by hsc748NLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gujiroberta_fan_en_5.1.1_3.0_1694602043407.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gujiroberta_fan_en_5.1.1_3.0_1694602043407.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("gujiroberta_fan","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("gujiroberta_fan", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gujiroberta_fan| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|420.2 MB| + +## References + +https://huggingface.co/hsc748NLP/GujiRoBERTa_fan \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hindi_bert_v2_hi.md b/docs/_posts/ahmedlone127/2023-09-13-hindi_bert_v2_hi.md new file mode 100644 index 00000000000000..5f8abc7871dc57 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hindi_bert_v2_hi.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hindi hindi_bert_v2 BertEmbeddings from l3cube-pune +author: John Snow Labs +name: hindi_bert_v2 +date: 2023-09-13 +tags: [bert, hi, open_source, fill_mask, onnx] +task: Embeddings +language: hi +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hindi_bert_v2` is a Hindi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hindi_bert_v2_hi_5.1.1_3.0_1694576005164.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hindi_bert_v2_hi_5.1.1_3.0_1694576005164.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hindi_bert_v2","hi") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hindi_bert_v2", "hi") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hindi_bert_v2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|hi| +|Size:|890.6 MB| + +## References + +https://huggingface.co/l3cube-pune/hindi-bert-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hindi_tweets_bert_scratch_hi.md b/docs/_posts/ahmedlone127/2023-09-13-hindi_tweets_bert_scratch_hi.md new file mode 100644 index 00000000000000..e4bae79be16f65 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hindi_tweets_bert_scratch_hi.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hindi hindi_tweets_bert_scratch BertEmbeddings from l3cube-pune +author: John Snow Labs +name: hindi_tweets_bert_scratch +date: 2023-09-13 +tags: [bert, hi, open_source, fill_mask, onnx] +task: Embeddings +language: hi +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hindi_tweets_bert_scratch` is a Hindi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hindi_tweets_bert_scratch_hi_5.1.1_3.0_1694632323915.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hindi_tweets_bert_scratch_hi_5.1.1_3.0_1694632323915.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hindi_tweets_bert_scratch","hi") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hindi_tweets_bert_scratch", "hi") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hindi_tweets_bert_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|hi| +|Size:|470.5 MB| + +## References + +https://huggingface.co/l3cube-pune/hindi-tweets-bert-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hing_mbert_mixed_hi.md b/docs/_posts/ahmedlone127/2023-09-13-hing_mbert_mixed_hi.md new file mode 100644 index 00000000000000..d326b13d1ee1a0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hing_mbert_mixed_hi.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hindi hing_mbert_mixed BertEmbeddings from l3cube-pune +author: John Snow Labs +name: hing_mbert_mixed +date: 2023-09-13 +tags: [bert, hi, open_source, fill_mask, onnx] +task: Embeddings +language: hi +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hing_mbert_mixed` is a Hindi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hing_mbert_mixed_hi_5.1.1_3.0_1694618447086.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hing_mbert_mixed_hi_5.1.1_3.0_1694618447086.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hing_mbert_mixed","hi") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hing_mbert_mixed", "hi") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hing_mbert_mixed| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|hi| +|Size:|664.9 MB| + +## References + +https://huggingface.co/l3cube-pune/hing-mbert-mixed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-hinglish_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-13-hinglish_finetuned_en.md new file mode 100644 index 00000000000000..d1358666fe5932 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-hinglish_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English hinglish_finetuned BertEmbeddings from ketan-rmcf +author: John Snow Labs +name: hinglish_finetuned +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hinglish_finetuned` is a English model originally trained by ketan-rmcf. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hinglish_finetuned_en_5.1.1_3.0_1694636325882.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hinglish_finetuned_en_5.1.1_3.0_1694636325882.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("hinglish_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("hinglish_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hinglish_finetuned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/ketan-rmcf/hinglish-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-incel_bert_base_multilingual_cased_1000k_english_xx.md b/docs/_posts/ahmedlone127/2023-09-13-incel_bert_base_multilingual_cased_1000k_english_xx.md new file mode 100644 index 00000000000000..2c7bf1886e701b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-incel_bert_base_multilingual_cased_1000k_english_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual incel_bert_base_multilingual_cased_1000k_english BertEmbeddings from pgajo +author: John Snow Labs +name: incel_bert_base_multilingual_cased_1000k_english +date: 2023-09-13 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`incel_bert_base_multilingual_cased_1000k_english` is a Multilingual model originally trained by pgajo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/incel_bert_base_multilingual_cased_1000k_english_xx_5.1.1_3.0_1694613006554.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/incel_bert_base_multilingual_cased_1000k_english_xx_5.1.1_3.0_1694613006554.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("incel_bert_base_multilingual_cased_1000k_english","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("incel_bert_base_multilingual_cased_1000k_english", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|incel_bert_base_multilingual_cased_1000k_english| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|665.1 MB| + +## References + +https://huggingface.co/pgajo/incel-bert-base-multilingual-cased-1000k_english \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-incel_bert_base_multilingual_cased_1000k_multi_xx.md b/docs/_posts/ahmedlone127/2023-09-13-incel_bert_base_multilingual_cased_1000k_multi_xx.md new file mode 100644 index 00000000000000..ce868db95ed37e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-incel_bert_base_multilingual_cased_1000k_multi_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual incel_bert_base_multilingual_cased_1000k_multi BertEmbeddings from pgajo +author: John Snow Labs +name: incel_bert_base_multilingual_cased_1000k_multi +date: 2023-09-13 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`incel_bert_base_multilingual_cased_1000k_multi` is a Multilingual model originally trained by pgajo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/incel_bert_base_multilingual_cased_1000k_multi_xx_5.1.1_3.0_1694613670535.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/incel_bert_base_multilingual_cased_1000k_multi_xx_5.1.1_3.0_1694613670535.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("incel_bert_base_multilingual_cased_1000k_multi","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("incel_bert_base_multilingual_cased_1000k_multi", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|incel_bert_base_multilingual_cased_1000k_multi| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|665.1 MB| + +## References + +https://huggingface.co/pgajo/incel-bert-base-multilingual-cased-1000k_multi \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-incel_bert_base_multilingual_cased_627k_italian_xx.md b/docs/_posts/ahmedlone127/2023-09-13-incel_bert_base_multilingual_cased_627k_italian_xx.md new file mode 100644 index 00000000000000..485adf1ed601bf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-incel_bert_base_multilingual_cased_627k_italian_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual incel_bert_base_multilingual_cased_627k_italian BertEmbeddings from pgajo +author: John Snow Labs +name: incel_bert_base_multilingual_cased_627k_italian +date: 2023-09-13 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`incel_bert_base_multilingual_cased_627k_italian` is a Multilingual model originally trained by pgajo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/incel_bert_base_multilingual_cased_627k_italian_xx_5.1.1_3.0_1694612397380.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/incel_bert_base_multilingual_cased_627k_italian_xx_5.1.1_3.0_1694612397380.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("incel_bert_base_multilingual_cased_627k_italian","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("incel_bert_base_multilingual_cased_627k_italian", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|incel_bert_base_multilingual_cased_627k_italian| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|665.0 MB| + +## References + +https://huggingface.co/pgajo/incel-bert-base-multilingual-cased-627k_italian \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-incel_bert_uncased_l_12_h_768_a_12_italian_alb3rt0_627k_italian_en.md b/docs/_posts/ahmedlone127/2023-09-13-incel_bert_uncased_l_12_h_768_a_12_italian_alb3rt0_627k_italian_en.md new file mode 100644 index 00000000000000..aa8032572192c6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-incel_bert_uncased_l_12_h_768_a_12_italian_alb3rt0_627k_italian_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English incel_bert_uncased_l_12_h_768_a_12_italian_alb3rt0_627k_italian BertEmbeddings from pgajo +author: John Snow Labs +name: incel_bert_uncased_l_12_h_768_a_12_italian_alb3rt0_627k_italian +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`incel_bert_uncased_l_12_h_768_a_12_italian_alb3rt0_627k_italian` is a English model originally trained by pgajo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/incel_bert_uncased_l_12_h_768_a_12_italian_alb3rt0_627k_italian_en_5.1.1_3.0_1694621049338.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/incel_bert_uncased_l_12_h_768_a_12_italian_alb3rt0_627k_italian_en_5.1.1_3.0_1694621049338.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("incel_bert_uncased_l_12_h_768_a_12_italian_alb3rt0_627k_italian","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("incel_bert_uncased_l_12_h_768_a_12_italian_alb3rt0_627k_italian", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|incel_bert_uncased_l_12_h_768_a_12_italian_alb3rt0_627k_italian| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|688.7 MB| + +## References + +https://huggingface.co/pgajo/incel-bert_uncased_L-12_H-768_A-12_italian_alb3rt0-627k_italian \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-indo_legalbert_id.md b/docs/_posts/ahmedlone127/2023-09-13-indo_legalbert_id.md new file mode 100644 index 00000000000000..3b082f0dec52ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-indo_legalbert_id.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Indonesian indo_legalbert BertEmbeddings from archi-ai +author: John Snow Labs +name: indo_legalbert +date: 2023-09-13 +tags: [bert, id, open_source, fill_mask, onnx] +task: Embeddings +language: id +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indo_legalbert` is a Indonesian model originally trained by archi-ai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indo_legalbert_id_5.1.1_3.0_1694623791636.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indo_legalbert_id_5.1.1_3.0_1694623791636.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("indo_legalbert","id") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("indo_legalbert", "id") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indo_legalbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|id| +|Size:|1.3 GB| + +## References + +https://huggingface.co/archi-ai/Indo-LegalBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-indobert_base_p2_finetuned_mer_10k_en.md b/docs/_posts/ahmedlone127/2023-09-13-indobert_base_p2_finetuned_mer_10k_en.md new file mode 100644 index 00000000000000..0d647abad05f0b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-indobert_base_p2_finetuned_mer_10k_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English indobert_base_p2_finetuned_mer_10k BertEmbeddings from stevenwh +author: John Snow Labs +name: indobert_base_p2_finetuned_mer_10k +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indobert_base_p2_finetuned_mer_10k` is a English model originally trained by stevenwh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indobert_base_p2_finetuned_mer_10k_en_5.1.1_3.0_1694636325821.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indobert_base_p2_finetuned_mer_10k_en_5.1.1_3.0_1694636325821.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("indobert_base_p2_finetuned_mer_10k","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("indobert_base_p2_finetuned_mer_10k", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indobert_base_p2_finetuned_mer_10k| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.2 MB| + +## References + +https://huggingface.co/stevenwh/indobert-base-p2-finetuned-mer-10k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-indobert_base_p2_finetuned_mer_80k_en.md b/docs/_posts/ahmedlone127/2023-09-13-indobert_base_p2_finetuned_mer_80k_en.md new file mode 100644 index 00000000000000..bdbc7e1959afe3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-indobert_base_p2_finetuned_mer_80k_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English indobert_base_p2_finetuned_mer_80k BertEmbeddings from stevenwh +author: John Snow Labs +name: indobert_base_p2_finetuned_mer_80k +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indobert_base_p2_finetuned_mer_80k` is a English model originally trained by stevenwh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indobert_base_p2_finetuned_mer_80k_en_5.1.1_3.0_1694636807078.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indobert_base_p2_finetuned_mer_80k_en_5.1.1_3.0_1694636807078.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("indobert_base_p2_finetuned_mer_80k","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("indobert_base_p2_finetuned_mer_80k", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indobert_base_p2_finetuned_mer_80k| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.2 MB| + +## References + +https://huggingface.co/stevenwh/indobert-base-p2-finetuned-mer-80k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-indobert_base_p2_finetuned_mer_en.md b/docs/_posts/ahmedlone127/2023-09-13-indobert_base_p2_finetuned_mer_en.md new file mode 100644 index 00000000000000..0f14ef376455f5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-indobert_base_p2_finetuned_mer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English indobert_base_p2_finetuned_mer BertEmbeddings from stevenwh +author: John Snow Labs +name: indobert_base_p2_finetuned_mer +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indobert_base_p2_finetuned_mer` is a English model originally trained by stevenwh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indobert_base_p2_finetuned_mer_en_5.1.1_3.0_1694629744633.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indobert_base_p2_finetuned_mer_en_5.1.1_3.0_1694629744633.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("indobert_base_p2_finetuned_mer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("indobert_base_p2_finetuned_mer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indobert_base_p2_finetuned_mer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.1 MB| + +## References + +https://huggingface.co/stevenwh/indobert-base-p2-finetuned-mer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-jobbert_base_cased_en.md b/docs/_posts/ahmedlone127/2023-09-13-jobbert_base_cased_en.md new file mode 100644 index 00000000000000..d189235ed2ed98 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-jobbert_base_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English jobbert_base_cased BertEmbeddings from jjzha +author: John Snow Labs +name: jobbert_base_cased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`jobbert_base_cased` is a English model originally trained by jjzha. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/jobbert_base_cased_en_5.1.1_3.0_1694631841420.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/jobbert_base_cased_en_5.1.1_3.0_1694631841420.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("jobbert_base_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("jobbert_base_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|jobbert_base_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.2 MB| + +## References + +https://huggingface.co/jjzha/jobbert-base-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kannada_bert_kn.md b/docs/_posts/ahmedlone127/2023-09-13-kannada_bert_kn.md new file mode 100644 index 00000000000000..d2953cc379d955 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kannada_bert_kn.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Kannada kannada_bert BertEmbeddings from l3cube-pune +author: John Snow Labs +name: kannada_bert +date: 2023-09-13 +tags: [bert, kn, open_source, fill_mask, onnx] +task: Embeddings +language: kn +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kannada_bert` is a Kannada model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kannada_bert_kn_5.1.1_3.0_1694638806956.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kannada_bert_kn_5.1.1_3.0_1694638806956.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kannada_bert","kn") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kannada_bert", "kn") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kannada_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|kn| +|Size:|890.5 MB| + +## References + +https://huggingface.co/l3cube-pune/kannada-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kc_900_en.md b/docs/_posts/ahmedlone127/2023-09-13-kc_900_en.md new file mode 100644 index 00000000000000..105e0c7462f8a5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kc_900_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kc_900 BertEmbeddings from erica +author: John Snow Labs +name: kc_900 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kc_900` is a English model originally trained by erica. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kc_900_en_5.1.1_3.0_1694633305275.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kc_900_en_5.1.1_3.0_1694633305275.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kc_900","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kc_900", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kc_900| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/erica/kc_900 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kcbase400_en.md b/docs/_posts/ahmedlone127/2023-09-13-kcbase400_en.md new file mode 100644 index 00000000000000..1e7a615c41a4b5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kcbase400_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kcbase400 BertEmbeddings from erica +author: John Snow Labs +name: kcbase400 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kcbase400` is a English model originally trained by erica. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kcbase400_en_5.1.1_3.0_1694633739043.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kcbase400_en_5.1.1_3.0_1694633739043.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kcbase400","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kcbase400", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kcbase400| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.3 MB| + +## References + +https://huggingface.co/erica/kcbase400 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kcbert_base_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-13-kcbert_base_finetuned_en.md new file mode 100644 index 00000000000000..eab51b72428795 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kcbert_base_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kcbert_base_finetuned BertEmbeddings from eno3940 +author: John Snow Labs +name: kcbert_base_finetuned +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kcbert_base_finetuned` is a English model originally trained by eno3940. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kcbert_base_finetuned_en_5.1.1_3.0_1694649289545.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kcbert_base_finetuned_en_5.1.1_3.0_1694649289545.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kcbert_base_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kcbert_base_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kcbert_base_finetuned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.2 MB| + +## References + +https://huggingface.co/eno3940/kcbert-base-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-kcbert_large_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-13-kcbert_large_finetuned_en.md new file mode 100644 index 00000000000000..6ea04c1b9af09d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-kcbert_large_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kcbert_large_finetuned BertEmbeddings from LoraBaek +author: John Snow Labs +name: kcbert_large_finetuned +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kcbert_large_finetuned` is a English model originally trained by LoraBaek. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kcbert_large_finetuned_en_5.1.1_3.0_1694644868795.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kcbert_large_finetuned_en_5.1.1_3.0_1694644868795.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kcbert_large_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kcbert_large_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kcbert_large_finetuned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/LoraBaek/kcbert-large-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-klue_base_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-13-klue_base_finetuned_en.md new file mode 100644 index 00000000000000..b8032d3fd51c7d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-klue_base_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English klue_base_finetuned BertEmbeddings from eno3940 +author: John Snow Labs +name: klue_base_finetuned +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`klue_base_finetuned` is a English model originally trained by eno3940. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/klue_base_finetuned_en_5.1.1_3.0_1694646857491.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/klue_base_finetuned_en_5.1.1_3.0_1694646857491.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("klue_base_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("klue_base_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|klue_base_finetuned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.4 MB| + +## References + +https://huggingface.co/eno3940/klue-base-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-klue_bert_epoch3_en.md b/docs/_posts/ahmedlone127/2023-09-13-klue_bert_epoch3_en.md new file mode 100644 index 00000000000000..bd821e3e444e85 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-klue_bert_epoch3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English klue_bert_epoch3 BertEmbeddings from eno3940 +author: John Snow Labs +name: klue_bert_epoch3 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`klue_bert_epoch3` is a English model originally trained by eno3940. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/klue_bert_epoch3_en_5.1.1_3.0_1694648970288.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/klue_bert_epoch3_en_5.1.1_3.0_1694648970288.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("klue_bert_epoch3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("klue_bert_epoch3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|klue_bert_epoch3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.4 MB| + +## References + +https://huggingface.co/eno3940/klue-bert-epoch3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-ksl_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-ksl_bert_en.md new file mode 100644 index 00000000000000..02249500c2f5a3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-ksl_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ksl_bert BertEmbeddings from dobbytk +author: John Snow Labs +name: ksl_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ksl_bert` is a English model originally trained by dobbytk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ksl_bert_en_5.1.1_3.0_1694626325742.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ksl_bert_en_5.1.1_3.0_1694626325742.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ksl_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ksl_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ksl_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.2 MB| + +## References + +https://huggingface.co/dobbytk/KSL-BERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-legal_hebert_en.md b/docs/_posts/ahmedlone127/2023-09-13-legal_hebert_en.md new file mode 100644 index 00000000000000..508141215a8c84 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-legal_hebert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English legal_hebert BertEmbeddings from avichr +author: John Snow Labs +name: legal_hebert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legal_hebert` is a English model originally trained by avichr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legal_hebert_en_5.1.1_3.0_1694641249619.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legal_hebert_en_5.1.1_3.0_1694641249619.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legal_hebert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legal_hebert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legal_hebert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|462.5 MB| + +## References + +https://huggingface.co/avichr/Legal-heBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-legal_indobert_pytorch_en.md b/docs/_posts/ahmedlone127/2023-09-13-legal_indobert_pytorch_en.md new file mode 100644 index 00000000000000..40968056fea7e2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-legal_indobert_pytorch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English legal_indobert_pytorch BertEmbeddings from kapanjagocoding +author: John Snow Labs +name: legal_indobert_pytorch +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legal_indobert_pytorch` is a English model originally trained by kapanjagocoding. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legal_indobert_pytorch_en_5.1.1_3.0_1694636800311.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legal_indobert_pytorch_en_5.1.1_3.0_1694636800311.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legal_indobert_pytorch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legal_indobert_pytorch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legal_indobert_pytorch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/kapanjagocoding/legal-indobert-pytorch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-legal_indobert_pytorch_v1_en.md b/docs/_posts/ahmedlone127/2023-09-13-legal_indobert_pytorch_v1_en.md new file mode 100644 index 00000000000000..446b310aff75d4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-legal_indobert_pytorch_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English legal_indobert_pytorch_v1 BertEmbeddings from kapanjagocoding +author: John Snow Labs +name: legal_indobert_pytorch_v1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legal_indobert_pytorch_v1` is a English model originally trained by kapanjagocoding. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legal_indobert_pytorch_v1_en_5.1.1_3.0_1694637779586.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legal_indobert_pytorch_v1_en_5.1.1_3.0_1694637779586.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legal_indobert_pytorch_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legal_indobert_pytorch_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legal_indobert_pytorch_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/kapanjagocoding/legal-indobert-pytorch-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-legal_indobert_pytorch_v2_en.md b/docs/_posts/ahmedlone127/2023-09-13-legal_indobert_pytorch_v2_en.md new file mode 100644 index 00000000000000..dca64da8b73945 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-legal_indobert_pytorch_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English legal_indobert_pytorch_v2 BertEmbeddings from kapanjagocoding +author: John Snow Labs +name: legal_indobert_pytorch_v2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legal_indobert_pytorch_v2` is a English model originally trained by kapanjagocoding. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legal_indobert_pytorch_v2_en_5.1.1_3.0_1694637348680.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legal_indobert_pytorch_v2_en_5.1.1_3.0_1694637348680.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legal_indobert_pytorch_v2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legal_indobert_pytorch_v2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legal_indobert_pytorch_v2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/kapanjagocoding/legal-indobert-pytorch-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-legal_indobert_pytorch_v3_en.md b/docs/_posts/ahmedlone127/2023-09-13-legal_indobert_pytorch_v3_en.md new file mode 100644 index 00000000000000..05faa53af7c6ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-legal_indobert_pytorch_v3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English legal_indobert_pytorch_v3 BertEmbeddings from kapanjagocoding +author: John Snow Labs +name: legal_indobert_pytorch_v3 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legal_indobert_pytorch_v3` is a English model originally trained by kapanjagocoding. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legal_indobert_pytorch_v3_en_5.1.1_3.0_1694638150002.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legal_indobert_pytorch_v3_en_5.1.1_3.0_1694638150002.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legal_indobert_pytorch_v3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legal_indobert_pytorch_v3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legal_indobert_pytorch_v3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.2 MB| + +## References + +https://huggingface.co/kapanjagocoding/legal-indobert-pytorch-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-legal_indobert_pytorch_v4_en.md b/docs/_posts/ahmedlone127/2023-09-13-legal_indobert_pytorch_v4_en.md new file mode 100644 index 00000000000000..3c192c002de763 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-legal_indobert_pytorch_v4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English legal_indobert_pytorch_v4 BertEmbeddings from kapanjagocoding +author: John Snow Labs +name: legal_indobert_pytorch_v4 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legal_indobert_pytorch_v4` is a English model originally trained by kapanjagocoding. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legal_indobert_pytorch_v4_en_5.1.1_3.0_1694643325435.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legal_indobert_pytorch_v4_en_5.1.1_3.0_1694643325435.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legal_indobert_pytorch_v4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legal_indobert_pytorch_v4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legal_indobert_pytorch_v4| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/kapanjagocoding/legal-indobert-pytorch-v4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-lht_bert_512_en.md b/docs/_posts/ahmedlone127/2023-09-13-lht_bert_512_en.md new file mode 100644 index 00000000000000..8e6a0c237b6dc7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-lht_bert_512_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English lht_bert_512 BertEmbeddings from Shanny +author: John Snow Labs +name: lht_bert_512 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lht_bert_512` is a English model originally trained by Shanny. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lht_bert_512_en_5.1.1_3.0_1694635559814.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lht_bert_512_en_5.1.1_3.0_1694635559814.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("lht_bert_512","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("lht_bert_512", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lht_bert_512| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Shanny/LHT_BERT_512 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-lht_bert_added_tokens_en.md b/docs/_posts/ahmedlone127/2023-09-13-lht_bert_added_tokens_en.md new file mode 100644 index 00000000000000..468f1bba828a96 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-lht_bert_added_tokens_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English lht_bert_added_tokens BertEmbeddings from Shanny +author: John Snow Labs +name: lht_bert_added_tokens +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lht_bert_added_tokens` is a English model originally trained by Shanny. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lht_bert_added_tokens_en_5.1.1_3.0_1694630512751.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lht_bert_added_tokens_en_5.1.1_3.0_1694630512751.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("lht_bert_added_tokens","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("lht_bert_added_tokens", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lht_bert_added_tokens| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Shanny/LHT_BERT_added_tokens \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-lht_bert_added_words_en.md b/docs/_posts/ahmedlone127/2023-09-13-lht_bert_added_words_en.md new file mode 100644 index 00000000000000..d52f808bf7aad1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-lht_bert_added_words_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English lht_bert_added_words BertEmbeddings from Shanny +author: John Snow Labs +name: lht_bert_added_words +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lht_bert_added_words` is a English model originally trained by Shanny. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lht_bert_added_words_en_5.1.1_3.0_1694629369350.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lht_bert_added_words_en_5.1.1_3.0_1694629369350.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("lht_bert_added_words","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("lht_bert_added_words", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lht_bert_added_words| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Shanny/LHT_BERT_added_words \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-lht_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-lht_bert_en.md new file mode 100644 index 00000000000000..4b569d0297cd15 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-lht_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English lht_bert BertEmbeddings from Shanny +author: John Snow Labs +name: lht_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lht_bert` is a English model originally trained by Shanny. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lht_bert_en_5.1.1_3.0_1694621223965.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lht_bert_en_5.1.1_3.0_1694621223965.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("lht_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("lht_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lht_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Shanny/LHT_BERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-linkbert_base_en.md b/docs/_posts/ahmedlone127/2023-09-13-linkbert_base_en.md new file mode 100644 index 00000000000000..23ffaa7b372c74 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-linkbert_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English linkbert_base BertEmbeddings from michiyasunaga +author: John Snow Labs +name: linkbert_base +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`linkbert_base` is a English model originally trained by michiyasunaga. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/linkbert_base_en_5.1.1_3.0_1694606637209.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/linkbert_base_en_5.1.1_3.0_1694606637209.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("linkbert_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("linkbert_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|linkbert_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.4 MB| + +## References + +https://huggingface.co/michiyasunaga/LinkBERT-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-linkbert_large_en.md b/docs/_posts/ahmedlone127/2023-09-13-linkbert_large_en.md new file mode 100644 index 00000000000000..8c6be65db73918 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-linkbert_large_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English linkbert_large BertEmbeddings from michiyasunaga +author: John Snow Labs +name: linkbert_large +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`linkbert_large` is a English model originally trained by michiyasunaga. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/linkbert_large_en_5.1.1_3.0_1694605675771.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/linkbert_large_en_5.1.1_3.0_1694605675771.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("linkbert_large","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("linkbert_large", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|linkbert_large| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/michiyasunaga/LinkBERT-large \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-luxembert_en.md b/docs/_posts/ahmedlone127/2023-09-13-luxembert_en.md new file mode 100644 index 00000000000000..390bb1ff2d62f0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-luxembert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English luxembert BertEmbeddings from lothritz +author: John Snow Labs +name: luxembert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`luxembert` is a English model originally trained by lothritz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/luxembert_en_5.1.1_3.0_1694643972873.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/luxembert_en_5.1.1_3.0_1694643972873.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("luxembert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("luxembert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|luxembert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.7 MB| + +## References + +https://huggingface.co/lothritz/LuxemBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mabel_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-13-mabel_bert_base_uncased_en.md new file mode 100644 index 00000000000000..08dec8669262a3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mabel_bert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mabel_bert_base_uncased BertEmbeddings from princeton-nlp +author: John Snow Labs +name: mabel_bert_base_uncased +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mabel_bert_base_uncased` is a English model originally trained by princeton-nlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mabel_bert_base_uncased_en_5.1.1_3.0_1694611081845.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mabel_bert_base_uncased_en_5.1.1_3.0_1694611081845.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mabel_bert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mabel_bert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mabel_bert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/princeton-nlp/mabel-bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-malayalam_bert_ml.md b/docs/_posts/ahmedlone127/2023-09-13-malayalam_bert_ml.md new file mode 100644 index 00000000000000..41ea31fd981233 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-malayalam_bert_ml.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Malayalam malayalam_bert BertEmbeddings from l3cube-pune +author: John Snow Labs +name: malayalam_bert +date: 2023-09-13 +tags: [bert, ml, open_source, fill_mask, onnx] +task: Embeddings +language: ml +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`malayalam_bert` is a Malayalam model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/malayalam_bert_ml_5.1.1_3.0_1694640553943.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/malayalam_bert_ml_5.1.1_3.0_1694640553943.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("malayalam_bert","ml") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("malayalam_bert", "ml") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|malayalam_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ml| +|Size:|890.5 MB| + +## References + +https://huggingface.co/l3cube-pune/malayalam-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-manubert_en.md b/docs/_posts/ahmedlone127/2023-09-13-manubert_en.md new file mode 100644 index 00000000000000..d0d6b24ffcc2f7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-manubert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English manubert BertEmbeddings from akumar33 +author: John Snow Labs +name: manubert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`manubert` is a English model originally trained by akumar33. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/manubert_en_5.1.1_3.0_1694648045075.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/manubert_en_5.1.1_3.0_1694648045075.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("manubert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("manubert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|manubert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/akumar33/ManuBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-marathi_bert_v2_mr.md b/docs/_posts/ahmedlone127/2023-09-13-marathi_bert_v2_mr.md new file mode 100644 index 00000000000000..f4caa9d6aa76ff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-marathi_bert_v2_mr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Marathi marathi_bert_v2 BertEmbeddings from l3cube-pune +author: John Snow Labs +name: marathi_bert_v2 +date: 2023-09-13 +tags: [bert, mr, open_source, fill_mask, onnx] +task: Embeddings +language: mr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`marathi_bert_v2` is a Marathi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/marathi_bert_v2_mr_5.1.1_3.0_1694573439496.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/marathi_bert_v2_mr_5.1.1_3.0_1694573439496.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("marathi_bert_v2","mr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("marathi_bert_v2", "mr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|marathi_bert_v2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|mr| +|Size:|890.6 MB| + +## References + +https://huggingface.co/l3cube-pune/marathi-bert-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-marathi_tweets_bert_scratch_mr.md b/docs/_posts/ahmedlone127/2023-09-13-marathi_tweets_bert_scratch_mr.md new file mode 100644 index 00000000000000..23d5443914b7c8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-marathi_tweets_bert_scratch_mr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Marathi marathi_tweets_bert_scratch BertEmbeddings from l3cube-pune +author: John Snow Labs +name: marathi_tweets_bert_scratch +date: 2023-09-13 +tags: [bert, mr, open_source, fill_mask, onnx] +task: Embeddings +language: mr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`marathi_tweets_bert_scratch` is a Marathi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/marathi_tweets_bert_scratch_mr_5.1.1_3.0_1694631845235.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/marathi_tweets_bert_scratch_mr_5.1.1_3.0_1694631845235.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("marathi_tweets_bert_scratch","mr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("marathi_tweets_bert_scratch", "mr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|marathi_tweets_bert_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|mr| +|Size:|470.4 MB| + +## References + +https://huggingface.co/l3cube-pune/marathi-tweets-bert-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-marbertv2_ar.md b/docs/_posts/ahmedlone127/2023-09-13-marbertv2_ar.md new file mode 100644 index 00000000000000..0ae0523e7cfbf1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-marbertv2_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic marbertv2 BertEmbeddings from UBC-NLP +author: John Snow Labs +name: marbertv2 +date: 2023-09-13 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`marbertv2` is a Arabic model originally trained by UBC-NLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/marbertv2_ar_5.1.1_3.0_1694574149293.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/marbertv2_ar_5.1.1_3.0_1694574149293.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("marbertv2","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("marbertv2", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|marbertv2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|606.5 MB| + +## References + +https://huggingface.co/UBC-NLP/MARBERTv2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mbert_deen_en.md b/docs/_posts/ahmedlone127/2023-09-13-mbert_deen_en.md new file mode 100644 index 00000000000000..62c910323cef2d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mbert_deen_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mbert_deen BertEmbeddings from miugod +author: John Snow Labs +name: mbert_deen +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbert_deen` is a English model originally trained by miugod. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbert_deen_en_5.1.1_3.0_1694644088821.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbert_deen_en_5.1.1_3.0_1694644088821.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbert_deen","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbert_deen", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbert_deen| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.1 MB| + +## References + +https://huggingface.co/miugod/mbert_deen \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mbertu_mt.md b/docs/_posts/ahmedlone127/2023-09-13-mbertu_mt.md new file mode 100644 index 00000000000000..dbdf2ab7dbdd6d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mbertu_mt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Maltese mbertu BertEmbeddings from MLRS +author: John Snow Labs +name: mbertu +date: 2023-09-13 +tags: [bert, mt, open_source, fill_mask, onnx] +task: Embeddings +language: mt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbertu` is a Maltese model originally trained by MLRS. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbertu_mt_5.1.1_3.0_1694635757980.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbertu_mt_5.1.1_3.0_1694635757980.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbertu","mt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbertu", "mt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbertu| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|mt| +|Size:|664.5 MB| + +## References + +https://huggingface.co/MLRS/mBERTu \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-me_bert_mixed_mr.md b/docs/_posts/ahmedlone127/2023-09-13-me_bert_mixed_mr.md new file mode 100644 index 00000000000000..08a957310142ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-me_bert_mixed_mr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Marathi me_bert_mixed BertEmbeddings from l3cube-pune +author: John Snow Labs +name: me_bert_mixed +date: 2023-09-13 +tags: [bert, mr, open_source, fill_mask, onnx] +task: Embeddings +language: mr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`me_bert_mixed` is a Marathi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/me_bert_mixed_mr_5.1.1_3.0_1694647549621.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/me_bert_mixed_mr_5.1.1_3.0_1694647549621.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("me_bert_mixed","mr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("me_bert_mixed", "mr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|me_bert_mixed| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|mr| +|Size:|665.0 MB| + +## References + +https://huggingface.co/l3cube-pune/me-bert-mixed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-me_bert_mr.md b/docs/_posts/ahmedlone127/2023-09-13-me_bert_mr.md new file mode 100644 index 00000000000000..c2e61fe5bcacd7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-me_bert_mr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Marathi me_bert BertEmbeddings from l3cube-pune +author: John Snow Labs +name: me_bert +date: 2023-09-13 +tags: [bert, mr, open_source, fill_mask, onnx] +task: Embeddings +language: mr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`me_bert` is a Marathi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/me_bert_mr_5.1.1_3.0_1694646862410.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/me_bert_mr_5.1.1_3.0_1694646862410.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("me_bert","mr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("me_bert", "mr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|me_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|mr| +|Size:|407.2 MB| + +## References + +https://huggingface.co/l3cube-pune/me-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-medical_bio_bert2_en.md b/docs/_posts/ahmedlone127/2023-09-13-medical_bio_bert2_en.md new file mode 100644 index 00000000000000..ef12b2cfe94345 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-medical_bio_bert2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English medical_bio_bert2 BertEmbeddings from fspanda +author: John Snow Labs +name: medical_bio_bert2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`medical_bio_bert2` is a English model originally trained by fspanda. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/medical_bio_bert2_en_5.1.1_3.0_1694647435608.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/medical_bio_bert2_en_5.1.1_3.0_1694647435608.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("medical_bio_bert2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("medical_bio_bert2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|medical_bio_bert2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.7 MB| + +## References + +https://huggingface.co/fspanda/Medical-Bio-BERT2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-metaphor_finetuned_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-metaphor_finetuned_bert_en.md new file mode 100644 index 00000000000000..f67dea2a490de9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-metaphor_finetuned_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English metaphor_finetuned_bert BertEmbeddings from kangela +author: John Snow Labs +name: metaphor_finetuned_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`metaphor_finetuned_bert` is a English model originally trained by kangela. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/metaphor_finetuned_bert_en_5.1.1_3.0_1694633662658.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/metaphor_finetuned_bert_en_5.1.1_3.0_1694633662658.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("metaphor_finetuned_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("metaphor_finetuned_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|metaphor_finetuned_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/kangela/Metaphor-FineTuned-BERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mlm_20230404_002_1_en.md b/docs/_posts/ahmedlone127/2023-09-13-mlm_20230404_002_1_en.md new file mode 100644 index 00000000000000..8c5b0f1c67eb01 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mlm_20230404_002_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230404_002_1 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230404_002_1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230404_002_1` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230404_002_1_en_5.1.1_3.0_1694610217733.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230404_002_1_en_5.1.1_3.0_1694610217733.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230404_002_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230404_002_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230404_002_1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.2 MB| + +## References + +https://huggingface.co/intanm/mlm-20230404-002-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mlm_20230405_002_2_en.md b/docs/_posts/ahmedlone127/2023-09-13-mlm_20230405_002_2_en.md new file mode 100644 index 00000000000000..3a0525308de1db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mlm_20230405_002_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230405_002_2 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230405_002_2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230405_002_2` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230405_002_2_en_5.1.1_3.0_1694611265207.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230405_002_2_en_5.1.1_3.0_1694611265207.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230405_002_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230405_002_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230405_002_2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.2 MB| + +## References + +https://huggingface.co/intanm/mlm-20230405-002-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mlm_20230405_002_3_en.md b/docs/_posts/ahmedlone127/2023-09-13-mlm_20230405_002_3_en.md new file mode 100644 index 00000000000000..e5a6ceb1a506fc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mlm_20230405_002_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230405_002_3 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230405_002_3 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230405_002_3` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230405_002_3_en_5.1.1_3.0_1694612950326.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230405_002_3_en_5.1.1_3.0_1694612950326.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230405_002_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230405_002_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230405_002_3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.2 MB| + +## References + +https://huggingface.co/intanm/mlm-20230405-002-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mlm_20230405_002_4_en.md b/docs/_posts/ahmedlone127/2023-09-13-mlm_20230405_002_4_en.md new file mode 100644 index 00000000000000..67725b1e5c7263 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mlm_20230405_002_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230405_002_4 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230405_002_4 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230405_002_4` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230405_002_4_en_5.1.1_3.0_1694614086684.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230405_002_4_en_5.1.1_3.0_1694614086684.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230405_002_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230405_002_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230405_002_4| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.2 MB| + +## References + +https://huggingface.co/intanm/mlm-20230405-002-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mlm_20230406_002_5_en.md b/docs/_posts/ahmedlone127/2023-09-13-mlm_20230406_002_5_en.md new file mode 100644 index 00000000000000..443a54d364e608 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mlm_20230406_002_5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230406_002_5 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230406_002_5 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230406_002_5` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230406_002_5_en_5.1.1_3.0_1694614626559.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230406_002_5_en_5.1.1_3.0_1694614626559.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230406_002_5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230406_002_5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230406_002_5| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.3 MB| + +## References + +https://huggingface.co/intanm/mlm-20230406-002-5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-model1_en.md b/docs/_posts/ahmedlone127/2023-09-13-model1_en.md new file mode 100644 index 00000000000000..b4e45db0bc3ce5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-model1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English model1 BertEmbeddings from flymushroom +author: John Snow Labs +name: model1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`model1` is a English model originally trained by flymushroom. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/model1_en_5.1.1_3.0_1694646795523.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/model1_en_5.1.1_3.0_1694646795523.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("model1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("model1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|model1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/flymushroom/model1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mybert_base_32k_en.md b/docs/_posts/ahmedlone127/2023-09-13-mybert_base_32k_en.md new file mode 100644 index 00000000000000..1a251c986969af --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mybert_base_32k_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mybert_base_32k BertEmbeddings from maveriq +author: John Snow Labs +name: mybert_base_32k +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mybert_base_32k` is a English model originally trained by maveriq. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mybert_base_32k_en_5.1.1_3.0_1694629972484.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mybert_base_32k_en_5.1.1_3.0_1694629972484.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mybert_base_32k","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mybert_base_32k", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mybert_base_32k| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.4 MB| + +## References + +https://huggingface.co/maveriq/mybert-base-32k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mybert_mini_172k_en.md b/docs/_posts/ahmedlone127/2023-09-13-mybert_mini_172k_en.md new file mode 100644 index 00000000000000..ca9d03fc99c778 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mybert_mini_172k_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mybert_mini_172k BertEmbeddings from maveriq +author: John Snow Labs +name: mybert_mini_172k +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mybert_mini_172k` is a English model originally trained by maveriq. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mybert_mini_172k_en_5.1.1_3.0_1694630527630.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mybert_mini_172k_en_5.1.1_3.0_1694630527630.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mybert_mini_172k","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mybert_mini_172k", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mybert_mini_172k| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|42.0 MB| + +## References + +https://huggingface.co/maveriq/mybert-mini-172k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mybert_mini_1m_en.md b/docs/_posts/ahmedlone127/2023-09-13-mybert_mini_1m_en.md new file mode 100644 index 00000000000000..b9603b419e6d4d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mybert_mini_1m_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mybert_mini_1m BertEmbeddings from maveriq +author: John Snow Labs +name: mybert_mini_1m +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mybert_mini_1m` is a English model originally trained by maveriq. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mybert_mini_1m_en_5.1.1_3.0_1694640791254.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mybert_mini_1m_en_5.1.1_3.0_1694640791254.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mybert_mini_1m","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mybert_mini_1m", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mybert_mini_1m| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|42.0 MB| + +## References + +https://huggingface.co/maveriq/mybert-mini-1M \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mybert_mini_500k_en.md b/docs/_posts/ahmedlone127/2023-09-13-mybert_mini_500k_en.md new file mode 100644 index 00000000000000..2f196e297605db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mybert_mini_500k_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mybert_mini_500k BertEmbeddings from maveriq +author: John Snow Labs +name: mybert_mini_500k +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mybert_mini_500k` is a English model originally trained by maveriq. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mybert_mini_500k_en_5.1.1_3.0_1694640484187.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mybert_mini_500k_en_5.1.1_3.0_1694640484187.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mybert_mini_500k","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mybert_mini_500k", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mybert_mini_500k| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|42.1 MB| + +## References + +https://huggingface.co/maveriq/mybert-mini-500k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-mymode03_en.md b/docs/_posts/ahmedlone127/2023-09-13-mymode03_en.md new file mode 100644 index 00000000000000..79221a5937bc9a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-mymode03_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mymode03 BertEmbeddings from wbmitcast +author: John Snow Labs +name: mymode03 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mymode03` is a English model originally trained by wbmitcast. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mymode03_en_5.1.1_3.0_1694583211320.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mymode03_en_5.1.1_3.0_1694583211320.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mymode03","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mymode03", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mymode03| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/wbmitcast/mymode03 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-nbme_bio_clinicalbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-nbme_bio_clinicalbert_en.md new file mode 100644 index 00000000000000..a8752a06674e57 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-nbme_bio_clinicalbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English nbme_bio_clinicalbert BertEmbeddings from smeoni +author: John Snow Labs +name: nbme_bio_clinicalbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nbme_bio_clinicalbert` is a English model originally trained by smeoni. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nbme_bio_clinicalbert_en_5.1.1_3.0_1694632921114.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nbme_bio_clinicalbert_en_5.1.1_3.0_1694632921114.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("nbme_bio_clinicalbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("nbme_bio_clinicalbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nbme_bio_clinicalbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.2 MB| + +## References + +https://huggingface.co/smeoni/nbme-Bio_ClinicalBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-news_pretrain_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-news_pretrain_bert_en.md new file mode 100644 index 00000000000000..811a7e392add61 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-news_pretrain_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English news_pretrain_bert BertEmbeddings from AnonymousSub +author: John Snow Labs +name: news_pretrain_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`news_pretrain_bert` is a English model originally trained by AnonymousSub. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/news_pretrain_bert_en_5.1.1_3.0_1694625096290.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/news_pretrain_bert_en_5.1.1_3.0_1694625096290.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("news_pretrain_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("news_pretrain_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|news_pretrain_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/AnonymousSub/news-pretrain-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-odia_bert_or.md b/docs/_posts/ahmedlone127/2023-09-13-odia_bert_or.md new file mode 100644 index 00000000000000..d68febbe294480 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-odia_bert_or.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Oriya (macrolanguage) odia_bert BertEmbeddings from l3cube-pune +author: John Snow Labs +name: odia_bert +date: 2023-09-13 +tags: [bert, or, open_source, fill_mask, onnx] +task: Embeddings +language: or +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`odia_bert` is a Oriya (macrolanguage) model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/odia_bert_or_5.1.1_3.0_1694643840678.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/odia_bert_or_5.1.1_3.0_1694643840678.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("odia_bert","or") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("odia_bert", "or") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|odia_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|or| +|Size:|890.4 MB| + +## References + +https://huggingface.co/l3cube-pune/odia-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-olm_bert_base_uncased_oct_2022_en.md b/docs/_posts/ahmedlone127/2023-09-13-olm_bert_base_uncased_oct_2022_en.md new file mode 100644 index 00000000000000..b56ea2b2160904 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-olm_bert_base_uncased_oct_2022_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English olm_bert_base_uncased_oct_2022 BertEmbeddings from Tristan +author: John Snow Labs +name: olm_bert_base_uncased_oct_2022 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`olm_bert_base_uncased_oct_2022` is a English model originally trained by Tristan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/olm_bert_base_uncased_oct_2022_en_5.1.1_3.0_1694626342322.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/olm_bert_base_uncased_oct_2022_en_5.1.1_3.0_1694626342322.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("olm_bert_base_uncased_oct_2022","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("olm_bert_base_uncased_oct_2022", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|olm_bert_base_uncased_oct_2022| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.6 MB| + +## References + +https://huggingface.co/Tristan/olm-bert-base-uncased-oct-2022 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-parlbert_german_v1_de.md b/docs/_posts/ahmedlone127/2023-09-13-parlbert_german_v1_de.md new file mode 100644 index 00000000000000..6ae3abde88faa7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-parlbert_german_v1_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German parlbert_german_v1 BertEmbeddings from chkla +author: John Snow Labs +name: parlbert_german_v1 +date: 2023-09-13 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`parlbert_german_v1` is a German model originally trained by chkla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/parlbert_german_v1_de_5.1.1_3.0_1694648419060.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/parlbert_german_v1_de_5.1.1_3.0_1694648419060.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("parlbert_german_v1","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("parlbert_german_v1", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|parlbert_german_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|406.8 MB| + +## References + +https://huggingface.co/chkla/parlbert-german-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-pashto_bert_c_en.md b/docs/_posts/ahmedlone127/2023-09-13-pashto_bert_c_en.md new file mode 100644 index 00000000000000..4370e423b498b6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-pashto_bert_c_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pashto_bert_c BertEmbeddings from ijazulhaq +author: John Snow Labs +name: pashto_bert_c +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pashto_bert_c` is a English model originally trained by ijazulhaq. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pashto_bert_c_en_5.1.1_3.0_1694629099684.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pashto_bert_c_en_5.1.1_3.0_1694629099684.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("pashto_bert_c","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("pashto_bert_c", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pashto_bert_c| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.3 MB| + +## References + +https://huggingface.co/ijazulhaq/pashto-bert-c \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-patana_chilean_spanish_bert_es.md b/docs/_posts/ahmedlone127/2023-09-13-patana_chilean_spanish_bert_es.md new file mode 100644 index 00000000000000..d244e1f29106cc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-patana_chilean_spanish_bert_es.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Castilian, Spanish patana_chilean_spanish_bert BertEmbeddings from dccuchile +author: John Snow Labs +name: patana_chilean_spanish_bert +date: 2023-09-13 +tags: [bert, es, open_source, fill_mask, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`patana_chilean_spanish_bert` is a Castilian, Spanish model originally trained by dccuchile. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/patana_chilean_spanish_bert_es_5.1.1_3.0_1694619206082.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/patana_chilean_spanish_bert_es_5.1.1_3.0_1694619206082.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("patana_chilean_spanish_bert","es") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("patana_chilean_spanish_bert", "es") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|patana_chilean_spanish_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|es| +|Size:|409.4 MB| + +## References + +https://huggingface.co/dccuchile/patana-chilean-spanish-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-phrase_bert_finetuned_imdb_en.md b/docs/_posts/ahmedlone127/2023-09-13-phrase_bert_finetuned_imdb_en.md new file mode 100644 index 00000000000000..853995c9977ccb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-phrase_bert_finetuned_imdb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English phrase_bert_finetuned_imdb BertEmbeddings from Sarmila +author: John Snow Labs +name: phrase_bert_finetuned_imdb +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`phrase_bert_finetuned_imdb` is a English model originally trained by Sarmila. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/phrase_bert_finetuned_imdb_en_5.1.1_3.0_1694641064662.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/phrase_bert_finetuned_imdb_en_5.1.1_3.0_1694641064662.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("phrase_bert_finetuned_imdb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("phrase_bert_finetuned_imdb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|phrase_bert_finetuned_imdb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Sarmila/phrase-bert-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-phs_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-phs_bert_en.md new file mode 100644 index 00000000000000..55e139407e13dc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-phs_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English phs_bert BertEmbeddings from publichealthsurveillance +author: John Snow Labs +name: phs_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`phs_bert` is a English model originally trained by publichealthsurveillance. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/phs_bert_en_5.1.1_3.0_1694631316270.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/phs_bert_en_5.1.1_3.0_1694631316270.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("phs_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("phs_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|phs_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/publichealthsurveillance/PHS-BERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-pretrained_kyw_e1_en.md b/docs/_posts/ahmedlone127/2023-09-13-pretrained_kyw_e1_en.md new file mode 100644 index 00000000000000..dd4ad5670e1bee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-pretrained_kyw_e1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pretrained_kyw_e1 BertEmbeddings from shahriargolchin +author: John Snow Labs +name: pretrained_kyw_e1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pretrained_kyw_e1` is a English model originally trained by shahriargolchin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pretrained_kyw_e1_en_5.1.1_3.0_1694646138393.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pretrained_kyw_e1_en_5.1.1_3.0_1694646138393.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("pretrained_kyw_e1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("pretrained_kyw_e1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pretrained_kyw_e1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/shahriargolchin/pretrained_kyw_e1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-prompt_ls_english_2_en.md b/docs/_posts/ahmedlone127/2023-09-13-prompt_ls_english_2_en.md new file mode 100644 index 00000000000000..d807d4577d1641 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-prompt_ls_english_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English prompt_ls_english_2 BertEmbeddings from lmvasque +author: John Snow Labs +name: prompt_ls_english_2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`prompt_ls_english_2` is a English model originally trained by lmvasque. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/prompt_ls_english_2_en_5.1.1_3.0_1694625527257.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/prompt_ls_english_2_en_5.1.1_3.0_1694625527257.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("prompt_ls_english_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("prompt_ls_english_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|prompt_ls_english_2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|625.5 MB| + +## References + +https://huggingface.co/lmvasque/prompt-ls-en-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-prunedbert_l12_h256_a4_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-13-prunedbert_l12_h256_a4_finetuned_en.md new file mode 100644 index 00000000000000..7841fe1b778e1c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-prunedbert_l12_h256_a4_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English prunedbert_l12_h256_a4_finetuned BertEmbeddings from eli4s +author: John Snow Labs +name: prunedbert_l12_h256_a4_finetuned +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`prunedbert_l12_h256_a4_finetuned` is a English model originally trained by eli4s. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/prunedbert_l12_h256_a4_finetuned_en_5.1.1_3.0_1694630185594.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/prunedbert_l12_h256_a4_finetuned_en_5.1.1_3.0_1694630185594.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("prunedbert_l12_h256_a4_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("prunedbert_l12_h256_a4_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|prunedbert_l12_h256_a4_finetuned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|111.9 MB| + +## References + +https://huggingface.co/eli4s/prunedBert-L12-h256-A4-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-prunedbert_l12_h384_a6_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-13-prunedbert_l12_h384_a6_finetuned_en.md new file mode 100644 index 00000000000000..3c9423988f0cf1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-prunedbert_l12_h384_a6_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English prunedbert_l12_h384_a6_finetuned BertEmbeddings from eli4s +author: John Snow Labs +name: prunedbert_l12_h384_a6_finetuned +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`prunedbert_l12_h384_a6_finetuned` is a English model originally trained by eli4s. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/prunedbert_l12_h384_a6_finetuned_en_5.1.1_3.0_1694630539022.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/prunedbert_l12_h384_a6_finetuned_en_5.1.1_3.0_1694630539022.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("prunedbert_l12_h384_a6_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("prunedbert_l12_h384_a6_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|prunedbert_l12_h384_a6_finetuned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|176.4 MB| + +## References + +https://huggingface.co/eli4s/prunedBert-L12-h384-A6-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-punjabi_bert_pa.md b/docs/_posts/ahmedlone127/2023-09-13-punjabi_bert_pa.md new file mode 100644 index 00000000000000..95cba25b441a16 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-punjabi_bert_pa.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Panjabi, Punjabi punjabi_bert BertEmbeddings from l3cube-pune +author: John Snow Labs +name: punjabi_bert +date: 2023-09-13 +tags: [bert, pa, open_source, fill_mask, onnx] +task: Embeddings +language: pa +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`punjabi_bert` is a Panjabi, Punjabi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/punjabi_bert_pa_5.1.1_3.0_1694645329932.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/punjabi_bert_pa_5.1.1_3.0_1694645329932.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("punjabi_bert","pa") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("punjabi_bert", "pa") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|punjabi_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pa| +|Size:|890.5 MB| + +## References + +https://huggingface.co/l3cube-pune/punjabi-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-pz_bert_kanuri_en.md b/docs/_posts/ahmedlone127/2023-09-13-pz_bert_kanuri_en.md new file mode 100644 index 00000000000000..0f41891579f9cb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-pz_bert_kanuri_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pz_bert_kanuri BertEmbeddings from Hanwoon +author: John Snow Labs +name: pz_bert_kanuri +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pz_bert_kanuri` is a English model originally trained by Hanwoon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pz_bert_kanuri_en_5.1.1_3.0_1694626706084.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pz_bert_kanuri_en_5.1.1_3.0_1694626706084.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("pz_bert_kanuri","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("pz_bert_kanuri", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pz_bert_kanuri| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|441.1 MB| + +## References + +https://huggingface.co/Hanwoon/pz-bert-kr \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-reddit_bert_text2_en.md b/docs/_posts/ahmedlone127/2023-09-13-reddit_bert_text2_en.md new file mode 100644 index 00000000000000..9284efa835afb5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-reddit_bert_text2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English reddit_bert_text2 BertEmbeddings from flboehm +author: John Snow Labs +name: reddit_bert_text2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`reddit_bert_text2` is a English model originally trained by flboehm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/reddit_bert_text2_en_5.1.1_3.0_1694643257404.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/reddit_bert_text2_en_5.1.1_3.0_1694643257404.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("reddit_bert_text2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("reddit_bert_text2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|reddit_bert_text2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|414.4 MB| + +## References + +https://huggingface.co/flboehm/reddit-bert-text2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-reddit_bert_text3_en.md b/docs/_posts/ahmedlone127/2023-09-13-reddit_bert_text3_en.md new file mode 100644 index 00000000000000..69533f6cfa486e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-reddit_bert_text3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English reddit_bert_text3 BertEmbeddings from flboehm +author: John Snow Labs +name: reddit_bert_text3 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`reddit_bert_text3` is a English model originally trained by flboehm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/reddit_bert_text3_en_5.1.1_3.0_1694643633284.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/reddit_bert_text3_en_5.1.1_3.0_1694643633284.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("reddit_bert_text3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("reddit_bert_text3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|reddit_bert_text3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|415.0 MB| + +## References + +https://huggingface.co/flboehm/reddit-bert-text3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-reddit_bert_text4_en.md b/docs/_posts/ahmedlone127/2023-09-13-reddit_bert_text4_en.md new file mode 100644 index 00000000000000..cef44591552f48 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-reddit_bert_text4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English reddit_bert_text4 BertEmbeddings from flboehm +author: John Snow Labs +name: reddit_bert_text4 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`reddit_bert_text4` is a English model originally trained by flboehm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/reddit_bert_text4_en_5.1.1_3.0_1694644221338.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/reddit_bert_text4_en_5.1.1_3.0_1694644221338.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("reddit_bert_text4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("reddit_bert_text4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|reddit_bert_text4| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.4 MB| + +## References + +https://huggingface.co/flboehm/reddit-bert-text4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-reddit_bert_text_10_en.md b/docs/_posts/ahmedlone127/2023-09-13-reddit_bert_text_10_en.md new file mode 100644 index 00000000000000..7b568bda23693f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-reddit_bert_text_10_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English reddit_bert_text_10 BertEmbeddings from flboehm +author: John Snow Labs +name: reddit_bert_text_10 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`reddit_bert_text_10` is a English model originally trained by flboehm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/reddit_bert_text_10_en_5.1.1_3.0_1694644674014.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/reddit_bert_text_10_en_5.1.1_3.0_1694644674014.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("reddit_bert_text_10","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("reddit_bert_text_10", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|reddit_bert_text_10| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.8 MB| + +## References + +https://huggingface.co/flboehm/reddit-bert-text_10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-reddit_bert_text_20_en.md b/docs/_posts/ahmedlone127/2023-09-13-reddit_bert_text_20_en.md new file mode 100644 index 00000000000000..4fb483f66bca0f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-reddit_bert_text_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English reddit_bert_text_20 BertEmbeddings from flboehm +author: John Snow Labs +name: reddit_bert_text_20 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`reddit_bert_text_20` is a English model originally trained by flboehm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/reddit_bert_text_20_en_5.1.1_3.0_1694645187415.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/reddit_bert_text_20_en_5.1.1_3.0_1694645187415.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("reddit_bert_text_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("reddit_bert_text_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|reddit_bert_text_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.4 MB| + +## References + +https://huggingface.co/flboehm/reddit-bert-text_20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-reddit_bert_text_5_en.md b/docs/_posts/ahmedlone127/2023-09-13-reddit_bert_text_5_en.md new file mode 100644 index 00000000000000..016c2e00b11dd6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-reddit_bert_text_5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English reddit_bert_text_5 BertEmbeddings from flboehm +author: John Snow Labs +name: reddit_bert_text_5 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`reddit_bert_text_5` is a English model originally trained by flboehm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/reddit_bert_text_5_en_5.1.1_3.0_1694645644655.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/reddit_bert_text_5_en_5.1.1_3.0_1694645644655.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("reddit_bert_text_5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("reddit_bert_text_5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|reddit_bert_text_5| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.7 MB| + +## References + +https://huggingface.co/flboehm/reddit-bert-text_5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-roberta_base_culinary_en.md b/docs/_posts/ahmedlone127/2023-09-13-roberta_base_culinary_en.md new file mode 100644 index 00000000000000..102a06c15282b6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-roberta_base_culinary_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English roberta_base_culinary BertEmbeddings from juancavallotti +author: John Snow Labs +name: roberta_base_culinary +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`roberta_base_culinary` is a English model originally trained by juancavallotti. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_base_culinary_en_5.1.1_3.0_1694642566778.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_base_culinary_en_5.1.1_3.0_1694642566778.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("roberta_base_culinary","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("roberta_base_culinary", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_base_culinary| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.5 MB| + +## References + +https://huggingface.co/juancavallotti/roberta-base-culinary \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-rubert_base_vet_en.md b/docs/_posts/ahmedlone127/2023-09-13-rubert_base_vet_en.md new file mode 100644 index 00000000000000..a118d7dacbd2a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-rubert_base_vet_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English rubert_base_vet BertEmbeddings from erasedwalt +author: John Snow Labs +name: rubert_base_vet +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`rubert_base_vet` is a English model originally trained by erasedwalt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/rubert_base_vet_en_5.1.1_3.0_1694632381694.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/rubert_base_vet_en_5.1.1_3.0_1694632381694.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("rubert_base_vet","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("rubert_base_vet", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rubert_base_vet| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|663.8 MB| + +## References + +https://huggingface.co/erasedwalt/rubert-base-vet \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-scinewsbert_en.md b/docs/_posts/ahmedlone127/2023-09-13-scinewsbert_en.md new file mode 100644 index 00000000000000..9d56fd0209afb9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-scinewsbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English scinewsbert BertEmbeddings from psmeros +author: John Snow Labs +name: scinewsbert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scinewsbert` is a English model originally trained by psmeros. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scinewsbert_en_5.1.1_3.0_1694596298829.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scinewsbert_en_5.1.1_3.0_1694596298829.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("scinewsbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("scinewsbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scinewsbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.0 MB| + +## References + +https://huggingface.co/psmeros/SciNewsBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-shangpin_pre_training_en.md b/docs/_posts/ahmedlone127/2023-09-13-shangpin_pre_training_en.md new file mode 100644 index 00000000000000..26ed799f5dfb2e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-shangpin_pre_training_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English shangpin_pre_training BertEmbeddings from nnn +author: John Snow Labs +name: shangpin_pre_training +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`shangpin_pre_training` is a English model originally trained by nnn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/shangpin_pre_training_en_5.1.1_3.0_1694643216154.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/shangpin_pre_training_en_5.1.1_3.0_1694643216154.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("shangpin_pre_training","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("shangpin_pre_training", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|shangpin_pre_training| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.6 MB| + +## References + +https://huggingface.co/nnn/shangpin-pre-training \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_wnli_custom_tokenizer_expand_vocab_en.md b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_wnli_custom_tokenizer_expand_vocab_en.md new file mode 100644 index 00000000000000..d92b404f4f39b4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-small_mlm_glue_wnli_custom_tokenizer_expand_vocab_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_glue_wnli_custom_tokenizer_expand_vocab BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_glue_wnli_custom_tokenizer_expand_vocab +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_glue_wnli_custom_tokenizer_expand_vocab` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_glue_wnli_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694564320620.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_glue_wnli_custom_tokenizer_expand_vocab_en_5.1.1_3.0_1694564320620.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_glue_wnli_custom_tokenizer_expand_vocab","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_glue_wnli_custom_tokenizer_expand_vocab", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_glue_wnli_custom_tokenizer_expand_vocab| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|108.1 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-glue-wnli-custom-tokenizer-expand-vocab \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-spanish_mlm_03_en.md b/docs/_posts/ahmedlone127/2023-09-13-spanish_mlm_03_en.md new file mode 100644 index 00000000000000..0ec8dd7175ef54 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-spanish_mlm_03_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English spanish_mlm_03 BertEmbeddings from ashwathjadhav23 +author: John Snow Labs +name: spanish_mlm_03 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`spanish_mlm_03` is a English model originally trained by ashwathjadhav23. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/spanish_mlm_03_en_5.1.1_3.0_1694612397384.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/spanish_mlm_03_en_5.1.1_3.0_1694612397384.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("spanish_mlm_03","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("spanish_mlm_03", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|spanish_mlm_03| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.5 MB| + +## References + +https://huggingface.co/ashwathjadhav23/Spanish_MLM_03 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-splade_all_bertnsp_220_en.md b/docs/_posts/ahmedlone127/2023-09-13-splade_all_bertnsp_220_en.md new file mode 100644 index 00000000000000..cb0f6a81171f89 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-splade_all_bertnsp_220_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English splade_all_bertnsp_220 BertEmbeddings from approach0 +author: John Snow Labs +name: splade_all_bertnsp_220 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`splade_all_bertnsp_220` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/splade_all_bertnsp_220_en_5.1.1_3.0_1694634798952.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/splade_all_bertnsp_220_en_5.1.1_3.0_1694634798952.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("splade_all_bertnsp_220","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("splade_all_bertnsp_220", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|splade_all_bertnsp_220| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/splade_all-bertnsp-220 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-splade_all_cocomae_220_en.md b/docs/_posts/ahmedlone127/2023-09-13-splade_all_cocomae_220_en.md new file mode 100644 index 00000000000000..9381130510091c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-splade_all_cocomae_220_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English splade_all_cocomae_220 BertEmbeddings from approach0 +author: John Snow Labs +name: splade_all_cocomae_220 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`splade_all_cocomae_220` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/splade_all_cocomae_220_en_5.1.1_3.0_1694633376860.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/splade_all_cocomae_220_en_5.1.1_3.0_1694633376860.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("splade_all_cocomae_220","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("splade_all_cocomae_220", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|splade_all_cocomae_220| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/approach0/splade_all-cocomae-220 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-splade_nomath_bertnsp_220_en.md b/docs/_posts/ahmedlone127/2023-09-13-splade_nomath_bertnsp_220_en.md new file mode 100644 index 00000000000000..2bdc74d8db74e3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-splade_nomath_bertnsp_220_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English splade_nomath_bertnsp_220 BertEmbeddings from approach0 +author: John Snow Labs +name: splade_nomath_bertnsp_220 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`splade_nomath_bertnsp_220` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/splade_nomath_bertnsp_220_en_5.1.1_3.0_1694635158811.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/splade_nomath_bertnsp_220_en_5.1.1_3.0_1694635158811.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("splade_nomath_bertnsp_220","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("splade_nomath_bertnsp_220", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|splade_nomath_bertnsp_220| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/splade_nomath-bertnsp-220 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-splade_nomath_cocomae_220_en.md b/docs/_posts/ahmedlone127/2023-09-13-splade_nomath_cocomae_220_en.md new file mode 100644 index 00000000000000..d90f7a4b5f6236 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-splade_nomath_cocomae_220_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English splade_nomath_cocomae_220 BertEmbeddings from approach0 +author: John Snow Labs +name: splade_nomath_cocomae_220 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`splade_nomath_cocomae_220` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/splade_nomath_cocomae_220_en_5.1.1_3.0_1694634002866.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/splade_nomath_cocomae_220_en_5.1.1_3.0_1694634002866.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("splade_nomath_cocomae_220","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("splade_nomath_cocomae_220", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|splade_nomath_cocomae_220| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/approach0/splade_nomath-cocomae-220 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-splade_somemath_bertnsp_220_en.md b/docs/_posts/ahmedlone127/2023-09-13-splade_somemath_bertnsp_220_en.md new file mode 100644 index 00000000000000..263c43c9c0f988 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-splade_somemath_bertnsp_220_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English splade_somemath_bertnsp_220 BertEmbeddings from approach0 +author: John Snow Labs +name: splade_somemath_bertnsp_220 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`splade_somemath_bertnsp_220` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/splade_somemath_bertnsp_220_en_5.1.1_3.0_1694635770562.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/splade_somemath_bertnsp_220_en_5.1.1_3.0_1694635770562.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("splade_somemath_bertnsp_220","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("splade_somemath_bertnsp_220", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|splade_somemath_bertnsp_220| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/approach0/splade_somemath-bertnsp-220 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-splade_somemath_cocomae_220_en.md b/docs/_posts/ahmedlone127/2023-09-13-splade_somemath_cocomae_220_en.md new file mode 100644 index 00000000000000..3d1debffa80e64 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-splade_somemath_cocomae_220_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English splade_somemath_cocomae_220 BertEmbeddings from approach0 +author: John Snow Labs +name: splade_somemath_cocomae_220 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`splade_somemath_cocomae_220` is a English model originally trained by approach0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/splade_somemath_cocomae_220_en_5.1.1_3.0_1694634379900.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/splade_somemath_cocomae_220_en_5.1.1_3.0_1694634379900.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("splade_somemath_cocomae_220","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("splade_somemath_cocomae_220", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|splade_somemath_cocomae_220| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/approach0/splade_somemath-cocomae-220 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tamil_bert_ta.md b/docs/_posts/ahmedlone127/2023-09-13-tamil_bert_ta.md new file mode 100644 index 00000000000000..8cb5121ac903b7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tamil_bert_ta.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Tamil tamil_bert BertEmbeddings from l3cube-pune +author: John Snow Labs +name: tamil_bert +date: 2023-09-13 +tags: [bert, ta, open_source, fill_mask, onnx] +task: Embeddings +language: ta +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tamil_bert` is a Tamil model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tamil_bert_ta_5.1.1_3.0_1694641183579.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tamil_bert_ta_5.1.1_3.0_1694641183579.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tamil_bert","ta") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tamil_bert", "ta") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tamil_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ta| +|Size:|890.7 MB| + +## References + +https://huggingface.co/l3cube-pune/tamil-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-telugu_bert_te.md b/docs/_posts/ahmedlone127/2023-09-13-telugu_bert_te.md new file mode 100644 index 00000000000000..c79075d944c5d4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-telugu_bert_te.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Telugu telugu_bert BertEmbeddings from l3cube-pune +author: John Snow Labs +name: telugu_bert +date: 2023-09-13 +tags: [bert, te, open_source, fill_mask, onnx] +task: Embeddings +language: te +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`telugu_bert` is a Telugu model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/telugu_bert_te_5.1.1_3.0_1694639562892.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/telugu_bert_te_5.1.1_3.0_1694639562892.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("telugu_bert","te") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("telugu_bert", "te") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|telugu_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|te| +|Size:|890.5 MB| + +## References + +https://huggingface.co/l3cube-pune/telugu-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-test_model_allispaul_en.md b/docs/_posts/ahmedlone127/2023-09-13-test_model_allispaul_en.md new file mode 100644 index 00000000000000..f76ff5249edd24 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-test_model_allispaul_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English test_model_allispaul BertEmbeddings from allispaul +author: John Snow Labs +name: test_model_allispaul +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`test_model_allispaul` is a English model originally trained by allispaul. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/test_model_allispaul_en_5.1.1_3.0_1694625763081.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/test_model_allispaul_en_5.1.1_3.0_1694625763081.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("test_model_allispaul","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("test_model_allispaul", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|test_model_allispaul| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/allispaul/test-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-testc8_1_en.md b/docs/_posts/ahmedlone127/2023-09-13-testc8_1_en.md new file mode 100644 index 00000000000000..8f4b77e91055a2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-testc8_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English testc8_1 BertEmbeddings from shafin +author: John Snow Labs +name: testc8_1 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`testc8_1` is a English model originally trained by shafin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/testc8_1_en_5.1.1_3.0_1694648911715.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/testc8_1_en_5.1.1_3.0_1694648911715.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("testc8_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("testc8_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|testc8_1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.1 MB| + +## References + +https://huggingface.co/shafin/testc8-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-testc8_2_en.md b/docs/_posts/ahmedlone127/2023-09-13-testc8_2_en.md new file mode 100644 index 00000000000000..b3e819239f89cc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-testc8_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English testc8_2 BertEmbeddings from shafin +author: John Snow Labs +name: testc8_2 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`testc8_2` is a English model originally trained by shafin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/testc8_2_en_5.1.1_3.0_1694649179749.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/testc8_2_en_5.1.1_3.0_1694649179749.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("testc8_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("testc8_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|testc8_2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.1 MB| + +## References + +https://huggingface.co/shafin/testc8-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tibert_base_ti.md b/docs/_posts/ahmedlone127/2023-09-13-tibert_base_ti.md new file mode 100644 index 00000000000000..a3dc47f3afca22 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tibert_base_ti.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Tigrinya tibert_base BertEmbeddings from fgaim +author: John Snow Labs +name: tibert_base +date: 2023-09-13 +tags: [bert, ti, open_source, fill_mask, onnx] +task: Embeddings +language: ti +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tibert_base` is a Tigrinya model originally trained by fgaim. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tibert_base_ti_5.1.1_3.0_1694637403635.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tibert_base_ti_5.1.1_3.0_1694637403635.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tibert_base","ti") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tibert_base", "ti") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tibert_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ti| +|Size:|407.8 MB| + +## References + +https://huggingface.co/fgaim/tibert-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tinybert_general_4l_312d_german_de.md b/docs/_posts/ahmedlone127/2023-09-13-tinybert_general_4l_312d_german_de.md new file mode 100644 index 00000000000000..2deadf696e9db2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tinybert_general_4l_312d_german_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German tinybert_general_4l_312d_german BertEmbeddings from dvm1983 +author: John Snow Labs +name: tinybert_general_4l_312d_german +date: 2023-09-13 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tinybert_general_4l_312d_german` is a German model originally trained by dvm1983. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tinybert_general_4l_312d_german_de_5.1.1_3.0_1694629239176.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tinybert_general_4l_312d_german_de_5.1.1_3.0_1694629239176.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tinybert_general_4l_312d_german","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tinybert_general_4l_312d_german", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tinybert_general_4l_312d_german| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|54.5 MB| + +## References + +https://huggingface.co/dvm1983/TinyBERT_General_4L_312D_de \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tinybert_general_6l_768d_finetuned_wikitext103_en.md b/docs/_posts/ahmedlone127/2023-09-13-tinybert_general_6l_768d_finetuned_wikitext103_en.md new file mode 100644 index 00000000000000..8fe297ac5f96af --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tinybert_general_6l_768d_finetuned_wikitext103_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tinybert_general_6l_768d_finetuned_wikitext103 BertEmbeddings from saghar +author: John Snow Labs +name: tinybert_general_6l_768d_finetuned_wikitext103 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tinybert_general_6l_768d_finetuned_wikitext103` is a English model originally trained by saghar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tinybert_general_6l_768d_finetuned_wikitext103_en_5.1.1_3.0_1694614398325.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tinybert_general_6l_768d_finetuned_wikitext103_en_5.1.1_3.0_1694614398325.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tinybert_general_6l_768d_finetuned_wikitext103","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tinybert_general_6l_768d_finetuned_wikitext103", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tinybert_general_6l_768d_finetuned_wikitext103| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.9 MB| + +## References + +https://huggingface.co/saghar/TinyBERT_General_6L_768D-finetuned-wikitext103 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tinybert_l_4_h_312_v2_finetuned_wikitext103_en.md b/docs/_posts/ahmedlone127/2023-09-13-tinybert_l_4_h_312_v2_finetuned_wikitext103_en.md new file mode 100644 index 00000000000000..617821d05bb8d5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tinybert_l_4_h_312_v2_finetuned_wikitext103_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tinybert_l_4_h_312_v2_finetuned_wikitext103 BertEmbeddings from saghar +author: John Snow Labs +name: tinybert_l_4_h_312_v2_finetuned_wikitext103 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tinybert_l_4_h_312_v2_finetuned_wikitext103` is a English model originally trained by saghar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tinybert_l_4_h_312_v2_finetuned_wikitext103_en_5.1.1_3.0_1694615065926.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tinybert_l_4_h_312_v2_finetuned_wikitext103_en_5.1.1_3.0_1694615065926.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tinybert_l_4_h_312_v2_finetuned_wikitext103","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tinybert_l_4_h_312_v2_finetuned_wikitext103", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tinybert_l_4_h_312_v2_finetuned_wikitext103| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|53.8 MB| + +## References + +https://huggingface.co/saghar/TinyBERT_L-4_H-312_v2-finetuned-wikitext103 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_amazon_large_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_amazon_large_scale_en.md new file mode 100644 index 00000000000000..b9c91319cbb7c8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_amazon_large_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_amazon_large_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_amazon_large_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_amazon_large_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_amazon_large_scale_en_5.1.1_3.0_1694588840311.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_amazon_large_scale_en_5.1.1_3.0_1694588840311.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_amazon_large_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_amazon_large_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_amazon_large_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/yxchar/tlm-amazon-large-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-tlm_hyp_medium_scale_en.md b/docs/_posts/ahmedlone127/2023-09-13-tlm_hyp_medium_scale_en.md new file mode 100644 index 00000000000000..7cfb252c763d5c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-tlm_hyp_medium_scale_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tlm_hyp_medium_scale BertEmbeddings from yxchar +author: John Snow Labs +name: tlm_hyp_medium_scale +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tlm_hyp_medium_scale` is a English model originally trained by yxchar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tlm_hyp_medium_scale_en_5.1.1_3.0_1694591052250.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tlm_hyp_medium_scale_en_5.1.1_3.0_1694591052250.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tlm_hyp_medium_scale","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tlm_hyp_medium_scale", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tlm_hyp_medium_scale| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.4 MB| + +## References + +https://huggingface.co/yxchar/tlm-hyp-medium-scale \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-turkish_base_bert_uncased_tr.md b/docs/_posts/ahmedlone127/2023-09-13-turkish_base_bert_uncased_tr.md new file mode 100644 index 00000000000000..a61127112b0517 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-turkish_base_bert_uncased_tr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Turkish turkish_base_bert_uncased BertEmbeddings from ytu-ce-cosmos +author: John Snow Labs +name: turkish_base_bert_uncased +date: 2023-09-13 +tags: [bert, tr, open_source, fill_mask, onnx] +task: Embeddings +language: tr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`turkish_base_bert_uncased` is a Turkish model originally trained by ytu-ce-cosmos. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/turkish_base_bert_uncased_tr_5.1.1_3.0_1694631273482.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/turkish_base_bert_uncased_tr_5.1.1_3.0_1694631273482.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("turkish_base_bert_uncased","tr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("turkish_base_bert_uncased", "tr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|turkish_base_bert_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|tr| +|Size:|413.0 MB| + +## References + +https://huggingface.co/ytu-ce-cosmos/turkish-base-bert-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-turkish_medium_bert_uncased_tr.md b/docs/_posts/ahmedlone127/2023-09-13-turkish_medium_bert_uncased_tr.md new file mode 100644 index 00000000000000..f2fc72a6a7920a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-turkish_medium_bert_uncased_tr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Turkish turkish_medium_bert_uncased BertEmbeddings from ytu-ce-cosmos +author: John Snow Labs +name: turkish_medium_bert_uncased +date: 2023-09-13 +tags: [bert, tr, open_source, fill_mask, onnx] +task: Embeddings +language: tr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`turkish_medium_bert_uncased` is a Turkish model originally trained by ytu-ce-cosmos. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/turkish_medium_bert_uncased_tr_5.1.1_3.0_1694629908740.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/turkish_medium_bert_uncased_tr_5.1.1_3.0_1694629908740.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("turkish_medium_bert_uncased","tr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("turkish_medium_bert_uncased", "tr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|turkish_medium_bert_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|tr| +|Size:|157.4 MB| + +## References + +https://huggingface.co/ytu-ce-cosmos/turkish-medium-bert-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-turkish_mini_bert_uncased_tr.md b/docs/_posts/ahmedlone127/2023-09-13-turkish_mini_bert_uncased_tr.md new file mode 100644 index 00000000000000..38b0dace2beb3b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-turkish_mini_bert_uncased_tr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Turkish turkish_mini_bert_uncased BertEmbeddings from ytu-ce-cosmos +author: John Snow Labs +name: turkish_mini_bert_uncased +date: 2023-09-13 +tags: [bert, tr, open_source, fill_mask, onnx] +task: Embeddings +language: tr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`turkish_mini_bert_uncased` is a Turkish model originally trained by ytu-ce-cosmos. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/turkish_mini_bert_uncased_tr_5.1.1_3.0_1694629425088.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/turkish_mini_bert_uncased_tr_5.1.1_3.0_1694629425088.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("turkish_mini_bert_uncased","tr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("turkish_mini_bert_uncased", "tr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|turkish_mini_bert_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|tr| +|Size:|43.3 MB| + +## References + +https://huggingface.co/ytu-ce-cosmos/turkish-mini-bert-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-turkish_small_bert_uncased_tr.md b/docs/_posts/ahmedlone127/2023-09-13-turkish_small_bert_uncased_tr.md new file mode 100644 index 00000000000000..7e662c5a7b01ae --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-turkish_small_bert_uncased_tr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Turkish turkish_small_bert_uncased BertEmbeddings from ytu-ce-cosmos +author: John Snow Labs +name: turkish_small_bert_uncased +date: 2023-09-13 +tags: [bert, tr, open_source, fill_mask, onnx] +task: Embeddings +language: tr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`turkish_small_bert_uncased` is a Turkish model originally trained by ytu-ce-cosmos. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/turkish_small_bert_uncased_tr_5.1.1_3.0_1694629693791.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/turkish_small_bert_uncased_tr_5.1.1_3.0_1694629693791.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("turkish_small_bert_uncased","tr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("turkish_small_bert_uncased", "tr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|turkish_small_bert_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|tr| +|Size:|109.9 MB| + +## References + +https://huggingface.co/ytu-ce-cosmos/turkish-small-bert-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-turkish_tiny_bert_uncased_tr.md b/docs/_posts/ahmedlone127/2023-09-13-turkish_tiny_bert_uncased_tr.md new file mode 100644 index 00000000000000..8b1f6a2abe9735 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-turkish_tiny_bert_uncased_tr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Turkish turkish_tiny_bert_uncased BertEmbeddings from ytu-ce-cosmos +author: John Snow Labs +name: turkish_tiny_bert_uncased +date: 2023-09-13 +tags: [bert, tr, open_source, fill_mask, onnx] +task: Embeddings +language: tr +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`turkish_tiny_bert_uncased` is a Turkish model originally trained by ytu-ce-cosmos. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/turkish_tiny_bert_uncased_tr_5.1.1_3.0_1694629276501.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/turkish_tiny_bert_uncased_tr_5.1.1_3.0_1694629276501.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("turkish_tiny_bert_uncased","tr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("turkish_tiny_bert_uncased", "tr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|turkish_tiny_bert_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|tr| +|Size:|17.4 MB| + +## References + +https://huggingface.co/ytu-ce-cosmos/turkish-tiny-bert-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-writter_bert_hep_en.md b/docs/_posts/ahmedlone127/2023-09-13-writter_bert_hep_en.md new file mode 100644 index 00000000000000..a2c65af1607008 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-writter_bert_hep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English writter_bert_hep BertEmbeddings from munozariasjm +author: John Snow Labs +name: writter_bert_hep +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`writter_bert_hep` is a English model originally trained by munozariasjm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/writter_bert_hep_en_5.1.1_3.0_1694626233603.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/writter_bert_hep_en_5.1.1_3.0_1694626233603.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("writter_bert_hep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("writter_bert_hep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|writter_bert_hep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|410.0 MB| + +## References + +https://huggingface.co/munozariasjm/writter_bert_hep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-xtremedistil_l12_h384_uncased_finetuned_wikitext103_en.md b/docs/_posts/ahmedlone127/2023-09-13-xtremedistil_l12_h384_uncased_finetuned_wikitext103_en.md new file mode 100644 index 00000000000000..966362a70b6660 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-xtremedistil_l12_h384_uncased_finetuned_wikitext103_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English xtremedistil_l12_h384_uncased_finetuned_wikitext103 BertEmbeddings from saghar +author: John Snow Labs +name: xtremedistil_l12_h384_uncased_finetuned_wikitext103 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`xtremedistil_l12_h384_uncased_finetuned_wikitext103` is a English model originally trained by saghar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xtremedistil_l12_h384_uncased_finetuned_wikitext103_en_5.1.1_3.0_1694616486581.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xtremedistil_l12_h384_uncased_finetuned_wikitext103_en_5.1.1_3.0_1694616486581.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("xtremedistil_l12_h384_uncased_finetuned_wikitext103","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("xtremedistil_l12_h384_uncased_finetuned_wikitext103", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xtremedistil_l12_h384_uncased_finetuned_wikitext103| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|123.6 MB| + +## References + +https://huggingface.co/saghar/xtremedistil-l12-h384-uncased-finetuned-wikitext103 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-xtremedistil_l6_h384_uncased_finetuned_wikitext103_en.md b/docs/_posts/ahmedlone127/2023-09-13-xtremedistil_l6_h384_uncased_finetuned_wikitext103_en.md new file mode 100644 index 00000000000000..478ff4678dc65a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-xtremedistil_l6_h384_uncased_finetuned_wikitext103_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English xtremedistil_l6_h384_uncased_finetuned_wikitext103 BertEmbeddings from saghar +author: John Snow Labs +name: xtremedistil_l6_h384_uncased_finetuned_wikitext103 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`xtremedistil_l6_h384_uncased_finetuned_wikitext103` is a English model originally trained by saghar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xtremedistil_l6_h384_uncased_finetuned_wikitext103_en_5.1.1_3.0_1694616303708.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xtremedistil_l6_h384_uncased_finetuned_wikitext103_en_5.1.1_3.0_1694616303708.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("xtremedistil_l6_h384_uncased_finetuned_wikitext103","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("xtremedistil_l6_h384_uncased_finetuned_wikitext103", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xtremedistil_l6_h384_uncased_finetuned_wikitext103| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|83.6 MB| + +## References + +https://huggingface.co/saghar/xtremedistil-l6-h384-uncased-finetuned-wikitext103 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-yelpy_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-yelpy_bert_en.md new file mode 100644 index 00000000000000..2283ab40fddadc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-yelpy_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English yelpy_bert BertEmbeddings from rttl-ai +author: John Snow Labs +name: yelpy_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`yelpy_bert` is a English model originally trained by rttl-ai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/yelpy_bert_en_5.1.1_3.0_1694583064434.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/yelpy_bert_en_5.1.1_3.0_1694583064434.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("yelpy_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("yelpy_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|yelpy_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/rttl-ai/yelpy-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-youtube_bert_10_en.md b/docs/_posts/ahmedlone127/2023-09-13-youtube_bert_10_en.md new file mode 100644 index 00000000000000..50a91796587ee1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-youtube_bert_10_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English youtube_bert_10 BertEmbeddings from flboehm +author: John Snow Labs +name: youtube_bert_10 +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`youtube_bert_10` is a English model originally trained by flboehm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/youtube_bert_10_en_5.1.1_3.0_1694646466454.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/youtube_bert_10_en_5.1.1_3.0_1694646466454.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("youtube_bert_10","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("youtube_bert_10", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|youtube_bert_10| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.6 MB| + +## References + +https://huggingface.co/flboehm/youtube-bert_10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-13-youtube_bert_en.md b/docs/_posts/ahmedlone127/2023-09-13-youtube_bert_en.md new file mode 100644 index 00000000000000..ffd52bceb7b195 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-13-youtube_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English youtube_bert BertEmbeddings from flboehm +author: John Snow Labs +name: youtube_bert +date: 2023-09-13 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`youtube_bert` is a English model originally trained by flboehm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/youtube_bert_en_5.1.1_3.0_1694646017109.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/youtube_bert_en_5.1.1_3.0_1694646017109.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("youtube_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("youtube_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|youtube_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.6 MB| + +## References + +https://huggingface.co/flboehm/youtube-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-9.4aistudy_en.md b/docs/_posts/ahmedlone127/2023-09-14-9.4aistudy_en.md new file mode 100644 index 00000000000000..a4c07e832d1b47 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-9.4aistudy_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English 9.4aistudy BertEmbeddings from hangmu +author: John Snow Labs +name: 9.4aistudy +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`9.4aistudy` is a English model originally trained by hangmu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/9.4aistudy_en_5.1.1_3.0_1694652490572.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/9.4aistudy_en_5.1.1_3.0_1694652490572.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("9.4aistudy","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("9.4aistudy", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|9.4aistudy| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/hangmu/9.4AIstudy \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-akkbert_en.md b/docs/_posts/ahmedlone127/2023-09-14-akkbert_en.md new file mode 100644 index 00000000000000..11ae896949b576 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-akkbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English akkbert BertEmbeddings from megamattc +author: John Snow Labs +name: akkbert +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`akkbert` is a English model originally trained by megamattc. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/akkbert_en_5.1.1_3.0_1694663523335.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/akkbert_en_5.1.1_3.0_1694663523335.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("akkbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("akkbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|akkbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|664.9 MB| + +## References + +https://huggingface.co/megamattc/AkkBert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-alephbertgimmel_base_512_he.md b/docs/_posts/ahmedlone127/2023-09-14-alephbertgimmel_base_512_he.md new file mode 100644 index 00000000000000..591248169da26e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-alephbertgimmel_base_512_he.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hebrew alephbertgimmel_base_512 BertEmbeddings from imvladikon +author: John Snow Labs +name: alephbertgimmel_base_512 +date: 2023-09-14 +tags: [bert, he, open_source, fill_mask, onnx] +task: Embeddings +language: he +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`alephbertgimmel_base_512` is a Hebrew model originally trained by imvladikon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/alephbertgimmel_base_512_he_5.1.1_3.0_1694658101075.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/alephbertgimmel_base_512_he_5.1.1_3.0_1694658101075.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("alephbertgimmel_base_512","he") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("alephbertgimmel_base_512", "he") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|alephbertgimmel_base_512| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|he| +|Size:|690.4 MB| + +## References + +https://huggingface.co/imvladikon/alephbertgimmel-base-512 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-aligner_english_vietnamese_en.md b/docs/_posts/ahmedlone127/2023-09-14-aligner_english_vietnamese_en.md new file mode 100644 index 00000000000000..1226fbe15e8e20 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-aligner_english_vietnamese_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English aligner_english_vietnamese BertEmbeddings from hdmt +author: John Snow Labs +name: aligner_english_vietnamese +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`aligner_english_vietnamese` is a English model originally trained by hdmt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/aligner_english_vietnamese_en_5.1.1_3.0_1694655583036.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/aligner_english_vietnamese_en_5.1.1_3.0_1694655583036.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("aligner_english_vietnamese","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("aligner_english_vietnamese", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|aligner_english_vietnamese| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/hdmt/aligner-en-vi \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-antismetisim1_finetuned_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-14-antismetisim1_finetuned_mlm_en.md new file mode 100644 index 00000000000000..8a81e08a25e7ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-antismetisim1_finetuned_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English antismetisim1_finetuned_mlm BertEmbeddings from Dhanush66 +author: John Snow Labs +name: antismetisim1_finetuned_mlm +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`antismetisim1_finetuned_mlm` is a English model originally trained by Dhanush66. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/antismetisim1_finetuned_mlm_en_5.1.1_3.0_1694667541883.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/antismetisim1_finetuned_mlm_en_5.1.1_3.0_1694667541883.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("antismetisim1_finetuned_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("antismetisim1_finetuned_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|antismetisim1_finetuned_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Dhanush66/Antismetisim1-finetuned-MLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-antismetisimlargedata_finetuned_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-14-antismetisimlargedata_finetuned_mlm_en.md new file mode 100644 index 00000000000000..3283bf335f3c73 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-antismetisimlargedata_finetuned_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English antismetisimlargedata_finetuned_mlm BertEmbeddings from Dhanush66 +author: John Snow Labs +name: antismetisimlargedata_finetuned_mlm +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`antismetisimlargedata_finetuned_mlm` is a English model originally trained by Dhanush66. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/antismetisimlargedata_finetuned_mlm_en_5.1.1_3.0_1694669960122.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/antismetisimlargedata_finetuned_mlm_en_5.1.1_3.0_1694669960122.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("antismetisimlargedata_finetuned_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("antismetisimlargedata_finetuned_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|antismetisimlargedata_finetuned_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|475.5 MB| + +## References + +https://huggingface.co/Dhanush66/AntismetisimLargedata-finetuned-MLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-applicationbert_en.md b/docs/_posts/ahmedlone127/2023-09-14-applicationbert_en.md new file mode 100644 index 00000000000000..400887f6307b1e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-applicationbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English applicationbert BertEmbeddings from EgilKarlsen +author: John Snow Labs +name: applicationbert +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`applicationbert` is a English model originally trained by EgilKarlsen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/applicationbert_en_5.1.1_3.0_1694664670828.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/applicationbert_en_5.1.1_3.0_1694664670828.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("applicationbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("applicationbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|applicationbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/EgilKarlsen/ApplicationBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-archaeobert_en.md b/docs/_posts/ahmedlone127/2023-09-14-archaeobert_en.md new file mode 100644 index 00000000000000..cd24212f4ff18d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-archaeobert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English archaeobert BertEmbeddings from alexbrandsen +author: John Snow Labs +name: archaeobert +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`archaeobert` is a English model originally trained by alexbrandsen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/archaeobert_en_5.1.1_3.0_1694650857417.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/archaeobert_en_5.1.1_3.0_1694650857417.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("archaeobert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("archaeobert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|archaeobert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.5 MB| + +## References + +https://huggingface.co/alexbrandsen/ArchaeoBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-autotrain_acc_keys_2347073860_en.md b/docs/_posts/ahmedlone127/2023-09-14-autotrain_acc_keys_2347073860_en.md new file mode 100644 index 00000000000000..6230f96852d273 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-autotrain_acc_keys_2347073860_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English autotrain_acc_keys_2347073860 BertEmbeddings from alanila +author: John Snow Labs +name: autotrain_acc_keys_2347073860 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`autotrain_acc_keys_2347073860` is a English model originally trained by alanila. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/autotrain_acc_keys_2347073860_en_5.1.1_3.0_1694660872048.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/autotrain_acc_keys_2347073860_en_5.1.1_3.0_1694660872048.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("autotrain_acc_keys_2347073860","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("autotrain_acc_keys_2347073860", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|autotrain_acc_keys_2347073860| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/alanila/autotrain-acc_keys-2347073860 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-awesome_align_with_corsican_xx.md b/docs/_posts/ahmedlone127/2023-09-14-awesome_align_with_corsican_xx.md new file mode 100644 index 00000000000000..2a88580dea8082 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-awesome_align_with_corsican_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual awesome_align_with_corsican BertEmbeddings from aneuraz +author: John Snow Labs +name: awesome_align_with_corsican +date: 2023-09-14 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`awesome_align_with_corsican` is a Multilingual model originally trained by aneuraz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/awesome_align_with_corsican_xx_5.1.1_3.0_1694653050397.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/awesome_align_with_corsican_xx_5.1.1_3.0_1694653050397.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("awesome_align_with_corsican","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("awesome_align_with_corsican", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|awesome_align_with_corsican| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|665.0 MB| + +## References + +https://huggingface.co/aneuraz/awesome-align-with-co \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-base_mlm_tweet_en.md b/docs/_posts/ahmedlone127/2023-09-14-base_mlm_tweet_en.md new file mode 100644 index 00000000000000..9196ab59237cd7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-base_mlm_tweet_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English base_mlm_tweet BertEmbeddings from muhtasham +author: John Snow Labs +name: base_mlm_tweet +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`base_mlm_tweet` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/base_mlm_tweet_en_5.1.1_3.0_1694664760038.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/base_mlm_tweet_en_5.1.1_3.0_1694664760038.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("base_mlm_tweet","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("base_mlm_tweet", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|base_mlm_tweet| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.6 MB| + +## References + +https://huggingface.co/muhtasham/base-mlm-tweet \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_application_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_application_en.md new file mode 100644 index 00000000000000..47bc2e814752ef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_application_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_application BertEmbeddings from EgilKarlsen +author: John Snow Labs +name: bert_application +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_application` is a English model originally trained by EgilKarlsen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_application_en_5.1.1_3.0_1694656899100.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_application_en_5.1.1_3.0_1694656899100.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_application","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_application", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_application| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/EgilKarlsen/BERT-Application \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_bangla_finetuned_summarization_dataset_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_bangla_finetuned_summarization_dataset_en.md new file mode 100644 index 00000000000000..0af6e45a148065 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_bangla_finetuned_summarization_dataset_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_bangla_finetuned_summarization_dataset BertEmbeddings from arbitropy +author: John Snow Labs +name: bert_base_bangla_finetuned_summarization_dataset +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_bangla_finetuned_summarization_dataset` is a English model originally trained by arbitropy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_bangla_finetuned_summarization_dataset_en_5.1.1_3.0_1694666948637.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_bangla_finetuned_summarization_dataset_en_5.1.1_3.0_1694666948637.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_bangla_finetuned_summarization_dataset","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_bangla_finetuned_summarization_dataset", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_bangla_finetuned_summarization_dataset| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.7 MB| + +## References + +https://huggingface.co/arbitropy/bert-base-bangla-finetuned-summarization-dataset \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_bookcorpus_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_bookcorpus_en.md new file mode 100644 index 00000000000000..3f0d44679ba69c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_bookcorpus_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_bookcorpus BertEmbeddings from nicholasKluge +author: John Snow Labs +name: bert_base_bookcorpus +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_bookcorpus` is a English model originally trained by nicholasKluge. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_bookcorpus_en_5.1.1_3.0_1694660865879.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_bookcorpus_en_5.1.1_3.0_1694660865879.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_bookcorpus","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_bookcorpus", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_bookcorpus| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.3 MB| + +## References + +https://huggingface.co/nicholasKluge/bert-base-bookcorpus \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_conversational_finetuned_wallisian_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_conversational_finetuned_wallisian_en.md new file mode 100644 index 00000000000000..c7bc69f37d0bc3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_conversational_finetuned_wallisian_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_conversational_finetuned_wallisian BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_conversational_finetuned_wallisian +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_conversational_finetuned_wallisian` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_conversational_finetuned_wallisian_en_5.1.1_3.0_1694652098523.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_conversational_finetuned_wallisian_en_5.1.1_3.0_1694652098523.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_conversational_finetuned_wallisian","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_conversational_finetuned_wallisian", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_conversational_finetuned_wallisian| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.8 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-conversational-finetuned-wls \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_chemistry_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_chemistry_en.md new file mode 100644 index 00000000000000..06b0d609693eb3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_chemistry_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_chemistry BertEmbeddings from Kuaaangwen +author: John Snow Labs +name: bert_base_cased_finetuned_chemistry +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_chemistry` is a English model originally trained by Kuaaangwen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_chemistry_en_5.1.1_3.0_1694662575342.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_chemistry_en_5.1.1_3.0_1694662575342.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_chemistry","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_chemistry", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_chemistry| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/Kuaaangwen/bert-base-cased-finetuned-chemistry \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_1ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_1ep_en.md new file mode 100644 index 00000000000000..d9329758bee6d7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_1ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_manual_1ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_manual_1ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_manual_1ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_1ep_en_5.1.1_3.0_1694671264388.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_1ep_en_5.1.1_3.0_1694671264388.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_manual_1ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_manual_1ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_manual_1ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-manual-1ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_2ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_2ep_en.md new file mode 100644 index 00000000000000..76cab26440f341 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_2ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_manual_2ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_manual_2ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_manual_2ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_2ep_en_5.1.1_3.0_1694671483581.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_2ep_en_5.1.1_3.0_1694671483581.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_manual_2ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_manual_2ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_manual_2ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-manual-2ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_3ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_3ep_en.md new file mode 100644 index 00000000000000..c2df8cab1a6d4a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_3ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_manual_3ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_manual_3ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_manual_3ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_3ep_en_5.1.1_3.0_1694671699838.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_3ep_en_5.1.1_3.0_1694671699838.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_manual_3ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_manual_3ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_manual_3ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-manual-3ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_4ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_4ep_en.md new file mode 100644 index 00000000000000..31140ef3ac2ca7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_4ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_manual_4ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_manual_4ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_manual_4ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_4ep_en_5.1.1_3.0_1694671916586.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_4ep_en_5.1.1_3.0_1694671916586.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_manual_4ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_manual_4ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_manual_4ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-manual-4ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_5ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_5ep_en.md new file mode 100644 index 00000000000000..8b557a038a1c52 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_5ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_manual_5ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_manual_5ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_manual_5ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_5ep_en_5.1.1_3.0_1694672136887.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_5ep_en_5.1.1_3.0_1694672136887.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_manual_5ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_manual_5ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_manual_5ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-manual-5ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_6ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_6ep_en.md new file mode 100644 index 00000000000000..5c362febe31be4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_6ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_manual_6ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_manual_6ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_manual_6ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_6ep_en_5.1.1_3.0_1694672358346.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_6ep_en_5.1.1_3.0_1694672358346.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_manual_6ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_manual_6ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_manual_6ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-manual-6ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_7ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_7ep_en.md new file mode 100644 index 00000000000000..a1c6991c44f6cd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_7ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_manual_7ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_manual_7ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_manual_7ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_7ep_en_5.1.1_3.0_1694672574132.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_7ep_en_5.1.1_3.0_1694672574132.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_manual_7ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_manual_7ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_manual_7ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-manual-7ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_8ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_8ep_en.md new file mode 100644 index 00000000000000..6023d76736cc6a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_8ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_manual_8ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_manual_8ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_manual_8ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_8ep_en_5.1.1_3.0_1694672793233.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_8ep_en_5.1.1_3.0_1694672793233.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_manual_8ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_manual_8ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_manual_8ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-manual-8ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_10ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_10ep_en.md new file mode 100644 index 00000000000000..8907e33aa75460 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_10ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_whisper_10ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_whisper_10ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_whisper_10ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_10ep_en_5.1.1_3.0_1694671157614.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_10ep_en_5.1.1_3.0_1694671157614.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_whisper_10ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_whisper_10ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_whisper_10ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-whisper-10ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_1ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_1ep_en.md new file mode 100644 index 00000000000000..586ef7cf3abcb5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_1ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_whisper_1ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_whisper_1ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_whisper_1ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_1ep_en_5.1.1_3.0_1694670183935.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_1ep_en_5.1.1_3.0_1694670183935.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_whisper_1ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_whisper_1ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_whisper_1ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-whisper-1ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_2ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_2ep_en.md new file mode 100644 index 00000000000000..9b838a1b0d25b5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_2ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_whisper_2ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_whisper_2ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_whisper_2ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_2ep_en_5.1.1_3.0_1694670290798.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_2ep_en_5.1.1_3.0_1694670290798.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_whisper_2ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_whisper_2ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_whisper_2ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-whisper-2ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_3ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_3ep_en.md new file mode 100644 index 00000000000000..ce81512a20d135 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_3ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_whisper_3ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_whisper_3ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_whisper_3ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_3ep_en_5.1.1_3.0_1694670399822.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_3ep_en_5.1.1_3.0_1694670399822.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_whisper_3ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_whisper_3ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_whisper_3ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-whisper-3ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_4ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_4ep_en.md new file mode 100644 index 00000000000000..ca7e21df625eee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_4ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_whisper_4ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_whisper_4ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_whisper_4ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_4ep_en_5.1.1_3.0_1694670508413.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_4ep_en_5.1.1_3.0_1694670508413.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_whisper_4ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_whisper_4ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_whisper_4ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-whisper-4ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_5ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_5ep_en.md new file mode 100644 index 00000000000000..0a0240cda3e66a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_5ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_whisper_5ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_whisper_5ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_whisper_5ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_5ep_en_5.1.1_3.0_1694670617445.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_5ep_en_5.1.1_3.0_1694670617445.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_whisper_5ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_whisper_5ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_whisper_5ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-whisper-5ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_6ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_6ep_en.md new file mode 100644 index 00000000000000..84352c6d887e03 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_6ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_whisper_6ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_whisper_6ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_whisper_6ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_6ep_en_5.1.1_3.0_1694670727672.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_6ep_en_5.1.1_3.0_1694670727672.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_whisper_6ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_whisper_6ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_whisper_6ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-whisper-6ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_7ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_7ep_en.md new file mode 100644 index 00000000000000..6e840d323af79f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_7ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_whisper_7ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_whisper_7ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_whisper_7ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_7ep_en_5.1.1_3.0_1694670836499.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_7ep_en_5.1.1_3.0_1694670836499.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_whisper_7ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_whisper_7ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_whisper_7ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-whisper-7ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_8ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_8ep_en.md new file mode 100644 index 00000000000000..e8996105cf1042 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_8ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_whisper_8ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_whisper_8ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_whisper_8ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_8ep_en_5.1.1_3.0_1694670943658.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_8ep_en_5.1.1_3.0_1694670943658.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_whisper_8ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_whisper_8ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_whisper_8ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-whisper-8ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_9ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_9ep_en.md new file mode 100644 index 00000000000000..03359371fcbeb5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_whisper_9ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_whisper_9ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_whisper_9ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_whisper_9ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_9ep_en_5.1.1_3.0_1694671051592.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_whisper_9ep_en_5.1.1_3.0_1694671051592.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_whisper_9ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_whisper_9ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_whisper_9ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-whisper-9ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_code_comments_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_code_comments_en.md new file mode 100644 index 00000000000000..2eaf964fb5fffd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_code_comments_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_code_comments BertEmbeddings from giganticode +author: John Snow Labs +name: bert_base_code_comments +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_code_comments` is a English model originally trained by giganticode. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_code_comments_en_5.1.1_3.0_1694649864357.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_code_comments_en_5.1.1_3.0_1694649864357.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_code_comments","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_code_comments", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_code_comments| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/giganticode/bert-base-code_comments \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_german_cased_mlm_basque_chemistry_regulation_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_german_cased_mlm_basque_chemistry_regulation_en.md new file mode 100644 index 00000000000000..e95d32ec292b16 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_german_cased_mlm_basque_chemistry_regulation_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_german_cased_mlm_basque_chemistry_regulation BertEmbeddings from jonas-luehrs +author: John Snow Labs +name: bert_base_german_cased_mlm_basque_chemistry_regulation +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_german_cased_mlm_basque_chemistry_regulation` is a English model originally trained by jonas-luehrs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_german_cased_mlm_basque_chemistry_regulation_en_5.1.1_3.0_1694669620686.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_german_cased_mlm_basque_chemistry_regulation_en_5.1.1_3.0_1694669620686.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_german_cased_mlm_basque_chemistry_regulation","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_german_cased_mlm_basque_chemistry_regulation", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_german_cased_mlm_basque_chemistry_regulation| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.9 MB| + +## References + +https://huggingface.co/jonas-luehrs/bert-base-german-cased-MLM-eu_chemistry_regulation \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_german_europeana_td_cased_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_german_europeana_td_cased_en.md new file mode 100644 index 00000000000000..c9a951f7840630 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_german_europeana_td_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_german_europeana_td_cased BertEmbeddings from dbmdz +author: John Snow Labs +name: bert_base_german_europeana_td_cased +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_german_europeana_td_cased` is a English model originally trained by dbmdz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_german_europeana_td_cased_en_5.1.1_3.0_1694652557402.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_german_europeana_td_cased_en_5.1.1_3.0_1694652557402.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_german_europeana_td_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_german_europeana_td_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_german_europeana_td_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.4 MB| + +## References + +https://huggingface.co/dbmdz/bert-base-german-europeana-td-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_greek_uncased_v5_finetuned_polylex_malagasy_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_greek_uncased_v5_finetuned_polylex_malagasy_en.md new file mode 100644 index 00000000000000..fbc8517052ed2e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_greek_uncased_v5_finetuned_polylex_malagasy_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_greek_uncased_v5_finetuned_polylex_malagasy BertEmbeddings from snousias +author: John Snow Labs +name: bert_base_greek_uncased_v5_finetuned_polylex_malagasy +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_greek_uncased_v5_finetuned_polylex_malagasy` is a English model originally trained by snousias. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_greek_uncased_v5_finetuned_polylex_malagasy_en_5.1.1_3.0_1694649633484.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_greek_uncased_v5_finetuned_polylex_malagasy_en_5.1.1_3.0_1694649633484.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_greek_uncased_v5_finetuned_polylex_malagasy","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_greek_uncased_v5_finetuned_polylex_malagasy", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_greek_uncased_v5_finetuned_polylex_malagasy| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|421.1 MB| + +## References + +https://huggingface.co/snousias/bert-base-greek-uncased-v5-finetuned-polylex-mg \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_greek_uncased_v6_finetuned_polylex_malagasy_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_greek_uncased_v6_finetuned_polylex_malagasy_en.md new file mode 100644 index 00000000000000..2d05099788c1f4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_greek_uncased_v6_finetuned_polylex_malagasy_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_greek_uncased_v6_finetuned_polylex_malagasy BertEmbeddings from polylexmg +author: John Snow Labs +name: bert_base_greek_uncased_v6_finetuned_polylex_malagasy +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_greek_uncased_v6_finetuned_polylex_malagasy` is a English model originally trained by polylexmg. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_greek_uncased_v6_finetuned_polylex_malagasy_en_5.1.1_3.0_1694649912799.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_greek_uncased_v6_finetuned_polylex_malagasy_en_5.1.1_3.0_1694649912799.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_greek_uncased_v6_finetuned_polylex_malagasy","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_greek_uncased_v6_finetuned_polylex_malagasy", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_greek_uncased_v6_finetuned_polylex_malagasy| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|421.1 MB| + +## References + +https://huggingface.co/polylexmg/bert-base-greek-uncased-v6-finetuned-polylex-mg \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_kor_v1_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_kor_v1_en.md new file mode 100644 index 00000000000000..687a9c8126d399 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_kor_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_kor_v1 BertEmbeddings from bongsoo +author: John Snow Labs +name: bert_base_kor_v1 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_kor_v1` is a English model originally trained by bongsoo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_kor_v1_en_5.1.1_3.0_1694653204759.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_kor_v1_en_5.1.1_3.0_1694653204759.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_kor_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_kor_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_kor_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.6 MB| + +## References + +https://huggingface.co/bongsoo/bert-base-kor-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_minipile_128_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_minipile_128_en.md new file mode 100644 index 00000000000000..f2cd21d687e58e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_minipile_128_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_minipile_128 BertEmbeddings from seba +author: John Snow Labs +name: bert_base_minipile_128 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_minipile_128` is a English model originally trained by seba. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_minipile_128_en_5.1.1_3.0_1694666014699.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_minipile_128_en_5.1.1_3.0_1694666014699.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_minipile_128","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_minipile_128", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_minipile_128| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.6 MB| + +## References + +https://huggingface.co/seba/bert-base-minipile-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_spanish_wwm_cased_finetuned_peppa_pig_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_spanish_wwm_cased_finetuned_peppa_pig_en.md new file mode 100644 index 00000000000000..24a4d3ea8867ea --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_spanish_wwm_cased_finetuned_peppa_pig_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_spanish_wwm_cased_finetuned_peppa_pig BertEmbeddings from guidoivetta +author: John Snow Labs +name: bert_base_spanish_wwm_cased_finetuned_peppa_pig +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_spanish_wwm_cased_finetuned_peppa_pig` is a English model originally trained by guidoivetta. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_peppa_pig_en_5.1.1_3.0_1694669728508.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_peppa_pig_en_5.1.1_3.0_1694669728508.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_spanish_wwm_cased_finetuned_peppa_pig","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_spanish_wwm_cased_finetuned_peppa_pig", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_spanish_wwm_cased_finetuned_peppa_pig| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/guidoivetta/bert-base-spanish-wwm-cased-finetuned-peppa-pig \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_spanish_wwm_cased_finetuned_wine_reviews_spanish_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_spanish_wwm_cased_finetuned_wine_reviews_spanish_en.md new file mode 100644 index 00000000000000..3c0e42b22f109e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_spanish_wwm_cased_finetuned_wine_reviews_spanish_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_spanish_wwm_cased_finetuned_wine_reviews_spanish BertEmbeddings from guidoivetta +author: John Snow Labs +name: bert_base_spanish_wwm_cased_finetuned_wine_reviews_spanish +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_spanish_wwm_cased_finetuned_wine_reviews_spanish` is a English model originally trained by guidoivetta. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_wine_reviews_spanish_en_5.1.1_3.0_1694669839262.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_wine_reviews_spanish_en_5.1.1_3.0_1694669839262.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_spanish_wwm_cased_finetuned_wine_reviews_spanish","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_spanish_wwm_cased_finetuned_wine_reviews_spanish", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_spanish_wwm_cased_finetuned_wine_reviews_spanish| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/guidoivetta/bert-base-spanish-wwm-cased-finetuned-wine-reviews_spanish \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_duplicate_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_duplicate_en.md new file mode 100644 index 00000000000000..80597bd7dbab61 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_duplicate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_duplicate BertEmbeddings from julien-c +author: John Snow Labs +name: bert_base_uncased_duplicate +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_duplicate` is a English model originally trained by julien-c. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_duplicate_en_5.1.1_3.0_1694665842003.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_duplicate_en_5.1.1_3.0_1694665842003.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_duplicate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_duplicate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_duplicate| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/julien-c/bert-base-uncased-duplicate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_char_hangman_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_char_hangman_en.md new file mode 100644 index 00000000000000..16454bec700c98 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_char_hangman_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_char_hangman BertEmbeddings from bhagasra-saurav +author: John Snow Labs +name: bert_base_uncased_finetuned_char_hangman +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_char_hangman` is a English model originally trained by bhagasra-saurav. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_char_hangman_en_5.1.1_3.0_1694659746036.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_char_hangman_en_5.1.1_3.0_1694659746036.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_char_hangman","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_char_hangman", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_char_hangman| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/bhagasra-saurav/bert-base-uncased-finetuned-char-hangman \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_en.md new file mode 100644 index 00000000000000..4c56d62315f39d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_wallisian BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_uncased_finetuned_wallisian +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_wallisian` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_en_5.1.1_3.0_1694653293253.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_en_5.1.1_3.0_1694653293253.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_wallisian","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_wallisian", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_wallisian| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/btamm12/bert-base-uncased-finetuned-wls \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_lower_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_lower_en.md new file mode 100644 index 00000000000000..98fcde4093640e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_lower_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_wallisian_lower BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_uncased_finetuned_wallisian_lower +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_wallisian_lower` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_lower_en_5.1.1_3.0_1694653735399.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_lower_en_5.1.1_3.0_1694653735399.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_wallisian_lower","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_wallisian_lower", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_wallisian_lower| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/btamm12/bert-base-uncased-finetuned-wls-lower \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_1ep_lower_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_1ep_lower_en.md new file mode 100644 index 00000000000000..039e69a18f7a89 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_1ep_lower_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_wallisian_manual_1ep_lower BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_uncased_finetuned_wallisian_manual_1ep_lower +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_wallisian_manual_1ep_lower` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_1ep_lower_en_5.1.1_3.0_1694671374906.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_1ep_lower_en_5.1.1_3.0_1694671374906.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_wallisian_manual_1ep_lower","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_wallisian_manual_1ep_lower", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_wallisian_manual_1ep_lower| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/btamm12/bert-base-uncased-finetuned-wls-manual-1ep-lower \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_2ep_lower_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_2ep_lower_en.md new file mode 100644 index 00000000000000..3311fb19c57a28 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_2ep_lower_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_wallisian_manual_2ep_lower BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_uncased_finetuned_wallisian_manual_2ep_lower +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_wallisian_manual_2ep_lower` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_2ep_lower_en_5.1.1_3.0_1694671592012.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_2ep_lower_en_5.1.1_3.0_1694671592012.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_wallisian_manual_2ep_lower","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_wallisian_manual_2ep_lower", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_wallisian_manual_2ep_lower| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/btamm12/bert-base-uncased-finetuned-wls-manual-2ep-lower \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_3ep_lower_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_3ep_lower_en.md new file mode 100644 index 00000000000000..2d5fd0bf93e234 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_3ep_lower_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_wallisian_manual_3ep_lower BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_uncased_finetuned_wallisian_manual_3ep_lower +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_wallisian_manual_3ep_lower` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_3ep_lower_en_5.1.1_3.0_1694671806791.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_3ep_lower_en_5.1.1_3.0_1694671806791.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_wallisian_manual_3ep_lower","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_wallisian_manual_3ep_lower", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_wallisian_manual_3ep_lower| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/btamm12/bert-base-uncased-finetuned-wls-manual-3ep-lower \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_4ep_lower_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_4ep_lower_en.md new file mode 100644 index 00000000000000..7f501fa3d04407 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_4ep_lower_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_wallisian_manual_4ep_lower BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_uncased_finetuned_wallisian_manual_4ep_lower +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_wallisian_manual_4ep_lower` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_4ep_lower_en_5.1.1_3.0_1694672025708.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_4ep_lower_en_5.1.1_3.0_1694672025708.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_wallisian_manual_4ep_lower","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_wallisian_manual_4ep_lower", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_wallisian_manual_4ep_lower| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/btamm12/bert-base-uncased-finetuned-wls-manual-4ep-lower \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_5ep_lower_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_5ep_lower_en.md new file mode 100644 index 00000000000000..be3bcbd50aaa7b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_5ep_lower_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_wallisian_manual_5ep_lower BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_uncased_finetuned_wallisian_manual_5ep_lower +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_wallisian_manual_5ep_lower` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_5ep_lower_en_5.1.1_3.0_1694672247698.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_5ep_lower_en_5.1.1_3.0_1694672247698.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_wallisian_manual_5ep_lower","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_wallisian_manual_5ep_lower", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_wallisian_manual_5ep_lower| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/btamm12/bert-base-uncased-finetuned-wls-manual-5ep-lower \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_6ep_lower_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_6ep_lower_en.md new file mode 100644 index 00000000000000..7e0b0f06d1899d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_6ep_lower_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_wallisian_manual_6ep_lower BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_uncased_finetuned_wallisian_manual_6ep_lower +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_wallisian_manual_6ep_lower` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_6ep_lower_en_5.1.1_3.0_1694672466472.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_6ep_lower_en_5.1.1_3.0_1694672466472.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_wallisian_manual_6ep_lower","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_wallisian_manual_6ep_lower", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_wallisian_manual_6ep_lower| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/btamm12/bert-base-uncased-finetuned-wls-manual-6ep-lower \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_7ep_lower_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_7ep_lower_en.md new file mode 100644 index 00000000000000..3c8ebc6681e002 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_7ep_lower_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_wallisian_manual_7ep_lower BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_uncased_finetuned_wallisian_manual_7ep_lower +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_wallisian_manual_7ep_lower` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_7ep_lower_en_5.1.1_3.0_1694672684834.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_7ep_lower_en_5.1.1_3.0_1694672684834.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_wallisian_manual_7ep_lower","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_wallisian_manual_7ep_lower", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_wallisian_manual_7ep_lower| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/btamm12/bert-base-uncased-finetuned-wls-manual-7ep-lower \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_8ep_lower_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_8ep_lower_en.md new file mode 100644 index 00000000000000..aaba40ad3784d7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_8ep_lower_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_wallisian_manual_8ep_lower BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_uncased_finetuned_wallisian_manual_8ep_lower +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_wallisian_manual_8ep_lower` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_8ep_lower_en_5.1.1_3.0_1694672900256.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_8ep_lower_en_5.1.1_3.0_1694672900256.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_wallisian_manual_8ep_lower","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_wallisian_manual_8ep_lower", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_wallisian_manual_8ep_lower| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/btamm12/bert-base-uncased-finetuned-wls-manual-8ep-lower \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wikitext_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wikitext_en.md new file mode 100644 index 00000000000000..0c88f303be7b25 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wikitext_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_wikitext BertEmbeddings from peteryushunli +author: John Snow Labs +name: bert_base_uncased_finetuned_wikitext +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_wikitext` is a English model originally trained by peteryushunli. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wikitext_en_5.1.1_3.0_1694667435437.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wikitext_en_5.1.1_3.0_1694667435437.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_wikitext","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_wikitext", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_wikitext| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/peteryushunli/bert-base-uncased-finetuned-wikitext \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_abhilashawasthi_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_abhilashawasthi_en.md new file mode 100644 index 00000000000000..1163955e9e3162 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_abhilashawasthi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_abhilashawasthi BertEmbeddings from abhilashawasthi +author: John Snow Labs +name: bert_base_uncased_issues_128_abhilashawasthi +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_abhilashawasthi` is a English model originally trained by abhilashawasthi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_abhilashawasthi_en_5.1.1_3.0_1694658897200.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_abhilashawasthi_en_5.1.1_3.0_1694658897200.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_abhilashawasthi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_abhilashawasthi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_abhilashawasthi| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/abhilashawasthi/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_bh8648_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_bh8648_en.md new file mode 100644 index 00000000000000..b462324ad6a751 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_bh8648_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_bh8648 BertEmbeddings from bh8648 +author: John Snow Labs +name: bert_base_uncased_issues_128_bh8648 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_bh8648` is a English model originally trained by bh8648. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_bh8648_en_5.1.1_3.0_1694652857821.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_bh8648_en_5.1.1_3.0_1694652857821.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_bh8648","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_bh8648", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_bh8648| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/bh8648/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_mabrouk_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_mabrouk_en.md new file mode 100644 index 00000000000000..01075aee7ecbd6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_mabrouk_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_mabrouk BertEmbeddings from mabrouk +author: John Snow Labs +name: bert_base_uncased_issues_128_mabrouk +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_mabrouk` is a English model originally trained by mabrouk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_mabrouk_en_5.1.1_3.0_1694651316234.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_mabrouk_en_5.1.1_3.0_1694651316234.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_mabrouk","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_mabrouk", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_mabrouk| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/mabrouk/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_reaverlee_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_reaverlee_en.md new file mode 100644 index 00000000000000..f288864d7ffbf1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_reaverlee_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_reaverlee BertEmbeddings from reaverlee +author: John Snow Labs +name: bert_base_uncased_issues_128_reaverlee +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_reaverlee` is a English model originally trained by reaverlee. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_reaverlee_en_5.1.1_3.0_1694659370450.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_reaverlee_en_5.1.1_3.0_1694659370450.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_reaverlee","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_reaverlee", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_reaverlee| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/reaverlee/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_veeps_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_veeps_en.md new file mode 100644 index 00000000000000..d44fce6405fd5f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_veeps_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_veeps BertEmbeddings from veeps +author: John Snow Labs +name: bert_base_uncased_issues_128_veeps +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_veeps` is a English model originally trained by veeps. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_veeps_en_5.1.1_3.0_1694652544895.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_veeps_en_5.1.1_3.0_1694652544895.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_veeps","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_veeps", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_veeps| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/veeps/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_mlm_scirepeval_fos_chemistry_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_mlm_scirepeval_fos_chemistry_en.md new file mode 100644 index 00000000000000..f7b0cea77a16f7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_mlm_scirepeval_fos_chemistry_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_mlm_scirepeval_fos_chemistry BertEmbeddings from jonas-luehrs +author: John Snow Labs +name: bert_base_uncased_mlm_scirepeval_fos_chemistry +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_mlm_scirepeval_fos_chemistry` is a English model originally trained by jonas-luehrs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mlm_scirepeval_fos_chemistry_en_5.1.1_3.0_1694658556245.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mlm_scirepeval_fos_chemistry_en_5.1.1_3.0_1694658556245.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_mlm_scirepeval_fos_chemistry","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_mlm_scirepeval_fos_chemistry", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_mlm_scirepeval_fos_chemistry| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/jonas-luehrs/bert-base-uncased-MLM-scirepeval_fos_chemistry \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_mlp_scirepeval_chemistry_large_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_mlp_scirepeval_chemistry_large_en.md new file mode 100644 index 00000000000000..aa64f8586a81d4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_mlp_scirepeval_chemistry_large_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_mlp_scirepeval_chemistry_large BertEmbeddings from jonas-luehrs +author: John Snow Labs +name: bert_base_uncased_mlp_scirepeval_chemistry_large +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_mlp_scirepeval_chemistry_large` is a English model originally trained by jonas-luehrs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mlp_scirepeval_chemistry_large_en_5.1.1_3.0_1694663054967.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mlp_scirepeval_chemistry_large_en_5.1.1_3.0_1694663054967.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_mlp_scirepeval_chemistry_large","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_mlp_scirepeval_chemistry_large", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_mlp_scirepeval_chemistry_large| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/jonas-luehrs/bert-base-uncased-MLP-scirepeval-chemistry-LARGE \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_narsil_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_narsil_en.md new file mode 100644 index 00000000000000..0112a7f88adebe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_narsil_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_narsil BertEmbeddings from Narsil +author: John Snow Labs +name: bert_base_uncased_narsil +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_narsil` is a English model originally trained by Narsil. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_narsil_en_5.1.1_3.0_1694650332859.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_narsil_en_5.1.1_3.0_1694650332859.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_narsil","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_narsil", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_narsil| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Narsil/bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_reviews_128_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_reviews_128_en.md new file mode 100644 index 00000000000000..40dc7540f16c97 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_reviews_128_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_reviews_128 BertEmbeddings from abhilashawasthi +author: John Snow Labs +name: bert_base_uncased_reviews_128 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_reviews_128` is a English model originally trained by abhilashawasthi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_reviews_128_en_5.1.1_3.0_1694659261358.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_reviews_128_en_5.1.1_3.0_1694659261358.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_reviews_128","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_reviews_128", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_reviews_128| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/abhilashawasthi/bert-base-uncased-reviews-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_wikitext_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_wikitext_en.md new file mode 100644 index 00000000000000..cb7988784efdd9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_wikitext_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_wikitext BertEmbeddings from nicholasKluge +author: John Snow Labs +name: bert_base_wikitext +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_wikitext` is a English model originally trained by nicholasKluge. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_wikitext_en_5.1.1_3.0_1694661280094.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_wikitext_en_5.1.1_3.0_1694661280094.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_wikitext","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_wikitext", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_wikitext| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.0 MB| + +## References + +https://huggingface.co/nicholasKluge/bert-base-wikitext \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_based_ner_models_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_based_ner_models_en.md new file mode 100644 index 00000000000000..281aeae64de7c3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_based_ner_models_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_based_ner_models BertEmbeddings from pragnakalp +author: John Snow Labs +name: bert_based_ner_models +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_based_ner_models` is a English model originally trained by pragnakalp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_based_ner_models_en_5.1.1_3.0_1694662008683.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_based_ner_models_en_5.1.1_3.0_1694662008683.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_based_ner_models","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_based_ner_models", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_based_ner_models| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/pragnakalp/bert_based_ner_models \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_10_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_10_en.md new file mode 100644 index 00000000000000..cf5335e2fe1041 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_10_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_10 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_10 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_10` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_10_en_5.1.1_3.0_1694657886347.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_10_en_5.1.1_3.0_1694657886347.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_10","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_10", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_10| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_11_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_11_en.md new file mode 100644 index 00000000000000..cabfb54e78437c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_11_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_11 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_11 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_11` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_11_en_5.1.1_3.0_1694658960750.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_11_en_5.1.1_3.0_1694658960750.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_11","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_11", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_11| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-11 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_12_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_12_en.md new file mode 100644 index 00000000000000..89d91289fa6aae --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_12_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_12 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_12 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_12` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_12_en_5.1.1_3.0_1694660226986.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_12_en_5.1.1_3.0_1694660226986.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_12","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_12", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_12| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-12 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_13_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_13_en.md new file mode 100644 index 00000000000000..1f323513727c72 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_13_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_13 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_13 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_13` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_13_en_5.1.1_3.0_1694660823117.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_13_en_5.1.1_3.0_1694660823117.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_13","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_13", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_13| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-13 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_20_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_20_en.md new file mode 100644 index 00000000000000..31f98a8bf31143 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_20_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_20 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_20 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_20` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_20_en_5.1.1_3.0_1694661462547.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_20_en_5.1.1_3.0_1694661462547.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_20","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_20", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_20| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_5_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_5_en.md new file mode 100644 index 00000000000000..8b0420e5ba0fee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_5 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_5 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_5` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_5_en_5.1.1_3.0_1694654880451.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_5_en_5.1.1_3.0_1694654880451.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_5| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_6_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_6_en.md new file mode 100644 index 00000000000000..5a4eaa348304a2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_6_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_6 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_6 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_6` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_6_en_5.1.1_3.0_1694655730374.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_6_en_5.1.1_3.0_1694655730374.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_6","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_6", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_6| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_7_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_7_en.md new file mode 100644 index 00000000000000..ca225039f9ad3a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_7_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_7 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_7 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_7` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_7_en_5.1.1_3.0_1694656705176.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_7_en_5.1.1_3.0_1694656705176.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_7","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_7", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_norwegian_label_40_2nd_test_lr10_8_7| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_large_nordic_pile_1m_steps_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_large_nordic_pile_1m_steps_en.md new file mode 100644 index 00000000000000..7244215eb4cd7e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_large_nordic_pile_1m_steps_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_nordic_pile_1m_steps BertEmbeddings from timpal0l +author: John Snow Labs +name: bert_large_nordic_pile_1m_steps +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_nordic_pile_1m_steps` is a English model originally trained by timpal0l. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_nordic_pile_1m_steps_en_5.1.1_3.0_1694666363768.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_nordic_pile_1m_steps_en_5.1.1_3.0_1694666363768.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_nordic_pile_1m_steps","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_nordic_pile_1m_steps", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_nordic_pile_1m_steps| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.4 GB| + +## References + +https://huggingface.co/timpal0l/bert-large-nordic-pile-1M-steps \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_large_nordic_pile_1m_steps_sv.md b/docs/_posts/ahmedlone127/2023-09-14-bert_large_nordic_pile_1m_steps_sv.md new file mode 100644 index 00000000000000..d3b6c68bf52137 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_large_nordic_pile_1m_steps_sv.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Swedish bert_large_nordic_pile_1m_steps BertEmbeddings from AI-Sweden-Models +author: John Snow Labs +name: bert_large_nordic_pile_1m_steps +date: 2023-09-14 +tags: [bert, sv, open_source, fill_mask, onnx] +task: Embeddings +language: sv +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_nordic_pile_1m_steps` is a Swedish model originally trained by AI-Sweden-Models. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_nordic_pile_1m_steps_sv_5.1.1_3.0_1694666623839.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_nordic_pile_1m_steps_sv_5.1.1_3.0_1694666623839.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_nordic_pile_1m_steps","sv") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_nordic_pile_1m_steps", "sv") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_nordic_pile_1m_steps| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|sv| +|Size:|1.4 GB| + +## References + +https://huggingface.co/AI-Sweden-Models/bert-large-nordic-pile-1M-steps \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_large_stackoverflow_comments_1m_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_large_stackoverflow_comments_1m_en.md new file mode 100644 index 00000000000000..97a55a6c69aebc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_large_stackoverflow_comments_1m_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_stackoverflow_comments_1m BertEmbeddings from giganticode +author: John Snow Labs +name: bert_large_stackoverflow_comments_1m +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_stackoverflow_comments_1m` is a English model originally trained by giganticode. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_stackoverflow_comments_1m_en_5.1.1_3.0_1694650551787.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_stackoverflow_comments_1m_en_5.1.1_3.0_1694650551787.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_stackoverflow_comments_1m","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_stackoverflow_comments_1m", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_stackoverflow_comments_1m| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/giganticode/bert-large-StackOverflow-comments_1M \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_multilang_finetune_bangla_summarization_dataset_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_multilang_finetune_bangla_summarization_dataset_en.md new file mode 100644 index 00000000000000..bc6fbe813d6aff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_multilang_finetune_bangla_summarization_dataset_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_multilang_finetune_bangla_summarization_dataset BertEmbeddings from arbitropy +author: John Snow Labs +name: bert_multilang_finetune_bangla_summarization_dataset +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_multilang_finetune_bangla_summarization_dataset` is a English model originally trained by arbitropy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_multilang_finetune_bangla_summarization_dataset_en_5.1.1_3.0_1694667323120.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_multilang_finetune_bangla_summarization_dataset_en_5.1.1_3.0_1694667323120.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_multilang_finetune_bangla_summarization_dataset","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_multilang_finetune_bangla_summarization_dataset", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_multilang_finetune_bangla_summarization_dataset| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/arbitropy/bert-multilang-finetune-bangla-summarization-dataset \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_nlp_project_google_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_nlp_project_google_en.md new file mode 100644 index 00000000000000..5ef87ba213725d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_nlp_project_google_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_nlp_project_google BertEmbeddings from jestemleon +author: John Snow Labs +name: bert_nlp_project_google +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_nlp_project_google` is a English model originally trained by jestemleon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_nlp_project_google_en_5.1.1_3.0_1694661280346.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_nlp_project_google_en_5.1.1_3.0_1694661280346.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_nlp_project_google","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_nlp_project_google", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_nlp_project_google| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/jestemleon/bert-nlp-project-google \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_nlp_project_imdb_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_nlp_project_imdb_en.md new file mode 100644 index 00000000000000..3cd3ae7bdcc554 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_nlp_project_imdb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_nlp_project_imdb BertEmbeddings from jestemleon +author: John Snow Labs +name: bert_nlp_project_imdb +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_nlp_project_imdb` is a English model originally trained by jestemleon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_nlp_project_imdb_en_5.1.1_3.0_1694659623230.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_nlp_project_imdb_en_5.1.1_3.0_1694659623230.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_nlp_project_imdb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_nlp_project_imdb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_nlp_project_imdb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/jestemleon/bert-nlp-project-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_system_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_system_en.md new file mode 100644 index 00000000000000..9a861bdd9c3c82 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_system_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_system BertEmbeddings from EgilKarlsen +author: John Snow Labs +name: bert_system +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_system` is a English model originally trained by EgilKarlsen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_system_en_5.1.1_3.0_1694654994124.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_system_en_5.1.1_3.0_1694654994124.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_system","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_system", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_system| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/EgilKarlsen/BERT-System \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_ucb_v1_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_ucb_v1_en.md new file mode 100644 index 00000000000000..d75034ad344dfa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_ucb_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_ucb_v1 BertEmbeddings from Diegomejia +author: John Snow Labs +name: bert_ucb_v1 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ucb_v1` is a English model originally trained by Diegomejia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ucb_v1_en_5.1.1_3.0_1694664280264.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ucb_v1_en_5.1.1_3.0_1694664280264.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_ucb_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_ucb_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ucb_v1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/Diegomejia/bert-ucb-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bertimbau_pt.md b/docs/_posts/ahmedlone127/2023-09-14-bertimbau_pt.md new file mode 100644 index 00000000000000..6a2709eb243966 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bertimbau_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese bertimbau BertEmbeddings from tubyneto +author: John Snow Labs +name: bertimbau +date: 2023-09-14 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertimbau` is a Portuguese model originally trained by tubyneto. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertimbau_pt_5.1.1_3.0_1694664877599.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertimbau_pt_5.1.1_3.0_1694664877599.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertimbau","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertimbau", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertimbau| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|405.9 MB| + +## References + +https://huggingface.co/tubyneto/bertimbau \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bertugues_base_portuguese_cased_pt.md b/docs/_posts/ahmedlone127/2023-09-14-bertugues_base_portuguese_cased_pt.md new file mode 100644 index 00000000000000..28838eb95b7cf8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bertugues_base_portuguese_cased_pt.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Portuguese bertugues_base_portuguese_cased BertEmbeddings from ricardoz +author: John Snow Labs +name: bertugues_base_portuguese_cased +date: 2023-09-14 +tags: [bert, pt, open_source, fill_mask, onnx] +task: Embeddings +language: pt +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertugues_base_portuguese_cased` is a Portuguese model originally trained by ricardoz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertugues_base_portuguese_cased_pt_5.1.1_3.0_1694650332853.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertugues_base_portuguese_cased_pt_5.1.1_3.0_1694650332853.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bertugues_base_portuguese_cased","pt") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bertugues_base_portuguese_cased", "pt") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertugues_base_portuguese_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|pt| +|Size:|408.1 MB| + +## References + +https://huggingface.co/ricardoz/BERTugues-base-portuguese-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bibert_v0.1_en.md b/docs/_posts/ahmedlone127/2023-09-14-bibert_v0.1_en.md new file mode 100644 index 00000000000000..c5d5aa8e4af490 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bibert_v0.1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bibert_v0.1 BertEmbeddings from yugen-ok +author: John Snow Labs +name: bibert_v0.1 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bibert_v0.1` is a English model originally trained by yugen-ok. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bibert_v0.1_en_5.1.1_3.0_1694666746036.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bibert_v0.1_en_5.1.1_3.0_1694666746036.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bibert_v0.1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bibert_v0.1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bibert_v0.1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/yugen-ok/bibert-v0.1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-biobit_it.md b/docs/_posts/ahmedlone127/2023-09-14-biobit_it.md new file mode 100644 index 00000000000000..c13cd6256893c1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-biobit_it.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Italian biobit BertEmbeddings from IVN-RIN +author: John Snow Labs +name: biobit +date: 2023-09-14 +tags: [bert, it, open_source, fill_mask, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biobit` is a Italian model originally trained by IVN-RIN. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biobit_it_5.1.1_3.0_1694659298869.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biobit_it_5.1.1_3.0_1694659298869.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biobit","it") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biobit", "it") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biobit| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|it| +|Size:|409.2 MB| + +## References + +https://huggingface.co/IVN-RIN/bioBIT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-biomedvlp_cxr_bert_general_en.md b/docs/_posts/ahmedlone127/2023-09-14-biomedvlp_cxr_bert_general_en.md new file mode 100644 index 00000000000000..a7f4d323893c69 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-biomedvlp_cxr_bert_general_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English biomedvlp_cxr_bert_general BertEmbeddings from microsoft +author: John Snow Labs +name: biomedvlp_cxr_bert_general +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biomedvlp_cxr_bert_general` is a English model originally trained by microsoft. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biomedvlp_cxr_bert_general_en_5.1.1_3.0_1694659655941.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biomedvlp_cxr_bert_general_en_5.1.1_3.0_1694659655941.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("biomedvlp_cxr_bert_general","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("biomedvlp_cxr_bert_general", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biomedvlp_cxr_bert_general| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|260.2 MB| + +## References + +https://huggingface.co/microsoft/BiomedVLP-CXR-BERT-general \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bnlp_tokenizer_paraphrase_mlm_bert_900001_en.md b/docs/_posts/ahmedlone127/2023-09-14-bnlp_tokenizer_paraphrase_mlm_bert_900001_en.md new file mode 100644 index 00000000000000..44cf910d36bcc3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bnlp_tokenizer_paraphrase_mlm_bert_900001_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bnlp_tokenizer_paraphrase_mlm_bert_900001 BertEmbeddings from arbitropy +author: John Snow Labs +name: bnlp_tokenizer_paraphrase_mlm_bert_900001 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bnlp_tokenizer_paraphrase_mlm_bert_900001` is a English model originally trained by arbitropy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bnlp_tokenizer_paraphrase_mlm_bert_900001_en_5.1.1_3.0_1694662292369.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bnlp_tokenizer_paraphrase_mlm_bert_900001_en_5.1.1_3.0_1694662292369.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bnlp_tokenizer_paraphrase_mlm_bert_900001","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bnlp_tokenizer_paraphrase_mlm_bert_900001", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bnlp_tokenizer_paraphrase_mlm_bert_900001| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.4 MB| + +## References + +https://huggingface.co/arbitropy/bnlp-tokenizer-paraphrase-mlm-bert-900001 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-clinicaltrialbiobert_en.md b/docs/_posts/ahmedlone127/2023-09-14-clinicaltrialbiobert_en.md new file mode 100644 index 00000000000000..0ec0ca24b6d969 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-clinicaltrialbiobert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English clinicaltrialbiobert BertEmbeddings from domenicrosati +author: John Snow Labs +name: clinicaltrialbiobert +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`clinicaltrialbiobert` is a English model originally trained by domenicrosati. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clinicaltrialbiobert_en_5.1.1_3.0_1694660034709.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clinicaltrialbiobert_en_5.1.1_3.0_1694660034709.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("clinicaltrialbiobert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("clinicaltrialbiobert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinicaltrialbiobert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.8 MB| + +## References + +https://huggingface.co/domenicrosati/ClinicalTrialBioBert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-closure_system_door_inne_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-14-closure_system_door_inne_bert_base_uncased_en.md new file mode 100644 index 00000000000000..dd9a2cd0616120 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-closure_system_door_inne_bert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English closure_system_door_inne_bert_base_uncased BertEmbeddings from Davincilee +author: John Snow Labs +name: closure_system_door_inne_bert_base_uncased +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`closure_system_door_inne_bert_base_uncased` is a English model originally trained by Davincilee. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/closure_system_door_inne_bert_base_uncased_en_5.1.1_3.0_1694654562553.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/closure_system_door_inne_bert_base_uncased_en_5.1.1_3.0_1694654562553.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("closure_system_door_inne_bert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("closure_system_door_inne_bert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|closure_system_door_inne_bert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Davincilee/closure_system_door_inne-bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-coronasentana_en.md b/docs/_posts/ahmedlone127/2023-09-14-coronasentana_en.md new file mode 100644 index 00000000000000..870dbc60f9b555 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-coronasentana_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English coronasentana BertEmbeddings from Peed911 +author: John Snow Labs +name: coronasentana +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`coronasentana` is a English model originally trained by Peed911. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/coronasentana_en_5.1.1_3.0_1694668810910.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/coronasentana_en_5.1.1_3.0_1694668810910.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("coronasentana","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("coronasentana", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|coronasentana| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.3 MB| + +## References + +https://huggingface.co/Peed911/CoronaSentAna \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-dictabert_he.md b/docs/_posts/ahmedlone127/2023-09-14-dictabert_he.md new file mode 100644 index 00000000000000..832d40238defcf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-dictabert_he.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hebrew dictabert BertEmbeddings from dicta-il +author: John Snow Labs +name: dictabert +date: 2023-09-14 +tags: [bert, he, open_source, fill_mask, onnx] +task: Embeddings +language: he +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dictabert` is a Hebrew model originally trained by dicta-il. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dictabert_he_5.1.1_3.0_1694668081428.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dictabert_he_5.1.1_3.0_1694668081428.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dictabert","he") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dictabert", "he") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dictabert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|he| +|Size:|440.2 MB| + +## References + +https://huggingface.co/dicta-il/dictabert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-dictabert_morph_he.md b/docs/_posts/ahmedlone127/2023-09-14-dictabert_morph_he.md new file mode 100644 index 00000000000000..c9fda4c69f2b7c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-dictabert_morph_he.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hebrew dictabert_morph BertEmbeddings from dicta-il +author: John Snow Labs +name: dictabert_morph +date: 2023-09-14 +tags: [bert, he, open_source, fill_mask, onnx] +task: Embeddings +language: he +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dictabert_morph` is a Hebrew model originally trained by dicta-il. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dictabert_morph_he_5.1.1_3.0_1694668253883.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dictabert_morph_he_5.1.1_3.0_1694668253883.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dictabert_morph","he") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dictabert_morph", "he") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dictabert_morph| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|he| +|Size:|639.8 MB| + +## References + +https://huggingface.co/dicta-il/dictabert-morph \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-dictabert_seg_he.md b/docs/_posts/ahmedlone127/2023-09-14-dictabert_seg_he.md new file mode 100644 index 00000000000000..1d928a59e56a6f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-dictabert_seg_he.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Hebrew dictabert_seg BertEmbeddings from dicta-il +author: John Snow Labs +name: dictabert_seg +date: 2023-09-14 +tags: [bert, he, open_source, fill_mask, onnx] +task: Embeddings +language: he +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dictabert_seg` is a Hebrew model originally trained by dicta-il. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dictabert_seg_he_5.1.1_3.0_1694667839991.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dictabert_seg_he_5.1.1_3.0_1694667839991.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dictabert_seg","he") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dictabert_seg", "he") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dictabert_seg| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|he| +|Size:|657.5 MB| + +## References + +https://huggingface.co/dicta-il/dictabert-seg \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-domain_adapted_arbert_goudma_bert_en.md b/docs/_posts/ahmedlone127/2023-09-14-domain_adapted_arbert_goudma_bert_en.md new file mode 100644 index 00000000000000..e5a0661b51bbf4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-domain_adapted_arbert_goudma_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English domain_adapted_arbert_goudma_bert BertEmbeddings from YassineToughrai +author: John Snow Labs +name: domain_adapted_arbert_goudma_bert +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`domain_adapted_arbert_goudma_bert` is a English model originally trained by YassineToughrai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/domain_adapted_arbert_goudma_bert_en_5.1.1_3.0_1694654039836.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/domain_adapted_arbert_goudma_bert_en_5.1.1_3.0_1694654039836.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("domain_adapted_arbert_goudma_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("domain_adapted_arbert_goudma_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|domain_adapted_arbert_goudma_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|605.0 MB| + +## References + +https://huggingface.co/YassineToughrai/Domain_adapted_ARBERT_GOUDMA_BERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-door_inner_with_sa_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-14-door_inner_with_sa_bert_base_uncased_en.md new file mode 100644 index 00000000000000..e2e74acf1e1674 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-door_inner_with_sa_bert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English door_inner_with_sa_bert_base_uncased BertEmbeddings from Davincilee +author: John Snow Labs +name: door_inner_with_sa_bert_base_uncased +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`door_inner_with_sa_bert_base_uncased` is a English model originally trained by Davincilee. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/door_inner_with_sa_bert_base_uncased_en_5.1.1_3.0_1694656043395.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/door_inner_with_sa_bert_base_uncased_en_5.1.1_3.0_1694656043395.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("door_inner_with_sa_bert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("door_inner_with_sa_bert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|door_inner_with_sa_bert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/Davincilee/door_inner_with_SA-bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-dummy_model_linbo_en.md b/docs/_posts/ahmedlone127/2023-09-14-dummy_model_linbo_en.md new file mode 100644 index 00000000000000..7f5458cb5f124f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-dummy_model_linbo_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dummy_model_linbo BertEmbeddings from Linbo +author: John Snow Labs +name: dummy_model_linbo +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dummy_model_linbo` is a English model originally trained by Linbo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dummy_model_linbo_en_5.1.1_3.0_1694649822787.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dummy_model_linbo_en_5.1.1_3.0_1694649822787.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dummy_model_linbo","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dummy_model_linbo", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dummy_model_linbo| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/Linbo/dummy-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-dzarabert_ar.md b/docs/_posts/ahmedlone127/2023-09-14-dzarabert_ar.md new file mode 100644 index 00000000000000..4b1abced8e95db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-dzarabert_ar.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Arabic dzarabert BertEmbeddings from Sifal +author: John Snow Labs +name: dzarabert +date: 2023-09-14 +tags: [bert, ar, open_source, fill_mask, onnx] +task: Embeddings +language: ar +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dzarabert` is a Arabic model originally trained by Sifal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dzarabert_ar_5.1.1_3.0_1694657627709.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dzarabert_ar_5.1.1_3.0_1694657627709.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dzarabert","ar") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dzarabert", "ar") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dzarabert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ar| +|Size:|418.8 MB| + +## References + +https://huggingface.co/Sifal/dzarabert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-frpile_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-14-frpile_mlm_en.md new file mode 100644 index 00000000000000..6f2bcb829de42a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-frpile_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English frpile_mlm BertEmbeddings from DragosGorduza +author: John Snow Labs +name: frpile_mlm +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`frpile_mlm` is a English model originally trained by DragosGorduza. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/frpile_mlm_en_5.1.1_3.0_1694656401826.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/frpile_mlm_en_5.1.1_3.0_1694656401826.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("frpile_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("frpile_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|frpile_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/DragosGorduza/FRPile_MLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-gbert_base_finetuned_twitter_janst_en.md b/docs/_posts/ahmedlone127/2023-09-14-gbert_base_finetuned_twitter_janst_en.md new file mode 100644 index 00000000000000..a7d41d1877bdf1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-gbert_base_finetuned_twitter_janst_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English gbert_base_finetuned_twitter_janst BertEmbeddings from JanSt +author: John Snow Labs +name: gbert_base_finetuned_twitter_janst +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`gbert_base_finetuned_twitter_janst` is a English model originally trained by JanSt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gbert_base_finetuned_twitter_janst_en_5.1.1_3.0_1694666124788.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gbert_base_finetuned_twitter_janst_en_5.1.1_3.0_1694666124788.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("gbert_base_finetuned_twitter_janst","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("gbert_base_finetuned_twitter_janst", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gbert_base_finetuned_twitter_janst| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.7 MB| + +## References + +https://huggingface.co/JanSt/gbert-base-finetuned-twitter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-gbert_large_autopart_en.md b/docs/_posts/ahmedlone127/2023-09-14-gbert_large_autopart_en.md new file mode 100644 index 00000000000000..fe87dd40038a5d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-gbert_large_autopart_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English gbert_large_autopart BertEmbeddings from luciore95 +author: John Snow Labs +name: gbert_large_autopart +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`gbert_large_autopart` is a English model originally trained by luciore95. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gbert_large_autopart_en_5.1.1_3.0_1694669047387.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gbert_large_autopart_en_5.1.1_3.0_1694669047387.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("gbert_large_autopart","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("gbert_large_autopart", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gbert_large_autopart| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/luciore95/gbert-large-autopart \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-gbert_large_finetuned_cust18_en.md b/docs/_posts/ahmedlone127/2023-09-14-gbert_large_finetuned_cust18_en.md new file mode 100644 index 00000000000000..ce2f6db90b1414 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-gbert_large_finetuned_cust18_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English gbert_large_finetuned_cust18 BertEmbeddings from shafin +author: John Snow Labs +name: gbert_large_finetuned_cust18 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`gbert_large_finetuned_cust18` is a English model originally trained by shafin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gbert_large_finetuned_cust18_en_5.1.1_3.0_1694664219714.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gbert_large_finetuned_cust18_en_5.1.1_3.0_1694664219714.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("gbert_large_finetuned_cust18","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("gbert_large_finetuned_cust18", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gbert_large_finetuned_cust18| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/shafin/gbert-large-finetuned-cust18 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-gbert_large_finetuned_cust_en.md b/docs/_posts/ahmedlone127/2023-09-14-gbert_large_finetuned_cust_en.md new file mode 100644 index 00000000000000..32310949e01b6b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-gbert_large_finetuned_cust_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English gbert_large_finetuned_cust BertEmbeddings from shafin +author: John Snow Labs +name: gbert_large_finetuned_cust +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`gbert_large_finetuned_cust` is a English model originally trained by shafin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gbert_large_finetuned_cust_en_5.1.1_3.0_1694662333720.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gbert_large_finetuned_cust_en_5.1.1_3.0_1694662333720.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("gbert_large_finetuned_cust","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("gbert_large_finetuned_cust", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gbert_large_finetuned_cust| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/shafin/gbert-large-finetuned-cust \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-gepabert_de.md b/docs/_posts/ahmedlone127/2023-09-14-gepabert_de.md new file mode 100644 index 00000000000000..725f463a85f721 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-gepabert_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German gepabert BertEmbeddings from aehrm +author: John Snow Labs +name: gepabert +date: 2023-09-14 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`gepabert` is a German model originally trained by aehrm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gepabert_de_5.1.1_3.0_1694654562569.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gepabert_de_5.1.1_3.0_1694654562569.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("gepabert","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("gepabert", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gepabert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|1.3 GB| + +## References + +https://huggingface.co/aehrm/gepabert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-gujarati_bert_scratch_gu.md b/docs/_posts/ahmedlone127/2023-09-14-gujarati_bert_scratch_gu.md new file mode 100644 index 00000000000000..8a8d4b1e24d396 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-gujarati_bert_scratch_gu.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Gujarati gujarati_bert_scratch BertEmbeddings from l3cube-pune +author: John Snow Labs +name: gujarati_bert_scratch +date: 2023-09-14 +tags: [bert, gu, open_source, fill_mask, onnx] +task: Embeddings +language: gu +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`gujarati_bert_scratch` is a Gujarati model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gujarati_bert_scratch_gu_5.1.1_3.0_1694652276670.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gujarati_bert_scratch_gu_5.1.1_3.0_1694652276670.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("gujarati_bert_scratch","gu") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("gujarati_bert_scratch", "gu") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gujarati_bert_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|gu| +|Size:|470.4 MB| + +## References + +https://huggingface.co/l3cube-pune/gujarati-bert-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-incorporation_of_company_related_factual_knowledge_into_pre_trained_language_models_en.md b/docs/_posts/ahmedlone127/2023-09-14-incorporation_of_company_related_factual_knowledge_into_pre_trained_language_models_en.md new file mode 100644 index 00000000000000..bee6ac16a86e3a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-incorporation_of_company_related_factual_knowledge_into_pre_trained_language_models_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English incorporation_of_company_related_factual_knowledge_into_pre_trained_language_models BertEmbeddings from sophia-jihye +author: John Snow Labs +name: incorporation_of_company_related_factual_knowledge_into_pre_trained_language_models +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`incorporation_of_company_related_factual_knowledge_into_pre_trained_language_models` is a English model originally trained by sophia-jihye. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/incorporation_of_company_related_factual_knowledge_into_pre_trained_language_models_en_5.1.1_3.0_1694667130967.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/incorporation_of_company_related_factual_knowledge_into_pre_trained_language_models_en_5.1.1_3.0_1694667130967.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("incorporation_of_company_related_factual_knowledge_into_pre_trained_language_models","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("incorporation_of_company_related_factual_knowledge_into_pre_trained_language_models", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|incorporation_of_company_related_factual_knowledge_into_pre_trained_language_models| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.4 MB| + +## References + +https://huggingface.co/sophia-jihye/Incorporation_of_Company-Related_Factual_Knowledge_into_Pre-trained_Language_Models \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-itd_bert_en.md b/docs/_posts/ahmedlone127/2023-09-14-itd_bert_en.md new file mode 100644 index 00000000000000..f5eccdd79bdf46 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-itd_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English itd_bert BertEmbeddings from melll-uff +author: John Snow Labs +name: itd_bert +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`itd_bert` is a English model originally trained by melll-uff. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/itd_bert_en_5.1.1_3.0_1694669236393.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/itd_bert_en_5.1.1_3.0_1694669236393.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("itd_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("itd_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|itd_bert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.3 MB| + +## References + +https://huggingface.co/melll-uff/itd_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-itd_longformer_en.md b/docs/_posts/ahmedlone127/2023-09-14-itd_longformer_en.md new file mode 100644 index 00000000000000..8a2a462ff6c2d8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-itd_longformer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English itd_longformer BertEmbeddings from melll-uff +author: John Snow Labs +name: itd_longformer +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`itd_longformer` is a English model originally trained by melll-uff. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/itd_longformer_en_5.1.1_3.0_1694669346528.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/itd_longformer_en_5.1.1_3.0_1694669346528.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("itd_longformer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("itd_longformer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|itd_longformer| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|415.9 MB| + +## References + +https://huggingface.co/melll-uff/itd_longformer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-jobs_pretraining_model_en.md b/docs/_posts/ahmedlone127/2023-09-14-jobs_pretraining_model_en.md new file mode 100644 index 00000000000000..75d3724bbd497b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-jobs_pretraining_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English jobs_pretraining_model BertEmbeddings from afif00 +author: John Snow Labs +name: jobs_pretraining_model +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`jobs_pretraining_model` is a English model originally trained by afif00. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/jobs_pretraining_model_en_5.1.1_3.0_1694661632301.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/jobs_pretraining_model_en_5.1.1_3.0_1694661632301.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("jobs_pretraining_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("jobs_pretraining_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|jobs_pretraining_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/afif00/jobs-pretraining-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-kannada_bert_scratch_kn.md b/docs/_posts/ahmedlone127/2023-09-14-kannada_bert_scratch_kn.md new file mode 100644 index 00000000000000..489c54b6534d66 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-kannada_bert_scratch_kn.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Kannada kannada_bert_scratch BertEmbeddings from l3cube-pune +author: John Snow Labs +name: kannada_bert_scratch +date: 2023-09-14 +tags: [bert, kn, open_source, fill_mask, onnx] +task: Embeddings +language: kn +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kannada_bert_scratch` is a Kannada model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kannada_bert_scratch_kn_5.1.1_3.0_1694652710651.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kannada_bert_scratch_kn_5.1.1_3.0_1694652710651.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kannada_bert_scratch","kn") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kannada_bert_scratch", "kn") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kannada_bert_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|kn| +|Size:|470.6 MB| + +## References + +https://huggingface.co/l3cube-pune/kannada-bert-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-klue_bert_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-14-klue_bert_mlm_en.md new file mode 100644 index 00000000000000..a4ec260a075c0f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-klue_bert_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English klue_bert_mlm BertEmbeddings from goodjw +author: John Snow Labs +name: klue_bert_mlm +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`klue_bert_mlm` is a English model originally trained by goodjw. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/klue_bert_mlm_en_5.1.1_3.0_1694651022503.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/klue_bert_mlm_en_5.1.1_3.0_1694651022503.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("klue_bert_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("klue_bert_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|klue_bert_mlm| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|412.4 MB| + +## References + +https://huggingface.co/goodjw/klue-bert-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-kw_pubmed_1000_0.000006_en.md b/docs/_posts/ahmedlone127/2023-09-14-kw_pubmed_1000_0.000006_en.md new file mode 100644 index 00000000000000..95500a982ee892 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-kw_pubmed_1000_0.000006_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kw_pubmed_1000_0.000006 BertEmbeddings from enoriega +author: John Snow Labs +name: kw_pubmed_1000_0.000006 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kw_pubmed_1000_0.000006` is a English model originally trained by enoriega. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kw_pubmed_1000_0.000006_en_5.1.1_3.0_1694663885237.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kw_pubmed_1000_0.000006_en_5.1.1_3.0_1694663885237.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kw_pubmed_1000_0.000006","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kw_pubmed_1000_0.000006", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kw_pubmed_1000_0.000006| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.2 MB| + +## References + +https://huggingface.co/enoriega/kw_pubmed_1000_0.000006 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-kw_pubmed_1000_0.00006_en.md b/docs/_posts/ahmedlone127/2023-09-14-kw_pubmed_1000_0.00006_en.md new file mode 100644 index 00000000000000..9467e699fed262 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-kw_pubmed_1000_0.00006_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kw_pubmed_1000_0.00006 BertEmbeddings from enoriega +author: John Snow Labs +name: kw_pubmed_1000_0.00006 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kw_pubmed_1000_0.00006` is a English model originally trained by enoriega. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kw_pubmed_1000_0.00006_en_5.1.1_3.0_1694663539113.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kw_pubmed_1000_0.00006_en_5.1.1_3.0_1694663539113.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kw_pubmed_1000_0.00006","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kw_pubmed_1000_0.00006", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kw_pubmed_1000_0.00006| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.2 MB| + +## References + +https://huggingface.co/enoriega/kw_pubmed_1000_0.00006 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-kw_pubmed_1000_0.0003_en.md b/docs/_posts/ahmedlone127/2023-09-14-kw_pubmed_1000_0.0003_en.md new file mode 100644 index 00000000000000..569429ac101f50 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-kw_pubmed_1000_0.0003_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kw_pubmed_1000_0.0003 BertEmbeddings from enoriega +author: John Snow Labs +name: kw_pubmed_1000_0.0003 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kw_pubmed_1000_0.0003` is a English model originally trained by enoriega. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kw_pubmed_1000_0.0003_en_5.1.1_3.0_1694663260280.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kw_pubmed_1000_0.0003_en_5.1.1_3.0_1694663260280.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("kw_pubmed_1000_0.0003","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("kw_pubmed_1000_0.0003", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kw_pubmed_1000_0.0003| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.2 MB| + +## References + +https://huggingface.co/enoriega/kw_pubmed_1000_0.0003 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-legal_hebert_ft_en.md b/docs/_posts/ahmedlone127/2023-09-14-legal_hebert_ft_en.md new file mode 100644 index 00000000000000..87a87782f0a4e6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-legal_hebert_ft_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English legal_hebert_ft BertEmbeddings from avichr +author: John Snow Labs +name: legal_hebert_ft +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legal_hebert_ft` is a English model originally trained by avichr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legal_hebert_ft_en_5.1.1_3.0_1694656937535.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legal_hebert_ft_en_5.1.1_3.0_1694656937535.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legal_hebert_ft","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legal_hebert_ft", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legal_hebert_ft| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.9 MB| + +## References + +https://huggingface.co/avichr/Legal-heBERT_ft \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-legalbert_large_1.7m_1_en.md b/docs/_posts/ahmedlone127/2023-09-14-legalbert_large_1.7m_1_en.md new file mode 100644 index 00000000000000..2129067405d6a5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-legalbert_large_1.7m_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English legalbert_large_1.7m_1 BertEmbeddings from pile-of-law +author: John Snow Labs +name: legalbert_large_1.7m_1 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legalbert_large_1.7m_1` is a English model originally trained by pile-of-law. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legalbert_large_1.7m_1_en_5.1.1_3.0_1694651512853.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legalbert_large_1.7m_1_en_5.1.1_3.0_1694651512853.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legalbert_large_1.7m_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legalbert_large_1.7m_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legalbert_large_1.7m_1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|643.5 MB| + +## References + +https://huggingface.co/pile-of-law/legalbert-large-1.7M-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-legalbert_large_1.7m_2_en.md b/docs/_posts/ahmedlone127/2023-09-14-legalbert_large_1.7m_2_en.md new file mode 100644 index 00000000000000..512842ee82e8d7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-legalbert_large_1.7m_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English legalbert_large_1.7m_2 BertEmbeddings from pile-of-law +author: John Snow Labs +name: legalbert_large_1.7m_2 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legalbert_large_1.7m_2` is a English model originally trained by pile-of-law. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legalbert_large_1.7m_2_en_5.1.1_3.0_1694653987323.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legalbert_large_1.7m_2_en_5.1.1_3.0_1694653987323.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("legalbert_large_1.7m_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("legalbert_large_1.7m_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legalbert_large_1.7m_2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|645.3 MB| + +## References + +https://huggingface.co/pile-of-law/legalbert-large-1.7M-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-logion_50k_wordpiece_en.md b/docs/_posts/ahmedlone127/2023-09-14-logion_50k_wordpiece_en.md new file mode 100644 index 00000000000000..18637a4b33e784 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-logion_50k_wordpiece_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English logion_50k_wordpiece BertEmbeddings from cabrooks +author: John Snow Labs +name: logion_50k_wordpiece +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`logion_50k_wordpiece` is a English model originally trained by cabrooks. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/logion_50k_wordpiece_en_5.1.1_3.0_1694661683131.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/logion_50k_wordpiece_en_5.1.1_3.0_1694661683131.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("logion_50k_wordpiece","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("logion_50k_wordpiece", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|logion_50k_wordpiece| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.1 MB| + +## References + +https://huggingface.co/cabrooks/LOGION-50k_wordpiece \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-louribert_en.md b/docs/_posts/ahmedlone127/2023-09-14-louribert_en.md new file mode 100644 index 00000000000000..74c2850a318111 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-louribert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English louribert BertEmbeddings from saeid7776 +author: John Snow Labs +name: louribert +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`louribert` is a English model originally trained by saeid7776. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/louribert_en_5.1.1_3.0_1694664381526.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/louribert_en_5.1.1_3.0_1694664381526.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("louribert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("louribert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|louribert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|668.1 MB| + +## References + +https://huggingface.co/saeid7776/LouriBert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-louribert_more_tokens_saeid7776_en.md b/docs/_posts/ahmedlone127/2023-09-14-louribert_more_tokens_saeid7776_en.md new file mode 100644 index 00000000000000..a0dbf24ce80480 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-louribert_more_tokens_saeid7776_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English louribert_more_tokens_saeid7776 BertEmbeddings from saeid7776 +author: John Snow Labs +name: louribert_more_tokens_saeid7776 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`louribert_more_tokens_saeid7776` is a English model originally trained by saeid7776. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/louribert_more_tokens_saeid7776_en_5.1.1_3.0_1694665209272.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/louribert_more_tokens_saeid7776_en_5.1.1_3.0_1694665209272.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("louribert_more_tokens_saeid7776","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("louribert_more_tokens_saeid7776", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|louribert_more_tokens_saeid7776| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|670.8 MB| + +## References + +https://huggingface.co/saeid7776/LouriBert_more_tokens \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-lumbarradiologyreports_en.md b/docs/_posts/ahmedlone127/2023-09-14-lumbarradiologyreports_en.md new file mode 100644 index 00000000000000..bfd7194db7ac69 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-lumbarradiologyreports_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English lumbarradiologyreports BertEmbeddings from YK96 +author: John Snow Labs +name: lumbarradiologyreports +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lumbarradiologyreports` is a English model originally trained by YK96. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lumbarradiologyreports_en_5.1.1_3.0_1694669457817.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lumbarradiologyreports_en_5.1.1_3.0_1694669457817.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("lumbarradiologyreports","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("lumbarradiologyreports", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lumbarradiologyreports| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/YK96/LumbarRadiologyReports \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-malayalam_bert_scratch_ml.md b/docs/_posts/ahmedlone127/2023-09-14-malayalam_bert_scratch_ml.md new file mode 100644 index 00000000000000..3a39ec6415ec0c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-malayalam_bert_scratch_ml.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Malayalam malayalam_bert_scratch BertEmbeddings from l3cube-pune +author: John Snow Labs +name: malayalam_bert_scratch +date: 2023-09-14 +tags: [bert, ml, open_source, fill_mask, onnx] +task: Embeddings +language: ml +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`malayalam_bert_scratch` is a Malayalam model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/malayalam_bert_scratch_ml_5.1.1_3.0_1694651911081.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/malayalam_bert_scratch_ml_5.1.1_3.0_1694651911081.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("malayalam_bert_scratch","ml") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("malayalam_bert_scratch", "ml") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|malayalam_bert_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ml| +|Size:|470.7 MB| + +## References + +https://huggingface.co/l3cube-pune/malayalam-bert-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-mbert_squad_en.md b/docs/_posts/ahmedlone127/2023-09-14-mbert_squad_en.md new file mode 100644 index 00000000000000..13c840d9731b59 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-mbert_squad_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mbert_squad BertEmbeddings from oceanpty +author: John Snow Labs +name: mbert_squad +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbert_squad` is a English model originally trained by oceanpty. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbert_squad_en_5.1.1_3.0_1694652176700.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbert_squad_en_5.1.1_3.0_1694652176700.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mbert_squad","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mbert_squad", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbert_squad| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|665.0 MB| + +## References + +https://huggingface.co/oceanpty/mbert-squad \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-medbert_512_norwegian_duplicates_de.md b/docs/_posts/ahmedlone127/2023-09-14-medbert_512_norwegian_duplicates_de.md new file mode 100644 index 00000000000000..4af11bf2aa10bb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-medbert_512_norwegian_duplicates_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German medbert_512_norwegian_duplicates BertEmbeddings from GerMedBERT +author: John Snow Labs +name: medbert_512_norwegian_duplicates +date: 2023-09-14 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`medbert_512_norwegian_duplicates` is a German model originally trained by GerMedBERT. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/medbert_512_norwegian_duplicates_de_5.1.1_3.0_1694654562610.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/medbert_512_norwegian_duplicates_de_5.1.1_3.0_1694654562610.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("medbert_512_norwegian_duplicates","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("medbert_512_norwegian_duplicates", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|medbert_512_norwegian_duplicates| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|406.8 MB| + +## References + +https://huggingface.co/GerMedBERT/medbert-512-no-duplicates \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-medbit_it.md b/docs/_posts/ahmedlone127/2023-09-14-medbit_it.md new file mode 100644 index 00000000000000..7742c28c348186 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-medbit_it.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Italian medbit BertEmbeddings from IVN-RIN +author: John Snow Labs +name: medbit +date: 2023-09-14 +tags: [bert, it, open_source, fill_mask, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`medbit` is a Italian model originally trained by IVN-RIN. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/medbit_it_5.1.1_3.0_1694658556151.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/medbit_it_5.1.1_3.0_1694658556151.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("medbit","it") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("medbit", "it") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|medbit| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|it| +|Size:|409.2 MB| + +## References + +https://huggingface.co/IVN-RIN/medBIT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-medbit_r3_plus_it.md b/docs/_posts/ahmedlone127/2023-09-14-medbit_r3_plus_it.md new file mode 100644 index 00000000000000..21c65a8b1c6da2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-medbit_r3_plus_it.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Italian medbit_r3_plus BertEmbeddings from IVN-RIN +author: John Snow Labs +name: medbit_r3_plus +date: 2023-09-14 +tags: [bert, it, open_source, fill_mask, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`medbit_r3_plus` is a Italian model originally trained by IVN-RIN. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/medbit_r3_plus_it_5.1.1_3.0_1694655730518.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/medbit_r3_plus_it_5.1.1_3.0_1694655730518.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("medbit_r3_plus","it") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("medbit_r3_plus", "it") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|medbit_r3_plus| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|it| +|Size:|409.2 MB| + +## References + +https://huggingface.co/IVN-RIN/medBIT-r3-plus \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-medium_mlm_tweet_en.md b/docs/_posts/ahmedlone127/2023-09-14-medium_mlm_tweet_en.md new file mode 100644 index 00000000000000..29d7db54fcfc96 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-medium_mlm_tweet_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English medium_mlm_tweet BertEmbeddings from muhtasham +author: John Snow Labs +name: medium_mlm_tweet +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`medium_mlm_tweet` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/medium_mlm_tweet_en_5.1.1_3.0_1694664476857.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/medium_mlm_tweet_en_5.1.1_3.0_1694664476857.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("medium_mlm_tweet","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("medium_mlm_tweet", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|medium_mlm_tweet| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|154.2 MB| + +## References + +https://huggingface.co/muhtasham/medium-mlm-tweet \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-medruberttiny2_ru.md b/docs/_posts/ahmedlone127/2023-09-14-medruberttiny2_ru.md new file mode 100644 index 00000000000000..5aefa2d60c1af0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-medruberttiny2_ru.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Russian medruberttiny2 BertEmbeddings from DmitryPogrebnoy +author: John Snow Labs +name: medruberttiny2 +date: 2023-09-14 +tags: [bert, ru, open_source, fill_mask, onnx] +task: Embeddings +language: ru +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`medruberttiny2` is a Russian model originally trained by DmitryPogrebnoy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/medruberttiny2_ru_5.1.1_3.0_1694658844568.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/medruberttiny2_ru_5.1.1_3.0_1694658844568.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("medruberttiny2","ru") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("medruberttiny2", "ru") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|medruberttiny2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ru| +|Size:|109.1 MB| + +## References + +https://huggingface.co/DmitryPogrebnoy/MedRuBertTiny2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-mergedistill_base_cased_anneal_en.md b/docs/_posts/ahmedlone127/2023-09-14-mergedistill_base_cased_anneal_en.md new file mode 100644 index 00000000000000..e7cccffcb1d689 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-mergedistill_base_cased_anneal_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mergedistill_base_cased_anneal BertEmbeddings from amitness +author: John Snow Labs +name: mergedistill_base_cased_anneal +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mergedistill_base_cased_anneal` is a English model originally trained by amitness. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mergedistill_base_cased_anneal_en_5.1.1_3.0_1694655583062.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mergedistill_base_cased_anneal_en_5.1.1_3.0_1694655583062.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mergedistill_base_cased_anneal","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mergedistill_base_cased_anneal", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mergedistill_base_cased_anneal| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|538.7 MB| + +## References + +https://huggingface.co/amitness/mergedistill-base-cased-anneal \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-mergedistill_base_cased_anneal_v4_en.md b/docs/_posts/ahmedlone127/2023-09-14-mergedistill_base_cased_anneal_v4_en.md new file mode 100644 index 00000000000000..db87564f46d8d5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-mergedistill_base_cased_anneal_v4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mergedistill_base_cased_anneal_v4 BertEmbeddings from amitness +author: John Snow Labs +name: mergedistill_base_cased_anneal_v4 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mergedistill_base_cased_anneal_v4` is a English model originally trained by amitness. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mergedistill_base_cased_anneal_v4_en_5.1.1_3.0_1694658171035.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mergedistill_base_cased_anneal_v4_en_5.1.1_3.0_1694658171035.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mergedistill_base_cased_anneal_v4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mergedistill_base_cased_anneal_v4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mergedistill_base_cased_anneal_v4| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|539.3 MB| + +## References + +https://huggingface.co/amitness/mergedistill-base-cased-anneal-v4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-mini_mlm_imdb_en.md b/docs/_posts/ahmedlone127/2023-09-14-mini_mlm_imdb_en.md new file mode 100644 index 00000000000000..08aa73b0b2ed25 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-mini_mlm_imdb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mini_mlm_imdb BertEmbeddings from muhtasham +author: John Snow Labs +name: mini_mlm_imdb +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mini_mlm_imdb` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mini_mlm_imdb_en_5.1.1_3.0_1694664315374.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mini_mlm_imdb_en_5.1.1_3.0_1694664315374.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mini_mlm_imdb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mini_mlm_imdb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mini_mlm_imdb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|41.9 MB| + +## References + +https://huggingface.co/muhtasham/mini-mlm-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-mini_mlm_tweet_en.md b/docs/_posts/ahmedlone127/2023-09-14-mini_mlm_tweet_en.md new file mode 100644 index 00000000000000..6d36dd181e8794 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-mini_mlm_tweet_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mini_mlm_tweet BertEmbeddings from muhtasham +author: John Snow Labs +name: mini_mlm_tweet +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mini_mlm_tweet` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mini_mlm_tweet_en_5.1.1_3.0_1694663987603.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mini_mlm_tweet_en_5.1.1_3.0_1694663987603.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mini_mlm_tweet","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mini_mlm_tweet", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mini_mlm_tweet| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|41.8 MB| + +## References + +https://huggingface.co/muhtasham/mini-mlm-tweet \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-mlm_20230416_003_1_en.md b/docs/_posts/ahmedlone127/2023-09-14-mlm_20230416_003_1_en.md new file mode 100644 index 00000000000000..564cc07291fc77 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-mlm_20230416_003_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230416_003_1 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230416_003_1 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230416_003_1` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230416_003_1_en_5.1.1_3.0_1694657044354.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230416_003_1_en_5.1.1_3.0_1694657044354.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230416_003_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230416_003_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230416_003_1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.2 MB| + +## References + +https://huggingface.co/intanm/mlm-20230416-003-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-mlm_20230416_003_2_en.md b/docs/_posts/ahmedlone127/2023-09-14-mlm_20230416_003_2_en.md new file mode 100644 index 00000000000000..32daa3fdf5ecd3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-mlm_20230416_003_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_20230416_003_2 BertEmbeddings from intanm +author: John Snow Labs +name: mlm_20230416_003_2 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_20230416_003_2` is a English model originally trained by intanm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_20230416_003_2_en_5.1.1_3.0_1694658343177.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_20230416_003_2_en_5.1.1_3.0_1694658343177.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlm_20230416_003_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlm_20230416_003_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_20230416_003_2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.2 MB| + +## References + +https://huggingface.co/intanm/mlm-20230416-003-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-mlperf_inference_bert_pytorch_fp32_squad_v1.1_en.md b/docs/_posts/ahmedlone127/2023-09-14-mlperf_inference_bert_pytorch_fp32_squad_v1.1_en.md new file mode 100644 index 00000000000000..5220b152bfa159 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-mlperf_inference_bert_pytorch_fp32_squad_v1.1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlperf_inference_bert_pytorch_fp32_squad_v1.1 BertEmbeddings from cknowledge +author: John Snow Labs +name: mlperf_inference_bert_pytorch_fp32_squad_v1.1 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlperf_inference_bert_pytorch_fp32_squad_v1.1` is a English model originally trained by cknowledge. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlperf_inference_bert_pytorch_fp32_squad_v1.1_en_5.1.1_3.0_1694660332701.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlperf_inference_bert_pytorch_fp32_squad_v1.1_en_5.1.1_3.0_1694660332701.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mlperf_inference_bert_pytorch_fp32_squad_v1.1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mlperf_inference_bert_pytorch_fp32_squad_v1.1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlperf_inference_bert_pytorch_fp32_squad_v1.1| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/cknowledge/mlperf-inference-bert-pytorch-fp32-squad-v1.1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-model_imdb_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-14-model_imdb_finetuned_en.md new file mode 100644 index 00000000000000..fb86215e144fba --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-model_imdb_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English model_imdb_finetuned BertEmbeddings from phanidhar +author: John Snow Labs +name: model_imdb_finetuned +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`model_imdb_finetuned` is a English model originally trained by phanidhar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/model_imdb_finetuned_en_5.1.1_3.0_1694662202940.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/model_imdb_finetuned_en_5.1.1_3.0_1694662202940.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("model_imdb_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("model_imdb_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|model_imdb_finetuned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/phanidhar/model-imdb-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-model_saeid7776_en.md b/docs/_posts/ahmedlone127/2023-09-14-model_saeid7776_en.md new file mode 100644 index 00000000000000..686979f1e283ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-model_saeid7776_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English model_saeid7776 BertEmbeddings from saeid7776 +author: John Snow Labs +name: model_saeid7776 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`model_saeid7776` is a English model originally trained by saeid7776. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/model_saeid7776_en_5.1.1_3.0_1694665486872.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/model_saeid7776_en_5.1.1_3.0_1694665486872.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("model_saeid7776","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("model_saeid7776", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|model_saeid7776| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|672.3 MB| + +## References + +https://huggingface.co/saeid7776/model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-model_v02_en.md b/docs/_posts/ahmedlone127/2023-09-14-model_v02_en.md new file mode 100644 index 00000000000000..c54d628bbc45d1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-model_v02_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English model_v02 BertEmbeddings from saeid7776 +author: John Snow Labs +name: model_v02 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`model_v02` is a English model originally trained by saeid7776. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/model_v02_en_5.1.1_3.0_1694665653448.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/model_v02_en_5.1.1_3.0_1694665653448.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("model_v02","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("model_v02", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|model_v02| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|672.8 MB| + +## References + +https://huggingface.co/saeid7776/model_v02 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-muril_base_cased_en.md b/docs/_posts/ahmedlone127/2023-09-14-muril_base_cased_en.md new file mode 100644 index 00000000000000..4a9eb0904bf4fb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-muril_base_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English muril_base_cased BertEmbeddings from google +author: John Snow Labs +name: muril_base_cased +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`muril_base_cased` is a English model originally trained by google. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/muril_base_cased_en_5.1.1_3.0_1694651581808.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/muril_base_cased_en_5.1.1_3.0_1694651581808.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("muril_base_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("muril_base_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|muril_base_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|890.4 MB| + +## References + +https://huggingface.co/google/muril-base-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-mvr_squad_bert_base_multilingual_cased_xx.md b/docs/_posts/ahmedlone127/2023-09-14-mvr_squad_bert_base_multilingual_cased_xx.md new file mode 100644 index 00000000000000..6f71bf2d7109eb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-mvr_squad_bert_base_multilingual_cased_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual mvr_squad_bert_base_multilingual_cased BertEmbeddings from dyyyyyyyy +author: John Snow Labs +name: mvr_squad_bert_base_multilingual_cased +date: 2023-09-14 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mvr_squad_bert_base_multilingual_cased` is a Multilingual model originally trained by dyyyyyyyy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mvr_squad_bert_base_multilingual_cased_xx_5.1.1_3.0_1694658258601.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mvr_squad_bert_base_multilingual_cased_xx_5.1.1_3.0_1694658258601.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mvr_squad_bert_base_multilingual_cased","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mvr_squad_bert_base_multilingual_cased", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mvr_squad_bert_base_multilingual_cased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|665.0 MB| + +## References + +https://huggingface.co/dyyyyyyyy/MVR_squad_BERT-base-multilingual-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-mymodel_en.md b/docs/_posts/ahmedlone127/2023-09-14-mymodel_en.md new file mode 100644 index 00000000000000..d47053633850ad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-mymodel_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mymodel BertEmbeddings from heima +author: John Snow Labs +name: mymodel +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mymodel` is a English model originally trained by heima. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mymodel_en_5.1.1_3.0_1694655891652.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mymodel_en_5.1.1_3.0_1694655891652.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("mymodel","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("mymodel", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mymodel| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/heima/mymodel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-oyo_bert_base_yo.md b/docs/_posts/ahmedlone127/2023-09-14-oyo_bert_base_yo.md new file mode 100644 index 00000000000000..a318f643294551 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-oyo_bert_base_yo.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Yoruba oyo_bert_base BertEmbeddings from Davlan +author: John Snow Labs +name: oyo_bert_base +date: 2023-09-14 +tags: [bert, yo, open_source, fill_mask, onnx] +task: Embeddings +language: yo +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`oyo_bert_base` is a Yoruba model originally trained by Davlan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/oyo_bert_base_yo_5.1.1_3.0_1694663927219.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/oyo_bert_base_yo_5.1.1_3.0_1694663927219.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("oyo_bert_base","yo") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("oyo_bert_base", "yo") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|oyo_bert_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|yo| +|Size:|412.5 MB| + +## References + +https://huggingface.co/Davlan/oyo-bert-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-parlbert_german_law_de.md b/docs/_posts/ahmedlone127/2023-09-14-parlbert_german_law_de.md new file mode 100644 index 00000000000000..f50623989eecdf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-parlbert_german_law_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German parlbert_german_law BertEmbeddings from InfAI +author: John Snow Labs +name: parlbert_german_law +date: 2023-09-14 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`parlbert_german_law` is a German model originally trained by InfAI. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/parlbert_german_law_de_5.1.1_3.0_1694667648647.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/parlbert_german_law_de_5.1.1_3.0_1694667648647.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("parlbert_german_law","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("parlbert_german_law", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|parlbert_german_law| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|406.8 MB| + +## References + +https://huggingface.co/InfAI/parlbert-german-law \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-project3_model_en.md b/docs/_posts/ahmedlone127/2023-09-14-project3_model_en.md new file mode 100644 index 00000000000000..3b96943457b19b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-project3_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English project3_model BertEmbeddings from nithya +author: John Snow Labs +name: project3_model +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`project3_model` is a English model originally trained by nithya. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/project3_model_en_5.1.1_3.0_1694662572357.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/project3_model_en_5.1.1_3.0_1694662572357.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("project3_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("project3_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|project3_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/nithya/project3-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-public_models_en.md b/docs/_posts/ahmedlone127/2023-09-14-public_models_en.md new file mode 100644 index 00000000000000..0c01d10eb05cd9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-public_models_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English public_models BertEmbeddings from helloNet +author: John Snow Labs +name: public_models +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`public_models` is a English model originally trained by helloNet. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/public_models_en_5.1.1_3.0_1694656362220.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/public_models_en_5.1.1_3.0_1694656362220.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("public_models","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("public_models", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|public_models| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/helloNet/public_models \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-radbert_en.md b/docs/_posts/ahmedlone127/2023-09-14-radbert_en.md new file mode 100644 index 00000000000000..48d8e85eaec6d3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-radbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English radbert BertEmbeddings from StanfordAIMI +author: John Snow Labs +name: radbert +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`radbert` is a English model originally trained by StanfordAIMI. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/radbert_en_5.1.1_3.0_1694656544468.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/radbert_en_5.1.1_3.0_1694656544468.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("radbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("radbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|radbert| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.5 MB| + +## References + +https://huggingface.co/StanfordAIMI/RadBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-retromae_msmarco_distill_en.md b/docs/_posts/ahmedlone127/2023-09-14-retromae_msmarco_distill_en.md new file mode 100644 index 00000000000000..c6bb0e888a5a06 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-retromae_msmarco_distill_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English retromae_msmarco_distill BertEmbeddings from Shitao +author: John Snow Labs +name: retromae_msmarco_distill +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`retromae_msmarco_distill` is a English model originally trained by Shitao. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/retromae_msmarco_distill_en_5.1.1_3.0_1694650755532.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/retromae_msmarco_distill_en_5.1.1_3.0_1694650755532.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("retromae_msmarco_distill","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("retromae_msmarco_distill", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|retromae_msmarco_distill| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.7 MB| + +## References + +https://huggingface.co/Shitao/RetroMAE_MSMARCO_distill \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-sagorbert_nwp_finetuning_test2_en.md b/docs/_posts/ahmedlone127/2023-09-14-sagorbert_nwp_finetuning_test2_en.md new file mode 100644 index 00000000000000..8445a53a13997a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-sagorbert_nwp_finetuning_test2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sagorbert_nwp_finetuning_test2 BertEmbeddings from amirhamza11 +author: John Snow Labs +name: sagorbert_nwp_finetuning_test2 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sagorbert_nwp_finetuning_test2` is a English model originally trained by amirhamza11. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sagorbert_nwp_finetuning_test2_en_5.1.1_3.0_1694659149656.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sagorbert_nwp_finetuning_test2_en_5.1.1_3.0_1694659149656.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sagorbert_nwp_finetuning_test2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sagorbert_nwp_finetuning_test2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sagorbert_nwp_finetuning_test2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|614.8 MB| + +## References + +https://huggingface.co/amirhamza11/sagorbert_nwp_finetuning_test2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-sagorbert_nwp_finetuning_test4_en.md b/docs/_posts/ahmedlone127/2023-09-14-sagorbert_nwp_finetuning_test4_en.md new file mode 100644 index 00000000000000..1a7e72dbe5b021 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-sagorbert_nwp_finetuning_test4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sagorbert_nwp_finetuning_test4 BertEmbeddings from amirhamza11 +author: John Snow Labs +name: sagorbert_nwp_finetuning_test4 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sagorbert_nwp_finetuning_test4` is a English model originally trained by amirhamza11. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sagorbert_nwp_finetuning_test4_en_5.1.1_3.0_1694662734694.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sagorbert_nwp_finetuning_test4_en_5.1.1_3.0_1694662734694.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("sagorbert_nwp_finetuning_test4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("sagorbert_nwp_finetuning_test4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sagorbert_nwp_finetuning_test4| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|614.9 MB| + +## References + +https://huggingface.co/amirhamza11/sagorbert_nwp_finetuning_test4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-scholarbert_100_64bit_en.md b/docs/_posts/ahmedlone127/2023-09-14-scholarbert_100_64bit_en.md new file mode 100644 index 00000000000000..f8902d9a65e07a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-scholarbert_100_64bit_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English scholarbert_100_64bit BertEmbeddings from globuslabs +author: John Snow Labs +name: scholarbert_100_64bit +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scholarbert_100_64bit` is a English model originally trained by globuslabs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scholarbert_100_64bit_en_5.1.1_3.0_1694668681788.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scholarbert_100_64bit_en_5.1.1_3.0_1694668681788.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("scholarbert_100_64bit","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("scholarbert_100_64bit", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scholarbert_100_64bit| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|842.4 MB| + +## References + +https://huggingface.co/globuslabs/ScholarBERT_100_64bit \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-skc_mlm_german_torch_de.md b/docs/_posts/ahmedlone127/2023-09-14-skc_mlm_german_torch_de.md new file mode 100644 index 00000000000000..ffb89c33cc68f3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-skc_mlm_german_torch_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German skc_mlm_german_torch BertEmbeddings from Tobias +author: John Snow Labs +name: skc_mlm_german_torch +date: 2023-09-14 +tags: [bert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`skc_mlm_german_torch` is a German model originally trained by Tobias. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/skc_mlm_german_torch_de_5.1.1_3.0_1694663119516.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/skc_mlm_german_torch_de_5.1.1_3.0_1694663119516.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("skc_mlm_german_torch","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("skc_mlm_german_torch", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|skc_mlm_german_torch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|406.9 MB| + +## References + +https://huggingface.co/Tobias/skc_MLM_German_torch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-small_mlm_imdb_en.md b/docs/_posts/ahmedlone127/2023-09-14-small_mlm_imdb_en.md new file mode 100644 index 00000000000000..08a988389d73b4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-small_mlm_imdb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_imdb BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_imdb +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_imdb` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_imdb_en_5.1.1_3.0_1694664931729.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_imdb_en_5.1.1_3.0_1694664931729.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_imdb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_imdb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_imdb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|107.0 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-small_mlm_tweet_en.md b/docs/_posts/ahmedlone127/2023-09-14-small_mlm_tweet_en.md new file mode 100644 index 00000000000000..e354adee2a69c3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-small_mlm_tweet_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English small_mlm_tweet BertEmbeddings from muhtasham +author: John Snow Labs +name: small_mlm_tweet +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`small_mlm_tweet` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/small_mlm_tweet_en_5.1.1_3.0_1694664219703.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/small_mlm_tweet_en_5.1.1_3.0_1694664219703.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("small_mlm_tweet","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("small_mlm_tweet", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|small_mlm_tweet| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|106.9 MB| + +## References + +https://huggingface.co/muhtasham/small-mlm-tweet \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-splade_cocondenser_ensembledistil_en.md b/docs/_posts/ahmedlone127/2023-09-14-splade_cocondenser_ensembledistil_en.md new file mode 100644 index 00000000000000..0f067aeb8edb23 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-splade_cocondenser_ensembledistil_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English splade_cocondenser_ensembledistil BertEmbeddings from naver +author: John Snow Labs +name: splade_cocondenser_ensembledistil +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`splade_cocondenser_ensembledistil` is a English model originally trained by naver. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/splade_cocondenser_ensembledistil_en_5.1.1_3.0_1694661773914.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/splade_cocondenser_ensembledistil_en_5.1.1_3.0_1694661773914.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("splade_cocondenser_ensembledistil","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("splade_cocondenser_ensembledistil", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|splade_cocondenser_ensembledistil| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.4 MB| + +## References + +https://huggingface.co/naver/splade-cocondenser-ensembledistil \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-splade_cocondenser_selfdistil_naver_en.md b/docs/_posts/ahmedlone127/2023-09-14-splade_cocondenser_selfdistil_naver_en.md new file mode 100644 index 00000000000000..837a77557b8ae5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-splade_cocondenser_selfdistil_naver_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English splade_cocondenser_selfdistil_naver BertEmbeddings from naver +author: John Snow Labs +name: splade_cocondenser_selfdistil_naver +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`splade_cocondenser_selfdistil_naver` is a English model originally trained by naver. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/splade_cocondenser_selfdistil_naver_en_5.1.1_3.0_1694661508394.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/splade_cocondenser_selfdistil_naver_en_5.1.1_3.0_1694661508394.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("splade_cocondenser_selfdistil_naver","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("splade_cocondenser_selfdistil_naver", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|splade_cocondenser_selfdistil_naver| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.4 MB| + +## References + +https://huggingface.co/naver/splade-cocondenser-selfdistil \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-telugu_bert_scratch_te.md b/docs/_posts/ahmedlone127/2023-09-14-telugu_bert_scratch_te.md new file mode 100644 index 00000000000000..4aa47d4b618289 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-telugu_bert_scratch_te.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Telugu telugu_bert_scratch BertEmbeddings from l3cube-pune +author: John Snow Labs +name: telugu_bert_scratch +date: 2023-09-14 +tags: [bert, te, open_source, fill_mask, onnx] +task: Embeddings +language: te +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`telugu_bert_scratch` is a Telugu model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/telugu_bert_scratch_te_5.1.1_3.0_1694651570626.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/telugu_bert_scratch_te_5.1.1_3.0_1694651570626.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("telugu_bert_scratch","te") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("telugu_bert_scratch", "te") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|telugu_bert_scratch| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|te| +|Size:|470.5 MB| + +## References + +https://huggingface.co/l3cube-pune/telugu-bert-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-test_bert_base_spanish_wwm_cased_finetuned_ultrasounds_en.md b/docs/_posts/ahmedlone127/2023-09-14-test_bert_base_spanish_wwm_cased_finetuned_ultrasounds_en.md new file mode 100644 index 00000000000000..a8a9587cb1dd00 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-test_bert_base_spanish_wwm_cased_finetuned_ultrasounds_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English test_bert_base_spanish_wwm_cased_finetuned_ultrasounds BertEmbeddings from manucos +author: John Snow Labs +name: test_bert_base_spanish_wwm_cased_finetuned_ultrasounds +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`test_bert_base_spanish_wwm_cased_finetuned_ultrasounds` is a English model originally trained by manucos. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/test_bert_base_spanish_wwm_cased_finetuned_ultrasounds_en_5.1.1_3.0_1694663393018.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/test_bert_base_spanish_wwm_cased_finetuned_ultrasounds_en_5.1.1_3.0_1694663393018.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("test_bert_base_spanish_wwm_cased_finetuned_ultrasounds","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("test_bert_base_spanish_wwm_cased_finetuned_ultrasounds", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|test_bert_base_spanish_wwm_cased_finetuned_ultrasounds| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/manucos/test-bert-base-spanish-wwm-cased-finetuned-ultrasounds \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-test_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-14-test_bert_base_uncased_en.md new file mode 100644 index 00000000000000..ca35916c7f1021 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-test_bert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English test_bert_base_uncased BertEmbeddings from kkkzzzkkk +author: John Snow Labs +name: test_bert_base_uncased +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`test_bert_base_uncased` is a English model originally trained by kkkzzzkkk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/test_bert_base_uncased_en_5.1.1_3.0_1694663392938.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/test_bert_base_uncased_en_5.1.1_3.0_1694663392938.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("test_bert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("test_bert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|test_bert_base_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/kkkzzzkkk/test_bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-test_dushen_en.md b/docs/_posts/ahmedlone127/2023-09-14-test_dushen_en.md new file mode 100644 index 00000000000000..80ab3cd98bf81c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-test_dushen_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English test_dushen BertEmbeddings from dushen +author: John Snow Labs +name: test_dushen +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`test_dushen` is a English model originally trained by dushen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/test_dushen_en_5.1.1_3.0_1694653639422.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/test_dushen_en_5.1.1_3.0_1694653639422.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("test_dushen","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("test_dushen", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|test_dushen| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/dushen/test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-tiny_mlm_imdb_en.md b/docs/_posts/ahmedlone127/2023-09-14-tiny_mlm_imdb_en.md new file mode 100644 index 00000000000000..a95b493f28606b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-tiny_mlm_imdb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_imdb BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_imdb +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_imdb` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_imdb_en_5.1.1_3.0_1694663665545.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_imdb_en_5.1.1_3.0_1694663665545.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_imdb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_imdb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_imdb| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-tiny_mlm_tweet_en.md b/docs/_posts/ahmedlone127/2023-09-14-tiny_mlm_tweet_en.md new file mode 100644 index 00000000000000..7ab71d2936ee20 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-tiny_mlm_tweet_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_mlm_tweet BertEmbeddings from muhtasham +author: John Snow Labs +name: tiny_mlm_tweet +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_mlm_tweet` is a English model originally trained by muhtasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_mlm_tweet_en_5.1.1_3.0_1694663791725.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_mlm_tweet_en_5.1.1_3.0_1694663791725.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("tiny_mlm_tweet","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("tiny_mlm_tweet", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_mlm_tweet| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/muhtasham/tiny-mlm-tweet \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-ucb_bert_finetunned_en.md b/docs/_posts/ahmedlone127/2023-09-14-ucb_bert_finetunned_en.md new file mode 100644 index 00000000000000..2ab6c71bbc765b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-ucb_bert_finetunned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ucb_bert_finetunned BertEmbeddings from Diegomejia +author: John Snow Labs +name: ucb_bert_finetunned +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ucb_bert_finetunned` is a English model originally trained by Diegomejia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ucb_bert_finetunned_en_5.1.1_3.0_1694661140033.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ucb_bert_finetunned_en_5.1.1_3.0_1694661140033.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ucb_bert_finetunned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ucb_bert_finetunned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ucb_bert_finetunned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/Diegomejia/ucb-bert-finetunned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-vatestnew_en.md b/docs/_posts/ahmedlone127/2023-09-14-vatestnew_en.md new file mode 100644 index 00000000000000..6eaaf25ddb1f10 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-vatestnew_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English vatestnew BertEmbeddings from mtluczek80 +author: John Snow Labs +name: vatestnew +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`vatestnew` is a English model originally trained by mtluczek80. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/vatestnew_en_5.1.1_3.0_1694657391978.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/vatestnew_en_5.1.1_3.0_1694657391978.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("vatestnew","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("vatestnew", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|vatestnew| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/mtluczek80/VATestNew \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-vbert_2021_base_en.md b/docs/_posts/ahmedlone127/2023-09-14-vbert_2021_base_en.md new file mode 100644 index 00000000000000..9f8d9584a3a562 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-vbert_2021_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English vbert_2021_base BertEmbeddings from VMware +author: John Snow Labs +name: vbert_2021_base +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`vbert_2021_base` is a English model originally trained by VMware. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/vbert_2021_base_en_5.1.1_3.0_1694664971607.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/vbert_2021_base_en_5.1.1_3.0_1694664971607.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("vbert_2021_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("vbert_2021_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|vbert_2021_base| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/VMware/vbert-2021-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-weights_bert_mlm_epoch50_en.md b/docs/_posts/ahmedlone127/2023-09-14-weights_bert_mlm_epoch50_en.md new file mode 100644 index 00000000000000..d3405b6f4c8b2f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-weights_bert_mlm_epoch50_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English weights_bert_mlm_epoch50 BertEmbeddings from grumpy +author: John Snow Labs +name: weights_bert_mlm_epoch50 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`weights_bert_mlm_epoch50` is a English model originally trained by grumpy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/weights_bert_mlm_epoch50_en_5.1.1_3.0_1694652096710.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/weights_bert_mlm_epoch50_en_5.1.1_3.0_1694652096710.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("weights_bert_mlm_epoch50","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("weights_bert_mlm_epoch50", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|weights_bert_mlm_epoch50| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/grumpy/weights_bert_mlm_epoch50 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-word_ethical_ko.md b/docs/_posts/ahmedlone127/2023-09-14-word_ethical_ko.md new file mode 100644 index 00000000000000..04b4d7f98c066d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-word_ethical_ko.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Korean word_ethical BertEmbeddings from julian5383 +author: John Snow Labs +name: word_ethical +date: 2023-09-14 +tags: [bert, ko, open_source, fill_mask, onnx] +task: Embeddings +language: ko +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`word_ethical` is a Korean model originally trained by julian5383. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/word_ethical_ko_5.1.1_3.0_1694670073884.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/word_ethical_ko_5.1.1_3.0_1694670073884.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("word_ethical","ko") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("word_ethical", "ko") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|word_ethical| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ko| +|Size:|421.2 MB| + +## References + +https://huggingface.co/julian5383/word_ethical \ No newline at end of file From 0f862373bafa6fc3cb7a847f7658fe9d5c42d911 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Fri, 15 Sep 2023 16:00:55 +0700 Subject: [PATCH 44/55] 2023-09-14-bert_base_cased_finetuned_wallisian_manual_9ep_en (#13982) * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_manual_9ep_en * Add model 2023-09-14-bert_base_uncased_finetuned_wallisian_manual_9ep_lower_en * Add model 2023-09-14-bert_base_cased_finetuned_wallisian_manual_10ep_en * Add model 2023-09-14-bert_base_uncased_finetuned_wallisian_manual_10ep_lower_en * Add model 2023-09-14-bert_csic_en * Add model 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+supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`AethiQs_GemBERT_bertje_50k` is a English model originally trained by AethiQs-Max. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/AethiQs_GemBERT_bertje_50k_en_5.1.1_3.0_1694692996070.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/AethiQs_GemBERT_bertje_50k_en_5.1.1_3.0_1694692996070.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("AethiQs_GemBERT_bertje_50k","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("AethiQs_GemBERT_bertje_50k", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|AethiQs_GemBERT_bertje_50k| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|406.8 MB| + +## References + +https://huggingface.co/AethiQs-Max/AethiQs_GemBERT_bertje_50k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-AlephBertGimmel_10_epochs_en.md b/docs/_posts/ahmedlone127/2023-09-14-AlephBertGimmel_10_epochs_en.md new file mode 100644 index 00000000000000..0886b811923735 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-AlephBertGimmel_10_epochs_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English AlephBertGimmel_10_epochs BertEmbeddings from Embible +author: John Snow Labs +name: AlephBertGimmel_10_epochs +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`AlephBertGimmel_10_epochs` is a English model originally trained by Embible. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/AlephBertGimmel_10_epochs_en_5.1.1_3.0_1694693579710.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/AlephBertGimmel_10_epochs_en_5.1.1_3.0_1694693579710.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("AlephBertGimmel_10_epochs","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("AlephBertGimmel_10_epochs", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|AlephBertGimmel_10_epochs| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|690.3 MB| + +## References + +https://huggingface.co/Embible/AlephBertGimmel-10-epochs \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-AlephBertGimmel_20_epochs_en.md b/docs/_posts/ahmedlone127/2023-09-14-AlephBertGimmel_20_epochs_en.md new file mode 100644 index 00000000000000..daf85f4af214f7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-AlephBertGimmel_20_epochs_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English AlephBertGimmel_20_epochs BertEmbeddings from Embible +author: John Snow Labs +name: AlephBertGimmel_20_epochs +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`AlephBertGimmel_20_epochs` is a English model originally trained by Embible. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/AlephBertGimmel_20_epochs_en_5.1.1_3.0_1694693813229.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/AlephBertGimmel_20_epochs_en_5.1.1_3.0_1694693813229.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("AlephBertGimmel_20_epochs","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("AlephBertGimmel_20_epochs", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|AlephBertGimmel_20_epochs| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|690.3 MB| + +## References + +https://huggingface.co/Embible/AlephBertGimmel-20-epochs \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-AlephBertGimmel_50_epochs_en.md b/docs/_posts/ahmedlone127/2023-09-14-AlephBertGimmel_50_epochs_en.md new file mode 100644 index 00000000000000..d7d958a3bbfdfb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-AlephBertGimmel_50_epochs_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English AlephBertGimmel_50_epochs BertEmbeddings from Embible +author: John Snow Labs +name: AlephBertGimmel_50_epochs +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`AlephBertGimmel_50_epochs` is a English model originally trained by Embible. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/AlephBertGimmel_50_epochs_en_5.1.1_3.0_1694694021882.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/AlephBertGimmel_50_epochs_en_5.1.1_3.0_1694694021882.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("AlephBertGimmel_50_epochs","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("AlephBertGimmel_50_epochs", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|AlephBertGimmel_50_epochs| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|690.0 MB| + +## References + +https://huggingface.co/Embible/AlephBertGimmel-50-epochs \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-BERTUNAM_en.md b/docs/_posts/ahmedlone127/2023-09-14-BERTUNAM_en.md new file mode 100644 index 00000000000000..49913ead495fe5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-BERTUNAM_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English BERTUNAM BertEmbeddings from benanxio +author: John Snow Labs +name: BERTUNAM +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`BERTUNAM` is a English model originally trained by benanxio. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/BERTUNAM_en_5.1.1_3.0_1694692836777.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/BERTUNAM_en_5.1.1_3.0_1694692836777.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("BERTUNAM","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("BERTUNAM", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|BERTUNAM| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/benanxio/BERTUNAM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-BERT_Base_CT_en.md b/docs/_posts/ahmedlone127/2023-09-14-BERT_Base_CT_en.md new file mode 100644 index 00000000000000..ba781f70be62c9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-BERT_Base_CT_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English BERT_Base_CT BertEmbeddings from Contrastive-Tension +author: John Snow Labs +name: BERT_Base_CT +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`BERT_Base_CT` is a English model originally trained by Contrastive-Tension. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/BERT_Base_CT_en_5.1.1_3.0_1694693914687.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/BERT_Base_CT_en_5.1.1_3.0_1694693914687.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("BERT_Base_CT","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("BERT_Base_CT", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|BERT_Base_CT| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Contrastive-Tension/BERT-Base-CT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-BERT_Base_NLI_CT_en.md b/docs/_posts/ahmedlone127/2023-09-14-BERT_Base_NLI_CT_en.md new file mode 100644 index 00000000000000..5da2fbd5db95a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-BERT_Base_NLI_CT_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English BERT_Base_NLI_CT BertEmbeddings from Contrastive-Tension +author: John Snow Labs +name: BERT_Base_NLI_CT +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`BERT_Base_NLI_CT` is a English model originally trained by Contrastive-Tension. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/BERT_Base_NLI_CT_en_5.1.1_3.0_1694694097832.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/BERT_Base_NLI_CT_en_5.1.1_3.0_1694694097832.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("BERT_Base_NLI_CT","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("BERT_Base_NLI_CT", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|BERT_Base_NLI_CT| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/Contrastive-Tension/BERT-Base-NLI-CT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-BERT_Distil_CT_en.md b/docs/_posts/ahmedlone127/2023-09-14-BERT_Distil_CT_en.md new file mode 100644 index 00000000000000..bade2aee450677 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-BERT_Distil_CT_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English BERT_Distil_CT DistilBertEmbeddings from Contrastive-Tension +author: John Snow Labs +name: BERT_Distil_CT +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`BERT_Distil_CT` is a English model originally trained by Contrastive-Tension. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/BERT_Distil_CT_en_5.1.2_3.0_1694735209512.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/BERT_Distil_CT_en_5.1.2_3.0_1694735209512.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("BERT_Distil_CT","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("BERT_Distil_CT", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|BERT_Distil_CT| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Contrastive-Tension/BERT-Distil-CT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-BERT_Distil_NLI_CT_en.md b/docs/_posts/ahmedlone127/2023-09-14-BERT_Distil_NLI_CT_en.md new file mode 100644 index 00000000000000..87bb6cbf1ba385 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-BERT_Distil_NLI_CT_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English BERT_Distil_NLI_CT DistilBertEmbeddings from Contrastive-Tension +author: John Snow Labs +name: BERT_Distil_NLI_CT +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`BERT_Distil_NLI_CT` is a English model originally trained by Contrastive-Tension. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/BERT_Distil_NLI_CT_en_5.1.2_3.0_1694735318073.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/BERT_Distil_NLI_CT_en_5.1.2_3.0_1694735318073.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("BERT_Distil_NLI_CT","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("BERT_Distil_NLI_CT", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|BERT_Distil_NLI_CT| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Contrastive-Tension/BERT-Distil-NLI-CT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-BERT_Large_CT_en.md b/docs/_posts/ahmedlone127/2023-09-14-BERT_Large_CT_en.md new file mode 100644 index 00000000000000..6db7fabbd1f717 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-BERT_Large_CT_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English BERT_Large_CT BertEmbeddings from Contrastive-Tension +author: John Snow Labs +name: BERT_Large_CT +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`BERT_Large_CT` is a English model originally trained by Contrastive-Tension. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/BERT_Large_CT_en_5.1.1_3.0_1694694383132.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/BERT_Large_CT_en_5.1.1_3.0_1694694383132.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("BERT_Large_CT","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("BERT_Large_CT", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|BERT_Large_CT| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/Contrastive-Tension/BERT-Large-CT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-BiodivBERT_en.md b/docs/_posts/ahmedlone127/2023-09-14-BiodivBERT_en.md new file mode 100644 index 00000000000000..1bec027e681000 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-BiodivBERT_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English BiodivBERT BertEmbeddings from NoYo25 +author: John Snow Labs +name: BiodivBERT +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`BiodivBERT` is a English model originally trained by NoYo25. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/BiodivBERT_en_5.1.1_3.0_1694693518067.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/BiodivBERT_en_5.1.1_3.0_1694693518067.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("BiodivBERT","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("BiodivBERT", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|BiodivBERT| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/NoYo25/BiodivBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-DistilBert_Finetuned_SpMLM_en.md b/docs/_posts/ahmedlone127/2023-09-14-DistilBert_Finetuned_SpMLM_en.md new file mode 100644 index 00000000000000..8a400ec9cac5ef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-DistilBert_Finetuned_SpMLM_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English DistilBert_Finetuned_SpMLM DistilBertEmbeddings from ashwathjadhav23 +author: John Snow Labs +name: DistilBert_Finetuned_SpMLM +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`DistilBert_Finetuned_SpMLM` is a English model originally trained by ashwathjadhav23. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/DistilBert_Finetuned_SpMLM_en_5.1.2_3.0_1694735812642.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/DistilBert_Finetuned_SpMLM_en_5.1.2_3.0_1694735812642.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("DistilBert_Finetuned_SpMLM","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("DistilBert_Finetuned_SpMLM", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|DistilBert_Finetuned_SpMLM| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|243.7 MB| + +## References + +https://huggingface.co/ashwathjadhav23/DistilBert_Finetuned_SpMLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-InLegalBERT_cbp_lkg_triples_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-14-InLegalBERT_cbp_lkg_triples_finetuned_en.md new file mode 100644 index 00000000000000..e77417718d3a58 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-InLegalBERT_cbp_lkg_triples_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English InLegalBERT_cbp_lkg_triples_finetuned BertEmbeddings from kinshuk-h +author: John Snow Labs +name: InLegalBERT_cbp_lkg_triples_finetuned +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`InLegalBERT_cbp_lkg_triples_finetuned` is a English model originally trained by kinshuk-h. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/InLegalBERT_cbp_lkg_triples_finetuned_en_5.1.1_3.0_1694694447567.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/InLegalBERT_cbp_lkg_triples_finetuned_en_5.1.1_3.0_1694694447567.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("InLegalBERT_cbp_lkg_triples_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("InLegalBERT_cbp_lkg_triples_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|InLegalBERT_cbp_lkg_triples_finetuned| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/kinshuk-h/InLegalBERT-cbp-lkg-triples-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-JavaBERT_en.md b/docs/_posts/ahmedlone127/2023-09-14-JavaBERT_en.md new file mode 100644 index 00000000000000..367a5d85fd1d59 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-JavaBERT_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English JavaBERT BertEmbeddings from CAUKiel +author: John Snow Labs +name: JavaBERT +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`JavaBERT` is a English model originally trained by CAUKiel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/JavaBERT_en_5.1.1_3.0_1694693773348.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/JavaBERT_en_5.1.1_3.0_1694693773348.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("JavaBERT","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("JavaBERT", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|JavaBERT| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.2 MB| + +## References + +https://huggingface.co/CAUKiel/JavaBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-JavaBERT_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-14-JavaBERT_uncased_en.md new file mode 100644 index 00000000000000..f7a98bf80dcd64 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-JavaBERT_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English JavaBERT_uncased BertEmbeddings from CAUKiel +author: John Snow Labs +name: JavaBERT_uncased +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`JavaBERT_uncased` is a English model originally trained by CAUKiel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/JavaBERT_uncased_en_5.1.1_3.0_1694693619422.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/JavaBERT_uncased_en_5.1.1_3.0_1694693619422.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("JavaBERT_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("JavaBERT_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|JavaBERT_uncased| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|408.2 MB| + +## References + +https://huggingface.co/CAUKiel/JavaBERT-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-Masked_Language_Model_en.md b/docs/_posts/ahmedlone127/2023-09-14-Masked_Language_Model_en.md new file mode 100644 index 00000000000000..b335eed5139d0c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-Masked_Language_Model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English Masked_Language_Model DistilBertEmbeddings from ayoolaolafenwa +author: John Snow Labs +name: Masked_Language_Model +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`Masked_Language_Model` is a English model originally trained by ayoolaolafenwa. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/Masked_Language_Model_en_5.1.2_3.0_1694735583713.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/Masked_Language_Model_en_5.1.2_3.0_1694735583713.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("Masked_Language_Model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("Masked_Language_Model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|Masked_Language_Model| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ayoolaolafenwa/Masked-Language-Model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-Medicaldistilbert_en.md b/docs/_posts/ahmedlone127/2023-09-14-Medicaldistilbert_en.md new file mode 100644 index 00000000000000..5f616b7ca0398b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-Medicaldistilbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English Medicaldistilbert DistilBertEmbeddings from Gaborandi +author: John Snow Labs +name: Medicaldistilbert +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`Medicaldistilbert` is a English model originally trained by Gaborandi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/Medicaldistilbert_en_5.1.2_3.0_1694735094024.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/Medicaldistilbert_en_5.1.2_3.0_1694735094024.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("Medicaldistilbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("Medicaldistilbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|Medicaldistilbert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Gaborandi/Medicaldistilbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-UKRI_DistilBERT_en.md b/docs/_posts/ahmedlone127/2023-09-14-UKRI_DistilBERT_en.md new file mode 100644 index 00000000000000..4594ffb756423e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-UKRI_DistilBERT_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English UKRI_DistilBERT DistilBertEmbeddings from Brawl +author: John Snow Labs +name: UKRI_DistilBERT +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`UKRI_DistilBERT` is a English model originally trained by Brawl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/UKRI_DistilBERT_en_5.1.2_3.0_1694734860415.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/UKRI_DistilBERT_en_5.1.2_3.0_1694734860415.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("UKRI_DistilBERT","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("UKRI_DistilBERT", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|UKRI_DistilBERT| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Brawl/UKRI_DistilBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-ar_mbertmodel_mberttok_en.md b/docs/_posts/ahmedlone127/2023-09-14-ar_mbertmodel_mberttok_en.md new file mode 100644 index 00000000000000..8c12c94259580b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-ar_mbertmodel_mberttok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ar_mbertmodel_mberttok BertEmbeddings from hgiyt +author: John Snow Labs +name: ar_mbertmodel_mberttok +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ar_mbertmodel_mberttok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ar_mbertmodel_mberttok_en_5.1.1_3.0_1694692763225.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ar_mbertmodel_mberttok_en_5.1.1_3.0_1694692763225.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ar_mbertmodel_mberttok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ar_mbertmodel_mberttok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ar_mbertmodel_mberttok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|545.9 MB| + +## References + +https://huggingface.co/hgiyt/ar-mbertmodel-mberttok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-ar_mbertmodel_monotok_adapter_en.md b/docs/_posts/ahmedlone127/2023-09-14-ar_mbertmodel_monotok_adapter_en.md new file mode 100644 index 00000000000000..eb3ef53356892b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-ar_mbertmodel_monotok_adapter_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ar_mbertmodel_monotok_adapter BertEmbeddings from hgiyt +author: John Snow Labs +name: ar_mbertmodel_monotok_adapter +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ar_mbertmodel_monotok_adapter` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ar_mbertmodel_monotok_adapter_en_5.1.1_3.0_1694692938173.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ar_mbertmodel_monotok_adapter_en_5.1.1_3.0_1694692938173.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ar_mbertmodel_monotok_adapter","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ar_mbertmodel_monotok_adapter", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ar_mbertmodel_monotok_adapter| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|505.0 MB| + +## References + +https://huggingface.co/hgiyt/ar-mbertmodel-monotok-adapter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-ar_mbertmodel_monotok_en.md b/docs/_posts/ahmedlone127/2023-09-14-ar_mbertmodel_monotok_en.md new file mode 100644 index 00000000000000..1ade663e7d7b4f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-ar_mbertmodel_monotok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ar_mbertmodel_monotok BertEmbeddings from hgiyt +author: John Snow Labs +name: ar_mbertmodel_monotok +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ar_mbertmodel_monotok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ar_mbertmodel_monotok_en_5.1.1_3.0_1694693109864.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ar_mbertmodel_monotok_en_5.1.1_3.0_1694693109864.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ar_mbertmodel_monotok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ar_mbertmodel_monotok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ar_mbertmodel_monotok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|505.0 MB| + +## References + +https://huggingface.co/hgiyt/ar-mbertmodel-monotok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-ar_monomodel_mberttok_en.md b/docs/_posts/ahmedlone127/2023-09-14-ar_monomodel_mberttok_en.md new file mode 100644 index 00000000000000..c358116c4f7da8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-ar_monomodel_mberttok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ar_monomodel_mberttok BertEmbeddings from hgiyt +author: John Snow Labs +name: ar_monomodel_mberttok +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ar_monomodel_mberttok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ar_monomodel_mberttok_en_5.1.1_3.0_1694693274766.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ar_monomodel_mberttok_en_5.1.1_3.0_1694693274766.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ar_monomodel_mberttok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ar_monomodel_mberttok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ar_monomodel_mberttok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|545.3 MB| + +## References + +https://huggingface.co/hgiyt/ar-monomodel-mberttok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-ar_monomodel_monotok_en.md b/docs/_posts/ahmedlone127/2023-09-14-ar_monomodel_monotok_en.md new file mode 100644 index 00000000000000..22cef2d18b56d9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-ar_monomodel_monotok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ar_monomodel_monotok BertEmbeddings from hgiyt +author: John Snow Labs +name: ar_monomodel_monotok +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ar_monomodel_monotok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ar_monomodel_monotok_en_5.1.1_3.0_1694693462819.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ar_monomodel_monotok_en_5.1.1_3.0_1694693462819.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("ar_monomodel_monotok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("ar_monomodel_monotok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ar_monomodel_monotok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|504.1 MB| + +## References + +https://huggingface.co/hgiyt/ar-monomodel-monotok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_adaptation_martin_fierro_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_adaptation_martin_fierro_en.md new file mode 100644 index 00000000000000..533a0b2d425d02 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_adaptation_martin_fierro_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_adaptation_martin_fierro BertEmbeddings from nanom +author: John Snow Labs +name: bert_adaptation_martin_fierro +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_adaptation_martin_fierro` is a English model originally trained by nanom. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_adaptation_martin_fierro_en_5.1.1_3.0_1694675031930.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_adaptation_martin_fierro_en_5.1.1_3.0_1694675031930.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_adaptation_martin_fierro","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_adaptation_martin_fierro", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_adaptation_martin_fierro| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.6 MB| + +## References + +https://huggingface.co/nanom/bert_adaptation_martin_fierro \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_adaptation_peppa_pig_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_adaptation_peppa_pig_en.md new file mode 100644 index 00000000000000..2222bfdf461800 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_adaptation_peppa_pig_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_adaptation_peppa_pig BertEmbeddings from nanom +author: John Snow Labs +name: bert_adaptation_peppa_pig +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_adaptation_peppa_pig` is a English model originally trained by nanom. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_adaptation_peppa_pig_en_5.1.1_3.0_1694675255366.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_adaptation_peppa_pig_en_5.1.1_3.0_1694675255366.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_adaptation_peppa_pig","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_adaptation_peppa_pig", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_adaptation_peppa_pig| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.6 MB| + +## References + +https://huggingface.co/nanom/bert_adaptation_peppa_pig \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_adaptation_referencias_german_vinos_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_adaptation_referencias_german_vinos_en.md new file mode 100644 index 00000000000000..d88347a9f4e451 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_adaptation_referencias_german_vinos_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_adaptation_referencias_german_vinos BertEmbeddings from nanom +author: John Snow Labs +name: bert_adaptation_referencias_german_vinos +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_adaptation_referencias_german_vinos` is a English model originally trained by nanom. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_adaptation_referencias_german_vinos_en_5.1.1_3.0_1694676519192.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_adaptation_referencias_german_vinos_en_5.1.1_3.0_1694676519192.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_adaptation_referencias_german_vinos","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_adaptation_referencias_german_vinos", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_adaptation_referencias_german_vinos| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|409.7 MB| + +## References + +https://huggingface.co/nanom/bert_adaptation_referencias_de_vinos \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_adaptation_vizwiz_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_adaptation_vizwiz_en.md new file mode 100644 index 00000000000000..62fe5a06105082 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_adaptation_vizwiz_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_adaptation_vizwiz BertEmbeddings from nanom +author: John Snow Labs +name: bert_adaptation_vizwiz +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_adaptation_vizwiz` is a English model originally trained by nanom. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_adaptation_vizwiz_en_5.1.1_3.0_1694675142841.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_adaptation_vizwiz_en_5.1.1_3.0_1694675142841.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_adaptation_vizwiz","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_adaptation_vizwiz", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_adaptation_vizwiz| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/nanom/bert_adaptation_vizwiz \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_arabertv02_finetuned_egyption_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_arabertv02_finetuned_egyption_en.md new file mode 100644 index 00000000000000..bc074294800f3e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_arabertv02_finetuned_egyption_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_arabertv02_finetuned_egyption BertEmbeddings from H-H-T-S +author: John Snow Labs +name: bert_base_arabertv02_finetuned_egyption +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabertv02_finetuned_egyption` is a English model originally trained by H-H-T-S. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabertv02_finetuned_egyption_en_5.1.1_3.0_1694674744031.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabertv02_finetuned_egyption_en_5.1.1_3.0_1694674744031.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_arabertv02_finetuned_egyption","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_arabertv02_finetuned_egyption", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabertv02_finetuned_egyption| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|505.1 MB| + +## References + +https://huggingface.co/H-H-T-S/bert-base-arabertv02-finetuned-egyption \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_10ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_10ep_en.md new file mode 100644 index 00000000000000..b1508164ef14fd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_10ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_manual_10ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_manual_10ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_manual_10ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_10ep_en_5.1.1_3.0_1694673230803.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_10ep_en_5.1.1_3.0_1694673230803.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_manual_10ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_manual_10ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_manual_10ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-manual-10ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_9ep_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_9ep_en.md new file mode 100644 index 00000000000000..3b7fe505732776 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_cased_finetuned_wallisian_manual_9ep_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_cased_finetuned_wallisian_manual_9ep BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_cased_finetuned_wallisian_manual_9ep +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_wallisian_manual_9ep` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_9ep_en_5.1.1_3.0_1694673007935.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_wallisian_manual_9ep_en_5.1.1_3.0_1694673007935.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_cased_finetuned_wallisian_manual_9ep","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_cased_finetuned_wallisian_manual_9ep", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_wallisian_manual_9ep| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/btamm12/bert-base-cased-finetuned-wls-manual-9ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_bert_auto2_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_bert_auto2_en.md new file mode 100644 index 00000000000000..1cc36fd98d0c61 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_bert_auto2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_bert_auto2 BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_base_uncased_finetuned_bert_auto2 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_bert_auto2` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_bert_auto2_en_5.1.1_3.0_1694674618662.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_bert_auto2_en_5.1.1_3.0_1694674618662.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_bert_auto2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_bert_auto2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_bert_auto2| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-base-uncased-finetuned-bert-auto2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_bert_auto3_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_bert_auto3_en.md new file mode 100644 index 00000000000000..1241d7849b3775 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_bert_auto3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_bert_auto3 BertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: bert_base_uncased_finetuned_bert_auto3 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_bert_auto3` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_bert_auto3_en_5.1.1_3.0_1694674856695.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_bert_auto3_en_5.1.1_3.0_1694674856695.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_bert_auto3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_bert_auto3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_bert_auto3| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/bert-base-uncased-finetuned-bert-auto3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_10ep_lower_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_10ep_lower_en.md new file mode 100644 index 00000000000000..a4a70f49e4b045 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_10ep_lower_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_wallisian_manual_10ep_lower BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_uncased_finetuned_wallisian_manual_10ep_lower +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_wallisian_manual_10ep_lower` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_10ep_lower_en_5.1.1_3.0_1694673340561.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_10ep_lower_en_5.1.1_3.0_1694673340561.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_wallisian_manual_10ep_lower","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_wallisian_manual_10ep_lower", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_wallisian_manual_10ep_lower| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/btamm12/bert-base-uncased-finetuned-wls-manual-10ep-lower \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_9ep_lower_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_9ep_lower_en.md new file mode 100644 index 00000000000000..6a36b8b63f5110 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_finetuned_wallisian_manual_9ep_lower_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_wallisian_manual_9ep_lower BertEmbeddings from btamm12 +author: John Snow Labs +name: bert_base_uncased_finetuned_wallisian_manual_9ep_lower +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_wallisian_manual_9ep_lower` is a English model originally trained by btamm12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_9ep_lower_en_5.1.1_3.0_1694673120991.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wallisian_manual_9ep_lower_en_5.1.1_3.0_1694673120991.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_finetuned_wallisian_manual_9ep_lower","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_finetuned_wallisian_manual_9ep_lower", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_wallisian_manual_9ep_lower| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.2 MB| + +## References + +https://huggingface.co/btamm12/bert-base-uncased-finetuned-wls-manual-9ep-lower \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_ckandemir_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_ckandemir_en.md new file mode 100644 index 00000000000000..94305278d80f21 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_ckandemir_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128_ckandemir BertEmbeddings from ckandemir +author: John Snow Labs +name: bert_base_uncased_issues_128_ckandemir +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128_ckandemir` is a English model originally trained by ckandemir. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_ckandemir_en_5.1.1_3.0_1694674334514.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_ckandemir_en_5.1.1_3.0_1694674334514.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128_ckandemir","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128_ckandemir", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128_ckandemir| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/ckandemir/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_en.md new file mode 100644 index 00000000000000..3e2162a7a25ab0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_base_uncased_issues_128_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_issues_128 BertEmbeddings from susnato +author: John Snow Labs +name: bert_base_uncased_issues_128 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_issues_128` is a English model originally trained by susnato. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_en_5.1.1_3.0_1694693094585.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_issues_128_en_5.1.1_3.0_1694693094585.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_base_uncased_issues_128","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_base_uncased_issues_128", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_issues_128| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.1 MB| + +## References + +https://huggingface.co/susnato/bert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_bgl_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_bgl_en.md new file mode 100644 index 00000000000000..b66b669f0eade3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_bgl_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_bgl BertEmbeddings from EgilKarlsen +author: John Snow Labs +name: bert_bgl +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_bgl` is a English model originally trained by EgilKarlsen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_bgl_en_5.1.1_3.0_1694673772401.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_bgl_en_5.1.1_3.0_1694673772401.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_bgl","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_bgl", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_bgl| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/EgilKarlsen/BERT-BGL \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_csic_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_csic_en.md new file mode 100644 index 00000000000000..05d72f2bc7620f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_csic_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_csic BertEmbeddings from EgilKarlsen +author: John Snow Labs +name: bert_csic +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_csic` is a English model originally trained by EgilKarlsen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_csic_en_5.1.1_3.0_1694673550522.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_csic_en_5.1.1_3.0_1694673550522.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_csic","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_csic", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_csic| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/EgilKarlsen/BERT-CSIC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_8_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_8_en.md new file mode 100644 index 00000000000000..d0d5ea09b7456f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_8_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_8 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_8 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_8` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_8_en_5.1.1_3.0_1694692978470.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_8_en_5.1.1_3.0_1694692978470.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_8","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_8", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_8| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-8 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_9_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_9_en.md new file mode 100644 index 00000000000000..3afaddd837f652 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_9_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_9 BertEmbeddings from jojoUla +author: John Snow Labs +name: bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_9 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_9` is a English model originally trained by jojoUla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_9_en_5.1.1_3.0_1694693264712.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_9_en_5.1.1_3.0_1694693264712.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_9","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_9", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_sigir_support_refute_no_label_40_2nd_test_LR10_8_9| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-9 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_pkdd_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_pkdd_en.md new file mode 100644 index 00000000000000..618789e42208b7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_pkdd_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_pkdd BertEmbeddings from EgilKarlsen +author: John Snow Labs +name: bert_pkdd +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_pkdd` is a English model originally trained by EgilKarlsen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_pkdd_en_5.1.1_3.0_1694673662056.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_pkdd_en_5.1.1_3.0_1694673662056.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_pkdd","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_pkdd", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_pkdd| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/EgilKarlsen/BERT-PKDD \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_spirit_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_spirit_en.md new file mode 100644 index 00000000000000..67b64e4cedec35 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_spirit_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_spirit BertEmbeddings from EgilKarlsen +author: John Snow Labs +name: bert_spirit +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_spirit` is a English model originally trained by EgilKarlsen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_spirit_en_5.1.1_3.0_1694673992319.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_spirit_en_5.1.1_3.0_1694673992319.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_spirit","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_spirit", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_spirit| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/EgilKarlsen/BERT-Spirit \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bert_thunderbird_en.md b/docs/_posts/ahmedlone127/2023-09-14-bert_thunderbird_en.md new file mode 100644 index 00000000000000..895ac1823d2833 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bert_thunderbird_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_thunderbird BertEmbeddings from EgilKarlsen +author: John Snow Labs +name: bert_thunderbird +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_thunderbird` is a English model originally trained by EgilKarlsen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_thunderbird_en_5.1.1_3.0_1694673882784.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_thunderbird_en_5.1.1_3.0_1694673882784.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("bert_thunderbird","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("bert_thunderbird", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_thunderbird| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/EgilKarlsen/BERT-Thunderbird \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-bertino_lsg_en.md b/docs/_posts/ahmedlone127/2023-09-14-bertino_lsg_en.md new file mode 100644 index 00000000000000..9b3c412bd78a64 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-bertino_lsg_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertino_lsg DistilBertEmbeddings from efederici +author: John Snow Labs +name: bertino_lsg +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertino_lsg` is a English model originally trained by efederici. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertino_lsg_en_5.1.2_3.0_1694735307524.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertino_lsg_en_5.1.2_3.0_1694735307524.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("bertino_lsg","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("bertino_lsg", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertino_lsg| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|263.3 MB| + +## References + +https://huggingface.co/efederici/bertino-lsg \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-burmese_bert_model_en.md b/docs/_posts/ahmedlone127/2023-09-14-burmese_bert_model_en.md new file mode 100644 index 00000000000000..c6c1ed507d66d6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-burmese_bert_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English burmese_bert_model BertEmbeddings from billfass +author: John Snow Labs +name: burmese_bert_model +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`burmese_bert_model` is a English model originally trained by billfass. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/burmese_bert_model_en_5.1.1_3.0_1694675726360.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/burmese_bert_model_en_5.1.1_3.0_1694675726360.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("burmese_bert_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("burmese_bert_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|burmese_bert_model| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|663.2 MB| + +## References + +https://huggingface.co/billfass/my_bert_model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-customer_data_tuned_trial_1_en.md b/docs/_posts/ahmedlone127/2023-09-14-customer_data_tuned_trial_1_en.md new file mode 100644 index 00000000000000..a9b0dfa807b05a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-customer_data_tuned_trial_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English customer_data_tuned_trial_1 DistilBertEmbeddings from EslamAhmed +author: John Snow Labs +name: customer_data_tuned_trial_1 +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`customer_data_tuned_trial_1` is a English model originally trained by EslamAhmed. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/customer_data_tuned_trial_1_en_5.1.2_3.0_1694734877512.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/customer_data_tuned_trial_1_en_5.1.2_3.0_1694734877512.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("customer_data_tuned_trial_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("customer_data_tuned_trial_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|customer_data_tuned_trial_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.3 MB| + +## References + +https://huggingface.co/EslamAhmed/customer_data_tuned_trial_1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-dbert_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-14-dbert_finetuned_en.md new file mode 100644 index 00000000000000..93876e4433068d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-dbert_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dbert_finetuned DistilBertEmbeddings from ksabeh +author: John Snow Labs +name: dbert_finetuned +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dbert_finetuned` is a English model originally trained by ksabeh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dbert_finetuned_en_5.1.2_3.0_1694735275130.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dbert_finetuned_en_5.1.2_3.0_1694735275130.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("dbert_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("dbert_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dbert_finetuned| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ksabeh/dbert-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-debiasing_pre_trained_contextualised_embeddings_distil_bert_en.md b/docs/_posts/ahmedlone127/2023-09-14-debiasing_pre_trained_contextualised_embeddings_distil_bert_en.md new file mode 100644 index 00000000000000..083d8a1ca717b2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-debiasing_pre_trained_contextualised_embeddings_distil_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English debiasing_pre_trained_contextualised_embeddings_distil_bert DistilBertEmbeddings from Daniel-Saeedi +author: John Snow Labs +name: debiasing_pre_trained_contextualised_embeddings_distil_bert +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`debiasing_pre_trained_contextualised_embeddings_distil_bert` is a English model originally trained by Daniel-Saeedi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/debiasing_pre_trained_contextualised_embeddings_distil_bert_en_5.1.2_3.0_1694734406797.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/debiasing_pre_trained_contextualised_embeddings_distil_bert_en_5.1.2_3.0_1694734406797.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("debiasing_pre_trained_contextualised_embeddings_distil_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("debiasing_pre_trained_contextualised_embeddings_distil_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|debiasing_pre_trained_contextualised_embeddings_distil_bert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Daniel-Saeedi/debiasing_pre-trained_contextualised_embeddings_distil_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_25lang_cased_xx.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_25lang_cased_xx.md new file mode 100644 index 00000000000000..28b38846c3cf87 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_25lang_cased_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual distilbert_base_25lang_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_25lang_cased +date: 2023-09-14 +tags: [distilbert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_25lang_cased` is a Multilingual model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_25lang_cased_xx_5.1.2_3.0_1694735571253.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_25lang_cased_xx_5.1.2_3.0_1694735571253.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_25lang_cased","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_25lang_cased", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_25lang_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|405.6 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-25lang-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_german_cased_de.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_german_cased_de.md new file mode 100644 index 00000000000000..d7e9c4f2aa5414 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_german_cased_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German distilbert_base_german_cased DistilBertEmbeddings from huggingface +author: John Snow Labs +name: distilbert_base_german_cased +date: 2023-09-14 +tags: [distilbert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_german_cased` is a German model originally trained by huggingface. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_german_cased_de_5.1.2_3.0_1694734364944.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_german_cased_de_5.1.2_3.0_1694734364944.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_german_cased","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_german_cased", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_german_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|250.3 MB| + +## References + +https://huggingface.co/distilbert-base-german-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_german_cased_finetuned_amazon_reviews_de.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_german_cased_finetuned_amazon_reviews_de.md new file mode 100644 index 00000000000000..d06a88ad176f8e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_german_cased_finetuned_amazon_reviews_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German distilbert_base_german_cased_finetuned_amazon_reviews DistilBertEmbeddings from mariav +author: John Snow Labs +name: distilbert_base_german_cased_finetuned_amazon_reviews +date: 2023-09-14 +tags: [distilbert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_german_cased_finetuned_amazon_reviews` is a German model originally trained by mariav. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_german_cased_finetuned_amazon_reviews_de_5.1.2_3.0_1694735975936.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_german_cased_finetuned_amazon_reviews_de_5.1.2_3.0_1694735975936.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_german_cased_finetuned_amazon_reviews","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_german_cased_finetuned_amazon_reviews", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_german_cased_finetuned_amazon_reviews| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|250.3 MB| + +## References + +https://huggingface.co/mariav/distilbert-base-german-cased-finetuned-amazon-reviews \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_multilingual_cased_finetuned_kintweetsE_xx.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_multilingual_cased_finetuned_kintweetsE_xx.md new file mode 100644 index 00000000000000..020608722279aa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_multilingual_cased_finetuned_kintweetsE_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual distilbert_base_multilingual_cased_finetuned_kintweetsE DistilBertEmbeddings from RogerB +author: John Snow Labs +name: distilbert_base_multilingual_cased_finetuned_kintweetsE +date: 2023-09-14 +tags: [distilbert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_multilingual_cased_finetuned_kintweetsE` is a Multilingual model originally trained by RogerB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_multilingual_cased_finetuned_kintweetsE_xx_5.1.2_3.0_1694735872200.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_multilingual_cased_finetuned_kintweetsE_xx_5.1.2_3.0_1694735872200.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_multilingual_cased_finetuned_kintweetsE","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_multilingual_cased_finetuned_kintweetsE", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_multilingual_cased_finetuned_kintweetsE| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|505.4 MB| + +## References + +https://huggingface.co/RogerB/distilbert-base-multilingual-cased-finetuned-kintweetsE \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_spanish_uncased_es.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_spanish_uncased_es.md new file mode 100644 index 00000000000000..c0b42334bbb1f0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_spanish_uncased_es.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Castilian, Spanish distilbert_base_spanish_uncased DistilBertEmbeddings from dccuchile +author: John Snow Labs +name: distilbert_base_spanish_uncased +date: 2023-09-14 +tags: [distilbert, es, open_source, fill_mask, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_spanish_uncased` is a Castilian, Spanish model originally trained by dccuchile. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_spanish_uncased_es_5.1.2_3.0_1694734760876.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_spanish_uncased_es_5.1.2_3.0_1694734760876.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_spanish_uncased","es") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_spanish_uncased", "es") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_spanish_uncased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|es| +|Size:|250.2 MB| + +## References + +https://huggingface.co/dccuchile/distilbert-base-spanish-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_aisera_texts_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_aisera_texts_en.md new file mode 100644 index 00000000000000..f6b77679f58191 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_aisera_texts_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_aisera_texts DistilBertEmbeddings from Theimisa +author: John Snow Labs +name: distilbert_base_uncased_aisera_texts +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_aisera_texts` is a English model originally trained by Theimisa. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_aisera_texts_en_5.1.2_3.0_1694735304909.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_aisera_texts_en_5.1.2_3.0_1694735304909.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_aisera_texts","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_aisera_texts", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_aisera_texts| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Theimisa/distilbert-base-uncased-aisera_texts \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_aisera_texts_v3_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_aisera_texts_v3_en.md new file mode 100644 index 00000000000000..b1058d44cb37fd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_aisera_texts_v3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_aisera_texts_v3 DistilBertEmbeddings from Theimisa +author: John Snow Labs +name: distilbert_base_uncased_aisera_texts_v3 +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_aisera_texts_v3` is a English model originally trained by Theimisa. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_aisera_texts_v3_en_5.1.2_3.0_1694735601782.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_aisera_texts_v3_en_5.1.2_3.0_1694735601782.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_aisera_texts_v3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_aisera_texts_v3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_aisera_texts_v3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Theimisa/distilbert-base-uncased-aisera_texts-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned2_imdb_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned2_imdb_en.md new file mode 100644 index 00000000000000..ebd1686949d1eb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned2_imdb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned2_imdb DistilBertEmbeddings from Ghost1 +author: John Snow Labs +name: distilbert_base_uncased_finetuned2_imdb +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned2_imdb` is a English model originally trained by Ghost1. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned2_imdb_en_5.1.2_3.0_1694734565679.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned2_imdb_en_5.1.2_3.0_1694734565679.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned2_imdb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned2_imdb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned2_imdb| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Ghost1/distilbert-base-uncased-finetuned2-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_CT_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_CT_en.md new file mode 100644 index 00000000000000..f0e25573804f94 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_CT_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_CT DistilBertEmbeddings from anthonyyazdani +author: John Snow Labs +name: distilbert_base_uncased_finetuned_CT +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_CT` is a English model originally trained by anthonyyazdani. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_CT_en_5.1.2_3.0_1694735035198.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_CT_en_5.1.2_3.0_1694735035198.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_CT","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_CT", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_CT| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/anthonyyazdani/distilbert-base-uncased-finetuned-CT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_cvent_2022_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_cvent_2022_en.md new file mode 100644 index 00000000000000..aa0915433f36f4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_cvent_2022_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_cvent_2022 DistilBertEmbeddings from vives +author: John Snow Labs +name: distilbert_base_uncased_finetuned_cvent_2022 +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_cvent_2022` is a English model originally trained by vives. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_cvent_2022_en_5.1.2_3.0_1694735949691.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_cvent_2022_en_5.1.2_3.0_1694735949691.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_cvent_2022","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_cvent_2022", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_cvent_2022| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/vives/distilbert-base-uncased-finetuned-cvent-2022 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_domain_adaptation_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_domain_adaptation_en.md new file mode 100644 index 00000000000000..b255e71e1dff7c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_domain_adaptation_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_domain_adaptation DistilBertEmbeddings from algiraldohe +author: John Snow Labs +name: distilbert_base_uncased_finetuned_domain_adaptation +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_domain_adaptation` is a English model originally trained by algiraldohe. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_domain_adaptation_en_5.1.2_3.0_1694734723970.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_domain_adaptation_en_5.1.2_3.0_1694734723970.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_domain_adaptation","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_domain_adaptation", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_domain_adaptation| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/algiraldohe/distilbert-base-uncased-finetuned-domain-adaptation \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_imdb_accelerate_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_imdb_accelerate_en.md new file mode 100644 index 00000000000000..2c740e9ca86528 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_imdb_accelerate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate DistilBertEmbeddings from liquannan +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate` is a English model originally trained by liquannan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_en_5.1.2_3.0_1694734614129.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_en_5.1.2_3.0_1694734614129.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/liquannan/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_imdb_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_imdb_en.md new file mode 100644 index 00000000000000..e02be7a94464bf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_imdb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb DistilBertEmbeddings from sabby +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb` is a English model originally trained by sabby. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_en_5.1.2_3.0_1694734499155.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_en_5.1.2_3.0_1694734499155.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/sabby/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_imdb_whole_word_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_imdb_whole_word_en.md new file mode 100644 index 00000000000000..7db36d8f23f0b8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_imdb_whole_word_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_whole_word DistilBertEmbeddings from PhysHunter +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_whole_word +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_whole_word` is a English model originally trained by PhysHunter. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_whole_word_en_5.1.2_3.0_1694735407983.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_whole_word_en_5.1.2_3.0_1694735407983.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_whole_word","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_whole_word", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_whole_word| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/PhysHunter/distilbert-base-uncased-finetuned-imdb-whole-word \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_kintweetsE_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_kintweetsE_en.md new file mode 100644 index 00000000000000..f39ccf9f0cf8be --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_kintweetsE_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_kintweetsE DistilBertEmbeddings from RogerB +author: John Snow Labs +name: distilbert_base_uncased_finetuned_kintweetsE +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_kintweetsE` is a English model originally trained by RogerB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_kintweetsE_en_5.1.2_3.0_1694735711522.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_kintweetsE_en_5.1.2_3.0_1694735711522.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_kintweetsE","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_kintweetsE", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_kintweetsE| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/RogerB/distilbert-base-uncased-finetuned-kintweetsE \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_mlm_1_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_mlm_1_en.md new file mode 100644 index 00000000000000..cfc0deb7ec7c34 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_mlm_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_mlm_1 DistilBertEmbeddings from aarroonn22 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_mlm_1 +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_mlm_1` is a English model originally trained by aarroonn22. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_mlm_1_en_5.1.2_3.0_1694735063576.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_mlm_1_en_5.1.2_3.0_1694735063576.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_mlm_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_mlm_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_mlm_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/aarroonn22/distilbert-base-uncased-finetuned-mlm-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_mlm_2_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_mlm_2_en.md new file mode 100644 index 00000000000000..51cc5f5702115c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_mlm_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_mlm_2 DistilBertEmbeddings from aarroonn22 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_mlm_2 +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_mlm_2` is a English model originally trained by aarroonn22. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_mlm_2_en_5.1.2_3.0_1694734954635.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_mlm_2_en_5.1.2_3.0_1694734954635.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_mlm_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_mlm_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_mlm_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/aarroonn22/distilbert-base-uncased-finetuned-mlm-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_nitro_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_nitro_en.md new file mode 100644 index 00000000000000..ac84a61ffad808 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_nitro_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_nitro DistilBertEmbeddings from dieexbr +author: John Snow Labs +name: distilbert_base_uncased_finetuned_nitro +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_nitro` is a English model originally trained by dieexbr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_nitro_en_5.1.2_3.0_1694735152823.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_nitro_en_5.1.2_3.0_1694735152823.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_nitro","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_nitro", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_nitro| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/dieexbr/distilbert-base-uncased-finetuned-nitro \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_recipe_accelerate_1_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_recipe_accelerate_1_en.md new file mode 100644 index 00000000000000..4762deca6f5c34 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_recipe_accelerate_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_recipe_accelerate_1 DistilBertEmbeddings from CennetOguz +author: John Snow Labs +name: distilbert_base_uncased_finetuned_recipe_accelerate_1 +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_recipe_accelerate_1` is a English model originally trained by CennetOguz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_recipe_accelerate_1_en_5.1.2_3.0_1694734929488.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_recipe_accelerate_1_en_5.1.2_3.0_1694734929488.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_recipe_accelerate_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_recipe_accelerate_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_recipe_accelerate_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_recipe_accelerate_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_recipe_accelerate_en.md new file mode 100644 index 00000000000000..6413138f4702d8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_recipe_accelerate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_recipe_accelerate DistilBertEmbeddings from CennetOguz +author: John Snow Labs +name: distilbert_base_uncased_finetuned_recipe_accelerate +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_recipe_accelerate` is a English model originally trained by CennetOguz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_recipe_accelerate_en_5.1.2_3.0_1694735054267.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_recipe_accelerate_en_5.1.2_3.0_1694735054267.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_recipe_accelerate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_recipe_accelerate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_recipe_accelerate| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_speeches_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_speeches_en.md new file mode 100644 index 00000000000000..dde8b49ff246fa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_speeches_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_speeches DistilBertEmbeddings from peterday +author: John Snow Labs +name: distilbert_base_uncased_finetuned_speeches +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_speeches` is a English model originally trained by peterday. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_speeches_en_5.1.2_3.0_1694734918396.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_speeches_en_5.1.2_3.0_1694734918396.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_speeches","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_speeches", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_speeches| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/peterday/distilbert-base-uncased-finetuned-speeches \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_squad_d5716d28_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_squad_d5716d28_en.md new file mode 100644 index 00000000000000..a1389289293835 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_finetuned_squad_d5716d28_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28 DistilBertEmbeddings from ysugawa +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28 +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28` is a English model originally trained by ysugawa. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_en_5.1.2_3.0_1694734604331.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_en_5.1.2_3.0_1694734604331.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ysugawa/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_linkedin_domain_adaptation_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_linkedin_domain_adaptation_en.md new file mode 100644 index 00000000000000..bd21c33cb2c48e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_base_uncased_linkedin_domain_adaptation_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_linkedin_domain_adaptation DistilBertEmbeddings from algiraldohe +author: John Snow Labs +name: distilbert_base_uncased_linkedin_domain_adaptation +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_linkedin_domain_adaptation` is a English model originally trained by algiraldohe. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_linkedin_domain_adaptation_en_5.1.2_3.0_1694735170425.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_linkedin_domain_adaptation_en_5.1.2_3.0_1694735170425.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_linkedin_domain_adaptation","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_linkedin_domain_adaptation", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_linkedin_domain_adaptation| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/algiraldohe/distilbert-base-uncased-linkedin-domain-adaptation \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_embeddings_clinical_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_embeddings_clinical_en.md new file mode 100644 index 00000000000000..a99e07c322312b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_embeddings_clinical_en.md @@ -0,0 +1,101 @@ +--- +layout: model +title: English Bert Embeddings model (from nlpie) +author: John Snow Labs +name: distilbert_embeddings_clinical +date: 2023-09-14 +tags: [open_source, distilbert, distilbert_embeddings, distilbertformaskedlm, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertForMaskedLM model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `clinical-distilbert` is a English model originally trained by `nlpie`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_clinical_en_5.1.2_3.0_1694735741619.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_clinical_en_5.1.2_3.0_1694735741619.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_clinical","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark-NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_clinical","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark-NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_clinical| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|243.6 MB| +|Case sensitive:|false| + +## References + +References + +https://huggingface.co/nlpie/clinical-distilbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_fa_zwnj_base_MLM_pquad_2_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_fa_zwnj_base_MLM_pquad_2_en.md new file mode 100644 index 00000000000000..1031457c93e365 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_fa_zwnj_base_MLM_pquad_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_fa_zwnj_base_MLM_pquad_2 DistilBertEmbeddings from Gholamreza +author: John Snow Labs +name: distilbert_fa_zwnj_base_MLM_pquad_2 +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_fa_zwnj_base_MLM_pquad_2` is a English model originally trained by Gholamreza. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_fa_zwnj_base_MLM_pquad_2_en_5.1.2_3.0_1694734812480.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_fa_zwnj_base_MLM_pquad_2_en_5.1.2_3.0_1694734812480.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_fa_zwnj_base_MLM_pquad_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_fa_zwnj_base_MLM_pquad_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_fa_zwnj_base_MLM_pquad_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|282.0 MB| + +## References + +https://huggingface.co/Gholamreza/distilbert-fa-zwnj-base-MLM-pquad_2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_fa_zwnj_base_MLM_pquad_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_fa_zwnj_base_MLM_pquad_en.md new file mode 100644 index 00000000000000..1f8209075e4924 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_fa_zwnj_base_MLM_pquad_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_fa_zwnj_base_MLM_pquad DistilBertEmbeddings from Gholamreza +author: John Snow Labs +name: distilbert_fa_zwnj_base_MLM_pquad +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_fa_zwnj_base_MLM_pquad` is a English model originally trained by Gholamreza. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_fa_zwnj_base_MLM_pquad_en_5.1.2_3.0_1694734709513.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_fa_zwnj_base_MLM_pquad_en_5.1.2_3.0_1694734709513.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_fa_zwnj_base_MLM_pquad","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_fa_zwnj_base_MLM_pquad", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_fa_zwnj_base_MLM_pquad| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|281.8 MB| + +## References + +https://huggingface.co/Gholamreza/distilbert-fa-zwnj-base-MLM-pquad \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distilbert_v1_en.md b/docs/_posts/ahmedlone127/2023-09-14-distilbert_v1_en.md new file mode 100644 index 00000000000000..1b4e103da6b3d4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distilbert_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_v1 DistilBertEmbeddings from Amirosein +author: John Snow Labs +name: distilbert_v1 +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_v1` is a English model originally trained by Amirosein. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_v1_en_5.1.2_3.0_1694734658336.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_v1_en_5.1.2_3.0_1694734658336.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_v1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|276.0 MB| + +## References + +https://huggingface.co/Amirosein/distilbert_v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-distillbert_base_spanish_uncased_finetuned_spanish_corpus_en.md b/docs/_posts/ahmedlone127/2023-09-14-distillbert_base_spanish_uncased_finetuned_spanish_corpus_en.md new file mode 100644 index 00000000000000..45e1a309454f20 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-distillbert_base_spanish_uncased_finetuned_spanish_corpus_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distillbert_base_spanish_uncased_finetuned_spanish_corpus DistilBertEmbeddings from franfram +author: John Snow Labs +name: distillbert_base_spanish_uncased_finetuned_spanish_corpus +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distillbert_base_spanish_uncased_finetuned_spanish_corpus` is a English model originally trained by franfram. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distillbert_base_spanish_uncased_finetuned_spanish_corpus_en_5.1.2_3.0_1694734373857.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distillbert_base_spanish_uncased_finetuned_spanish_corpus_en_5.1.2_3.0_1694734373857.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distillbert_base_spanish_uncased_finetuned_spanish_corpus","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distillbert_base_spanish_uncased_finetuned_spanish_corpus", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distillbert_base_spanish_uncased_finetuned_spanish_corpus| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|250.2 MB| + +## References + +https://huggingface.co/franfram/distillbert-base-spanish-uncased-finetuned-spanish-corpus \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-dummy_model_sasikarn_en.md b/docs/_posts/ahmedlone127/2023-09-14-dummy_model_sasikarn_en.md new file mode 100644 index 00000000000000..358517d641bf8e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-dummy_model_sasikarn_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dummy_model_sasikarn BertEmbeddings from Sasikarn +author: John Snow Labs +name: dummy_model_sasikarn +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dummy_model_sasikarn` is a English model originally trained by Sasikarn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dummy_model_sasikarn_en_5.1.1_3.0_1694675578031.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dummy_model_sasikarn_en_5.1.1_3.0_1694675578031.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("dummy_model_sasikarn","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("dummy_model_sasikarn", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dummy_model_sasikarn| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|403.6 MB| + +## References + +https://huggingface.co/Sasikarn/dummy-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-fi_mbertmodel_mberttok_en.md b/docs/_posts/ahmedlone127/2023-09-14-fi_mbertmodel_mberttok_en.md new file mode 100644 index 00000000000000..fd76997befd39f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-fi_mbertmodel_mberttok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English fi_mbertmodel_mberttok BertEmbeddings from hgiyt +author: John Snow Labs +name: fi_mbertmodel_mberttok +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fi_mbertmodel_mberttok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fi_mbertmodel_mberttok_en_5.1.1_3.0_1694693637004.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fi_mbertmodel_mberttok_en_5.1.1_3.0_1694693637004.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("fi_mbertmodel_mberttok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("fi_mbertmodel_mberttok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fi_mbertmodel_mberttok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|495.8 MB| + +## References + +https://huggingface.co/hgiyt/fi-mbertmodel-mberttok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-fi_mbertmodel_monotok_adapter_en.md b/docs/_posts/ahmedlone127/2023-09-14-fi_mbertmodel_monotok_adapter_en.md new file mode 100644 index 00000000000000..240a390a9027bc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-fi_mbertmodel_monotok_adapter_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English fi_mbertmodel_monotok_adapter BertEmbeddings from hgiyt +author: John Snow Labs +name: fi_mbertmodel_monotok_adapter +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fi_mbertmodel_monotok_adapter` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fi_mbertmodel_monotok_adapter_en_5.1.1_3.0_1694693799221.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fi_mbertmodel_monotok_adapter_en_5.1.1_3.0_1694693799221.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("fi_mbertmodel_monotok_adapter","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("fi_mbertmodel_monotok_adapter", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fi_mbertmodel_monotok_adapter| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.9 MB| + +## References + +https://huggingface.co/hgiyt/fi-mbertmodel-monotok-adapter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-fi_mbertmodel_monotok_en.md b/docs/_posts/ahmedlone127/2023-09-14-fi_mbertmodel_monotok_en.md new file mode 100644 index 00000000000000..44d484cfba9eac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-fi_mbertmodel_monotok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English fi_mbertmodel_monotok BertEmbeddings from hgiyt +author: John Snow Labs +name: fi_mbertmodel_monotok +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fi_mbertmodel_monotok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fi_mbertmodel_monotok_en_5.1.1_3.0_1694693956177.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fi_mbertmodel_monotok_en_5.1.1_3.0_1694693956177.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("fi_mbertmodel_monotok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("fi_mbertmodel_monotok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fi_mbertmodel_monotok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.8 MB| + +## References + +https://huggingface.co/hgiyt/fi-mbertmodel-monotok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-fi_monomodel_mberttok_en.md b/docs/_posts/ahmedlone127/2023-09-14-fi_monomodel_mberttok_en.md new file mode 100644 index 00000000000000..b17ee8b1448d47 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-fi_monomodel_mberttok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English fi_monomodel_mberttok BertEmbeddings from hgiyt +author: John Snow Labs +name: fi_monomodel_mberttok +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fi_monomodel_mberttok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fi_monomodel_mberttok_en_5.1.1_3.0_1694694133556.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fi_monomodel_mberttok_en_5.1.1_3.0_1694694133556.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("fi_monomodel_mberttok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("fi_monomodel_mberttok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fi_monomodel_mberttok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|495.0 MB| + +## References + +https://huggingface.co/hgiyt/fi-monomodel-mberttok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-fi_monomodel_monotok_en.md b/docs/_posts/ahmedlone127/2023-09-14-fi_monomodel_monotok_en.md new file mode 100644 index 00000000000000..07d9a6c53cd28e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-fi_monomodel_monotok_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English fi_monomodel_monotok BertEmbeddings from hgiyt +author: John Snow Labs +name: fi_monomodel_monotok +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fi_monomodel_monotok` is a English model originally trained by hgiyt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fi_monomodel_monotok_en_5.1.1_3.0_1694694310500.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fi_monomodel_monotok_en_5.1.1_3.0_1694694310500.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("fi_monomodel_monotok","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("fi_monomodel_monotok", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fi_monomodel_monotok| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|464.3 MB| + +## References + +https://huggingface.co/hgiyt/fi-monomodel-monotok \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-first_try_4_en.md b/docs/_posts/ahmedlone127/2023-09-14-first_try_4_en.md new file mode 100644 index 00000000000000..7eec5d6f9072f4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-first_try_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English first_try_4 DistilBertEmbeddings from disanda +author: John Snow Labs +name: first_try_4 +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`first_try_4` is a English model originally trained by disanda. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/first_try_4_en_5.1.2_3.0_1694735581056.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/first_try_4_en_5.1.2_3.0_1694735581056.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("first_try_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("first_try_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|first_try_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/disanda/first_try_4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-google_Job_data_tuned_trial_1_en.md b/docs/_posts/ahmedlone127/2023-09-14-google_Job_data_tuned_trial_1_en.md new file mode 100644 index 00000000000000..054ab1e0a89b4d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-google_Job_data_tuned_trial_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English google_Job_data_tuned_trial_1 DistilBertEmbeddings from EslamAhmed +author: John Snow Labs +name: google_Job_data_tuned_trial_1 +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`google_Job_data_tuned_trial_1` is a English model originally trained by EslamAhmed. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/google_Job_data_tuned_trial_1_en_5.1.2_3.0_1694734998102.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/google_Job_data_tuned_trial_1_en_5.1.2_3.0_1694734998102.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("google_Job_data_tuned_trial_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("google_Job_data_tuned_trial_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|google_Job_data_tuned_trial_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.3 MB| + +## References + +https://huggingface.co/EslamAhmed/google_Job_data_tuned_trial_1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-hf_distilbert_imdb_mlm_cosine_en.md b/docs/_posts/ahmedlone127/2023-09-14-hf_distilbert_imdb_mlm_cosine_en.md new file mode 100644 index 00000000000000..f2a91829c87dac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-hf_distilbert_imdb_mlm_cosine_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English hf_distilbert_imdb_mlm_cosine DistilBertEmbeddings from nos1de +author: John Snow Labs +name: hf_distilbert_imdb_mlm_cosine +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hf_distilbert_imdb_mlm_cosine` is a English model originally trained by nos1de. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hf_distilbert_imdb_mlm_cosine_en_5.1.2_3.0_1694734368264.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hf_distilbert_imdb_mlm_cosine_en_5.1.2_3.0_1694734368264.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("hf_distilbert_imdb_mlm_cosine","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("hf_distilbert_imdb_mlm_cosine", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hf_distilbert_imdb_mlm_cosine| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/nos1de/hf-distilbert-imdb-mlm-cosine \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-louribert_more_tokens_zahrabahmani61_en.md b/docs/_posts/ahmedlone127/2023-09-14-louribert_more_tokens_zahrabahmani61_en.md new file mode 100644 index 00000000000000..b267727fc08d49 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-louribert_more_tokens_zahrabahmani61_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English louribert_more_tokens_zahrabahmani61 BertEmbeddings from zahrabahmani61 +author: John Snow Labs +name: louribert_more_tokens_zahrabahmani61 +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`louribert_more_tokens_zahrabahmani61` is a English model originally trained by zahrabahmani61. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/louribert_more_tokens_zahrabahmani61_en_5.1.1_3.0_1694674505303.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/louribert_more_tokens_zahrabahmani61_en_5.1.1_3.0_1694674505303.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("louribert_more_tokens_zahrabahmani61","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("louribert_more_tokens_zahrabahmani61", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|louribert_more_tokens_zahrabahmani61| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|668.1 MB| + +## References + +https://huggingface.co/zahrabahmani61/LouriBert_more_tokens \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-lsg16k_Italian_Legal_BERT_it.md b/docs/_posts/ahmedlone127/2023-09-14-lsg16k_Italian_Legal_BERT_it.md new file mode 100644 index 00000000000000..9db6537f150c15 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-lsg16k_Italian_Legal_BERT_it.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Italian lsg16k_Italian_Legal_BERT BertEmbeddings from dlicari +author: John Snow Labs +name: lsg16k_Italian_Legal_BERT +date: 2023-09-14 +tags: [bert, it, open_source, fill_mask, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lsg16k_Italian_Legal_BERT` is a Italian model originally trained by dlicari. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lsg16k_Italian_Legal_BERT_it_5.1.1_3.0_1694693338486.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lsg16k_Italian_Legal_BERT_it_5.1.1_3.0_1694693338486.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("lsg16k_Italian_Legal_BERT","it") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("lsg16k_Italian_Legal_BERT", "it") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lsg16k_Italian_Legal_BERT| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|it| +|Size:|454.6 MB| + +## References + +https://huggingface.co/dlicari/lsg16k-Italian-Legal-BERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-marathi_distilbert_mr.md b/docs/_posts/ahmedlone127/2023-09-14-marathi_distilbert_mr.md new file mode 100644 index 00000000000000..5e73fdeb53bf6e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-marathi_distilbert_mr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Marathi marathi_distilbert DistilBertEmbeddings from DarshanDeshpande +author: John Snow Labs +name: marathi_distilbert +date: 2023-09-14 +tags: [distilbert, mr, open_source, fill_mask, onnx] +task: Embeddings +language: mr +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`marathi_distilbert` is a Marathi model originally trained by DarshanDeshpande. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/marathi_distilbert_mr_5.1.2_3.0_1694735432160.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/marathi_distilbert_mr_5.1.2_3.0_1694735432160.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("marathi_distilbert","mr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("marathi_distilbert", "mr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|marathi_distilbert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|mr| +|Size:|247.5 MB| + +## References + +https://huggingface.co/DarshanDeshpande/marathi-distilbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-mdistilbertV3.1_en.md b/docs/_posts/ahmedlone127/2023-09-14-mdistilbertV3.1_en.md new file mode 100644 index 00000000000000..a780a45ca4aaca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-mdistilbertV3.1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mdistilbertV3.1 DistilBertEmbeddings from bongsoo +author: John Snow Labs +name: mdistilbertV3.1 +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mdistilbertV3.1` is a English model originally trained by bongsoo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mdistilbertV3.1_en_5.1.2_3.0_1694735345489.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mdistilbertV3.1_en_5.1.2_3.0_1694735345489.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("mdistilbertV3.1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("mdistilbertV3.1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mdistilbertV3.1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|621.1 MB| + +## References + +https://huggingface.co/bongsoo/mdistilbertV3.1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-mlm_model_en.md b/docs/_posts/ahmedlone127/2023-09-14-mlm_model_en.md new file mode 100644 index 00000000000000..342d9ff186049d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-mlm_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_model DistilBertEmbeddings from amkorba +author: John Snow Labs +name: mlm_model +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_model` is a English model originally trained by amkorba. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_model_en_5.1.2_3.0_1694735450221.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_model_en_5.1.2_3.0_1694735450221.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("mlm_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("mlm_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_model| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/amkorba/mlm-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-multilingual_bert_model_classiffication_xx.md b/docs/_posts/ahmedlone127/2023-09-14-multilingual_bert_model_classiffication_xx.md new file mode 100644 index 00000000000000..2bf7dd4ebb7d34 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-multilingual_bert_model_classiffication_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual multilingual_bert_model_classiffication BertEmbeddings from billfass +author: John Snow Labs +name: multilingual_bert_model_classiffication +date: 2023-09-14 +tags: [bert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multilingual_bert_model_classiffication` is a Multilingual model originally trained by billfass. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/multilingual_bert_model_classiffication_xx_5.1.1_3.0_1694675880530.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multilingual_bert_model_classiffication_xx_5.1.1_3.0_1694675880530.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("multilingual_bert_model_classiffication","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("multilingual_bert_model_classiffication", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|multilingual_bert_model_classiffication| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|663.2 MB| + +## References + +https://huggingface.co/billfass/multilingual_bert_model_classiffication \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-news_contrastive_pretrain_en.md b/docs/_posts/ahmedlone127/2023-09-14-news_contrastive_pretrain_en.md new file mode 100644 index 00000000000000..6490b428eea3e0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-news_contrastive_pretrain_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English news_contrastive_pretrain BertEmbeddings from yyu +author: John Snow Labs +name: news_contrastive_pretrain +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`news_contrastive_pretrain` is a English model originally trained by yyu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/news_contrastive_pretrain_en_5.1.1_3.0_1694675368213.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/news_contrastive_pretrain_en_5.1.1_3.0_1694675368213.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("news_contrastive_pretrain","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("news_contrastive_pretrain", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|news_contrastive_pretrain| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.3 MB| + +## References + +https://huggingface.co/yyu/news_contrastive_pretrain \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-remote_sensing_distilbert_cased_en.md b/docs/_posts/ahmedlone127/2023-09-14-remote_sensing_distilbert_cased_en.md new file mode 100644 index 00000000000000..08457f46c61478 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-remote_sensing_distilbert_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English remote_sensing_distilbert_cased DistilBertEmbeddings from Chramer +author: John Snow Labs +name: remote_sensing_distilbert_cased +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`remote_sensing_distilbert_cased` is a English model originally trained by Chramer. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/remote_sensing_distilbert_cased_en_5.1.2_3.0_1694735724243.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/remote_sensing_distilbert_cased_en_5.1.2_3.0_1694735724243.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("remote_sensing_distilbert_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("remote_sensing_distilbert_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|remote_sensing_distilbert_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|243.7 MB| + +## References + +https://huggingface.co/Chramer/remote-sensing-distilbert-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-review_contrastive_pretrain_en.md b/docs/_posts/ahmedlone127/2023-09-14-review_contrastive_pretrain_en.md new file mode 100644 index 00000000000000..71b5eb863ebba0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-review_contrastive_pretrain_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English review_contrastive_pretrain BertEmbeddings from yyu +author: John Snow Labs +name: review_contrastive_pretrain +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`review_contrastive_pretrain` is a English model originally trained by yyu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/review_contrastive_pretrain_en_5.1.1_3.0_1694676140447.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/review_contrastive_pretrain_en_5.1.1_3.0_1694676140447.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("review_contrastive_pretrain","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("review_contrastive_pretrain", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|review_contrastive_pretrain| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.3 MB| + +## References + +https://huggingface.co/yyu/review_contrastive_pretrain \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-train_mask_language_model_en.md b/docs/_posts/ahmedlone127/2023-09-14-train_mask_language_model_en.md new file mode 100644 index 00000000000000..fefc807be955e1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-train_mask_language_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English train_mask_language_model DistilBertEmbeddings from hubert10 +author: John Snow Labs +name: train_mask_language_model +date: 2023-09-14 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`train_mask_language_model` is a English model originally trained by hubert10. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/train_mask_language_model_en_5.1.2_3.0_1694735909215.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/train_mask_language_model_en_5.1.2_3.0_1694735909215.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("train_mask_language_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("train_mask_language_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|train_mask_language_model| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/hubert10/train_mask_language_model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-14-wiki_contrastive_pretrain_en.md b/docs/_posts/ahmedlone127/2023-09-14-wiki_contrastive_pretrain_en.md new file mode 100644 index 00000000000000..f5c798e105a71a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-14-wiki_contrastive_pretrain_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English wiki_contrastive_pretrain BertEmbeddings from yyu +author: John Snow Labs +name: wiki_contrastive_pretrain +date: 2023-09-14 +tags: [bert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.1 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`wiki_contrastive_pretrain` is a English model originally trained by yyu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/wiki_contrastive_pretrain_en_5.1.1_3.0_1694676032643.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/wiki_contrastive_pretrain_en_5.1.1_3.0_1694676032643.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =BertEmbeddings.pretrained("wiki_contrastive_pretrain","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = BertEmbeddings + .pretrained("wiki_contrastive_pretrain", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|wiki_contrastive_pretrain| +|Compatibility:|Spark NLP 5.1.1+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|407.4 MB| + +## References + +https://huggingface.co/yyu/wiki_contrastive_pretrain \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-debiasing_pre_trained_contextualised_embeddings_distil_bert_en.md b/docs/_posts/ahmedlone127/2023-09-15-debiasing_pre_trained_contextualised_embeddings_distil_bert_en.md new file mode 100644 index 00000000000000..f7f40c0501d222 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-debiasing_pre_trained_contextualised_embeddings_distil_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English debiasing_pre_trained_contextualised_embeddings_distil_bert DistilBertEmbeddings from Daniel-Saeedi +author: John Snow Labs +name: debiasing_pre_trained_contextualised_embeddings_distil_bert +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`debiasing_pre_trained_contextualised_embeddings_distil_bert` is a English model originally trained by Daniel-Saeedi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/debiasing_pre_trained_contextualised_embeddings_distil_bert_en_5.1.2_3.0_1694736696151.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/debiasing_pre_trained_contextualised_embeddings_distil_bert_en_5.1.2_3.0_1694736696151.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("debiasing_pre_trained_contextualised_embeddings_distil_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("debiasing_pre_trained_contextualised_embeddings_distil_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|debiasing_pre_trained_contextualised_embeddings_distil_bert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Daniel-Saeedi/debiasing_pre-trained_contextualised_embeddings_distil_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_en_ar_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_en_ar_cased_en.md new file mode 100644 index 00000000000000..6a41bd16c4a3ad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_en_ar_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_en_ar_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_en_ar_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_en_ar_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_en_ar_cased_en_5.1.2_3.0_1694736147424.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_en_ar_cased_en_5.1.2_3.0_1694736147424.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_en_ar_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_en_ar_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_en_ar_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|252.8 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-ar-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_en_bg_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_en_bg_cased_en.md new file mode 100644 index 00000000000000..0cebd8e9fc7bfc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_en_bg_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_en_bg_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_en_bg_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_en_bg_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_en_bg_cased_en_5.1.2_3.0_1694736259560.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_en_bg_cased_en_5.1.2_3.0_1694736259560.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_en_bg_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_en_bg_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_en_bg_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|260.5 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-bg-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_german_cased_de.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_german_cased_de.md new file mode 100644 index 00000000000000..cdb44bb6e9fe85 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_german_cased_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German distilbert_base_german_cased DistilBertEmbeddings from huggingface +author: John Snow Labs +name: distilbert_base_german_cased +date: 2023-09-15 +tags: [distilbert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_german_cased` is a German model originally trained by huggingface. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_german_cased_de_5.1.2_3.0_1694736667910.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_german_cased_de_5.1.2_3.0_1694736667910.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_german_cased","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_german_cased", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_german_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|250.3 MB| + +## References + +https://huggingface.co/distilbert-base-german-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned2_imdb_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned2_imdb_en.md new file mode 100644 index 00000000000000..288f7f544c168e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned2_imdb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned2_imdb DistilBertEmbeddings from Ghost1 +author: John Snow Labs +name: distilbert_base_uncased_finetuned2_imdb +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned2_imdb` is a English model originally trained by Ghost1. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned2_imdb_en_5.1.2_3.0_1694736806970.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned2_imdb_en_5.1.2_3.0_1694736806970.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned2_imdb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned2_imdb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned2_imdb| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Ghost1/distilbert-base-uncased-finetuned2-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_civi_cooments_accelerate_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_civi_cooments_accelerate_en.md new file mode 100644 index 00000000000000..bbe130d0a2833d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_civi_cooments_accelerate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_civi_cooments_accelerate DistilBertEmbeddings from SmartPy +author: John Snow Labs +name: distilbert_base_uncased_finetuned_civi_cooments_accelerate +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_civi_cooments_accelerate` is a English model originally trained by SmartPy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_civi_cooments_accelerate_en_5.1.2_3.0_1694736212479.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_civi_cooments_accelerate_en_5.1.2_3.0_1694736212479.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_civi_cooments_accelerate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_civi_cooments_accelerate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_civi_cooments_accelerate| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/SmartPy/distilbert-base-uncased-finetuned-civi-cooments-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_cvent_2019_2022_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_cvent_2019_2022_en.md new file mode 100644 index 00000000000000..fc23d579765c5f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_cvent_2019_2022_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_cvent_2019_2022 DistilBertEmbeddings from vives +author: John Snow Labs +name: distilbert_base_uncased_finetuned_cvent_2019_2022 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_cvent_2019_2022` is a English model originally trained by vives. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_cvent_2019_2022_en_5.1.2_3.0_1694736129695.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_cvent_2019_2022_en_5.1.2_3.0_1694736129695.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_cvent_2019_2022","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_cvent_2019_2022", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_cvent_2019_2022| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/vives/distilbert-base-uncased-finetuned-cvent-2019_2022 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_homedepot_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_homedepot_en.md new file mode 100644 index 00000000000000..e958f9e7829bd9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_homedepot_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_homedepot DistilBertEmbeddings from Ukhushn +author: John Snow Labs +name: distilbert_base_uncased_finetuned_homedepot +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_homedepot` is a English model originally trained by Ukhushn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_homedepot_en_5.1.2_3.0_1694736318696.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_homedepot_en_5.1.2_3.0_1694736318696.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_homedepot","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_homedepot", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_homedepot| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Ukhushn/distilbert-base-uncased-finetuned-homedepot \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_ghost1_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_ghost1_en.md new file mode 100644 index 00000000000000..59397cf11b26c3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_ghost1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_ghost1 DistilBertEmbeddings from Ghost1 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_ghost1 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_ghost1` is a English model originally trained by Ghost1. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_ghost1_en_5.1.2_3.0_1694736935663.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_ghost1_en_5.1.2_3.0_1694736935663.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_ghost1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_ghost1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_ghost1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Ghost1/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_liquannan_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_liquannan_en.md new file mode 100644 index 00000000000000..497cf8cf207580 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_liquannan_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_liquannan DistilBertEmbeddings from liquannan +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_liquannan +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_liquannan` is a English model originally trained by liquannan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_liquannan_en_5.1.2_3.0_1694736911779.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_liquannan_en_5.1.2_3.0_1694736911779.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_liquannan","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_liquannan", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_liquannan| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/liquannan/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sabby_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sabby_en.md new file mode 100644 index 00000000000000..155b5987b5e13d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sabby_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_sabby DistilBertEmbeddings from sabby +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_sabby +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_sabby` is a English model originally trained by sabby. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sabby_en_5.1.2_3.0_1694736793793.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sabby_en_5.1.2_3.0_1694736793793.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_sabby","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_sabby", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_sabby| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/sabby/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_ysugawa_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_ysugawa_en.md new file mode 100644 index 00000000000000..1926befd573ad4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_ysugawa_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_ysugawa DistilBertEmbeddings from ysugawa +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_ysugawa +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_ysugawa` is a English model originally trained by ysugawa. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_ysugawa_en_5.1.2_3.0_1694736922524.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_ysugawa_en_5.1.2_3.0_1694736922524.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_ysugawa","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_ysugawa", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_ysugawa| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ysugawa/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_v1_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_v1_en.md new file mode 100644 index 00000000000000..661174247fb785 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_v1 DistilBertEmbeddings from Amirosein +author: John Snow Labs +name: distilbert_v1 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_v1` is a English model originally trained by Amirosein. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_v1_en_5.1.2_3.0_1694736931572.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_v1_en_5.1.2_3.0_1694736931572.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_v1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|276.0 MB| + +## References + +https://huggingface.co/Amirosein/distilbert_v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distillbert_base_spanish_uncased_finetuned_spanish_corpus_en.md b/docs/_posts/ahmedlone127/2023-09-15-distillbert_base_spanish_uncased_finetuned_spanish_corpus_en.md new file mode 100644 index 00000000000000..29817fb96f563c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distillbert_base_spanish_uncased_finetuned_spanish_corpus_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distillbert_base_spanish_uncased_finetuned_spanish_corpus DistilBertEmbeddings from franfram +author: John Snow Labs +name: distillbert_base_spanish_uncased_finetuned_spanish_corpus +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distillbert_base_spanish_uncased_finetuned_spanish_corpus` is a English model originally trained by franfram. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distillbert_base_spanish_uncased_finetuned_spanish_corpus_en_5.1.2_3.0_1694736667906.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distillbert_base_spanish_uncased_finetuned_spanish_corpus_en_5.1.2_3.0_1694736667906.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distillbert_base_spanish_uncased_finetuned_spanish_corpus","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distillbert_base_spanish_uncased_finetuned_spanish_corpus", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distillbert_base_spanish_uncased_finetuned_spanish_corpus| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|250.2 MB| + +## References + +https://huggingface.co/franfram/distillbert-base-spanish-uncased-finetuned-spanish-corpus \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-film20000distilbert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-15-film20000distilbert_base_uncased_en.md new file mode 100644 index 00000000000000..74d1ca7c856994 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-film20000distilbert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English film20000distilbert_base_uncased DistilBertEmbeddings from AmaiaSolaun +author: John Snow Labs +name: film20000distilbert_base_uncased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`film20000distilbert_base_uncased` is a English model originally trained by AmaiaSolaun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/film20000distilbert_base_uncased_en_5.1.2_3.0_1694736242255.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/film20000distilbert_base_uncased_en_5.1.2_3.0_1694736242255.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("film20000distilbert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("film20000distilbert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|film20000distilbert_base_uncased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.3 MB| + +## References + +https://huggingface.co/AmaiaSolaun/film20000distilbert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-google_Job_data_tuned_trial_2_11_2_2022_en.md b/docs/_posts/ahmedlone127/2023-09-15-google_Job_data_tuned_trial_2_11_2_2022_en.md new file mode 100644 index 00000000000000..53f8d0bd36e848 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-google_Job_data_tuned_trial_2_11_2_2022_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English google_Job_data_tuned_trial_2_11_2_2022 DistilBertEmbeddings from EslamAhmed +author: John Snow Labs +name: google_Job_data_tuned_trial_2_11_2_2022 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`google_Job_data_tuned_trial_2_11_2_2022` is a English model originally trained by EslamAhmed. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/google_Job_data_tuned_trial_2_11_2_2022_en_5.1.2_3.0_1694736099439.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/google_Job_data_tuned_trial_2_11_2_2022_en_5.1.2_3.0_1694736099439.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("google_Job_data_tuned_trial_2_11_2_2022","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("google_Job_data_tuned_trial_2_11_2_2022", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|google_Job_data_tuned_trial_2_11_2_2022| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.3 MB| + +## References + +https://huggingface.co/EslamAhmed/google_Job_data_tuned_trial_2_11-2-2022 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-hf_distilbert_imdb_mlm_cosine_en.md b/docs/_posts/ahmedlone127/2023-09-15-hf_distilbert_imdb_mlm_cosine_en.md new file mode 100644 index 00000000000000..f1b4ae3231f99c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-hf_distilbert_imdb_mlm_cosine_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English hf_distilbert_imdb_mlm_cosine DistilBertEmbeddings from nos1de +author: John Snow Labs +name: hf_distilbert_imdb_mlm_cosine +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hf_distilbert_imdb_mlm_cosine` is a English model originally trained by nos1de. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hf_distilbert_imdb_mlm_cosine_en_5.1.2_3.0_1694736657876.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hf_distilbert_imdb_mlm_cosine_en_5.1.2_3.0_1694736657876.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("hf_distilbert_imdb_mlm_cosine","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("hf_distilbert_imdb_mlm_cosine", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hf_distilbert_imdb_mlm_cosine| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/nos1de/hf-distilbert-imdb-mlm-cosine \ No newline at end of file From d9fdbf04e11f7900a1b26d5a58ca6a74d0b4654b Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Sat, 16 Sep 2023 18:00:47 +0700 Subject: [PATCH 45/55] 2023-09-15-distilbert_base_german_cased_de (#13984) * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_jaese_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_apatidar0_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_gg1313_en * Add model 2023-09-15-we4lkd_aml_distilbert_1921_2006_en * Add model 2023-09-15-distilbert_mlm_best_en * Add model 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2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_yangwooko_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_cchychen_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_gautam1989_en * Add model 2023-09-15-distilbert_embeddings_base_uncased_finetuned_imdb_accelerate_en * Add model 2023-09-15-bertino_lsg_en * Add model 2023-09-15-distilbert_base_spanish_uncased_model_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_cssupport_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_terps_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_vanhoan_en * Add model 2023-09-15-fine_tuned_distilbert_nosql_injection_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_elggman_en * Add model 2023-09-15-sae_distilbert_base_uncased_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_iotengtr_en * Add model 2023-09-15-distilbert_base_uncased_malayalam_arxiv_papers_en * Add model 2023-09-15-clr_pretrained_distilbert_base_uncased_en * Add model 2023-09-15-we4lkd_aml_distilbert_1921_2020_en * Add model 2023-09-15-we4lkd_aml_distilbert_1921_2011_en * Add model 2023-09-15-distilbert_base_uncased_imdb_distilbert_en * Add model 2023-09-15-inisw08_distilbert_mlm_adamw_torch_fused_en * Add model 2023-09-15-distilbert_mlm_500k_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_eusojk_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_crypto_en * Add model 2023-09-15-spladex_zs_en * Add model 2023-09-15-hf_distilbert_imdb_mlm_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_fadliaulawi_en * Add model 2023-09-15-distilabena_base_asante_twi_uncased_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_hilariooliveira_en * Add model 2023-09-15-distilbert_classification_eplorer_en * Add model 2023-09-15-distilbert_hemingway_sar_en * Add model 2023-09-15-distilbert_base_english_lithuanian_cased_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_himani_m_en * Add model 2023-09-15-splade_v2_max_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_sgr23_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_peterhsu_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_shadowtwin41_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_evincent18_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_fetch_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_cleandata_en * Add model 2023-09-15-distilbert_base_multilingual_cased_finetuned_kintweetse_xx * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_tlapusan_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_tsobolev_en * Add model 2023-09-15-dummy_model_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_himani_auto_gen_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_surjray_en * Add model 2023-09-15-we4lkd_aml_distilbert_1921_2022_en * Add model 2023-09-15-clinical_bert_finetuned_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_thangvip_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_coreyabs_db_en * Add model 2023-09-15-distilbert_base_uncased_mask_finetuned_imdb_v1_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_spasis_en * Add model 2023-09-15-erwt_year_en * Add model 2023-09-15-bertino_it * Add model 2023-09-15-distilbert_base_uncased_finetuned_himani_auto_textgeneration_en * Add model 2023-09-15-distilbert_base_uncased_imdb_accelerate_en * Add model 2023-09-15-film20000film20000distilbert_base_uncased_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_fadliaulawi_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_ttmusic_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_lakecrimsonn_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_huggingface_course_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_coreyabs_db_en * Add model 2023-09-15-dbert_finetuned_g_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_cl_wood_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_raulgdp_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jwchung_en * Add model 2023-09-15-distilbert_base_uncased_sparse_80_1x4_block_pruneofa_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_averageandyyy_en * Add model 2023-09-15-test_text_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_averageandyyy_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_francesc_en * Add model 2023-09-15-distilbert_base_turkish_cased_offensive_mlm_tr * Add model 2023-09-15-distilbert_base_english_french_spanish_portuguese_italian_cased_xx * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_techtank_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_cvent_2019_2022_en * Add model 2023-09-15-distilbert_base_uncased_mlm_scirepeval_fos_chemistry_en * Add model 2023-09-15-100_sdb_tbb_en * Add model 2023-09-15-we4lkd_aml_distilbert_1921_2009_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_yuto01_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_thetaphipsi_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_rugo_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_guoguo_en * Add model 2023-09-15-yolochess_mlm_azure_cloud_35_en * Add model 2023-09-15-we4lkd_aml_distilbert_1921_2019_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_ryanlai_en * Add model 2023-09-15-distilbert_embeddings_base_uncased_continued_training_medqa_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_luzimu_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_thutrang_en * Add model 2023-09-15-javanese_distilbert_small_jv * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_johnyyhk_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_finetuned_imdb_chenyanjin_en * Add model 2023-09-15-spladex_tt_spanish_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_gtxygyzb_en * Add model 2023-09-15-erwt_year_masked_75_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_andrewr_en * Add model 2023-09-15-distilbert_base_english_urdu_cased_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_sungchun71_en * Add model 2023-09-15-we4lkd_aml_distilbert_1921_1992_en * Add model 2023-09-15-pt_distilbert_base_en * Add model 2023-09-15-distilbert_base_english_french_german_cased_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_mlm_accelerate_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_nugget00_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_tux_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_gostrive_en * Add model 2023-09-15-we4lkd_aml_distilbert_1921_1965_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_renyulin_en * Add model 2023-09-15-we4lkd_aml_distilbert_1921_2016_en * Add model 2023-09-15-distilbert_finetuned_spmlm_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_dipika09_en * Add model 2023-09-15-distilbert_mlm_750k_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_golightly_en * Add model 2023-09-15-distilbert_base_english_french_spanish_cased_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_minye819_en * Add model 2023-09-15-distilbert_base_english_russian_cased_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_ryanlai_en * Add model 2023-09-15-kaz_legal_distilbert_legal_corpus_312818008_words_4.945454545454545_en * Add model 2023-09-15-distilbert_base_indonesian_id * Add model 2023-09-15-we4lkd_aml_distilbert_1921_1977_en * Add model 2023-09-15-inisw08_distilbert_mlm_adamw_torch_0608_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jjinbbangman_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_dmlea_en * Add model 2023-09-15-distilbert_base_uncased_finetuned_himani_auto_text_en --------- Co-authored-by: ahmedlone127 --- .../ahmedlone127/2023-09-15-100_sdb_tbb_en.md | 93 ++++++++++++++++ .../2023-09-15-20split_dataset_version1_en.md | 93 ++++++++++++++++ .../2023-09-15-20split_dataset_version2_en.md | 93 ++++++++++++++++ .../2023-09-15-20split_dataset_version3_en.md | 93 ++++++++++++++++ .../2023-09-15-20split_dataset_version4_en.md | 93 ++++++++++++++++ .../ahmedlone127/2023-09-15-500_sdb_tbb_en.md | 93 ++++++++++++++++ .../2023-09-15-aave_distil_bert_en.md | 93 ++++++++++++++++ ...sa_with_maskedlm_finetuned_sentihood_en.md | 93 ++++++++++++++++ ...bert_base_uncased_finetuned_dantest2_en.md | 93 ++++++++++++++++ ...se_uncased_finetuned_imdb_accelerate_en.md | 93 ++++++++++++++++ .../2023-09-15-bert_distil_ct_en.md | 93 ++++++++++++++++ .../2023-09-15-bert_distil_nli_ct_en.md | 93 ++++++++++++++++ .../ahmedlone127/2023-09-15-bert_name_en.md | 93 ++++++++++++++++ .../ahmedlone127/2023-09-15-bert_news_en.md | 93 ++++++++++++++++ .../2023-09-15-bert_pretrain_en.md | 93 ++++++++++++++++ ...23-09-15-bert_tuned_trial_20_12_2022_en.md | 93 ++++++++++++++++ ...15-bertfined_finetunedmodel_fakenews_en.md | 93 ++++++++++++++++ .../ahmedlone127/2023-09-15-bertino_it.md | 93 ++++++++++++++++ .../ahmedlone127/2023-09-15-bertino_lsg_en.md | 93 ++++++++++++++++ ...mese_finetuned_distilbert_portuguese_en.md | 93 ++++++++++++++++ .../2023-09-15-carrot_tuned_model_en.md | 93 ++++++++++++++++ .../2023-09-15-clinical_bert_finetuned_en.md | 93 ++++++++++++++++ .../2023-09-15-clinicalbert_medicalai_en.md | 93 ++++++++++++++++ ...r_pretrained_distilbert_base_uncased_en.md | 93 ++++++++++++++++ .../2023-09-15-crypto_bert_model_en.md | 93 ++++++++++++++++ ...23-09-15-customer_data_tuned_trial_1_en.md | 93 ++++++++++++++++ .../2023-09-15-dbert_finetuned_en.md | 93 ++++++++++++++++ .../2023-09-15-dbert_finetuned_g_en.md | 93 ++++++++++++++++ .../ahmedlone127/2023-09-15-dbert_rda_en.md | 93 ++++++++++++++++ ...ontextualised_embeddings_distil_bert_en.md | 4 +- .../2023-09-15-distil_bert_aave_large_en.md | 93 ++++++++++++++++ .../ahmedlone127/2023-09-15-distil_eng_en.md | 93 ++++++++++++++++ ...5-distilabena_base_akuapem_twi_cased_en.md | 93 ++++++++++++++++ ...-distilabena_base_asante_twi_uncased_en.md | 93 ++++++++++++++++ ...istilabena_base_v2_akuapem_twi_cased_en.md | 93 ++++++++++++++++ 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docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2022_en.md create mode 100644 docs/_posts/ahmedlone127/2023-09-15-yolochess_mlm_azure_cloud_35_en.md create mode 100644 docs/_posts/ahmedlone127/2023-09-15-zero_shot_cross_lingual_transfer_demo_masked_en.md diff --git a/docs/_posts/ahmedlone127/2023-09-15-100_sdb_tbb_en.md b/docs/_posts/ahmedlone127/2023-09-15-100_sdb_tbb_en.md new file mode 100644 index 00000000000000..eea668d4b339e8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-100_sdb_tbb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English 100_sdb_tbb DistilBertEmbeddings from sripadhstudy +author: John Snow Labs +name: 100_sdb_tbb +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`100_sdb_tbb` is a English model originally trained by sripadhstudy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/100_sdb_tbb_en_5.1.2_3.0_1694784123220.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/100_sdb_tbb_en_5.1.2_3.0_1694784123220.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("100_sdb_tbb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("100_sdb_tbb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|100_sdb_tbb| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.0 MB| + +## References + +https://huggingface.co/sripadhstudy/100_SDB_TBB \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-20split_dataset_version1_en.md b/docs/_posts/ahmedlone127/2023-09-15-20split_dataset_version1_en.md new file mode 100644 index 00000000000000..fea219717006e5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-20split_dataset_version1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English 20split_dataset_version1 DistilBertEmbeddings from Billwzl +author: John Snow Labs +name: 20split_dataset_version1 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`20split_dataset_version1` is a English model originally trained by Billwzl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/20split_dataset_version1_en_5.1.2_3.0_1694780611703.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/20split_dataset_version1_en_5.1.2_3.0_1694780611703.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("20split_dataset_version1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("20split_dataset_version1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|20split_dataset_version1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.3 MB| + +## References + +https://huggingface.co/Billwzl/20split_dataset_version1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-20split_dataset_version2_en.md b/docs/_posts/ahmedlone127/2023-09-15-20split_dataset_version2_en.md new file mode 100644 index 00000000000000..5a5ae58f9c6d35 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-20split_dataset_version2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English 20split_dataset_version2 DistilBertEmbeddings from Billwzl +author: John Snow Labs +name: 20split_dataset_version2 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`20split_dataset_version2` is a English model originally trained by Billwzl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/20split_dataset_version2_en_5.1.2_3.0_1694781304116.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/20split_dataset_version2_en_5.1.2_3.0_1694781304116.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("20split_dataset_version2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("20split_dataset_version2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|20split_dataset_version2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.0 MB| + +## References + +https://huggingface.co/Billwzl/20split_dataset_version2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-20split_dataset_version3_en.md b/docs/_posts/ahmedlone127/2023-09-15-20split_dataset_version3_en.md new file mode 100644 index 00000000000000..1676b2b1aa852d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-20split_dataset_version3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English 20split_dataset_version3 DistilBertEmbeddings from Billwzl +author: John Snow Labs +name: 20split_dataset_version3 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`20split_dataset_version3` is a English model originally trained by Billwzl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/20split_dataset_version3_en_5.1.2_3.0_1694781556318.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/20split_dataset_version3_en_5.1.2_3.0_1694781556318.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("20split_dataset_version3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("20split_dataset_version3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|20split_dataset_version3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Billwzl/20split_dataset_version3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-20split_dataset_version4_en.md b/docs/_posts/ahmedlone127/2023-09-15-20split_dataset_version4_en.md new file mode 100644 index 00000000000000..4114cdc080b445 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-20split_dataset_version4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English 20split_dataset_version4 DistilBertEmbeddings from Billwzl +author: John Snow Labs +name: 20split_dataset_version4 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`20split_dataset_version4` is a English model originally trained by Billwzl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/20split_dataset_version4_en_5.1.2_3.0_1694782055167.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/20split_dataset_version4_en_5.1.2_3.0_1694782055167.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("20split_dataset_version4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("20split_dataset_version4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|20split_dataset_version4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Billwzl/20split_dataset_version4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-500_sdb_tbb_en.md b/docs/_posts/ahmedlone127/2023-09-15-500_sdb_tbb_en.md new file mode 100644 index 00000000000000..43e9f72176801e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-500_sdb_tbb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English 500_sdb_tbb DistilBertEmbeddings from sripadhstudy +author: John Snow Labs +name: 500_sdb_tbb +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`500_sdb_tbb` is a English model originally trained by sripadhstudy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/500_sdb_tbb_en_5.1.2_3.0_1694784390417.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/500_sdb_tbb_en_5.1.2_3.0_1694784390417.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("500_sdb_tbb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("500_sdb_tbb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|500_sdb_tbb| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.9 MB| + +## References + +https://huggingface.co/sripadhstudy/500_SDB_TBB \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-aave_distil_bert_en.md b/docs/_posts/ahmedlone127/2023-09-15-aave_distil_bert_en.md new file mode 100644 index 00000000000000..0c6b3141a32a29 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-aave_distil_bert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English aave_distil_bert DistilBertEmbeddings from csalaam +author: John Snow Labs +name: aave_distil_bert +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`aave_distil_bert` is a English model originally trained by csalaam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/aave_distil_bert_en_5.1.2_3.0_1694781245405.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/aave_distil_bert_en_5.1.2_3.0_1694781245405.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("aave_distil_bert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("aave_distil_bert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|aave_distil_bert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.5 MB| + +## References + +https://huggingface.co/csalaam/AAVE-distil-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-absa_with_maskedlm_finetuned_sentihood_en.md b/docs/_posts/ahmedlone127/2023-09-15-absa_with_maskedlm_finetuned_sentihood_en.md new file mode 100644 index 00000000000000..94283894826b54 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-absa_with_maskedlm_finetuned_sentihood_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English absa_with_maskedlm_finetuned_sentihood DistilBertEmbeddings from UchihaMadara +author: John Snow Labs +name: absa_with_maskedlm_finetuned_sentihood +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`absa_with_maskedlm_finetuned_sentihood` is a English model originally trained by UchihaMadara. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/absa_with_maskedlm_finetuned_sentihood_en_5.1.2_3.0_1694786043453.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/absa_with_maskedlm_finetuned_sentihood_en_5.1.2_3.0_1694786043453.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("absa_with_maskedlm_finetuned_sentihood","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("absa_with_maskedlm_finetuned_sentihood", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|absa_with_maskedlm_finetuned_sentihood| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/UchihaMadara/ABSA-with-MaskedLM-finetuned-sentihood \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-ad_distilbert_base_uncased_finetuned_dantest2_en.md b/docs/_posts/ahmedlone127/2023-09-15-ad_distilbert_base_uncased_finetuned_dantest2_en.md new file mode 100644 index 00000000000000..2ac718547ae413 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-ad_distilbert_base_uncased_finetuned_dantest2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ad_distilbert_base_uncased_finetuned_dantest2 DistilBertEmbeddings from refringence +author: John Snow Labs +name: ad_distilbert_base_uncased_finetuned_dantest2 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ad_distilbert_base_uncased_finetuned_dantest2` is a English model originally trained by refringence. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ad_distilbert_base_uncased_finetuned_dantest2_en_5.1.2_3.0_1694777623781.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ad_distilbert_base_uncased_finetuned_dantest2_en_5.1.2_3.0_1694777623781.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("ad_distilbert_base_uncased_finetuned_dantest2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("ad_distilbert_base_uncased_finetuned_dantest2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ad_distilbert_base_uncased_finetuned_dantest2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/refringence/ad-distilbert-base-uncased-finetuned-dantest2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-bert_base_uncased_finetuned_imdb_accelerate_en.md b/docs/_posts/ahmedlone127/2023-09-15-bert_base_uncased_finetuned_imdb_accelerate_en.md new file mode 100644 index 00000000000000..8b2b3e14125407 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-bert_base_uncased_finetuned_imdb_accelerate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_imdb_accelerate DistilBertEmbeddings from Arthuerwang +author: John Snow Labs +name: bert_base_uncased_finetuned_imdb_accelerate +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_imdb_accelerate` is a English model originally trained by Arthuerwang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_imdb_accelerate_en_5.1.2_3.0_1694783561288.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_imdb_accelerate_en_5.1.2_3.0_1694783561288.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("bert_base_uncased_finetuned_imdb_accelerate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("bert_base_uncased_finetuned_imdb_accelerate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_imdb_accelerate| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Arthuerwang/bert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-bert_distil_ct_en.md b/docs/_posts/ahmedlone127/2023-09-15-bert_distil_ct_en.md new file mode 100644 index 00000000000000..59445a5eb45611 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-bert_distil_ct_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_distil_ct DistilBertEmbeddings from Contrastive-Tension +author: John Snow Labs +name: bert_distil_ct +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_distil_ct` is a English model originally trained by Contrastive-Tension. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_distil_ct_en_5.1.2_3.0_1694770957596.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_distil_ct_en_5.1.2_3.0_1694770957596.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("bert_distil_ct","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("bert_distil_ct", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_distil_ct| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Contrastive-Tension/BERT-Distil-CT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-bert_distil_nli_ct_en.md b/docs/_posts/ahmedlone127/2023-09-15-bert_distil_nli_ct_en.md new file mode 100644 index 00000000000000..263aedc5f9ebe9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-bert_distil_nli_ct_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_distil_nli_ct DistilBertEmbeddings from Contrastive-Tension +author: John Snow Labs +name: bert_distil_nli_ct +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_distil_nli_ct` is a English model originally trained by Contrastive-Tension. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_distil_nli_ct_en_5.1.2_3.0_1694771080239.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_distil_nli_ct_en_5.1.2_3.0_1694771080239.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("bert_distil_nli_ct","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("bert_distil_nli_ct", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_distil_nli_ct| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Contrastive-Tension/BERT-Distil-NLI-CT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-bert_name_en.md b/docs/_posts/ahmedlone127/2023-09-15-bert_name_en.md new file mode 100644 index 00000000000000..adb4957f0414db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-bert_name_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_name DistilBertEmbeddings from Ondiet +author: John Snow Labs +name: bert_name +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_name` is a English model originally trained by Ondiet. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_name_en_5.1.2_3.0_1694788522767.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_name_en_5.1.2_3.0_1694788522767.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("bert_name","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("bert_name", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_name| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Ondiet/bert_name \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-bert_news_en.md b/docs/_posts/ahmedlone127/2023-09-15-bert_news_en.md new file mode 100644 index 00000000000000..b8f3700c4de564 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-bert_news_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_news DistilBertEmbeddings from harvinder676 +author: John Snow Labs +name: bert_news +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_news` is a English model originally trained by harvinder676. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_news_en_5.1.2_3.0_1694782914172.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_news_en_5.1.2_3.0_1694782914172.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("bert_news","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("bert_news", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_news| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/harvinder676/bert-news \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-bert_pretrain_en.md b/docs/_posts/ahmedlone127/2023-09-15-bert_pretrain_en.md new file mode 100644 index 00000000000000..34886cf1e9c64d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-bert_pretrain_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_pretrain DistilBertEmbeddings from catofnull +author: John Snow Labs +name: bert_pretrain +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_pretrain` is a English model originally trained by catofnull. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_pretrain_en_5.1.2_3.0_1694775655895.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_pretrain_en_5.1.2_3.0_1694775655895.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("bert_pretrain","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("bert_pretrain", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_pretrain| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.8 MB| + +## References + +https://huggingface.co/catofnull/BERT-Pretrain \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-bert_tuned_trial_20_12_2022_en.md b/docs/_posts/ahmedlone127/2023-09-15-bert_tuned_trial_20_12_2022_en.md new file mode 100644 index 00000000000000..dc68fbe89125c0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-bert_tuned_trial_20_12_2022_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bert_tuned_trial_20_12_2022 DistilBertEmbeddings from EslamAhmed +author: John Snow Labs +name: bert_tuned_trial_20_12_2022 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tuned_trial_20_12_2022` is a English model originally trained by EslamAhmed. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tuned_trial_20_12_2022_en_5.1.2_3.0_1694784215749.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tuned_trial_20_12_2022_en_5.1.2_3.0_1694784215749.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("bert_tuned_trial_20_12_2022","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("bert_tuned_trial_20_12_2022", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tuned_trial_20_12_2022| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|447.5 MB| + +## References + +https://huggingface.co/EslamAhmed/BERT_tuned_trial_20-12-2022 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-bertfined_finetunedmodel_fakenews_en.md b/docs/_posts/ahmedlone127/2023-09-15-bertfined_finetunedmodel_fakenews_en.md new file mode 100644 index 00000000000000..e11b830920c741 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-bertfined_finetunedmodel_fakenews_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertfined_finetunedmodel_fakenews DistilBertEmbeddings from Sakil +author: John Snow Labs +name: bertfined_finetunedmodel_fakenews +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertfined_finetunedmodel_fakenews` is a English model originally trained by Sakil. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertfined_finetunedmodel_fakenews_en_5.1.2_3.0_1694790199446.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertfined_finetunedmodel_fakenews_en_5.1.2_3.0_1694790199446.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("bertfined_finetunedmodel_fakenews","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("bertfined_finetunedmodel_fakenews", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertfined_finetunedmodel_fakenews| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.3 MB| + +## References + +https://huggingface.co/Sakil/bertfined_finetunedmodel_fakenews \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-bertino_it.md b/docs/_posts/ahmedlone127/2023-09-15-bertino_it.md new file mode 100644 index 00000000000000..154883d0285a3d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-bertino_it.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Italian bertino DistilBertEmbeddings from indigo-ai +author: John Snow Labs +name: bertino +date: 2023-09-15 +tags: [distilbert, it, open_source, fill_mask, onnx] +task: Embeddings +language: it +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertino` is a Italian model originally trained by indigo-ai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertino_it_5.1.2_3.0_1694780792709.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertino_it_5.1.2_3.0_1694780792709.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("bertino","it") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("bertino", "it") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertino| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|it| +|Size:|253.0 MB| + +## References + +https://huggingface.co/indigo-ai/BERTino \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-bertino_lsg_en.md b/docs/_posts/ahmedlone127/2023-09-15-bertino_lsg_en.md new file mode 100644 index 00000000000000..30ab6840313ef8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-bertino_lsg_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English bertino_lsg DistilBertEmbeddings from efederici +author: John Snow Labs +name: bertino_lsg +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertino_lsg` is a English model originally trained by efederici. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertino_lsg_en_5.1.2_3.0_1694772729108.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertino_lsg_en_5.1.2_3.0_1694772729108.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("bertino_lsg","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("bertino_lsg", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertino_lsg| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|263.3 MB| + +## References + +https://huggingface.co/efederici/bertino-lsg \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-burmese_finetuned_distilbert_portuguese_en.md b/docs/_posts/ahmedlone127/2023-09-15-burmese_finetuned_distilbert_portuguese_en.md new file mode 100644 index 00000000000000..2f8110527e6642 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-burmese_finetuned_distilbert_portuguese_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English burmese_finetuned_distilbert_portuguese DistilBertEmbeddings from jibi2906 +author: John Snow Labs +name: burmese_finetuned_distilbert_portuguese +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`burmese_finetuned_distilbert_portuguese` is a English model originally trained by jibi2906. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/burmese_finetuned_distilbert_portuguese_en_5.1.2_3.0_1694782684337.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/burmese_finetuned_distilbert_portuguese_en_5.1.2_3.0_1694782684337.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("burmese_finetuned_distilbert_portuguese","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("burmese_finetuned_distilbert_portuguese", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|burmese_finetuned_distilbert_portuguese| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/jibi2906/my-finetuned-distilbert-pt \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-carrot_tuned_model_en.md b/docs/_posts/ahmedlone127/2023-09-15-carrot_tuned_model_en.md new file mode 100644 index 00000000000000..287a83bbc35942 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-carrot_tuned_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English carrot_tuned_model DistilBertEmbeddings from DaniloH +author: John Snow Labs +name: carrot_tuned_model +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`carrot_tuned_model` is a English model originally trained by DaniloH. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/carrot_tuned_model_en_5.1.2_3.0_1694781032170.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/carrot_tuned_model_en_5.1.2_3.0_1694781032170.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("carrot_tuned_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("carrot_tuned_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|carrot_tuned_model| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/DaniloH/carrot-tuned-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-clinical_bert_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-15-clinical_bert_finetuned_en.md new file mode 100644 index 00000000000000..f26d4c79c8962b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-clinical_bert_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English clinical_bert_finetuned DistilBertEmbeddings from aminghias +author: John Snow Labs +name: clinical_bert_finetuned +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`clinical_bert_finetuned` is a English model originally trained by aminghias. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clinical_bert_finetuned_en_5.1.2_3.0_1694784623028.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clinical_bert_finetuned_en_5.1.2_3.0_1694784623028.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("clinical_bert_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("clinical_bert_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinical_bert_finetuned| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|505.3 MB| + +## References + +https://huggingface.co/aminghias/Clinical-BERT-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-clinicalbert_medicalai_en.md b/docs/_posts/ahmedlone127/2023-09-15-clinicalbert_medicalai_en.md new file mode 100644 index 00000000000000..aa008cdd4f9abb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-clinicalbert_medicalai_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English clinicalbert_medicalai DistilBertEmbeddings from medicalai +author: John Snow Labs +name: clinicalbert_medicalai +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`clinicalbert_medicalai` is a English model originally trained by medicalai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clinicalbert_medicalai_en_5.1.2_3.0_1694775464907.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clinicalbert_medicalai_en_5.1.2_3.0_1694775464907.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("clinicalbert_medicalai","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("clinicalbert_medicalai", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clinicalbert_medicalai| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|505.3 MB| + +## References + +https://huggingface.co/medicalai/ClinicalBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-clr_pretrained_distilbert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-15-clr_pretrained_distilbert_base_uncased_en.md new file mode 100644 index 00000000000000..33d06d45924af0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-clr_pretrained_distilbert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English clr_pretrained_distilbert_base_uncased DistilBertEmbeddings from SauravMaheshkar +author: John Snow Labs +name: clr_pretrained_distilbert_base_uncased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`clr_pretrained_distilbert_base_uncased` is a English model originally trained by SauravMaheshkar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/clr_pretrained_distilbert_base_uncased_en_5.1.2_3.0_1694779351433.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/clr_pretrained_distilbert_base_uncased_en_5.1.2_3.0_1694779351433.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("clr_pretrained_distilbert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("clr_pretrained_distilbert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|clr_pretrained_distilbert_base_uncased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/SauravMaheshkar/clr-pretrained-distilbert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-crypto_bert_model_en.md b/docs/_posts/ahmedlone127/2023-09-15-crypto_bert_model_en.md new file mode 100644 index 00000000000000..ab9953467ca37a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-crypto_bert_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English crypto_bert_model DistilBertEmbeddings from vedantgoswami +author: John Snow Labs +name: crypto_bert_model +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`crypto_bert_model` is a English model originally trained by vedantgoswami. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/crypto_bert_model_en_5.1.2_3.0_1694787260868.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/crypto_bert_model_en_5.1.2_3.0_1694787260868.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("crypto_bert_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("crypto_bert_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|crypto_bert_model| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.3 MB| + +## References + +https://huggingface.co/vedantgoswami/crypto-bert-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-customer_data_tuned_trial_1_en.md b/docs/_posts/ahmedlone127/2023-09-15-customer_data_tuned_trial_1_en.md new file mode 100644 index 00000000000000..de65fef610203f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-customer_data_tuned_trial_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English customer_data_tuned_trial_1 DistilBertEmbeddings from EslamAhmed +author: John Snow Labs +name: customer_data_tuned_trial_1 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`customer_data_tuned_trial_1` is a English model originally trained by EslamAhmed. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/customer_data_tuned_trial_1_en_5.1.2_3.0_1694770508030.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/customer_data_tuned_trial_1_en_5.1.2_3.0_1694770508030.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("customer_data_tuned_trial_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("customer_data_tuned_trial_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|customer_data_tuned_trial_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.3 MB| + +## References + +https://huggingface.co/EslamAhmed/customer_data_tuned_trial_1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-dbert_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-15-dbert_finetuned_en.md new file mode 100644 index 00000000000000..f6124834e65b4d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-dbert_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dbert_finetuned DistilBertEmbeddings from ksabeh +author: John Snow Labs +name: dbert_finetuned +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dbert_finetuned` is a English model originally trained by ksabeh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dbert_finetuned_en_5.1.2_3.0_1694772576304.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dbert_finetuned_en_5.1.2_3.0_1694772576304.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("dbert_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("dbert_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dbert_finetuned| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ksabeh/dbert-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-dbert_finetuned_g_en.md b/docs/_posts/ahmedlone127/2023-09-15-dbert_finetuned_g_en.md new file mode 100644 index 00000000000000..248dd699f61a0e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-dbert_finetuned_g_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dbert_finetuned_g DistilBertEmbeddings from e-hossam96 +author: John Snow Labs +name: dbert_finetuned_g +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dbert_finetuned_g` is a English model originally trained by e-hossam96. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dbert_finetuned_g_en_5.1.2_3.0_1694775531375.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dbert_finetuned_g_en_5.1.2_3.0_1694775531375.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("dbert_finetuned_g","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("dbert_finetuned_g", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dbert_finetuned_g| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/e-hossam96/dbert-finetuned-g \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-dbert_rda_en.md b/docs/_posts/ahmedlone127/2023-09-15-dbert_rda_en.md new file mode 100644 index 00000000000000..2a5b3183080fd7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-dbert_rda_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dbert_rda DistilBertEmbeddings from nkul +author: John Snow Labs +name: dbert_rda +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dbert_rda` is a English model originally trained by nkul. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dbert_rda_en_5.1.2_3.0_1694783312876.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dbert_rda_en_5.1.2_3.0_1694783312876.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("dbert_rda","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("dbert_rda", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dbert_rda| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/nkul/dbert-rda \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-debiasing_pre_trained_contextualised_embeddings_distil_bert_en.md b/docs/_posts/ahmedlone127/2023-09-15-debiasing_pre_trained_contextualised_embeddings_distil_bert_en.md index f7f40c0501d222..6073c0a320d86f 100644 --- a/docs/_posts/ahmedlone127/2023-09-15-debiasing_pre_trained_contextualised_embeddings_distil_bert_en.md +++ b/docs/_posts/ahmedlone127/2023-09-15-debiasing_pre_trained_contextualised_embeddings_distil_bert_en.md @@ -24,8 +24,8 @@ Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/debiasing_pre_trained_contextualised_embeddings_distil_bert_en_5.1.2_3.0_1694736696151.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/debiasing_pre_trained_contextualised_embeddings_distil_bert_en_5.1.2_3.0_1694736696151.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/debiasing_pre_trained_contextualised_embeddings_distil_bert_en_5.1.2_3.0_1694770008501.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/debiasing_pre_trained_contextualised_embeddings_distil_bert_en_5.1.2_3.0_1694770008501.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-09-15-distil_bert_aave_large_en.md b/docs/_posts/ahmedlone127/2023-09-15-distil_bert_aave_large_en.md new file mode 100644 index 00000000000000..fc36f7295e21c1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distil_bert_aave_large_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distil_bert_aave_large DistilBertEmbeddings from csalaam +author: John Snow Labs +name: distil_bert_aave_large +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distil_bert_aave_large` is a English model originally trained by csalaam. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distil_bert_aave_large_en_5.1.2_3.0_1694783341558.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distil_bert_aave_large_en_5.1.2_3.0_1694783341558.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distil_bert_aave_large","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distil_bert_aave_large", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distil_bert_aave_large| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.4 MB| + +## References + +https://huggingface.co/csalaam/distil-bert-aave-large \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distil_eng_en.md b/docs/_posts/ahmedlone127/2023-09-15-distil_eng_en.md new file mode 100644 index 00000000000000..81b436f220bfff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distil_eng_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distil_eng DistilBertEmbeddings from mboth +author: John Snow Labs +name: distil_eng +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distil_eng` is a English model originally trained by mboth. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distil_eng_en_5.1.2_3.0_1694782258463.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distil_eng_en_5.1.2_3.0_1694782258463.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distil_eng","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distil_eng", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distil_eng| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.5 MB| + +## References + +https://huggingface.co/mboth/distil-eng \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilabena_base_akuapem_twi_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilabena_base_akuapem_twi_cased_en.md new file mode 100644 index 00000000000000..12589472a53379 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilabena_base_akuapem_twi_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilabena_base_akuapem_twi_cased DistilBertEmbeddings from Ghana-NLP +author: John Snow Labs +name: distilabena_base_akuapem_twi_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilabena_base_akuapem_twi_cased` is a English model originally trained by Ghana-NLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilabena_base_akuapem_twi_cased_en_5.1.2_3.0_1694777845278.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilabena_base_akuapem_twi_cased_en_5.1.2_3.0_1694777845278.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilabena_base_akuapem_twi_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilabena_base_akuapem_twi_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilabena_base_akuapem_twi_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|505.3 MB| + +## References + +https://huggingface.co/Ghana-NLP/distilabena-base-akuapem-twi-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilabena_base_asante_twi_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilabena_base_asante_twi_uncased_en.md new file mode 100644 index 00000000000000..21f83b38462445 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilabena_base_asante_twi_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilabena_base_asante_twi_uncased DistilBertEmbeddings from Ghana-NLP +author: John Snow Labs +name: distilabena_base_asante_twi_uncased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilabena_base_asante_twi_uncased` is a English model originally trained by Ghana-NLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilabena_base_asante_twi_uncased_en_5.1.2_3.0_1694778027436.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilabena_base_asante_twi_uncased_en_5.1.2_3.0_1694778027436.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilabena_base_asante_twi_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilabena_base_asante_twi_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilabena_base_asante_twi_uncased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|505.2 MB| + +## References + +https://huggingface.co/Ghana-NLP/distilabena-base-asante-twi-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilabena_base_v2_akuapem_twi_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilabena_base_v2_akuapem_twi_cased_en.md new file mode 100644 index 00000000000000..e4af1362a76f4f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilabena_base_v2_akuapem_twi_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilabena_base_v2_akuapem_twi_cased DistilBertEmbeddings from Ghana-NLP +author: John Snow Labs +name: distilabena_base_v2_akuapem_twi_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilabena_base_v2_akuapem_twi_cased` is a English model originally trained by Ghana-NLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilabena_base_v2_akuapem_twi_cased_en_5.1.2_3.0_1694778202672.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilabena_base_v2_akuapem_twi_cased_en_5.1.2_3.0_1694778202672.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilabena_base_v2_akuapem_twi_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilabena_base_v2_akuapem_twi_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilabena_base_v2_akuapem_twi_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|505.0 MB| + +## References + +https://huggingface.co/Ghana-NLP/distilabena-base-v2-akuapem-twi-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilabena_base_v2_asante_twi_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilabena_base_v2_asante_twi_uncased_en.md new file mode 100644 index 00000000000000..6f00bae234db9b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilabena_base_v2_asante_twi_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilabena_base_v2_asante_twi_uncased DistilBertEmbeddings from Ghana-NLP +author: John Snow Labs +name: distilabena_base_v2_asante_twi_uncased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilabena_base_v2_asante_twi_uncased` is a English model originally trained by Ghana-NLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilabena_base_v2_asante_twi_uncased_en_5.1.2_3.0_1694778363788.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilabena_base_v2_asante_twi_uncased_en_5.1.2_3.0_1694778363788.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilabena_base_v2_asante_twi_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilabena_base_v2_asante_twi_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilabena_base_v2_asante_twi_uncased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|505.1 MB| + +## References + +https://huggingface.co/Ghana-NLP/distilabena-base-v2-asante-twi-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_add_pre_training_complete_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_add_pre_training_complete_en.md new file mode 100644 index 00000000000000..4df7e0415d31e4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_add_pre_training_complete_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_add_pre_training_complete DistilBertEmbeddings from gokuls +author: John Snow Labs +name: distilbert_add_pre_training_complete +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_add_pre_training_complete` is a English model originally trained by gokuls. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_add_pre_training_complete_en_5.1.2_3.0_1694788672681.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_add_pre_training_complete_en_5.1.2_3.0_1694788672681.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_add_pre_training_complete","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_add_pre_training_complete", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_add_pre_training_complete| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/gokuls/distilbert_add_pre-training-complete \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_add_pre_training_dim_96_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_add_pre_training_dim_96_en.md new file mode 100644 index 00000000000000..57c958c3c0f54c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_add_pre_training_dim_96_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_add_pre_training_dim_96 DistilBertEmbeddings from gokuls +author: John Snow Labs +name: distilbert_add_pre_training_dim_96 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_add_pre_training_dim_96` is a English model originally trained by gokuls. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_add_pre_training_dim_96_en_5.1.2_3.0_1694789132049.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_add_pre_training_dim_96_en_5.1.2_3.0_1694789132049.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_add_pre_training_dim_96","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_add_pre_training_dim_96", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_add_pre_training_dim_96| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|25.5 MB| + +## References + +https://huggingface.co/gokuls/distilbert_add_pre-training-dim-96 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_25lang_cased_xx.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_25lang_cased_xx.md new file mode 100644 index 00000000000000..c65abeb6e688bb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_25lang_cased_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual distilbert_base_25lang_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_25lang_cased +date: 2023-09-15 +tags: [distilbert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_25lang_cased` is a Multilingual model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_25lang_cased_xx_5.1.2_3.0_1694771346236.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_25lang_cased_xx_5.1.2_3.0_1694771346236.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_25lang_cased","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_25lang_cased", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_25lang_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|405.6 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-25lang-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_cased_fine_tuned_blbooksgenre_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_cased_fine_tuned_blbooksgenre_en.md new file mode 100644 index 00000000000000..8f237f066a1f42 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_cased_fine_tuned_blbooksgenre_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_cased_fine_tuned_blbooksgenre DistilBertEmbeddings from BritishLibraryLabs +author: John Snow Labs +name: distilbert_base_cased_fine_tuned_blbooksgenre +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_cased_fine_tuned_blbooksgenre` is a English model originally trained by BritishLibraryLabs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_cased_fine_tuned_blbooksgenre_en_5.1.2_3.0_1694777850511.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_cased_fine_tuned_blbooksgenre_en_5.1.2_3.0_1694777850511.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_cased_fine_tuned_blbooksgenre","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_cased_fine_tuned_blbooksgenre", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_cased_fine_tuned_blbooksgenre| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|243.7 MB| + +## References + +https://huggingface.co/BritishLibraryLabs/distilbert-base-cased-fine-tuned-blbooksgenre \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_arabic_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_arabic_cased_en.md new file mode 100644 index 00000000000000..d8b6aa20b2e8cd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_arabic_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_arabic_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_arabic_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_arabic_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_arabic_cased_en_5.1.2_3.0_1694771816351.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_arabic_cased_en_5.1.2_3.0_1694771816351.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_arabic_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_arabic_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_arabic_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|252.8 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-ar-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_bulgarian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_bulgarian_cased_en.md new file mode 100644 index 00000000000000..5f54413e5771a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_bulgarian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_bulgarian_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_bulgarian_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_bulgarian_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_bulgarian_cased_en_5.1.2_3.0_1694771936312.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_bulgarian_cased_en_5.1.2_3.0_1694771936312.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_bulgarian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_bulgarian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_bulgarian_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|260.5 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-bg-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_chinese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_chinese_cased_en.md new file mode 100644 index 00000000000000..a2db36c483b540 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_chinese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_chinese_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_chinese_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_chinese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_chinese_cased_en_5.1.2_3.0_1694776185915.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_chinese_cased_en_5.1.2_3.0_1694776185915.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_chinese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_chinese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_chinese_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|262.2 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-zh-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_chinese_hindi_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_chinese_hindi_cased_en.md new file mode 100644 index 00000000000000..415ac319d7a072 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_chinese_hindi_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_chinese_hindi_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_chinese_hindi_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_chinese_hindi_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_chinese_hindi_cased_en_5.1.2_3.0_1694776303232.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_chinese_hindi_cased_en_5.1.2_3.0_1694776303232.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_chinese_hindi_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_chinese_hindi_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_chinese_hindi_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|266.8 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-zh-hi-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_danish_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_danish_cased_en.md new file mode 100644 index 00000000000000..2d9e1196e841eb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_danish_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_danish_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_danish_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_danish_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_danish_cased_en_5.1.2_3.0_1694772113636.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_danish_cased_en_5.1.2_3.0_1694772113636.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_danish_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_danish_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_danish_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|255.0 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-da-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_dutch_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_dutch_cased_en.md new file mode 100644 index 00000000000000..61cb1497036289 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_dutch_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_dutch_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_dutch_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_dutch_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_dutch_cased_en_5.1.2_3.0_1694774832781.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_dutch_cased_en_5.1.2_3.0_1694774832781.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_dutch_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_dutch_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_dutch_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|256.4 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-nl-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_arabic_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_arabic_cased_en.md new file mode 100644 index 00000000000000..8f09ebf3492900 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_arabic_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_french_arabic_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_french_arabic_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_french_arabic_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_arabic_cased_en_5.1.2_3.0_1694772831618.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_arabic_cased_en_5.1.2_3.0_1694772831618.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_french_arabic_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_french_arabic_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_french_arabic_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|266.4 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-fr-ar-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_cased_en.md new file mode 100644 index 00000000000000..f14dada027cdb0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_french_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_french_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_french_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_cased_en_5.1.2_3.0_1694772943858.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_cased_en_5.1.2_3.0_1694772943858.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_french_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_french_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_french_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|257.0 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-fr-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_chinese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_chinese_cased_en.md new file mode 100644 index 00000000000000..36d70dc96125db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_chinese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_french_chinese_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_french_chinese_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_french_chinese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_chinese_cased_en_5.1.2_3.0_1694774157815.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_chinese_cased_en_5.1.2_3.0_1694774157815.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_french_chinese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_french_chinese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_french_chinese_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|275.7 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-fr-zh-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_chinese_japanese_vietnamese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_chinese_japanese_vietnamese_cased_en.md new file mode 100644 index 00000000000000..0677b778b8aeb0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_chinese_japanese_vietnamese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_french_chinese_japanese_vietnamese_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_french_chinese_japanese_vietnamese_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_french_chinese_japanese_vietnamese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_chinese_japanese_vietnamese_cased_en_5.1.2_3.0_1694774268638.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_chinese_japanese_vietnamese_cased_en_5.1.2_3.0_1694774268638.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_french_chinese_japanese_vietnamese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_french_chinese_japanese_vietnamese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_french_chinese_japanese_vietnamese_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|287.0 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-fr-zh-ja-vi-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_danish_japanese_vietnamese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_danish_japanese_vietnamese_cased_en.md new file mode 100644 index 00000000000000..26ea43a59387ec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_danish_japanese_vietnamese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_french_danish_japanese_vietnamese_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_french_danish_japanese_vietnamese_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_french_danish_japanese_vietnamese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_danish_japanese_vietnamese_cased_en_5.1.2_3.0_1694773081995.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_danish_japanese_vietnamese_cased_en_5.1.2_3.0_1694773081995.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_french_danish_japanese_vietnamese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_french_danish_japanese_vietnamese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_french_danish_japanese_vietnamese_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|287.0 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-fr-da-ja-vi-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_dutch_russian_arabic_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_dutch_russian_arabic_cased_en.md new file mode 100644 index 00000000000000..723c68a70aafef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_dutch_russian_arabic_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_french_dutch_russian_arabic_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_french_dutch_russian_arabic_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_french_dutch_russian_arabic_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_dutch_russian_arabic_cased_en_5.1.2_3.0_1694774052258.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_dutch_russian_arabic_cased_en_5.1.2_3.0_1694774052258.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_french_dutch_russian_arabic_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_french_dutch_russian_arabic_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_french_dutch_russian_arabic_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|301.9 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-fr-nl-ru-ar-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_german_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_german_cased_en.md new file mode 100644 index 00000000000000..9b3994bfcaa4ea --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_german_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_french_german_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_french_german_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_french_german_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_german_cased_en_5.1.2_3.0_1694773218512.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_german_cased_en_5.1.2_3.0_1694773218512.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_french_german_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_french_german_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_french_german_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|273.1 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-fr-de-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_german_norwegian_danish_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_german_norwegian_danish_cased_en.md new file mode 100644 index 00000000000000..6f0459344ad57f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_german_norwegian_danish_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_french_german_norwegian_danish_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_french_german_norwegian_danish_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_french_german_norwegian_danish_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_german_norwegian_danish_cased_en_5.1.2_3.0_1694773335862.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_german_norwegian_danish_cased_en_5.1.2_3.0_1694773335862.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_french_german_norwegian_danish_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_french_german_norwegian_danish_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_french_german_norwegian_danish_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|280.9 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-fr-de-no-da-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_italian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_italian_cased_en.md new file mode 100644 index 00000000000000..97d9954b4c2ee2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_italian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_french_italian_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_french_italian_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_french_italian_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_italian_cased_en_5.1.2_3.0_1694773810141.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_italian_cased_en_5.1.2_3.0_1694773810141.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_french_italian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_french_italian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_french_italian_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|268.6 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-fr-it-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_lithuanian_norwegian_polish_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_lithuanian_norwegian_polish_cased_en.md new file mode 100644 index 00000000000000..bd6dc3c0e5832e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_lithuanian_norwegian_polish_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_french_lithuanian_norwegian_polish_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_french_lithuanian_norwegian_polish_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_french_lithuanian_norwegian_polish_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_lithuanian_norwegian_polish_cased_en_5.1.2_3.0_1694773937924.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_lithuanian_norwegian_polish_cased_en_5.1.2_3.0_1694773937924.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_french_lithuanian_norwegian_polish_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_french_lithuanian_norwegian_polish_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_french_lithuanian_norwegian_polish_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|282.3 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-fr-lt-no-pl-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_spanish_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_spanish_cased_en.md new file mode 100644 index 00000000000000..d3d77074005313 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_spanish_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_french_spanish_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_french_spanish_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_french_spanish_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_spanish_cased_en_5.1.2_3.0_1694773457609.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_spanish_cased_en_5.1.2_3.0_1694773457609.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_french_spanish_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_french_spanish_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_french_spanish_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|273.5 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-fr-es-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_spanish_german_chinese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_spanish_german_chinese_cased_en.md new file mode 100644 index 00000000000000..4c8ac82883c4a9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_spanish_german_chinese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_french_spanish_german_chinese_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_french_spanish_german_chinese_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_french_spanish_german_chinese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_spanish_german_chinese_cased_en_5.1.2_3.0_1694773578439.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_spanish_german_chinese_cased_en_5.1.2_3.0_1694773578439.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_french_spanish_german_chinese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_french_spanish_german_chinese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_french_spanish_german_chinese_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|307.1 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-fr-es-de-zh-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_spanish_portuguese_italian_cased_xx.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_spanish_portuguese_italian_cased_xx.md new file mode 100644 index 00000000000000..fb3d04ae622e55 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_french_spanish_portuguese_italian_cased_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual distilbert_base_english_french_spanish_portuguese_italian_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_french_spanish_portuguese_italian_cased +date: 2023-09-15 +tags: [distilbert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_french_spanish_portuguese_italian_cased` is a Multilingual model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_spanish_portuguese_italian_cased_xx_5.1.2_3.0_1694773689639.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_french_spanish_portuguese_italian_cased_xx_5.1.2_3.0_1694773689639.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_french_spanish_portuguese_italian_cased","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_french_spanish_portuguese_italian_cased", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_french_spanish_portuguese_italian_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|285.0 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-fr-es-pt-it-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_german_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_german_cased_en.md new file mode 100644 index 00000000000000..f41b333cf9a22a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_german_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_german_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_german_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_german_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_german_cased_en_5.1.2_3.0_1694772216088.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_german_cased_en_5.1.2_3.0_1694772216088.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_german_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_german_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_german_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|262.2 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-de-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_hindi_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_hindi_cased_en.md new file mode 100644 index 00000000000000..e7019a696e680a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_hindi_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_hindi_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_hindi_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_hindi_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_hindi_cased_en_5.1.2_3.0_1694774382188.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_hindi_cased_en_5.1.2_3.0_1694774382188.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_hindi_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_hindi_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_hindi_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.4 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-hi-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_italian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_italian_cased_en.md new file mode 100644 index 00000000000000..586f8d76357949 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_italian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_italian_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_italian_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_italian_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_italian_cased_en_5.1.2_3.0_1694774487693.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_italian_cased_en_5.1.2_3.0_1694774487693.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_italian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_italian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_italian_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|258.3 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-it-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_japanese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_japanese_cased_en.md new file mode 100644 index 00000000000000..606d482826af62 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_japanese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_japanese_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_japanese_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_japanese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_japanese_cased_en_5.1.2_3.0_1694774608716.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_japanese_cased_en_5.1.2_3.0_1694774608716.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_japanese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_japanese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_japanese_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|256.7 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-ja-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_lithuanian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_lithuanian_cased_en.md new file mode 100644 index 00000000000000..e40c9a0ef0a05d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_lithuanian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_lithuanian_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_lithuanian_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_lithuanian_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_lithuanian_cased_en_5.1.2_3.0_1694774717030.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_lithuanian_cased_en_5.1.2_3.0_1694774717030.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_lithuanian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_lithuanian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_lithuanian_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|252.4 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-lt-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_norwegian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_norwegian_cased_en.md new file mode 100644 index 00000000000000..fc156ef81deef1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_norwegian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_norwegian_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_norwegian_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_norwegian_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_norwegian_cased_en_5.1.2_3.0_1694774950666.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_norwegian_cased_en_5.1.2_3.0_1694774950666.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_norwegian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_norwegian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_norwegian_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|256.0 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-no-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_polish_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_polish_cased_en.md new file mode 100644 index 00000000000000..1c6551a361cb40 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_polish_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_polish_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_polish_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_polish_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_polish_cased_en_5.1.2_3.0_1694775064040.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_polish_cased_en_5.1.2_3.0_1694775064040.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_polish_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_polish_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_polish_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|258.3 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-pl-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_portuguese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_portuguese_cased_en.md new file mode 100644 index 00000000000000..66c1afe09eb274 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_portuguese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_portuguese_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_portuguese_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_portuguese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_portuguese_cased_en_5.1.2_3.0_1694775172152.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_portuguese_cased_en_5.1.2_3.0_1694775172152.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_portuguese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_portuguese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_portuguese_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|259.6 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-pt-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_romanian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_romanian_cased_en.md new file mode 100644 index 00000000000000..16175fa13ef393 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_romanian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_romanian_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_romanian_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_romanian_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_romanian_cased_en_5.1.2_3.0_1694775272352.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_romanian_cased_en_5.1.2_3.0_1694775272352.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_romanian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_romanian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_romanian_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|253.7 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-ro-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_russian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_russian_cased_en.md new file mode 100644 index 00000000000000..7e6a1f9547a0c9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_russian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_russian_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_russian_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_russian_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_russian_cased_en_5.1.2_3.0_1694775407575.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_russian_cased_en_5.1.2_3.0_1694775407575.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_russian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_russian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_russian_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|268.7 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-ru-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_spanish_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_spanish_cased_en.md new file mode 100644 index 00000000000000..ac4013218ebf90 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_spanish_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_spanish_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_spanish_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_spanish_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_spanish_cased_en_5.1.2_3.0_1694772349323.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_spanish_cased_en_5.1.2_3.0_1694772349323.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_spanish_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_spanish_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_spanish_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|262.6 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-es-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_spanish_chinese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_spanish_chinese_cased_en.md new file mode 100644 index 00000000000000..de8fa75ccc422c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_spanish_chinese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_spanish_chinese_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_spanish_chinese_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_spanish_chinese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_spanish_chinese_cased_en_5.1.2_3.0_1694772709372.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_spanish_chinese_cased_en_5.1.2_3.0_1694772709372.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_spanish_chinese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_spanish_chinese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_spanish_chinese_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|281.7 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-es-zh-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_spanish_italian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_spanish_italian_cased_en.md new file mode 100644 index 00000000000000..132dccba1d6e08 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_spanish_italian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_spanish_italian_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_spanish_italian_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_spanish_italian_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_spanish_italian_cased_en_5.1.2_3.0_1694772463123.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_spanish_italian_cased_en_5.1.2_3.0_1694772463123.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_spanish_italian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_spanish_italian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_spanish_italian_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|272.0 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-es-it-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_spanish_portuguese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_spanish_portuguese_cased_en.md new file mode 100644 index 00000000000000..e97b5e4fd7e93a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_spanish_portuguese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_spanish_portuguese_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_spanish_portuguese_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_spanish_portuguese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_spanish_portuguese_cased_en_5.1.2_3.0_1694772586206.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_spanish_portuguese_cased_en_5.1.2_3.0_1694772586206.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_spanish_portuguese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_spanish_portuguese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_spanish_portuguese_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|268.4 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-es-pt-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_swahili_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_swahili_cased_en.md new file mode 100644 index 00000000000000..126f16b2350ba3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_swahili_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_swahili_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_swahili_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_swahili_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_swahili_cased_en_5.1.2_3.0_1694775528652.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_swahili_cased_en_5.1.2_3.0_1694775528652.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_swahili_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_swahili_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_swahili_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.1 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-sw-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_thai_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_thai_cased_en.md new file mode 100644 index 00000000000000..67daf96a0fd159 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_thai_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_thai_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_thai_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_thai_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_thai_cased_en_5.1.2_3.0_1694775652118.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_thai_cased_en_5.1.2_3.0_1694775652118.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_thai_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_thai_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_thai_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|244.7 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-th-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_turkish_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_turkish_cased_en.md new file mode 100644 index 00000000000000..ed2d8baf7aa504 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_turkish_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_turkish_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_turkish_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_turkish_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_turkish_cased_en_5.1.2_3.0_1694775755844.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_turkish_cased_en_5.1.2_3.0_1694775755844.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_turkish_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_turkish_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_turkish_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|251.1 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-tr-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_ukrainian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_ukrainian_cased_en.md new file mode 100644 index 00000000000000..86887587730333 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_ukrainian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_ukrainian_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_ukrainian_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_ukrainian_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_ukrainian_cased_en_5.1.2_3.0_1694775863815.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_ukrainian_cased_en_5.1.2_3.0_1694775863815.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_ukrainian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_ukrainian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_ukrainian_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|262.9 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-uk-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_urdu_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_urdu_cased_en.md new file mode 100644 index 00000000000000..f552acc2b40c20 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_urdu_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_urdu_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_urdu_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_urdu_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_urdu_cased_en_5.1.2_3.0_1694775970383.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_urdu_cased_en_5.1.2_3.0_1694775970383.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_urdu_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_urdu_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_urdu_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|251.1 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-ur-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_vietnamese_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_vietnamese_cased_en.md new file mode 100644 index 00000000000000..db1dac48cd374a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_english_vietnamese_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_english_vietnamese_cased DistilBertEmbeddings from Geotrend +author: John Snow Labs +name: distilbert_base_english_vietnamese_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_english_vietnamese_cased` is a English model originally trained by Geotrend. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_english_vietnamese_cased_en_5.1.2_3.0_1694776082127.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_english_vietnamese_cased_en_5.1.2_3.0_1694776082127.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_english_vietnamese_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_english_vietnamese_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_english_vietnamese_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|251.3 MB| + +## References + +https://huggingface.co/Geotrend/distilbert-base-en-vi-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_food_review_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_food_review_en.md new file mode 100644 index 00000000000000..e19e3205926823 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_food_review_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_food_review DistilBertEmbeddings from breakjl +author: John Snow Labs +name: distilbert_base_food_review +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_food_review` is a English model originally trained by breakjl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_food_review_en_5.1.2_3.0_1694781544212.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_food_review_en_5.1.2_3.0_1694781544212.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_food_review","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_food_review", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_food_review| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/breakjl/distilbert-base-food_review \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_german_cased_de.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_german_cased_de.md index cdb44bb6e9fe85..09b6d2a1fe6097 100644 --- a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_german_cased_de.md +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_german_cased_de.md @@ -24,8 +24,8 @@ Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_german_cased_de_5.1.2_3.0_1694736667910.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_german_cased_de_5.1.2_3.0_1694736667910.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_german_cased_de_5.1.2_3.0_1694769969382.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_german_cased_de_5.1.2_3.0_1694769969382.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_german_cased_finetuned_amazon_reviews_de.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_german_cased_finetuned_amazon_reviews_de.md new file mode 100644 index 00000000000000..ad8b9c47eb6bcd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_german_cased_finetuned_amazon_reviews_de.md @@ -0,0 +1,93 @@ +--- +layout: model +title: German distilbert_base_german_cased_finetuned_amazon_reviews DistilBertEmbeddings from mariav +author: John Snow Labs +name: distilbert_base_german_cased_finetuned_amazon_reviews +date: 2023-09-15 +tags: [distilbert, de, open_source, fill_mask, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_german_cased_finetuned_amazon_reviews` is a German model originally trained by mariav. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_german_cased_finetuned_amazon_reviews_de_5.1.2_3.0_1694774283101.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_german_cased_finetuned_amazon_reviews_de_5.1.2_3.0_1694774283101.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_german_cased_finetuned_amazon_reviews","de") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_german_cased_finetuned_amazon_reviews", "de") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_german_cased_finetuned_amazon_reviews| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|de| +|Size:|250.3 MB| + +## References + +https://huggingface.co/mariav/distilbert-base-german-cased-finetuned-amazon-reviews \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_indonesian_id.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_indonesian_id.md new file mode 100644 index 00000000000000..6ffbb1a7dfb19c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_indonesian_id.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Indonesian distilbert_base_indonesian DistilBertEmbeddings from cahya +author: John Snow Labs +name: distilbert_base_indonesian +date: 2023-09-15 +tags: [distilbert, id, open_source, fill_mask, onnx] +task: Embeddings +language: id +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_indonesian` is a Indonesian model originally trained by cahya. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_indonesian_id_5.1.2_3.0_1694780162345.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_indonesian_id_5.1.2_3.0_1694780162345.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_indonesian","id") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_indonesian", "id") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_indonesian| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|id| +|Size:|253.0 MB| + +## References + +https://huggingface.co/cahya/distilbert-base-indonesian \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_multilingual_cased_bulgarian_wikipedia_xx.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_multilingual_cased_bulgarian_wikipedia_xx.md new file mode 100644 index 00000000000000..013ad0c084893a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_multilingual_cased_bulgarian_wikipedia_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual distilbert_base_multilingual_cased_bulgarian_wikipedia DistilBertEmbeddings from mor40 +author: John Snow Labs +name: distilbert_base_multilingual_cased_bulgarian_wikipedia +date: 2023-09-15 +tags: [distilbert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_multilingual_cased_bulgarian_wikipedia` is a Multilingual model originally trained by mor40. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_multilingual_cased_bulgarian_wikipedia_xx_5.1.2_3.0_1694784992621.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_multilingual_cased_bulgarian_wikipedia_xx_5.1.2_3.0_1694784992621.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_multilingual_cased_bulgarian_wikipedia","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_multilingual_cased_bulgarian_wikipedia", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_multilingual_cased_bulgarian_wikipedia| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|505.3 MB| + +## References + +https://huggingface.co/mor40/distilbert-base-multilingual-cased-bg-wikipedia \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_multilingual_cased_finetuned_kintweetse_xx.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_multilingual_cased_finetuned_kintweetse_xx.md new file mode 100644 index 00000000000000..9205b7afab9408 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_multilingual_cased_finetuned_kintweetse_xx.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Multilingual distilbert_base_multilingual_cased_finetuned_kintweetse DistilBertEmbeddings from RogerB +author: John Snow Labs +name: distilbert_base_multilingual_cased_finetuned_kintweetse +date: 2023-09-15 +tags: [distilbert, xx, open_source, fill_mask, onnx] +task: Embeddings +language: xx +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_multilingual_cased_finetuned_kintweetse` is a Multilingual model originally trained by RogerB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_multilingual_cased_finetuned_kintweetse_xx_5.1.2_3.0_1694773789090.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_multilingual_cased_finetuned_kintweetse_xx_5.1.2_3.0_1694773789090.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_multilingual_cased_finetuned_kintweetse","xx") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_multilingual_cased_finetuned_kintweetse", "xx") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_multilingual_cased_finetuned_kintweetse| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|505.4 MB| + +## References + +https://huggingface.co/RogerB/distilbert-base-multilingual-cased-finetuned-kintweetsE \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_spanish_uncased_es.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_spanish_uncased_es.md new file mode 100644 index 00000000000000..c91d1b9b7089d4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_spanish_uncased_es.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Castilian, Spanish distilbert_base_spanish_uncased DistilBertEmbeddings from dccuchile +author: John Snow Labs +name: distilbert_base_spanish_uncased +date: 2023-09-15 +tags: [distilbert, es, open_source, fill_mask, onnx] +task: Embeddings +language: es +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_spanish_uncased` is a Castilian, Spanish model originally trained by dccuchile. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_spanish_uncased_es_5.1.2_3.0_1694770464754.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_spanish_uncased_es_5.1.2_3.0_1694770464754.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_spanish_uncased","es") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_spanish_uncased", "es") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_spanish_uncased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|es| +|Size:|250.2 MB| + +## References + +https://huggingface.co/dccuchile/distilbert-base-spanish-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_spanish_uncased_finetuned_amazon_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_spanish_uncased_finetuned_amazon_en.md new file mode 100644 index 00000000000000..1a929d4ef1b1b0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_spanish_uncased_finetuned_amazon_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_spanish_uncased_finetuned_amazon DistilBertEmbeddings from Marianoleiras +author: John Snow Labs +name: distilbert_base_spanish_uncased_finetuned_amazon +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_spanish_uncased_finetuned_amazon` is a English model originally trained by Marianoleiras. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_spanish_uncased_finetuned_amazon_en_5.1.2_3.0_1694777013368.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_spanish_uncased_finetuned_amazon_en_5.1.2_3.0_1694777013368.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_spanish_uncased_finetuned_amazon","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_spanish_uncased_finetuned_amazon", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_spanish_uncased_finetuned_amazon| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|250.2 MB| + +## References + +https://huggingface.co/Marianoleiras/distilbert-base-spanish-uncased-finetuned-amazon \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_spanish_uncased_finetuned_clinais_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_spanish_uncased_finetuned_clinais_en.md new file mode 100644 index 00000000000000..2180d32468bab9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_spanish_uncased_finetuned_clinais_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_spanish_uncased_finetuned_clinais DistilBertEmbeddings from joheras +author: John Snow Labs +name: distilbert_base_spanish_uncased_finetuned_clinais +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_spanish_uncased_finetuned_clinais` is a English model originally trained by joheras. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_spanish_uncased_finetuned_clinais_en_5.1.2_3.0_1694774679930.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_spanish_uncased_finetuned_clinais_en_5.1.2_3.0_1694774679930.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_spanish_uncased_finetuned_clinais","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_spanish_uncased_finetuned_clinais", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_spanish_uncased_finetuned_clinais| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|250.2 MB| + +## References + +https://huggingface.co/joheras/distilbert-base-spanish-uncased-finetuned-clinais \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_spanish_uncased_model_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_spanish_uncased_model_en.md new file mode 100644 index 00000000000000..7154505408b50d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_spanish_uncased_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_spanish_uncased_model DistilBertEmbeddings from AleNunezArroyo +author: John Snow Labs +name: distilbert_base_spanish_uncased_model +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_spanish_uncased_model` is a English model originally trained by AleNunezArroyo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_spanish_uncased_model_en_5.1.2_3.0_1694786476451.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_spanish_uncased_model_en_5.1.2_3.0_1694786476451.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_spanish_uncased_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_spanish_uncased_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_spanish_uncased_model| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|250.2 MB| + +## References + +https://huggingface.co/AleNunezArroyo/distilbert-base-spanish-uncased-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_turkish_cased_offensive_mlm_tr.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_turkish_cased_offensive_mlm_tr.md new file mode 100644 index 00000000000000..73eaf8472f9710 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_turkish_cased_offensive_mlm_tr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Turkish distilbert_base_turkish_cased_offensive_mlm DistilBertEmbeddings from Overfit-GM +author: John Snow Labs +name: distilbert_base_turkish_cased_offensive_mlm +date: 2023-09-15 +tags: [distilbert, tr, open_source, fill_mask, onnx] +task: Embeddings +language: tr +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_turkish_cased_offensive_mlm` is a Turkish model originally trained by Overfit-GM. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_turkish_cased_offensive_mlm_tr_5.1.2_3.0_1694777986744.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_turkish_cased_offensive_mlm_tr_5.1.2_3.0_1694777986744.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_turkish_cased_offensive_mlm","tr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_turkish_cased_offensive_mlm", "tr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_turkish_cased_offensive_mlm| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|tr| +|Size:|251.8 MB| + +## References + +https://huggingface.co/Overfit-GM/distilbert-base-turkish-cased-offensive-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_aisera_texts_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_aisera_texts_en.md new file mode 100644 index 00000000000000..98e8bbc60c2f76 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_aisera_texts_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_aisera_texts DistilBertEmbeddings from Theimisa +author: John Snow Labs +name: distilbert_base_uncased_aisera_texts +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_aisera_texts` is a English model originally trained by Theimisa. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_aisera_texts_en_5.1.2_3.0_1694771537071.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_aisera_texts_en_5.1.2_3.0_1694771537071.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_aisera_texts","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_aisera_texts", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_aisera_texts| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Theimisa/distilbert-base-uncased-aisera_texts \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_aisera_texts_v3_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_aisera_texts_v3_en.md new file mode 100644 index 00000000000000..41c888ba9aa83a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_aisera_texts_v3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_aisera_texts_v3 DistilBertEmbeddings from Theimisa +author: John Snow Labs +name: distilbert_base_uncased_aisera_texts_v3 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_aisera_texts_v3` is a English model originally trained by Theimisa. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_aisera_texts_v3_en_5.1.2_3.0_1694771955323.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_aisera_texts_v3_en_5.1.2_3.0_1694771955323.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_aisera_texts_v3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_aisera_texts_v3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_aisera_texts_v3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Theimisa/distilbert-base-uncased-aisera_texts-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_arxiv_abstracts_10k_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_arxiv_abstracts_10k_en.md new file mode 100644 index 00000000000000..244d68acd24e74 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_arxiv_abstracts_10k_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_arxiv_abstracts_10k DistilBertEmbeddings from timetoai +author: John Snow Labs +name: distilbert_base_uncased_arxiv_abstracts_10k +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_arxiv_abstracts_10k` is a English model originally trained by timetoai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_arxiv_abstracts_10k_en_5.1.2_3.0_1694786407387.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_arxiv_abstracts_10k_en_5.1.2_3.0_1694786407387.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_arxiv_abstracts_10k","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_arxiv_abstracts_10k", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_arxiv_abstracts_10k| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/timetoai/distilbert-base-uncased-arxiv-abstracts-10k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_bert_yoga_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_bert_yoga_finetuned_en.md new file mode 100644 index 00000000000000..dd84bcaf8d1de4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_bert_yoga_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_bert_yoga_finetuned DistilBertEmbeddings from dsantistevan +author: John Snow Labs +name: distilbert_base_uncased_bert_yoga_finetuned +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_bert_yoga_finetuned` is a English model originally trained by dsantistevan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_bert_yoga_finetuned_en_5.1.2_3.0_1694782584370.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_bert_yoga_finetuned_en_5.1.2_3.0_1694782584370.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_bert_yoga_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_bert_yoga_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_bert_yoga_finetuned| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/dsantistevan/distilbert-base-uncased-bert-yoga-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_cohl_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_cohl_en.md new file mode 100644 index 00000000000000..20254050842eb7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_cohl_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_cohl DistilBertEmbeddings from shafin +author: John Snow Labs +name: distilbert_base_uncased_cohl +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_cohl` is a English model originally trained by shafin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_cohl_en_5.1.2_3.0_1694789103082.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_cohl_en_5.1.2_3.0_1694789103082.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_cohl","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_cohl", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_cohl| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/shafin/distilbert-base-uncased-cohl \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned2_imdb_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned2_imdb_en.md index 288f7f544c168e..192a81712410a8 100644 --- a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned2_imdb_en.md +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned2_imdb_en.md @@ -24,8 +24,8 @@ Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned2_imdb_en_5.1.2_3.0_1694736806970.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned2_imdb_en_5.1.2_3.0_1694736806970.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned2_imdb_en_5.1.2_3.0_1694770185421.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned2_imdb_en_5.1.2_3.0_1694770185421.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_allocation_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_allocation_en.md new file mode 100644 index 00000000000000..408432551a1b7e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_allocation_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_allocation DistilBertEmbeddings from shubham7 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_allocation +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_allocation` is a English model originally trained by shubham7. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_allocation_en_5.1.2_3.0_1694789203683.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_allocation_en_5.1.2_3.0_1694789203683.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_allocation","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_allocation", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_allocation| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.4 MB| + +## References + +https://huggingface.co/shubham7/distilbert-base-uncased-finetuned-allocation \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_amazon_review_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_amazon_review_en.md new file mode 100644 index 00000000000000..61cd2d11f30b99 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_amazon_review_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_amazon_review DistilBertEmbeddings from soyisauce +author: John Snow Labs +name: distilbert_base_uncased_finetuned_amazon_review +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_amazon_review` is a English model originally trained by soyisauce. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_amazon_review_en_5.1.2_3.0_1694790068018.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_amazon_review_en_5.1.2_3.0_1694790068018.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_amazon_review","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_amazon_review", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_amazon_review| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/soyisauce/distilbert-base-uncased-finetuned-amazon_review \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_ccnews_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_ccnews_en.md new file mode 100644 index 00000000000000..c592f2cc267708 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_ccnews_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_ccnews DistilBertEmbeddings from mchalek +author: John Snow Labs +name: distilbert_base_uncased_finetuned_ccnews +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_ccnews` is a English model originally trained by mchalek. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_ccnews_en_5.1.2_3.0_1694790900189.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_ccnews_en_5.1.2_3.0_1694790900189.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_ccnews","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_ccnews", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_ccnews| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/mchalek/distilbert-base-uncased-finetuned-ccnews \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_char_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_char_en.md new file mode 100644 index 00000000000000..6a364cc025f64c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_char_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_char DistilBertEmbeddings from bhagasra-saurav +author: John Snow Labs +name: distilbert_base_uncased_finetuned_char +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_char` is a English model originally trained by bhagasra-saurav. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_char_en_5.1.2_3.0_1694784715338.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_char_en_5.1.2_3.0_1694784715338.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_char","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_char", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_char| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/bhagasra-saurav/distilbert-base-uncased-finetuned-char \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_char_v1_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_char_v1_en.md new file mode 100644 index 00000000000000..c2bd6fd8bcad8e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_char_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_char_v1 DistilBertEmbeddings from bhagasra-saurav +author: John Snow Labs +name: distilbert_base_uncased_finetuned_char_v1 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_char_v1` is a English model originally trained by bhagasra-saurav. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_char_v1_en_5.1.2_3.0_1694785235023.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_char_v1_en_5.1.2_3.0_1694785235023.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_char_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_char_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_char_v1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.3 MB| + +## References + +https://huggingface.co/bhagasra-saurav/distilbert-base-uncased-finetuned-char-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_char_v2_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_char_v2_en.md new file mode 100644 index 00000000000000..66095204915f14 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_char_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_char_v2 DistilBertEmbeddings from bhagasra-saurav +author: John Snow Labs +name: distilbert_base_uncased_finetuned_char_v2 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_char_v2` is a English model originally trained by bhagasra-saurav. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_char_v2_en_5.1.2_3.0_1694785386613.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_char_v2_en_5.1.2_3.0_1694785386613.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_char_v2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_char_v2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_char_v2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.3 MB| + +## References + +https://huggingface.co/bhagasra-saurav/distilbert-base-uncased-finetuned-char-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_char_v3_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_char_v3_en.md new file mode 100644 index 00000000000000..9224f204e3d4a5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_char_v3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_char_v3 DistilBertEmbeddings from bhagasra-saurav +author: John Snow Labs +name: distilbert_base_uncased_finetuned_char_v3 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_char_v3` is a English model originally trained by bhagasra-saurav. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_char_v3_en_5.1.2_3.0_1694785507806.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_char_v3_en_5.1.2_3.0_1694785507806.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_char_v3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_char_v3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_char_v3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/bhagasra-saurav/distilbert-base-uncased-finetuned-char-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_civi_cooments_accelerate_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_civi_cooments_accelerate_en.md index bbe130d0a2833d..6849c3f4264606 100644 --- a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_civi_cooments_accelerate_en.md +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_civi_cooments_accelerate_en.md @@ -24,8 +24,8 @@ Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_civi_cooments_accelerate_en_5.1.2_3.0_1694736212479.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_civi_cooments_accelerate_en_5.1.2_3.0_1694736212479.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_civi_cooments_accelerate_en_5.1.2_3.0_1694772882674.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_civi_cooments_accelerate_en_5.1.2_3.0_1694772882674.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_clinc150_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_clinc150_en.md new file mode 100644 index 00000000000000..62008c1952d486 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_clinc150_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_clinc150 DistilBertEmbeddings from FilippoComastri +author: John Snow Labs +name: distilbert_base_uncased_finetuned_clinc150 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_clinc150` is a English model originally trained by FilippoComastri. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_clinc150_en_5.1.2_3.0_1694780894423.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_clinc150_en_5.1.2_3.0_1694780894423.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_clinc150","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_clinc150", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_clinc150| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/FilippoComastri/distilbert-base-uncased-finetuned-clinc150 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_cnn_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_cnn_en.md new file mode 100644 index 00000000000000..1dd8748f045f58 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_cnn_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_cnn DistilBertEmbeddings from SmartPy +author: John Snow Labs +name: distilbert_base_uncased_finetuned_cnn +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_cnn` is a English model originally trained by SmartPy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_cnn_en_5.1.2_3.0_1694783567189.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_cnn_en_5.1.2_3.0_1694783567189.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_cnn","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_cnn", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_cnn| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/SmartPy/distilbert-base-uncased-finetuned-cnn \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_crypto_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_crypto_en.md new file mode 100644 index 00000000000000..73aaf1d6d8970c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_crypto_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_crypto DistilBertEmbeddings from smarquie +author: John Snow Labs +name: distilbert_base_uncased_finetuned_crypto +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_crypto` is a English model originally trained by smarquie. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_crypto_en_5.1.2_3.0_1694780201008.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_crypto_en_5.1.2_3.0_1694780201008.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_crypto","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_crypto", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_crypto| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/smarquie/distilbert-base-uncased-finetuned-crypto \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_ct_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_ct_en.md new file mode 100644 index 00000000000000..00d315670b1fed --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_ct_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_ct DistilBertEmbeddings from anthonyyazdani +author: John Snow Labs +name: distilbert_base_uncased_finetuned_ct +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_ct` is a English model originally trained by anthonyyazdani. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_ct_en_5.1.2_3.0_1694771385621.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_ct_en_5.1.2_3.0_1694771385621.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_ct","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_ct", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_ct| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/anthonyyazdani/distilbert-base-uncased-finetuned-CT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_cust_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_cust_en.md new file mode 100644 index 00000000000000..5ded3ab0043fd3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_cust_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_cust DistilBertEmbeddings from shafin +author: John Snow Labs +name: distilbert_base_uncased_finetuned_cust +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_cust` is a English model originally trained by shafin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_cust_en_5.1.2_3.0_1694773996911.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_cust_en_5.1.2_3.0_1694773996911.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_cust","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_cust", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_cust| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/shafin/distilbert-base-uncased-finetuned-cust \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_cvent_2019_2022_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_cvent_2019_2022_en.md index fc23d579765c5f..b85ccee1867399 100644 --- a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_cvent_2019_2022_en.md +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_cvent_2019_2022_en.md @@ -24,8 +24,8 @@ Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_cvent_2019_2022_en_5.1.2_3.0_1694736129695.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_cvent_2019_2022_en_5.1.2_3.0_1694736129695.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_cvent_2019_2022_en_5.1.2_3.0_1694773096960.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_cvent_2019_2022_en_5.1.2_3.0_1694773096960.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_cvent_2022_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_cvent_2022_en.md new file mode 100644 index 00000000000000..aa6933e92831f5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_cvent_2022_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_cvent_2022 DistilBertEmbeddings from vives +author: John Snow Labs +name: distilbert_base_uncased_finetuned_cvent_2022 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_cvent_2022` is a English model originally trained by vives. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_cvent_2022_en_5.1.2_3.0_1694772739443.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_cvent_2022_en_5.1.2_3.0_1694772739443.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_cvent_2022","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_cvent_2022", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_cvent_2022| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/vives/distilbert-base-uncased-finetuned-cvent-2022 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_dis_mlm5_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_dis_mlm5_en.md new file mode 100644 index 00000000000000..76160242691f13 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_dis_mlm5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_dis_mlm5 DistilBertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_dis_mlm5 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_dis_mlm5` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_dis_mlm5_en_5.1.2_3.0_1694781655821.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_dis_mlm5_en_5.1.2_3.0_1694781655821.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_dis_mlm5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_dis_mlm5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_dis_mlm5| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/distilbert-base-uncased-finetuned-DIS-mlm5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_discord_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_discord_en.md new file mode 100644 index 00000000000000..8f57fc772ed09d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_discord_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_discord DistilBertEmbeddings from Phroggu +author: John Snow Labs +name: distilbert_base_uncased_finetuned_discord +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_discord` is a English model originally trained by Phroggu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_discord_en_5.1.2_3.0_1694782495752.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_discord_en_5.1.2_3.0_1694782495752.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_discord","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_discord", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_discord| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Phroggu/distilbert-base-uncased-finetuned-discord \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_domain_adaptation_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_domain_adaptation_en.md new file mode 100644 index 00000000000000..b9678c9f78f3fa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_domain_adaptation_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_domain_adaptation DistilBertEmbeddings from algiraldohe +author: John Snow Labs +name: distilbert_base_uncased_finetuned_domain_adaptation +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_domain_adaptation` is a English model originally trained by algiraldohe. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_domain_adaptation_en_5.1.2_3.0_1694770565017.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_domain_adaptation_en_5.1.2_3.0_1694770565017.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_domain_adaptation","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_domain_adaptation", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_domain_adaptation| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/algiraldohe/distilbert-base-uncased-finetuned-domain-adaptation \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_fetch_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_fetch_en.md new file mode 100644 index 00000000000000..156d63873a6b60 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_fetch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_fetch DistilBertEmbeddings from rtreptow +author: John Snow Labs +name: distilbert_base_uncased_finetuned_fetch +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_fetch` is a English model originally trained by rtreptow. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_fetch_en_5.1.2_3.0_1694792165514.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_fetch_en_5.1.2_3.0_1694792165514.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_fetch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_fetch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_fetch| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/rtreptow/distilbert-base-uncased-finetuned-fetch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_game_accelerate_v2_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_game_accelerate_v2_en.md new file mode 100644 index 00000000000000..e7d87711bde367 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_game_accelerate_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_game_accelerate_v2 DistilBertEmbeddings from kaiku03 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_game_accelerate_v2 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_game_accelerate_v2` is a English model originally trained by kaiku03. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_game_accelerate_v2_en_5.1.2_3.0_1694789931049.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_game_accelerate_v2_en_5.1.2_3.0_1694789931049.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_game_accelerate_v2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_game_accelerate_v2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_game_accelerate_v2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/kaiku03/distilbert-base-uncased-finetuned-game-accelerate_V2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_auto_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_auto_en.md new file mode 100644 index 00000000000000..dce335a6e8a9d0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_auto_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_himani_auto DistilBertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_himani_auto +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_himani_auto` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_himani_auto_en_5.1.2_3.0_1694780256193.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_himani_auto_en_5.1.2_3.0_1694780256193.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_himani_auto","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_himani_auto", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_himani_auto| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/distilbert-base-uncased-finetuned-himani_auto \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_auto_gen_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_auto_gen_en.md new file mode 100644 index 00000000000000..80b471a8a49cce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_auto_gen_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_himani_auto_gen DistilBertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_himani_auto_gen +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_himani_auto_gen` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_himani_auto_gen_en_5.1.2_3.0_1694780783124.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_himani_auto_gen_en_5.1.2_3.0_1694780783124.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_himani_auto_gen","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_himani_auto_gen", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_himani_auto_gen| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/distilbert-base-uncased-finetuned-himani_auto-gen \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_auto_text_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_auto_text_en.md new file mode 100644 index 00000000000000..c24c8aa3930423 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_auto_text_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_himani_auto_text DistilBertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_himani_auto_text +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_himani_auto_text` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_himani_auto_text_en_5.1.2_3.0_1694780380500.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_himani_auto_text_en_5.1.2_3.0_1694780380500.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_himani_auto_text","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_himani_auto_text", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_himani_auto_text| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/distilbert-base-uncased-finetuned-himani_auto-TEXT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_auto_text_gen_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_auto_text_gen_en.md new file mode 100644 index 00000000000000..764413b2f1274f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_auto_text_gen_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_himani_auto_text_gen DistilBertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_himani_auto_text_gen +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_himani_auto_text_gen` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_himani_auto_text_gen_en_5.1.2_3.0_1694780620171.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_himani_auto_text_gen_en_5.1.2_3.0_1694780620171.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_himani_auto_text_gen","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_himani_auto_text_gen", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_himani_auto_text_gen| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/distilbert-base-uncased-finetuned-himani_auto-text-gen \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_auto_textgeneration_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_auto_textgeneration_en.md new file mode 100644 index 00000000000000..a2b0235a2c909f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_auto_textgeneration_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_himani_auto_textgeneration DistilBertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_himani_auto_textgeneration +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_himani_auto_textgeneration` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_himani_auto_textgeneration_en_5.1.2_3.0_1694780500428.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_himani_auto_textgeneration_en_5.1.2_3.0_1694780500428.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_himani_auto_textgeneration","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_himani_auto_textgeneration", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_himani_auto_textgeneration| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/distilbert-base-uncased-finetuned-himani_auto-TEXTgeneration \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_gen_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_gen_en.md new file mode 100644 index 00000000000000..a4254b89309dad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_gen_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_himani_gen DistilBertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_himani_gen +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_himani_gen` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_himani_gen_en_5.1.2_3.0_1694780899595.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_himani_gen_en_5.1.2_3.0_1694780899595.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_himani_gen","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_himani_gen", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_himani_gen| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/distilbert-base-uncased-finetuned-himani-gen \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_m_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_m_en.md new file mode 100644 index 00000000000000..8d3e1610cd4e11 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_himani_m_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_himani_m DistilBertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_himani_m +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_himani_m` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_himani_m_en_5.1.2_3.0_1694781279459.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_himani_m_en_5.1.2_3.0_1694781279459.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_himani_m","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_himani_m", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_himani_m| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/distilbert-base-uncased-finetuned-himani-m \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_hina_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_hina_en.md new file mode 100644 index 00000000000000..3d3ce4fea0c69f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_hina_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_hina DistilBertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_hina +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_hina` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_hina_en_5.1.2_3.0_1694780144155.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_hina_en_5.1.2_3.0_1694780144155.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_hina","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_hina", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_hina| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/distilbert-base-uncased-finetuned-hina \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_homedepot_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_homedepot_en.md index e958f9e7829bd9..ee78b576460382 100644 --- a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_homedepot_en.md +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_homedepot_en.md @@ -24,8 +24,8 @@ Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_homedepot_en_5.1.2_3.0_1694736318696.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_homedepot_en_5.1.2_3.0_1694736318696.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_homedepot_en_5.1.2_3.0_1694773345195.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_homedepot_en_5.1.2_3.0_1694773345195.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_im_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_im_en.md new file mode 100644 index 00000000000000..60c44348217532 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_im_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_im DistilBertEmbeddings from shahriarebrampour +author: John Snow Labs +name: distilbert_base_uncased_finetuned_im +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_im` is a English model originally trained by shahriarebrampour. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_im_en_5.1.2_3.0_1694788569896.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_im_en_5.1.2_3.0_1694788569896.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_im","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_im", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_im| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/shahriarebrampour/distilbert-base-uncased-finetuned-im \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb1_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb1_en.md new file mode 100644 index 00000000000000..5126fed5979abf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb1 DistilBertEmbeddings from pulkitkumar13 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb1 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb1` is a English model originally trained by pulkitkumar13. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb1_en_5.1.2_3.0_1694787665201.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb1_en_5.1.2_3.0_1694787665201.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/pulkitkumar13/distilbert-base-uncased-finetuned-imdb1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_anikaai_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_anikaai_en.md new file mode 100644 index 00000000000000..85514f63f942d3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_anikaai_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_anikaai DistilBertEmbeddings from AnikaAI +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_anikaai +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_anikaai` is a English model originally trained by AnikaAI. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_anikaai_en_5.1.2_3.0_1694789143868.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_anikaai_en_5.1.2_3.0_1694789143868.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_anikaai","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_anikaai", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_anikaai| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/AnikaAI/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_arthuerwang_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_arthuerwang_en.md new file mode 100644 index 00000000000000..bde3235d5932a5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_arthuerwang_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_arthuerwang DistilBertEmbeddings from Arthuerwang +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_arthuerwang +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_arthuerwang` is a English model originally trained by Arthuerwang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_arthuerwang_en_5.1.2_3.0_1694783071999.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_arthuerwang_en_5.1.2_3.0_1694783071999.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_arthuerwang","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_arthuerwang", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_arthuerwang| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Arthuerwang/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_averageandyyy_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_averageandyyy_en.md new file mode 100644 index 00000000000000..45255bc0e2c683 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_averageandyyy_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_averageandyyy DistilBertEmbeddings from averageandyyy +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_averageandyyy +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_averageandyyy` is a English model originally trained by averageandyyy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_averageandyyy_en_5.1.2_3.0_1694776377229.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_averageandyyy_en_5.1.2_3.0_1694776377229.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_averageandyyy","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_averageandyyy", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_averageandyyy| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/averageandyyy/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_binaryy_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_binaryy_en.md new file mode 100644 index 00000000000000..c4baa5b2af5634 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_binaryy_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_binaryy DistilBertEmbeddings from Binaryy +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_binaryy +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_binaryy` is a English model originally trained by Binaryy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_binaryy_en_5.1.2_3.0_1694783198520.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_binaryy_en_5.1.2_3.0_1694783198520.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_binaryy","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_binaryy", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_binaryy| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Binaryy/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_caroline_betbeze_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_caroline_betbeze_en.md new file mode 100644 index 00000000000000..9c83bd18214daf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_caroline_betbeze_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_caroline_betbeze DistilBertEmbeddings from caroline-betbeze +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_caroline_betbeze +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_caroline_betbeze` is a English model originally trained by caroline-betbeze. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_caroline_betbeze_en_5.1.2_3.0_1694782749314.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_caroline_betbeze_en_5.1.2_3.0_1694782749314.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_caroline_betbeze","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_caroline_betbeze", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_caroline_betbeze| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/caroline-betbeze/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_cchychen_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_cchychen_en.md new file mode 100644 index 00000000000000..382a9dbe2a65dd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_cchychen_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_cchychen DistilBertEmbeddings from Cchychen +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_cchychen +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_cchychen` is a English model originally trained by Cchychen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_cchychen_en_5.1.2_3.0_1694786049897.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_cchychen_en_5.1.2_3.0_1694786049897.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_cchychen","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_cchychen", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_cchychen| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Cchychen/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_chenxingphh_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_chenxingphh_en.md new file mode 100644 index 00000000000000..7c345712b2ccc1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_chenxingphh_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_chenxingphh DistilBertEmbeddings from chenxingphh +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_chenxingphh +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_chenxingphh` is a English model originally trained by chenxingphh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_chenxingphh_en_5.1.2_3.0_1694771845243.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_chenxingphh_en_5.1.2_3.0_1694771845243.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_chenxingphh","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_chenxingphh", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_chenxingphh| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/chenxingphh/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_coreyabs_db_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_coreyabs_db_en.md new file mode 100644 index 00000000000000..334f5cd53e2113 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_coreyabs_db_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_coreyabs_db DistilBertEmbeddings from coreyabs-db +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_coreyabs_db +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_coreyabs_db` is a English model originally trained by coreyabs-db. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_coreyabs_db_en_5.1.2_3.0_1694781387285.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_coreyabs_db_en_5.1.2_3.0_1694781387285.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_coreyabs_db","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_coreyabs_db", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_coreyabs_db| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/coreyabs-db/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_delusionaldreams_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_delusionaldreams_en.md new file mode 100644 index 00000000000000..be0d71be38ae57 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_delusionaldreams_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_delusionaldreams DistilBertEmbeddings from DelusionalDreams +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_delusionaldreams +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_delusionaldreams` is a English model originally trained by DelusionalDreams. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_delusionaldreams_en_5.1.2_3.0_1694772163662.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_delusionaldreams_en_5.1.2_3.0_1694772163662.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_delusionaldreams","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_delusionaldreams", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_delusionaldreams| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/DelusionalDreams/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_dewa_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_dewa_en.md new file mode 100644 index 00000000000000..c8e5d61dfef8ed --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_dewa_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_dewa DistilBertEmbeddings from Dewa +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_dewa +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_dewa` is a English model originally trained by Dewa. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_dewa_en_5.1.2_3.0_1694777452620.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_dewa_en_5.1.2_3.0_1694777452620.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_dewa","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_dewa", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_dewa| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Dewa/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_dmlea_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_dmlea_en.md new file mode 100644 index 00000000000000..5b4fa23b7f93a0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_dmlea_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_dmlea DistilBertEmbeddings from dmlea +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_dmlea +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_dmlea` is a English model originally trained by dmlea. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_dmlea_en_5.1.2_3.0_1694790142387.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_dmlea_en_5.1.2_3.0_1694790142387.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_dmlea","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_dmlea", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_dmlea| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/dmlea/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_elhamagk_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_elhamagk_en.md new file mode 100644 index 00000000000000..3d028683b975b4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_elhamagk_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_elhamagk DistilBertEmbeddings from elhamagk +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_elhamagk +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_elhamagk` is a English model originally trained by elhamagk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_elhamagk_en_5.1.2_3.0_1694778234781.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_elhamagk_en_5.1.2_3.0_1694778234781.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_elhamagk","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_elhamagk", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_elhamagk| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/elhamagk/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_emergix_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_emergix_en.md new file mode 100644 index 00000000000000..c314c1d6a4000e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_emergix_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_emergix DistilBertEmbeddings from emergix +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_emergix +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_emergix` is a English model originally trained by emergix. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_emergix_en_5.1.2_3.0_1694776509650.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_emergix_en_5.1.2_3.0_1694776509650.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_emergix","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_emergix", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_emergix| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/emergix/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_evincent18_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_evincent18_en.md new file mode 100644 index 00000000000000..6bf4a3c0e91d36 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_evincent18_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_evincent18 DistilBertEmbeddings from evincent18 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_evincent18 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_evincent18` is a English model originally trained by evincent18. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_evincent18_en_5.1.2_3.0_1694771587269.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_evincent18_en_5.1.2_3.0_1694771587269.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_evincent18","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_evincent18", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_evincent18| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/evincent18/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_fadliaulawi_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_fadliaulawi_en.md new file mode 100644 index 00000000000000..197be0f2a3fb30 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_fadliaulawi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_fadliaulawi DistilBertEmbeddings from fadliaulawi +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_fadliaulawi +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_fadliaulawi` is a English model originally trained by fadliaulawi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_fadliaulawi_en_5.1.2_3.0_1694775951105.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_fadliaulawi_en_5.1.2_3.0_1694775951105.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_fadliaulawi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_fadliaulawi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_fadliaulawi| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/fadliaulawi/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_francesc_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_francesc_en.md new file mode 100644 index 00000000000000..675ac5f986fe1b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_francesc_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_francesc DistilBertEmbeddings from Francesc +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_francesc +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_francesc` is a English model originally trained by Francesc. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_francesc_en_5.1.2_3.0_1694787169835.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_francesc_en_5.1.2_3.0_1694787169835.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_francesc","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_francesc", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_francesc| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Francesc/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_ghost1_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_ghost1_en.md index 59397cf11b26c3..4220b4ef365618 100644 --- a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_ghost1_en.md +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_ghost1_en.md @@ -24,8 +24,8 @@ Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_ghost1_en_5.1.2_3.0_1694736935663.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_ghost1_en_5.1.2_3.0_1694736935663.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_ghost1_en_5.1.2_3.0_1694770280423.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_ghost1_en_5.1.2_3.0_1694770280423.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_golightly_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_golightly_en.md new file mode 100644 index 00000000000000..262d8869f48e30 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_golightly_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_golightly DistilBertEmbeddings from golightly +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_golightly +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_golightly` is a English model originally trained by golightly. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_golightly_en_5.1.2_3.0_1694790895420.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_golightly_en_5.1.2_3.0_1694790895420.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_golightly","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_golightly", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_golightly| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/golightly/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_gyronee_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_gyronee_en.md new file mode 100644 index 00000000000000..0a1097aa2f652b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_gyronee_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_gyronee DistilBertEmbeddings from gyronee +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_gyronee +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_gyronee` is a English model originally trained by gyronee. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_gyronee_en_5.1.2_3.0_1694778898355.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_gyronee_en_5.1.2_3.0_1694778898355.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_gyronee","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_gyronee", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_gyronee| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/gyronee/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_hilariooliveira_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_hilariooliveira_en.md new file mode 100644 index 00000000000000..ddb2c158e6c39e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_hilariooliveira_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_hilariooliveira DistilBertEmbeddings from hilariooliveira +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_hilariooliveira +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_hilariooliveira` is a English model originally trained by hilariooliveira. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_hilariooliveira_en_5.1.2_3.0_1694791662511.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_hilariooliveira_en_5.1.2_3.0_1694791662511.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_hilariooliveira","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_hilariooliveira", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_hilariooliveira| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/hilariooliveira/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_hsiehpinghan_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_hsiehpinghan_en.md new file mode 100644 index 00000000000000..78b51cd3e879dc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_hsiehpinghan_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_hsiehpinghan DistilBertEmbeddings from hsiehpinghan +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_hsiehpinghan +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_hsiehpinghan` is a English model originally trained by hsiehpinghan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_hsiehpinghan_en_5.1.2_3.0_1694771644406.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_hsiehpinghan_en_5.1.2_3.0_1694771644406.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_hsiehpinghan","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_hsiehpinghan", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_hsiehpinghan| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/hsiehpinghan/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_icity_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_icity_en.md new file mode 100644 index 00000000000000..ed3134fc2b2935 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_icity_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_icity DistilBertEmbeddings from icity +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_icity +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_icity` is a English model originally trained by icity. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_icity_en_5.1.2_3.0_1694770827492.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_icity_en_5.1.2_3.0_1694770827492.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_icity","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_icity", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_icity| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/icity/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jakeyunwookim_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jakeyunwookim_en.md new file mode 100644 index 00000000000000..b42c32039f9035 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jakeyunwookim_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_jakeyunwookim DistilBertEmbeddings from JakeYunwooKim +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_jakeyunwookim +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_jakeyunwookim` is a English model originally trained by JakeYunwooKim. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_jakeyunwookim_en_5.1.2_3.0_1694786783189.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_jakeyunwookim_en_5.1.2_3.0_1694786783189.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_jakeyunwookim","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_jakeyunwookim", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_jakeyunwookim| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/JakeYunwooKim/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jcai1_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jcai1_en.md new file mode 100644 index 00000000000000..e7b062a92e71a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jcai1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_jcai1 DistilBertEmbeddings from jcai1 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_jcai1 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_jcai1` is a English model originally trained by jcai1. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_jcai1_en_5.1.2_3.0_1694771220693.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_jcai1_en_5.1.2_3.0_1694771220693.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_jcai1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_jcai1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_jcai1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/jcai1/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jdang_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jdang_en.md new file mode 100644 index 00000000000000..8a483e649a4d0a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jdang_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_jdang DistilBertEmbeddings from jdang +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_jdang +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_jdang` is a English model originally trained by jdang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_jdang_en_5.1.2_3.0_1694778406635.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_jdang_en_5.1.2_3.0_1694778406635.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_jdang","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_jdang", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_jdang| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/jdang/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jjinbbangman_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jjinbbangman_en.md new file mode 100644 index 00000000000000..ba8fc381e23e65 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jjinbbangman_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_jjinbbangman DistilBertEmbeddings from JJinBBangMan +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_jjinbbangman +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_jjinbbangman` is a English model originally trained by JJinBBangMan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_jjinbbangman_en_5.1.2_3.0_1694787149226.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_jjinbbangman_en_5.1.2_3.0_1694787149226.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_jjinbbangman","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_jjinbbangman", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_jjinbbangman| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/JJinBBangMan/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_johnyyhk_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_johnyyhk_en.md new file mode 100644 index 00000000000000..920e66d668be9d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_johnyyhk_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_johnyyhk DistilBertEmbeddings from johnyyhk +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_johnyyhk +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_johnyyhk` is a English model originally trained by johnyyhk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_johnyyhk_en_5.1.2_3.0_1694785389324.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_johnyyhk_en_5.1.2_3.0_1694785389324.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_johnyyhk","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_johnyyhk", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_johnyyhk| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/johnyyhk/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jordanblatter_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jordanblatter_en.md new file mode 100644 index 00000000000000..d7c4c082430879 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jordanblatter_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_jordanblatter DistilBertEmbeddings from jordanblatter +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_jordanblatter +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_jordanblatter` is a English model originally trained by jordanblatter. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_jordanblatter_en_5.1.2_3.0_1694783143004.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_jordanblatter_en_5.1.2_3.0_1694783143004.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_jordanblatter","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_jordanblatter", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_jordanblatter| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/jordanblatter/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jwchung_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jwchung_en.md new file mode 100644 index 00000000000000..092eb224e14fc9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_jwchung_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_jwchung DistilBertEmbeddings from jwchung +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_jwchung +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_jwchung` is a English model originally trained by jwchung. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_jwchung_en_5.1.2_3.0_1694774483686.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_jwchung_en_5.1.2_3.0_1694774483686.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_jwchung","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_jwchung", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_jwchung| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/jwchung/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_larryboy825_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_larryboy825_en.md new file mode 100644 index 00000000000000..544093deb279cb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_larryboy825_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_larryboy825 DistilBertEmbeddings from larryboy825 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_larryboy825 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_larryboy825` is a English model originally trained by larryboy825. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_larryboy825_en_5.1.2_3.0_1694775543945.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_larryboy825_en_5.1.2_3.0_1694775543945.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_larryboy825","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_larryboy825", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_larryboy825| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/larryboy825/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_lewtun_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_lewtun_en.md new file mode 100644 index 00000000000000..b6851fc15b528a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_lewtun_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_lewtun DistilBertEmbeddings from lewtun +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_lewtun +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_lewtun` is a English model originally trained by lewtun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_lewtun_en_5.1.2_3.0_1694781429360.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_lewtun_en_5.1.2_3.0_1694781429360.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_lewtun","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_lewtun", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_lewtun| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/lewtun/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_liquannan_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_liquannan_en.md new file mode 100644 index 00000000000000..45619a606744cb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_liquannan_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_liquannan DistilBertEmbeddings from liquannan +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_liquannan +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_liquannan` is a English model originally trained by liquannan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_liquannan_en_5.1.2_3.0_1694770331398.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_liquannan_en_5.1.2_3.0_1694770331398.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_liquannan","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_liquannan", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_liquannan| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/liquannan/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_luzimu_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_luzimu_en.md new file mode 100644 index 00000000000000..07af92acd37c4e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_luzimu_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_luzimu DistilBertEmbeddings from luzimu +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_luzimu +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_luzimu` is a English model originally trained by luzimu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_luzimu_en_5.1.2_3.0_1694783067580.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_luzimu_en_5.1.2_3.0_1694783067580.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_luzimu","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_luzimu", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_luzimu| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/luzimu/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_lyk0013_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_lyk0013_en.md new file mode 100644 index 00000000000000..def64ecc5b5171 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_lyk0013_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_lyk0013 DistilBertEmbeddings from lyk0013 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_lyk0013 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_lyk0013` is a English model originally trained by lyk0013. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_lyk0013_en_5.1.2_3.0_1694790463669.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_lyk0013_en_5.1.2_3.0_1694790463669.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_lyk0013","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_lyk0013", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_lyk0013| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/lyk0013/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_magnustragardh_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_magnustragardh_en.md new file mode 100644 index 00000000000000..a9542906b549d2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_magnustragardh_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_magnustragardh DistilBertEmbeddings from magnustragardh +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_magnustragardh +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_magnustragardh` is a English model originally trained by magnustragardh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_magnustragardh_en_5.1.2_3.0_1694784482897.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_magnustragardh_en_5.1.2_3.0_1694784482897.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_magnustragardh","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_magnustragardh", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_magnustragardh| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/magnustragardh/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_mascariddu8_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_mascariddu8_en.md new file mode 100644 index 00000000000000..2b3e5da9589bc4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_mascariddu8_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_mascariddu8 DistilBertEmbeddings from Mascariddu8 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_mascariddu8 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_mascariddu8` is a English model originally trained by Mascariddu8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_mascariddu8_en_5.1.2_3.0_1694779288740.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_mascariddu8_en_5.1.2_3.0_1694779288740.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_mascariddu8","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_mascariddu8", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_mascariddu8| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Mascariddu8/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_mbateman_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_mbateman_en.md new file mode 100644 index 00000000000000..22a0f28c52b9a1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_mbateman_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_mbateman DistilBertEmbeddings from mbateman +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_mbateman +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_mbateman` is a English model originally trained by mbateman. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_mbateman_en_5.1.2_3.0_1694781933141.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_mbateman_en_5.1.2_3.0_1694781933141.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_mbateman","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_mbateman", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_mbateman| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/mbateman/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_mulinski_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_mulinski_en.md new file mode 100644 index 00000000000000..dd2d6c101a66db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_mulinski_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_mulinski DistilBertEmbeddings from mulinski +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_mulinski +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_mulinski` is a English model originally trained by mulinski. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_mulinski_en_5.1.2_3.0_1694776279605.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_mulinski_en_5.1.2_3.0_1694776279605.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_mulinski","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_mulinski", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_mulinski| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/mulinski/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_pavle_tsotskolauri_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_pavle_tsotskolauri_en.md new file mode 100644 index 00000000000000..2775e40d77279a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_pavle_tsotskolauri_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_pavle_tsotskolauri DistilBertEmbeddings from pavle-tsotskolauri +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_pavle_tsotskolauri +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_pavle_tsotskolauri` is a English model originally trained by pavle-tsotskolauri. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_pavle_tsotskolauri_en_5.1.2_3.0_1694771323246.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_pavle_tsotskolauri_en_5.1.2_3.0_1694771323246.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_pavle_tsotskolauri","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_pavle_tsotskolauri", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_pavle_tsotskolauri| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/pavle-tsotskolauri/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_peterhsu_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_peterhsu_en.md new file mode 100644 index 00000000000000..ebf6dd4c7409e5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_peterhsu_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_peterhsu DistilBertEmbeddings from peterhsu +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_peterhsu +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_peterhsu` is a English model originally trained by peterhsu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_peterhsu_en_5.1.2_3.0_1694783408621.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_peterhsu_en_5.1.2_3.0_1694783408621.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_peterhsu","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_peterhsu", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_peterhsu| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/peterhsu/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_physhunter_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_physhunter_en.md new file mode 100644 index 00000000000000..cf2aef600f03a9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_physhunter_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_physhunter DistilBertEmbeddings from PhysHunter +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_physhunter +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_physhunter` is a English model originally trained by PhysHunter. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_physhunter_en_5.1.2_3.0_1694773235874.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_physhunter_en_5.1.2_3.0_1694773235874.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_physhunter","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_physhunter", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_physhunter| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/PhysHunter/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_qianyu88_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_qianyu88_en.md new file mode 100644 index 00000000000000..34649d44d2013b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_qianyu88_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_qianyu88 DistilBertEmbeddings from qianyu88 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_qianyu88 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_qianyu88` is a English model originally trained by qianyu88. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_qianyu88_en_5.1.2_3.0_1694791575645.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_qianyu88_en_5.1.2_3.0_1694791575645.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_qianyu88","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_qianyu88", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_qianyu88| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/qianyu88/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_rajknakka_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_rajknakka_en.md new file mode 100644 index 00000000000000..da7a86034d5757 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_rajknakka_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_rajknakka DistilBertEmbeddings from RajkNakka +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_rajknakka +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_rajknakka` is a English model originally trained by RajkNakka. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_rajknakka_en_5.1.2_3.0_1694789718500.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_rajknakka_en_5.1.2_3.0_1694789718500.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_rajknakka","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_rajknakka", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_rajknakka| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/RajkNakka/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_rdvdsn_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_rdvdsn_en.md new file mode 100644 index 00000000000000..ff062179b335a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_rdvdsn_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_rdvdsn DistilBertEmbeddings from rdvdsn +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_rdvdsn +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_rdvdsn` is a English model originally trained by rdvdsn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_rdvdsn_en_5.1.2_3.0_1694786749532.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_rdvdsn_en_5.1.2_3.0_1694786749532.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_rdvdsn","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_rdvdsn", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_rdvdsn| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/rdvdsn/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_rugo_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_rugo_en.md new file mode 100644 index 00000000000000..3958d058e5c25f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_rugo_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_rugo DistilBertEmbeddings from rugo +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_rugo +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_rugo` is a English model originally trained by rugo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_rugo_en_5.1.2_3.0_1694783338406.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_rugo_en_5.1.2_3.0_1694783338406.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_rugo","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_rugo", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_rugo| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/rugo/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_rushikesh_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_rushikesh_en.md new file mode 100644 index 00000000000000..f3ac492a2c078a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_rushikesh_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_rushikesh DistilBertEmbeddings from Rushikesh +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_rushikesh +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_rushikesh` is a English model originally trained by Rushikesh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_rushikesh_en_5.1.2_3.0_1694782340471.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_rushikesh_en_5.1.2_3.0_1694782340471.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_rushikesh","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_rushikesh", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_rushikesh| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Rushikesh/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_ryanlai_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_ryanlai_en.md new file mode 100644 index 00000000000000..e5c2a2540731da --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_ryanlai_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_ryanlai DistilBertEmbeddings from ryanlai +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_ryanlai +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_ryanlai` is a English model originally trained by ryanlai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_ryanlai_en_5.1.2_3.0_1694787087716.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_ryanlai_en_5.1.2_3.0_1694787087716.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_ryanlai","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_ryanlai", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_ryanlai| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ryanlai/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_sarthakc44_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_sarthakc44_en.md new file mode 100644 index 00000000000000..a6de82726a327b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_sarthakc44_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_sarthakc44 DistilBertEmbeddings from sarthakc44 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_sarthakc44 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_sarthakc44` is a English model originally trained by sarthakc44. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_sarthakc44_en_5.1.2_3.0_1694773431276.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_sarthakc44_en_5.1.2_3.0_1694773431276.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_sarthakc44","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_sarthakc44", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_sarthakc44| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/sarthakc44/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_shadowtwin41_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_shadowtwin41_en.md new file mode 100644 index 00000000000000..5671f78b475aa1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_shadowtwin41_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_shadowtwin41 DistilBertEmbeddings from ShadowTwin41 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_shadowtwin41 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_shadowtwin41` is a English model originally trained by ShadowTwin41. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_shadowtwin41_en_5.1.2_3.0_1694787894599.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_shadowtwin41_en_5.1.2_3.0_1694787894599.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_shadowtwin41","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_shadowtwin41", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_shadowtwin41| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ShadowTwin41/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_spasis_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_spasis_en.md new file mode 100644 index 00000000000000..59dd4caf3680e9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_spasis_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_spasis DistilBertEmbeddings from spasis +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_spasis +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_spasis` is a English model originally trained by spasis. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_spasis_en_5.1.2_3.0_1694787046946.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_spasis_en_5.1.2_3.0_1694787046946.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_spasis","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_spasis", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_spasis| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/spasis/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_sungchun71_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_sungchun71_en.md new file mode 100644 index 00000000000000..168ad312f748ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_sungchun71_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_sungchun71 DistilBertEmbeddings from sungchun71 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_sungchun71 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_sungchun71` is a English model originally trained by sungchun71. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_sungchun71_en_5.1.2_3.0_1694774001255.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_sungchun71_en_5.1.2_3.0_1694774001255.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_sungchun71","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_sungchun71", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_sungchun71| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/sungchun71/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_surjray_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_surjray_en.md new file mode 100644 index 00000000000000..50675e3b7168be --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_surjray_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_surjray DistilBertEmbeddings from surjray +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_surjray +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_surjray` is a English model originally trained by surjray. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_surjray_en_5.1.2_3.0_1694778413933.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_surjray_en_5.1.2_3.0_1694778413933.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_surjray","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_surjray", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_surjray| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/surjray/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_talhaa_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_talhaa_en.md new file mode 100644 index 00000000000000..bb2995994b8b52 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_talhaa_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_talhaa DistilBertEmbeddings from talhaa +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_talhaa +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_talhaa` is a English model originally trained by talhaa. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_talhaa_en_5.1.2_3.0_1694786575831.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_talhaa_en_5.1.2_3.0_1694786575831.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_talhaa","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_talhaa", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_talhaa| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/talhaa/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_tanvirkhan_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_tanvirkhan_en.md new file mode 100644 index 00000000000000..2f71074cd131bb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_tanvirkhan_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_tanvirkhan DistilBertEmbeddings from tanvirkhan +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_tanvirkhan +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_tanvirkhan` is a English model originally trained by tanvirkhan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_tanvirkhan_en_5.1.2_3.0_1694785375336.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_tanvirkhan_en_5.1.2_3.0_1694785375336.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_tanvirkhan","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_tanvirkhan", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_tanvirkhan| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/tanvirkhan/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_terps_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_terps_en.md new file mode 100644 index 00000000000000..c0c74f701c926f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_terps_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_terps DistilBertEmbeddings from Terps +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_terps +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_terps` is a English model originally trained by Terps. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_terps_en_5.1.2_3.0_1694790594312.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_terps_en_5.1.2_3.0_1694790594312.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_terps","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_terps", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_terps| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Terps/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_thangvip_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_thangvip_en.md new file mode 100644 index 00000000000000..63313f6803eaca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_thangvip_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_thangvip DistilBertEmbeddings from thangvip +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_thangvip +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_thangvip` is a English model originally trained by thangvip. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_thangvip_en_5.1.2_3.0_1694791316149.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_thangvip_en_5.1.2_3.0_1694791316149.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_thangvip","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_thangvip", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_thangvip| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/thangvip/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_thetaphipsi_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_thetaphipsi_en.md new file mode 100644 index 00000000000000..2e448cb14dad75 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_thetaphipsi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_thetaphipsi DistilBertEmbeddings from ThetaPhiPsi +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_thetaphipsi +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_thetaphipsi` is a English model originally trained by ThetaPhiPsi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_thetaphipsi_en_5.1.2_3.0_1694781360414.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_thetaphipsi_en_5.1.2_3.0_1694781360414.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_thetaphipsi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_thetaphipsi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_thetaphipsi| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ThetaPhiPsi/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_threite_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_threite_en.md new file mode 100644 index 00000000000000..3904d3683f92ec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_threite_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_threite DistilBertEmbeddings from threite +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_threite +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_threite` is a English model originally trained by threite. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_threite_en_5.1.2_3.0_1694788276759.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_threite_en_5.1.2_3.0_1694788276759.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_threite","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_threite", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_threite| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/threite/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_timtl_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_timtl_en.md new file mode 100644 index 00000000000000..66aae5f7a59396 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_timtl_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_timtl DistilBertEmbeddings from TimTL +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_timtl +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_timtl` is a English model originally trained by TimTL. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_timtl_en_5.1.2_3.0_1694787470542.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_timtl_en_5.1.2_3.0_1694787470542.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_timtl","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_timtl", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_timtl| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/TimTL/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_tkoyama_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_tkoyama_en.md new file mode 100644 index 00000000000000..9f848af5608b19 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_tkoyama_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_tkoyama DistilBertEmbeddings from tkoyama +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_tkoyama +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_tkoyama` is a English model originally trained by tkoyama. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_tkoyama_en_5.1.2_3.0_1694788742865.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_tkoyama_en_5.1.2_3.0_1694788742865.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_tkoyama","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_tkoyama", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_tkoyama| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/tkoyama/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_tsobolev_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_tsobolev_en.md new file mode 100644 index 00000000000000..8ea35a9d79670e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_tsobolev_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_tsobolev DistilBertEmbeddings from tsobolev +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_tsobolev +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_tsobolev` is a English model originally trained by tsobolev. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_tsobolev_en_5.1.2_3.0_1694789508240.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_tsobolev_en_5.1.2_3.0_1694789508240.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_tsobolev","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_tsobolev", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_tsobolev| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/tsobolev/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_ttmusic_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_ttmusic_en.md new file mode 100644 index 00000000000000..51cdd81703037a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_ttmusic_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_ttmusic DistilBertEmbeddings from ttmusic +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_ttmusic +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_ttmusic` is a English model originally trained by ttmusic. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_ttmusic_en_5.1.2_3.0_1694786680946.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_ttmusic_en_5.1.2_3.0_1694786680946.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_ttmusic","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_ttmusic", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_ttmusic| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ttmusic/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_udoy_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_udoy_en.md new file mode 100644 index 00000000000000..ae85c58cf729ae --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_udoy_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_udoy DistilBertEmbeddings from Udoy +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_udoy +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_udoy` is a English model originally trained by Udoy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_udoy_en_5.1.2_3.0_1694787044089.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_udoy_en_5.1.2_3.0_1694787044089.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_udoy","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_udoy", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_udoy| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Udoy/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_vanhoan_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_vanhoan_en.md new file mode 100644 index 00000000000000..9e9e16648533d1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_vanhoan_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_vanhoan DistilBertEmbeddings from VanHoan +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_vanhoan +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_vanhoan` is a English model originally trained by VanHoan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_vanhoan_en_5.1.2_3.0_1694784218282.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_vanhoan_en_5.1.2_3.0_1694784218282.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_vanhoan","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_vanhoan", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_vanhoan| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/VanHoan/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_vives_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_vives_en.md new file mode 100644 index 00000000000000..0fa2a9ebe6b44c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_vives_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_vives DistilBertEmbeddings from vives +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_vives +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_vives` is a English model originally trained by vives. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_vives_en_5.1.2_3.0_1694772625562.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_vives_en_5.1.2_3.0_1694772625562.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_vives","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_vives", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_vives| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/vives/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_vsrinivas_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_vsrinivas_en.md new file mode 100644 index 00000000000000..353f294c12a3b1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_vsrinivas_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_vsrinivas DistilBertEmbeddings from vsrinivas +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_vsrinivas +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_vsrinivas` is a English model originally trained by vsrinivas. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_vsrinivas_en_5.1.2_3.0_1694788081981.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_vsrinivas_en_5.1.2_3.0_1694788081981.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_vsrinivas","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_vsrinivas", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_vsrinivas| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/vsrinivas/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_wangmiaobeng_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_wangmiaobeng_en.md new file mode 100644 index 00000000000000..f55dcf70c098ce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_wangmiaobeng_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_wangmiaobeng DistilBertEmbeddings from wangmiaobeng +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_wangmiaobeng +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_wangmiaobeng` is a English model originally trained by wangmiaobeng. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_wangmiaobeng_en_5.1.2_3.0_1694790638480.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_wangmiaobeng_en_5.1.2_3.0_1694790638480.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_wangmiaobeng","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_wangmiaobeng", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_wangmiaobeng| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/wangmiaobeng/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_waynechiu_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_waynechiu_en.md new file mode 100644 index 00000000000000..62724f9be9198a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_waynechiu_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_waynechiu DistilBertEmbeddings from WayneChiu +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_waynechiu +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_waynechiu` is a English model originally trained by WayneChiu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_waynechiu_en_5.1.2_3.0_1694788869882.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_waynechiu_en_5.1.2_3.0_1694788869882.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_waynechiu","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_waynechiu", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_waynechiu| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/WayneChiu/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_winson_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_winson_en.md new file mode 100644 index 00000000000000..4769f7d193d153 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_winson_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_winson DistilBertEmbeddings from winson +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_winson +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_winson` is a English model originally trained by winson. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_winson_en_5.1.2_3.0_1694776770579.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_winson_en_5.1.2_3.0_1694776770579.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_winson","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_winson", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_winson| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/winson/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_xkang_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_xkang_en.md new file mode 100644 index 00000000000000..4e0213c75e57b7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_xkang_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_xkang DistilBertEmbeddings from xkang +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_xkang +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_xkang` is a English model originally trained by xkang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_xkang_en_5.1.2_3.0_1694785805536.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_xkang_en_5.1.2_3.0_1694785805536.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_xkang","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_xkang", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_xkang| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/xkang/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_yangwooko_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_yangwooko_en.md new file mode 100644 index 00000000000000..4f460dbcbda848 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_yangwooko_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_yangwooko DistilBertEmbeddings from yangwooko +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_yangwooko +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_yangwooko` is a English model originally trained by yangwooko. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_yangwooko_en_5.1.2_3.0_1694777159804.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_yangwooko_en_5.1.2_3.0_1694777159804.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_yangwooko","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_yangwooko", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_yangwooko| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/yangwooko/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_yuto01_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_yuto01_en.md new file mode 100644 index 00000000000000..38b5cea503fb62 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_accelerate_yuto01_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_accelerate_yuto01 DistilBertEmbeddings from Yuto01 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_accelerate_yuto01 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_accelerate_yuto01` is a English model originally trained by Yuto01. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_yuto01_en_5.1.2_3.0_1694789597628.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_accelerate_yuto01_en_5.1.2_3.0_1694789597628.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_yuto01","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_accelerate_yuto01", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_accelerate_yuto01| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Yuto01/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_akashmaggon_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_akashmaggon_en.md new file mode 100644 index 00000000000000..d074260299e72b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_akashmaggon_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_akashmaggon DistilBertEmbeddings from akashmaggon +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_akashmaggon +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_akashmaggon` is a English model originally trained by akashmaggon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_akashmaggon_en_5.1.2_3.0_1694786916828.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_akashmaggon_en_5.1.2_3.0_1694786916828.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_akashmaggon","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_akashmaggon", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_akashmaggon| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/akashmaggon/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_akazad_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_akazad_en.md new file mode 100644 index 00000000000000..c92d705e708910 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_akazad_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_akazad DistilBertEmbeddings from akazad +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_akazad +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_akazad` is a English model originally trained by akazad. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_akazad_en_5.1.2_3.0_1694789344568.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_akazad_en_5.1.2_3.0_1694789344568.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_akazad","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_akazad", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_akazad| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/akazad/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_andrewr_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_andrewr_en.md new file mode 100644 index 00000000000000..6caaef76de79df --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_andrewr_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_andrewr DistilBertEmbeddings from AndrewR +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_andrewr +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_andrewr` is a English model originally trained by AndrewR. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_andrewr_en_5.1.2_3.0_1694790355579.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_andrewr_en_5.1.2_3.0_1694790355579.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_andrewr","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_andrewr", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_andrewr| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/AndrewR/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_anikaai_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_anikaai_en.md new file mode 100644 index 00000000000000..cd332606000491 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_anikaai_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_anikaai DistilBertEmbeddings from AnikaAI +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_anikaai +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_anikaai` is a English model originally trained by AnikaAI. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_anikaai_en_5.1.2_3.0_1694789041659.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_anikaai_en_5.1.2_3.0_1694789041659.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_anikaai","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_anikaai", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_anikaai| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/AnikaAI/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_anthonyyazdani_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_anthonyyazdani_en.md new file mode 100644 index 00000000000000..deccf083caa8ce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_anthonyyazdani_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_anthonyyazdani DistilBertEmbeddings from anthonyyazdani +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_anthonyyazdani +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_anthonyyazdani` is a English model originally trained by anthonyyazdani. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_anthonyyazdani_en_5.1.2_3.0_1694771270494.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_anthonyyazdani_en_5.1.2_3.0_1694771270494.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_anthonyyazdani","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_anthonyyazdani", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_anthonyyazdani| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/anthonyyazdani/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_aoill_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_aoill_en.md new file mode 100644 index 00000000000000..dee27866081dae --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_aoill_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_aoill DistilBertEmbeddings from aoill +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_aoill +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_aoill` is a English model originally trained by aoill. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_aoill_en_5.1.2_3.0_1694775321830.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_aoill_en_5.1.2_3.0_1694775321830.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_aoill","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_aoill", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_aoill| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/aoill/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_apatidar0_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_apatidar0_en.md new file mode 100644 index 00000000000000..5195f31d24a28d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_apatidar0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_apatidar0 DistilBertEmbeddings from apatidar0 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_apatidar0 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_apatidar0` is a English model originally trained by apatidar0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_apatidar0_en_5.1.2_3.0_1694789699531.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_apatidar0_en_5.1.2_3.0_1694789699531.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_apatidar0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_apatidar0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_apatidar0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/apatidar0/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_arjun9689_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_arjun9689_en.md new file mode 100644 index 00000000000000..c07f8a4f293a7e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_arjun9689_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_arjun9689 DistilBertEmbeddings from arjun9689 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_arjun9689 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_arjun9689` is a English model originally trained by arjun9689. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_arjun9689_en_5.1.2_3.0_1694777211475.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_arjun9689_en_5.1.2_3.0_1694777211475.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_arjun9689","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_arjun9689", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_arjun9689| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/arjun9689/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_arthuerwang_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_arthuerwang_en.md new file mode 100644 index 00000000000000..c58b8fd2284322 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_arthuerwang_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_arthuerwang DistilBertEmbeddings from Arthuerwang +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_arthuerwang +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_arthuerwang` is a English model originally trained by Arthuerwang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_arthuerwang_en_5.1.2_3.0_1694782618860.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_arthuerwang_en_5.1.2_3.0_1694782618860.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_arthuerwang","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_arthuerwang", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_arthuerwang| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Arthuerwang/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_arunadiraju_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_arunadiraju_en.md new file mode 100644 index 00000000000000..665da3a304af55 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_arunadiraju_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_arunadiraju DistilBertEmbeddings from arunadiraju +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_arunadiraju +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_arunadiraju` is a English model originally trained by arunadiraju. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_arunadiraju_en_5.1.2_3.0_1694779661933.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_arunadiraju_en_5.1.2_3.0_1694779661933.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_arunadiraju","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_arunadiraju", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_arunadiraju| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/arunadiraju/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_at2507_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_at2507_en.md new file mode 100644 index 00000000000000..e3722b6b69c050 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_at2507_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_at2507 DistilBertEmbeddings from at2507 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_at2507 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_at2507` is a English model originally trained by at2507. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_at2507_en_5.1.2_3.0_1694772852025.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_at2507_en_5.1.2_3.0_1694772852025.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_at2507","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_at2507", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_at2507| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/at2507/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_atiiisham988_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_atiiisham988_en.md new file mode 100644 index 00000000000000..06e68f572a0e5a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_atiiisham988_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_atiiisham988 DistilBertEmbeddings from atiiisham988 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_atiiisham988 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_atiiisham988` is a English model originally trained by atiiisham988. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_atiiisham988_en_5.1.2_3.0_1694772504810.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_atiiisham988_en_5.1.2_3.0_1694772504810.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_atiiisham988","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_atiiisham988", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_atiiisham988| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/atiiisham988/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_averageandyyy_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_averageandyyy_en.md new file mode 100644 index 00000000000000..c785d4e81bc0c5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_averageandyyy_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_averageandyyy DistilBertEmbeddings from averageandyyy +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_averageandyyy +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_averageandyyy` is a English model originally trained by averageandyyy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_averageandyyy_en_5.1.2_3.0_1694776247161.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_averageandyyy_en_5.1.2_3.0_1694776247161.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_averageandyyy","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_averageandyyy", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_averageandyyy| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/averageandyyy/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_bigearhututu_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_bigearhututu_en.md new file mode 100644 index 00000000000000..8be5c1fca2c171 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_bigearhututu_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_bigearhututu DistilBertEmbeddings from bigearhututu +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_bigearhututu +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_bigearhututu` is a English model originally trained by bigearhututu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_bigearhututu_en_5.1.2_3.0_1694775113143.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_bigearhututu_en_5.1.2_3.0_1694775113143.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_bigearhututu","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_bigearhututu", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_bigearhututu| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/bigearhututu/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_brenton_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_brenton_en.md new file mode 100644 index 00000000000000..882b5cfb3e4a5b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_brenton_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_brenton DistilBertEmbeddings from brenton +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_brenton +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_brenton` is a English model originally trained by brenton. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_brenton_en_5.1.2_3.0_1694787537664.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_brenton_en_5.1.2_3.0_1694787537664.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_brenton","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_brenton", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_brenton| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/brenton/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_bubblejoe_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_bubblejoe_en.md new file mode 100644 index 00000000000000..95708a24fcafd8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_bubblejoe_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_bubblejoe DistilBertEmbeddings from BubbleJoe +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_bubblejoe +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_bubblejoe` is a English model originally trained by BubbleJoe. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_bubblejoe_en_5.1.2_3.0_1694782227859.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_bubblejoe_en_5.1.2_3.0_1694782227859.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_bubblejoe","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_bubblejoe", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_bubblejoe| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/BubbleJoe/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_caroline_betbeze_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_caroline_betbeze_en.md new file mode 100644 index 00000000000000..757fdc53be5b41 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_caroline_betbeze_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_caroline_betbeze DistilBertEmbeddings from caroline-betbeze +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_caroline_betbeze +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_caroline_betbeze` is a English model originally trained by caroline-betbeze. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_caroline_betbeze_en_5.1.2_3.0_1694782648414.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_caroline_betbeze_en_5.1.2_3.0_1694782648414.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_caroline_betbeze","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_caroline_betbeze", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_caroline_betbeze| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/caroline-betbeze/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_cartinoe5930_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_cartinoe5930_en.md new file mode 100644 index 00000000000000..e859e17b692462 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_cartinoe5930_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_cartinoe5930 DistilBertEmbeddings from Cartinoe5930 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_cartinoe5930 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_cartinoe5930` is a English model originally trained by Cartinoe5930. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_cartinoe5930_en_5.1.2_3.0_1694784085673.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_cartinoe5930_en_5.1.2_3.0_1694784085673.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_cartinoe5930","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_cartinoe5930", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_cartinoe5930| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Cartinoe5930/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_cchychen_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_cchychen_en.md new file mode 100644 index 00000000000000..cf78c891438e85 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_cchychen_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_cchychen DistilBertEmbeddings from Cchychen +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_cchychen +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_cchychen` is a English model originally trained by Cchychen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_cchychen_en_5.1.2_3.0_1694786158859.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_cchychen_en_5.1.2_3.0_1694786158859.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_cchychen","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_cchychen", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_cchychen| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Cchychen/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_chenxingphh_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_chenxingphh_en.md new file mode 100644 index 00000000000000..14a89ef3a0f937 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_chenxingphh_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_chenxingphh DistilBertEmbeddings from chenxingphh +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_chenxingphh +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_chenxingphh` is a English model originally trained by chenxingphh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_chenxingphh_en_5.1.2_3.0_1694771729067.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_chenxingphh_en_5.1.2_3.0_1694771729067.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_chenxingphh","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_chenxingphh", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_chenxingphh| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/chenxingphh/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_cindymc_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_cindymc_en.md new file mode 100644 index 00000000000000..64f068843d732a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_cindymc_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_cindymc DistilBertEmbeddings from cindymc +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_cindymc +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_cindymc` is a English model originally trained by cindymc. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_cindymc_en_5.1.2_3.0_1694786805038.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_cindymc_en_5.1.2_3.0_1694786805038.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_cindymc","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_cindymc", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_cindymc| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/cindymc/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_cl_wood_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_cl_wood_en.md new file mode 100644 index 00000000000000..5ce33cfc4167bf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_cl_wood_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_cl_wood DistilBertEmbeddings from cl-wood +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_cl_wood +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_cl_wood` is a English model originally trained by cl-wood. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_cl_wood_en_5.1.2_3.0_1694770937006.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_cl_wood_en_5.1.2_3.0_1694770937006.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_cl_wood","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_cl_wood", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_cl_wood| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/cl-wood/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_cleandata_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_cleandata_en.md new file mode 100644 index 00000000000000..038ccccc225a70 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_cleandata_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_cleandata DistilBertEmbeddings from cleandata +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_cleandata +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_cleandata` is a English model originally trained by cleandata. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_cleandata_en_5.1.2_3.0_1694787303361.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_cleandata_en_5.1.2_3.0_1694787303361.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_cleandata","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_cleandata", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_cleandata| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/cleandata/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_codeplay_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_codeplay_en.md new file mode 100644 index 00000000000000..65603cc5098af2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_codeplay_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_codeplay DistilBertEmbeddings from codeplay +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_codeplay +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_codeplay` is a English model originally trained by codeplay. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_codeplay_en_5.1.2_3.0_1694787263429.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_codeplay_en_5.1.2_3.0_1694787263429.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_codeplay","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_codeplay", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_codeplay| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/codeplay/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_coreyabs_db_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_coreyabs_db_en.md new file mode 100644 index 00000000000000..4d0585f4f3aa94 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_coreyabs_db_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_coreyabs_db DistilBertEmbeddings from coreyabs-db +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_coreyabs_db +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_coreyabs_db` is a English model originally trained by coreyabs-db. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_coreyabs_db_en_5.1.2_3.0_1694781268724.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_coreyabs_db_en_5.1.2_3.0_1694781268724.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_coreyabs_db","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_coreyabs_db", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_coreyabs_db| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/coreyabs-db/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_cssupport_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_cssupport_en.md new file mode 100644 index 00000000000000..8b1b618b1fe744 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_cssupport_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_cssupport DistilBertEmbeddings from cssupport +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_cssupport +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_cssupport` is a English model originally trained by cssupport. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_cssupport_en_5.1.2_3.0_1694781174746.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_cssupport_en_5.1.2_3.0_1694781174746.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_cssupport","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_cssupport", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_cssupport| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/cssupport/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_dave_sheets_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_dave_sheets_en.md new file mode 100644 index 00000000000000..6f5258d356e46f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_dave_sheets_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_dave_sheets DistilBertEmbeddings from Dave-Sheets +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_dave_sheets +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_dave_sheets` is a English model originally trained by Dave-Sheets. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_dave_sheets_en_5.1.2_3.0_1694774885308.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_dave_sheets_en_5.1.2_3.0_1694774885308.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_dave_sheets","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_dave_sheets", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_dave_sheets| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Dave-Sheets/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_debug_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_debug_en.md new file mode 100644 index 00000000000000..1c2260c9ed02fa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_debug_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_debug DistilBertEmbeddings from lewtun +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_debug +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_debug` is a English model originally trained by lewtun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_debug_en_5.1.2_3.0_1694785359537.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_debug_en_5.1.2_3.0_1694785359537.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_debug","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_debug", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_debug| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/lewtun/distilbert-base-uncased-finetuned-imdb-debug \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_delusionaldreams_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_delusionaldreams_en.md new file mode 100644 index 00000000000000..30583928480413 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_delusionaldreams_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_delusionaldreams DistilBertEmbeddings from DelusionalDreams +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_delusionaldreams +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_delusionaldreams` is a English model originally trained by DelusionalDreams. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_delusionaldreams_en_5.1.2_3.0_1694772062647.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_delusionaldreams_en_5.1.2_3.0_1694772062647.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_delusionaldreams","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_delusionaldreams", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_delusionaldreams| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/DelusionalDreams/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_dewa_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_dewa_en.md new file mode 100644 index 00000000000000..f0bf012d043671 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_dewa_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_dewa DistilBertEmbeddings from Dewa +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_dewa +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_dewa` is a English model originally trained by Dewa. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_dewa_en_5.1.2_3.0_1694777331208.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_dewa_en_5.1.2_3.0_1694777331208.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_dewa","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_dewa", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_dewa| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Dewa/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_dieexbr_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_dieexbr_en.md new file mode 100644 index 00000000000000..c0fc37a5751a9a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_dieexbr_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_dieexbr DistilBertEmbeddings from dieexbr +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_dieexbr +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_dieexbr` is a English model originally trained by dieexbr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_dieexbr_en_5.1.2_3.0_1694771700734.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_dieexbr_en_5.1.2_3.0_1694771700734.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_dieexbr","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_dieexbr", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_dieexbr| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/dieexbr/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_dipika09_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_dipika09_en.md new file mode 100644 index 00000000000000..9454ae3c2c31f5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_dipika09_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_dipika09 DistilBertEmbeddings from Dipika09 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_dipika09 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_dipika09` is a English model originally trained by Dipika09. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_dipika09_en_5.1.2_3.0_1694789723756.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_dipika09_en_5.1.2_3.0_1694789723756.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_dipika09","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_dipika09", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_dipika09| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Dipika09/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_dmlea_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_dmlea_en.md new file mode 100644 index 00000000000000..bcc0834ba821ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_dmlea_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_dmlea DistilBertEmbeddings from dmlea +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_dmlea +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_dmlea` is a English model originally trained by dmlea. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_dmlea_en_5.1.2_3.0_1694790044524.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_dmlea_en_5.1.2_3.0_1694790044524.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_dmlea","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_dmlea", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_dmlea| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/dmlea/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_drdspace_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_drdspace_en.md new file mode 100644 index 00000000000000..dbd7a7611ec4b5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_drdspace_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_drdspace DistilBertEmbeddings from drdspace +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_drdspace +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_drdspace` is a English model originally trained by drdspace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_drdspace_en_5.1.2_3.0_1694783734326.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_drdspace_en_5.1.2_3.0_1694783734326.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_drdspace","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_drdspace", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_drdspace| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/drdspace/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_dshvetsov_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_dshvetsov_en.md new file mode 100644 index 00000000000000..855ea38f4559aa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_dshvetsov_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_dshvetsov DistilBertEmbeddings from dshvetsov +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_dshvetsov +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_dshvetsov` is a English model originally trained by dshvetsov. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_dshvetsov_en_5.1.2_3.0_1694782739474.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_dshvetsov_en_5.1.2_3.0_1694782739474.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_dshvetsov","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_dshvetsov", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_dshvetsov| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/dshvetsov/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_ecosystem_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_ecosystem_en.md new file mode 100644 index 00000000000000..4f12510ac207f2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_ecosystem_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_ecosystem DistilBertEmbeddings from ecosystem +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_ecosystem +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_ecosystem` is a English model originally trained by ecosystem. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_ecosystem_en_5.1.2_3.0_1694776959436.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_ecosystem_en_5.1.2_3.0_1694776959436.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_ecosystem","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_ecosystem", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_ecosystem| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ecosystem/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_edraper88_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_edraper88_en.md new file mode 100644 index 00000000000000..7f067a64f33b16 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_edraper88_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_edraper88 DistilBertEmbeddings from edraper88 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_edraper88 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_edraper88` is a English model originally trained by edraper88. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_edraper88_en_5.1.2_3.0_1694791144038.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_edraper88_en_5.1.2_3.0_1694791144038.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_edraper88","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_edraper88", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_edraper88| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/edraper88/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_eitanli_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_eitanli_en.md new file mode 100644 index 00000000000000..7b9b7ffbf63ba6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_eitanli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_eitanli DistilBertEmbeddings from Eitanli +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_eitanli +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_eitanli` is a English model originally trained by Eitanli. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_eitanli_en_5.1.2_3.0_1694785816192.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_eitanli_en_5.1.2_3.0_1694785816192.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_eitanli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_eitanli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_eitanli| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Eitanli/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_elggman_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_elggman_en.md new file mode 100644 index 00000000000000..6fc02e83c10865 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_elggman_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_elggman DistilBertEmbeddings from ELggman +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_elggman +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_elggman` is a English model originally trained by ELggman. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_elggman_en_5.1.2_3.0_1694784844116.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_elggman_en_5.1.2_3.0_1694784844116.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_elggman","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_elggman", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_elggman| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ELggman/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_eusojk_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_eusojk_en.md new file mode 100644 index 00000000000000..e4096b978b26cc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_eusojk_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_eusojk DistilBertEmbeddings from eusojk +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_eusojk +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_eusojk` is a English model originally trained by eusojk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_eusojk_en_5.1.2_3.0_1694784211104.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_eusojk_en_5.1.2_3.0_1694784211104.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_eusojk","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_eusojk", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_eusojk| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/eusojk/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_evincent18_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_evincent18_en.md new file mode 100644 index 00000000000000..fcf7eb07b7d19d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_evincent18_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_evincent18 DistilBertEmbeddings from evincent18 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_evincent18 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_evincent18` is a English model originally trained by evincent18. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_evincent18_en_5.1.2_3.0_1694771481378.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_evincent18_en_5.1.2_3.0_1694771481378.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_evincent18","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_evincent18", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_evincent18| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/evincent18/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_fadliaulawi_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_fadliaulawi_en.md new file mode 100644 index 00000000000000..23f3b87d5a0281 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_fadliaulawi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_fadliaulawi DistilBertEmbeddings from fadliaulawi +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_fadliaulawi +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_fadliaulawi` is a English model originally trained by fadliaulawi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_fadliaulawi_en_5.1.2_3.0_1694775718136.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_fadliaulawi_en_5.1.2_3.0_1694775718136.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_fadliaulawi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_fadliaulawi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_fadliaulawi| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/fadliaulawi/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_feeeper_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_feeeper_en.md new file mode 100644 index 00000000000000..e1ef4d25ccb1a5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_feeeper_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_feeeper DistilBertEmbeddings from feeeper +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_feeeper +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_feeeper` is a English model originally trained by feeeper. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_feeeper_en_5.1.2_3.0_1694778391045.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_feeeper_en_5.1.2_3.0_1694778391045.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_feeeper","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_feeeper", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_feeeper| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/feeeper/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_finetuned_imdb_chenyanjin_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_finetuned_imdb_chenyanjin_en.md new file mode 100644 index 00000000000000..2e0723b6dccd75 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_finetuned_imdb_chenyanjin_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_finetuned_imdb_chenyanjin DistilBertEmbeddings from chenyanjin +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_finetuned_imdb_chenyanjin +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_finetuned_imdb_chenyanjin` is a English model originally trained by chenyanjin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_finetuned_imdb_chenyanjin_en_5.1.2_3.0_1694783843728.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_finetuned_imdb_chenyanjin_en_5.1.2_3.0_1694783843728.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_finetuned_imdb_chenyanjin","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_finetuned_imdb_chenyanjin", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_finetuned_imdb_chenyanjin| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/chenyanjin/distilbert-base-uncased-finetuned-imdb-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_geolearner_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_geolearner_en.md new file mode 100644 index 00000000000000..52165837706ebe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_geolearner_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_geolearner DistilBertEmbeddings from geolearner +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_geolearner +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_geolearner` is a English model originally trained by geolearner. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_geolearner_en_5.1.2_3.0_1694776548569.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_geolearner_en_5.1.2_3.0_1694776548569.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_geolearner","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_geolearner", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_geolearner| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/geolearner/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_gg1313_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_gg1313_en.md new file mode 100644 index 00000000000000..c2c14b7105869c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_gg1313_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_gg1313 DistilBertEmbeddings from Gg1313 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_gg1313 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_gg1313` is a English model originally trained by Gg1313. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_gg1313_en_5.1.2_3.0_1694791141518.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_gg1313_en_5.1.2_3.0_1694791141518.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_gg1313","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_gg1313", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_gg1313| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Gg1313/distilbert-base-uncased-finetuned_imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_golightly_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_golightly_en.md new file mode 100644 index 00000000000000..c5be2b207d61bb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_golightly_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_golightly DistilBertEmbeddings from golightly +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_golightly +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_golightly` is a English model originally trained by golightly. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_golightly_en_5.1.2_3.0_1694790800704.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_golightly_en_5.1.2_3.0_1694790800704.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_golightly","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_golightly", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_golightly| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/golightly/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_gtxygyzb_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_gtxygyzb_en.md new file mode 100644 index 00000000000000..74657841cfd68e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_gtxygyzb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_gtxygyzb DistilBertEmbeddings from gtxygyzb +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_gtxygyzb +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_gtxygyzb` is a English model originally trained by gtxygyzb. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_gtxygyzb_en_5.1.2_3.0_1694783633507.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_gtxygyzb_en_5.1.2_3.0_1694783633507.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_gtxygyzb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_gtxygyzb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_gtxygyzb| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/gtxygyzb/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_guidoivetta_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_guidoivetta_en.md new file mode 100644 index 00000000000000..f5f7a5157b5230 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_guidoivetta_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_guidoivetta DistilBertEmbeddings from guidoivetta +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_guidoivetta +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_guidoivetta` is a English model originally trained by guidoivetta. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_guidoivetta_en_5.1.2_3.0_1694788838001.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_guidoivetta_en_5.1.2_3.0_1694788838001.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_guidoivetta","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_guidoivetta", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_guidoivetta| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/guidoivetta/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_harangus_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_harangus_en.md new file mode 100644 index 00000000000000..439cdf3b1dbe0c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_harangus_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_harangus DistilBertEmbeddings from Harangus +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_harangus +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_harangus` is a English model originally trained by Harangus. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_harangus_en_5.1.2_3.0_1694790359797.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_harangus_en_5.1.2_3.0_1694790359797.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_harangus","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_harangus", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_harangus| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Harangus/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_harshseth_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_harshseth_en.md new file mode 100644 index 00000000000000..80bee2d129ddce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_harshseth_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_harshseth DistilBertEmbeddings from harshseth +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_harshseth +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_harshseth` is a English model originally trained by harshseth. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_harshseth_en_5.1.2_3.0_1694789500099.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_harshseth_en_5.1.2_3.0_1694789500099.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_harshseth","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_harshseth", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_harshseth| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/harshseth/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_hemanth11_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_hemanth11_en.md new file mode 100644 index 00000000000000..665fcbc5416e32 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_hemanth11_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_hemanth11 DistilBertEmbeddings from hemanth11 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_hemanth11 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_hemanth11` is a English model originally trained by hemanth11. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_hemanth11_en_5.1.2_3.0_1694791749406.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_hemanth11_en_5.1.2_3.0_1694791749406.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_hemanth11","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_hemanth11", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_hemanth11| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/hemanth11/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_holtbui_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_holtbui_en.md new file mode 100644 index 00000000000000..47096c27f24d02 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_holtbui_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_holtbui DistilBertEmbeddings from holtbui +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_holtbui +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_holtbui` is a English model originally trained by holtbui. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_holtbui_en_5.1.2_3.0_1694787990455.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_holtbui_en_5.1.2_3.0_1694787990455.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_holtbui","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_holtbui", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_holtbui| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/holtbui/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_huggingface_course_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_huggingface_course_en.md new file mode 100644 index 00000000000000..893e4babd3a088 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_huggingface_course_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_huggingface_course DistilBertEmbeddings from huggingface-course +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_huggingface_course +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_huggingface_course` is a English model originally trained by huggingface-course. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_huggingface_course_en_5.1.2_3.0_1694780655866.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_huggingface_course_en_5.1.2_3.0_1694780655866.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_huggingface_course","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_huggingface_course", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_huggingface_course| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/huggingface-course/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_hxshen_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_hxshen_en.md new file mode 100644 index 00000000000000..aa74c5c6ede594 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_hxshen_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_hxshen DistilBertEmbeddings from hxshen +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_hxshen +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_hxshen` is a English model originally trained by hxshen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_hxshen_en_5.1.2_3.0_1694788924518.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_hxshen_en_5.1.2_3.0_1694788924518.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_hxshen","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_hxshen", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_hxshen| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/hxshen/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_imxxn_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_imxxn_en.md new file mode 100644 index 00000000000000..7e47a9ed008b02 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_imxxn_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_imxxn DistilBertEmbeddings from Imxxn +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_imxxn +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_imxxn` is a English model originally trained by Imxxn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_imxxn_en_5.1.2_3.0_1694777338856.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_imxxn_en_5.1.2_3.0_1694777338856.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_imxxn","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_imxxn", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_imxxn| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Imxxn/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_insub_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_insub_en.md new file mode 100644 index 00000000000000..fde7962a8d9c00 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_insub_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_insub DistilBertEmbeddings from insub +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_insub +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_insub` is a English model originally trained by insub. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_insub_en_5.1.2_3.0_1694772369747.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_insub_en_5.1.2_3.0_1694772369747.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_insub","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_insub", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_insub| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/insub/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_iotengtr_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_iotengtr_en.md new file mode 100644 index 00000000000000..b946a0f32dfe1b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_iotengtr_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_iotengtr DistilBertEmbeddings from iotengtr +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_iotengtr +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_iotengtr` is a English model originally trained by iotengtr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_iotengtr_en_5.1.2_3.0_1694773541551.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_iotengtr_en_5.1.2_3.0_1694773541551.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_iotengtr","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_iotengtr", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_iotengtr| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/iotengtr/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_iven5880_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_iven5880_en.md new file mode 100644 index 00000000000000..ce21c11521f919 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_iven5880_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_iven5880 DistilBertEmbeddings from iven5880 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_iven5880 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_iven5880` is a English model originally trained by iven5880. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_iven5880_en_5.1.2_3.0_1694791025301.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_iven5880_en_5.1.2_3.0_1694791025301.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_iven5880","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_iven5880", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_iven5880| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/iven5880/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_jaese_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_jaese_en.md new file mode 100644 index 00000000000000..ab1c8c738d5acf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_jaese_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_jaese DistilBertEmbeddings from jaese +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_jaese +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_jaese` is a English model originally trained by jaese. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_jaese_en_5.1.2_3.0_1694791022657.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_jaese_en_5.1.2_3.0_1694791022657.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_jaese","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_jaese", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_jaese| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/jaese/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_jake777_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_jake777_en.md new file mode 100644 index 00000000000000..f1ea6bca6c5b72 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_jake777_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_jake777 DistilBertEmbeddings from JAKE777 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_jake777 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_jake777` is a English model originally trained by JAKE777. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_jake777_en_5.1.2_3.0_1694777853032.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_jake777_en_5.1.2_3.0_1694777853032.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_jake777","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_jake777", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_jake777| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/JAKE777/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_jchhabra_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_jchhabra_en.md new file mode 100644 index 00000000000000..dbd8ea6e5bc839 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_jchhabra_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_jchhabra DistilBertEmbeddings from jchhabra +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_jchhabra +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_jchhabra` is a English model originally trained by jchhabra. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_jchhabra_en_5.1.2_3.0_1694788774737.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_jchhabra_en_5.1.2_3.0_1694788774737.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_jchhabra","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_jchhabra", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_jchhabra| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/jchhabra/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_jjinbbangman_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_jjinbbangman_en.md new file mode 100644 index 00000000000000..c3b178dfca6292 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_jjinbbangman_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_jjinbbangman DistilBertEmbeddings from JJinBBangMan +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_jjinbbangman +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_jjinbbangman` is a English model originally trained by JJinBBangMan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_jjinbbangman_en_5.1.2_3.0_1694787034924.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_jjinbbangman_en_5.1.2_3.0_1694787034924.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_jjinbbangman","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_jjinbbangman", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_jjinbbangman| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/JJinBBangMan/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_johnyyhk_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_johnyyhk_en.md new file mode 100644 index 00000000000000..4d0c68de8b954c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_johnyyhk_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_johnyyhk DistilBertEmbeddings from johnyyhk +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_johnyyhk +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_johnyyhk` is a English model originally trained by johnyyhk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_johnyyhk_en_5.1.2_3.0_1694785221188.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_johnyyhk_en_5.1.2_3.0_1694785221188.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_johnyyhk","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_johnyyhk", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_johnyyhk| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/johnyyhk/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_junchengding_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_junchengding_en.md new file mode 100644 index 00000000000000..1d08533d2c1d00 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_junchengding_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_junchengding DistilBertEmbeddings from JunchengDing +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_junchengding +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_junchengding` is a English model originally trained by JunchengDing. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_junchengding_en_5.1.2_3.0_1694780502980.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_junchengding_en_5.1.2_3.0_1694780502980.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_junchengding","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_junchengding", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_junchengding| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/JunchengDing/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_jwchung_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_jwchung_en.md new file mode 100644 index 00000000000000..94047821a62227 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_jwchung_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_jwchung DistilBertEmbeddings from jwchung +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_jwchung +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_jwchung` is a English model originally trained by jwchung. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_jwchung_en_5.1.2_3.0_1694774375800.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_jwchung_en_5.1.2_3.0_1694774375800.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_jwchung","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_jwchung", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_jwchung| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/jwchung/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_kar1sumax_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_kar1sumax_en.md new file mode 100644 index 00000000000000..0f0af95c4077d2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_kar1sumax_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_kar1sumax DistilBertEmbeddings from Kar1suMAX +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_kar1sumax +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_kar1sumax` is a English model originally trained by Kar1suMAX. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_kar1sumax_en_5.1.2_3.0_1694771169950.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_kar1sumax_en_5.1.2_3.0_1694771169950.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_kar1sumax","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_kar1sumax", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_kar1sumax| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Kar1suMAX/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_kosec39_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_kosec39_en.md new file mode 100644 index 00000000000000..8aa415a32b6c90 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_kosec39_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_kosec39 DistilBertEmbeddings from kosec39 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_kosec39 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_kosec39` is a English model originally trained by kosec39. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_kosec39_en_5.1.2_3.0_1694771918349.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_kosec39_en_5.1.2_3.0_1694771918349.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_kosec39","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_kosec39", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_kosec39| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/kosec39/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_ksaml_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_ksaml_en.md new file mode 100644 index 00000000000000..85a16d6a391374 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_ksaml_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_ksaml DistilBertEmbeddings from ksaml +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_ksaml +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_ksaml` is a English model originally trained by ksaml. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_ksaml_en_5.1.2_3.0_1694772454474.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_ksaml_en_5.1.2_3.0_1694772454474.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_ksaml","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_ksaml", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_ksaml| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ksaml/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_kyle2023_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_kyle2023_en.md new file mode 100644 index 00000000000000..e617536c967736 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_kyle2023_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_kyle2023 DistilBertEmbeddings from kyle2023 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_kyle2023 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_kyle2023` is a English model originally trained by kyle2023. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_kyle2023_en_5.1.2_3.0_1694788760520.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_kyle2023_en_5.1.2_3.0_1694788760520.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_kyle2023","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_kyle2023", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_kyle2023| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/kyle2023/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_lindarz_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_lindarz_en.md new file mode 100644 index 00000000000000..1316363d009c86 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_lindarz_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_lindarz DistilBertEmbeddings from lindarz +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_lindarz +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_lindarz` is a English model originally trained by lindarz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_lindarz_en_5.1.2_3.0_1694771058189.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_lindarz_en_5.1.2_3.0_1694771058189.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_lindarz","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_lindarz", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_lindarz| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/lindarz/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_liquannan_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_liquannan_en.md index 497cf8cf207580..1e789097ea533d 100644 --- a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_liquannan_en.md +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_liquannan_en.md @@ -24,8 +24,8 @@ Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_liquannan_en_5.1.2_3.0_1694736911779.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_liquannan_en_5.1.2_3.0_1694736911779.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_liquannan_en_5.1.2_3.0_1694770219474.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_liquannan_en_5.1.2_3.0_1694770219474.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_lokeshsoni2801_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_lokeshsoni2801_en.md new file mode 100644 index 00000000000000..51233de9251ce7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_lokeshsoni2801_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_lokeshsoni2801 DistilBertEmbeddings from Lokeshsoni2801 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_lokeshsoni2801 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_lokeshsoni2801` is a English model originally trained by Lokeshsoni2801. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_lokeshsoni2801_en_5.1.2_3.0_1694770947224.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_lokeshsoni2801_en_5.1.2_3.0_1694770947224.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_lokeshsoni2801","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_lokeshsoni2801", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_lokeshsoni2801| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Lokeshsoni2801/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_lsimon_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_lsimon_en.md new file mode 100644 index 00000000000000..066072d4e9bdae --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_lsimon_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_lsimon DistilBertEmbeddings from lsimon +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_lsimon +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_lsimon` is a English model originally trained by lsimon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_lsimon_en_5.1.2_3.0_1694783337770.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_lsimon_en_5.1.2_3.0_1694783337770.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_lsimon","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_lsimon", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_lsimon| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/lsimon/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_luzimu_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_luzimu_en.md new file mode 100644 index 00000000000000..2432be0cfaa61e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_luzimu_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_luzimu DistilBertEmbeddings from luzimu +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_luzimu +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_luzimu` is a English model originally trained by luzimu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_luzimu_en_5.1.2_3.0_1694782954592.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_luzimu_en_5.1.2_3.0_1694782954592.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_luzimu","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_luzimu", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_luzimu| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/luzimu/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_magnustragardh_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_magnustragardh_en.md new file mode 100644 index 00000000000000..59d521aa257ae9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_magnustragardh_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_magnustragardh DistilBertEmbeddings from magnustragardh +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_magnustragardh +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_magnustragardh` is a English model originally trained by magnustragardh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_magnustragardh_en_5.1.2_3.0_1694784349082.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_magnustragardh_en_5.1.2_3.0_1694784349082.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_magnustragardh","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_magnustragardh", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_magnustragardh| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/magnustragardh/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_manishw_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_manishw_en.md new file mode 100644 index 00000000000000..7adeccce72cddf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_manishw_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_manishw DistilBertEmbeddings from ManishW +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_manishw +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_manishw` is a English model originally trained by ManishW. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_manishw_en_5.1.2_3.0_1694779007105.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_manishw_en_5.1.2_3.0_1694779007105.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_manishw","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_manishw", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_manishw| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ManishW/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_marccram_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_marccram_en.md new file mode 100644 index 00000000000000..359af8942ad663 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_marccram_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_marccram DistilBertEmbeddings from marccram +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_marccram +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_marccram` is a English model originally trained by marccram. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_marccram_en_5.1.2_3.0_1694771311229.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_marccram_en_5.1.2_3.0_1694771311229.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_marccram","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_marccram", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_marccram| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/marccram/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_martingui_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_martingui_en.md new file mode 100644 index 00000000000000..17cc99ea75ac7b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_martingui_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_martingui DistilBertEmbeddings from MartinGui +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_martingui +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_martingui` is a English model originally trained by MartinGui. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_martingui_en_5.1.2_3.0_1694786646812.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_martingui_en_5.1.2_3.0_1694786646812.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_martingui","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_martingui", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_martingui| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/MartinGui/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mattiaparavisi_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mattiaparavisi_en.md new file mode 100644 index 00000000000000..738ad55426e54b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mattiaparavisi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_mattiaparavisi DistilBertEmbeddings from MattiaParavisi +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_mattiaparavisi +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_mattiaparavisi` is a English model originally trained by MattiaParavisi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_mattiaparavisi_en_5.1.2_3.0_1694782470244.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_mattiaparavisi_en_5.1.2_3.0_1694782470244.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_mattiaparavisi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_mattiaparavisi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_mattiaparavisi| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/MattiaParavisi/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_maysamalfiza_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_maysamalfiza_en.md new file mode 100644 index 00000000000000..133c958c38779c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_maysamalfiza_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_maysamalfiza DistilBertEmbeddings from maysamalfiza +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_maysamalfiza +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_maysamalfiza` is a English model originally trained by maysamalfiza. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_maysamalfiza_en_5.1.2_3.0_1694789818242.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_maysamalfiza_en_5.1.2_3.0_1694789818242.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_maysamalfiza","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_maysamalfiza", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_maysamalfiza| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/maysamalfiza/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mchalek_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mchalek_en.md new file mode 100644 index 00000000000000..6cd24d86760a96 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mchalek_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_mchalek DistilBertEmbeddings from mchalek +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_mchalek +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_mchalek` is a English model originally trained by mchalek. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_mchalek_en_5.1.2_3.0_1694790810503.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_mchalek_en_5.1.2_3.0_1694790810503.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_mchalek","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_mchalek", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_mchalek| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/mchalek/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mholi_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mholi_en.md new file mode 100644 index 00000000000000..0a48e83d439f16 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mholi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_mholi DistilBertEmbeddings from mholi +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_mholi +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_mholi` is a English model originally trained by mholi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_mholi_en_5.1.2_3.0_1694774114982.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_mholi_en_5.1.2_3.0_1694774114982.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_mholi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_mholi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_mholi| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/mholi/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mildmillard_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mildmillard_en.md new file mode 100644 index 00000000000000..8053e8d6830bb3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mildmillard_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_mildmillard DistilBertEmbeddings from mildmillard +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_mildmillard +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_mildmillard` is a English model originally trained by mildmillard. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_mildmillard_en_5.1.2_3.0_1694788468751.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_mildmillard_en_5.1.2_3.0_1694788468751.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_mildmillard","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_mildmillard", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_mildmillard| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/mildmillard/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mintz1104_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mintz1104_en.md new file mode 100644 index 00000000000000..ce1a8e679e3743 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mintz1104_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_mintz1104 DistilBertEmbeddings from mintz1104 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_mintz1104 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_mintz1104` is a English model originally trained by mintz1104. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_mintz1104_en_5.1.2_3.0_1694783458456.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_mintz1104_en_5.1.2_3.0_1694783458456.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_mintz1104","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_mintz1104", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_mintz1104| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/mintz1104/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_minye819_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_minye819_en.md new file mode 100644 index 00000000000000..981398a5275064 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_minye819_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_minye819 DistilBertEmbeddings from minye819 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_minye819 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_minye819` is a English model originally trained by minye819. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_minye819_en_5.1.2_3.0_1694785732543.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_minye819_en_5.1.2_3.0_1694785732543.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_minye819","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_minye819", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_minye819| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/minye819/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mlm_accelerate_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mlm_accelerate_en.md new file mode 100644 index 00000000000000..0e9588d860230f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mlm_accelerate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_mlm_accelerate DistilBertEmbeddings from pritam3355 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_mlm_accelerate +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_mlm_accelerate` is a English model originally trained by pritam3355. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_mlm_accelerate_en_5.1.2_3.0_1694783358290.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_mlm_accelerate_en_5.1.2_3.0_1694783358290.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_mlm_accelerate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_mlm_accelerate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_mlm_accelerate| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/pritam3355/distilbert-base-uncased-finetuned-imdb-mlm-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mulinski_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mulinski_en.md new file mode 100644 index 00000000000000..a9a4057f89695c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mulinski_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_mulinski DistilBertEmbeddings from mulinski +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_mulinski +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_mulinski` is a English model originally trained by mulinski. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_mulinski_en_5.1.2_3.0_1694776177033.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_mulinski_en_5.1.2_3.0_1694776177033.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_mulinski","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_mulinski", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_mulinski| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/mulinski/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mxalmeida_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mxalmeida_en.md new file mode 100644 index 00000000000000..41c4c1bbd6112e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_mxalmeida_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_mxalmeida DistilBertEmbeddings from mxalmeida +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_mxalmeida +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_mxalmeida` is a English model originally trained by mxalmeida. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_mxalmeida_en_5.1.2_3.0_1694783674388.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_mxalmeida_en_5.1.2_3.0_1694783674388.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_mxalmeida","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_mxalmeida", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_mxalmeida| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/mxalmeida/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_nugget00_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_nugget00_en.md new file mode 100644 index 00000000000000..4409f03c189a88 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_nugget00_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_nugget00 DistilBertEmbeddings from nugget00 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_nugget00 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_nugget00` is a English model originally trained by nugget00. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_nugget00_en_5.1.2_3.0_1694788096126.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_nugget00_en_5.1.2_3.0_1694788096126.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_nugget00","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_nugget00", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_nugget00| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/nugget00/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_orangelu_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_orangelu_en.md new file mode 100644 index 00000000000000..97c078a6e5b710 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_orangelu_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_orangelu DistilBertEmbeddings from orangelu +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_orangelu +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_orangelu` is a English model originally trained by orangelu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_orangelu_en_5.1.2_3.0_1694786367577.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_orangelu_en_5.1.2_3.0_1694786367577.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_orangelu","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_orangelu", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_orangelu| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/orangelu/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_parchiev_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_parchiev_en.md new file mode 100644 index 00000000000000..7341b84fd633a3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_parchiev_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_parchiev DistilBertEmbeddings from parchiev +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_parchiev +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_parchiev` is a English model originally trained by parchiev. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_parchiev_en_5.1.2_3.0_1694771616239.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_parchiev_en_5.1.2_3.0_1694771616239.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_parchiev","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_parchiev", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_parchiev| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/parchiev/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_pattom_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_pattom_en.md new file mode 100644 index 00000000000000..9fb28b2b1fab35 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_pattom_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_pattom DistilBertEmbeddings from pattom +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_pattom +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_pattom` is a English model originally trained by pattom. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_pattom_en_5.1.2_3.0_1694785696930.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_pattom_en_5.1.2_3.0_1694785696930.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_pattom","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_pattom", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_pattom| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/pattom/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_peterhsu_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_peterhsu_en.md new file mode 100644 index 00000000000000..b71c0181d15293 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_peterhsu_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_peterhsu DistilBertEmbeddings from peterhsu +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_peterhsu +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_peterhsu` is a English model originally trained by peterhsu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_peterhsu_en_5.1.2_3.0_1694783528508.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_peterhsu_en_5.1.2_3.0_1694783528508.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_peterhsu","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_peterhsu", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_peterhsu| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/peterhsu/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_peteryushunli_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_peteryushunli_en.md new file mode 100644 index 00000000000000..237e7a73f09e5e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_peteryushunli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_peteryushunli DistilBertEmbeddings from peteryushunli +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_peteryushunli +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_peteryushunli` is a English model originally trained by peteryushunli. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_peteryushunli_en_5.1.2_3.0_1694784593034.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_peteryushunli_en_5.1.2_3.0_1694784593034.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_peteryushunli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_peteryushunli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_peteryushunli| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/peteryushunli/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_physhunter_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_physhunter_en.md new file mode 100644 index 00000000000000..41adc211b1df3f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_physhunter_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_physhunter DistilBertEmbeddings from PhysHunter +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_physhunter +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_physhunter` is a English model originally trained by PhysHunter. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_physhunter_en_5.1.2_3.0_1694772969529.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_physhunter_en_5.1.2_3.0_1694772969529.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_physhunter","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_physhunter", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_physhunter| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/PhysHunter/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_pierre_arthur_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_pierre_arthur_en.md new file mode 100644 index 00000000000000..7654d1053146fd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_pierre_arthur_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_pierre_arthur DistilBertEmbeddings from Pierre-Arthur +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_pierre_arthur +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_pierre_arthur` is a English model originally trained by Pierre-Arthur. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_pierre_arthur_en_5.1.2_3.0_1694778085444.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_pierre_arthur_en_5.1.2_3.0_1694778085444.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_pierre_arthur","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_pierre_arthur", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_pierre_arthur| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Pierre-Arthur/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_poplkl_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_poplkl_en.md new file mode 100644 index 00000000000000..ac1d92179e7b0f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_poplkl_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_poplkl DistilBertEmbeddings from poplkl +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_poplkl +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_poplkl` is a English model originally trained by poplkl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_poplkl_en_5.1.2_3.0_1694778090892.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_poplkl_en_5.1.2_3.0_1694778090892.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_poplkl","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_poplkl", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_poplkl| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/poplkl/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_prasanthin_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_prasanthin_en.md new file mode 100644 index 00000000000000..6884d0f95bdbcc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_prasanthin_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_prasanthin DistilBertEmbeddings from Prasanthin +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_prasanthin +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_prasanthin` is a English model originally trained by Prasanthin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_prasanthin_en_5.1.2_3.0_1694791836770.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_prasanthin_en_5.1.2_3.0_1694791836770.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_prasanthin","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_prasanthin", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_prasanthin| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Prasanthin/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_qianyu88_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_qianyu88_en.md new file mode 100644 index 00000000000000..bd4fc552267259 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_qianyu88_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_qianyu88 DistilBertEmbeddings from qianyu88 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_qianyu88 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_qianyu88` is a English model originally trained by qianyu88. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_qianyu88_en_5.1.2_3.0_1694791401990.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_qianyu88_en_5.1.2_3.0_1694791401990.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_qianyu88","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_qianyu88", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_qianyu88| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/qianyu88/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_rajknakka_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_rajknakka_en.md new file mode 100644 index 00000000000000..1ba868fc50d4b4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_rajknakka_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_rajknakka DistilBertEmbeddings from RajkNakka +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_rajknakka +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_rajknakka` is a English model originally trained by RajkNakka. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_rajknakka_en_5.1.2_3.0_1694789607833.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_rajknakka_en_5.1.2_3.0_1694789607833.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_rajknakka","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_rajknakka", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_rajknakka| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/RajkNakka/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_raphaelmerx_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_raphaelmerx_en.md new file mode 100644 index 00000000000000..7d8ba517a2bac4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_raphaelmerx_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_raphaelmerx DistilBertEmbeddings from raphaelmerx +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_raphaelmerx +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_raphaelmerx` is a English model originally trained by raphaelmerx. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_raphaelmerx_en_5.1.2_3.0_1694783693258.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_raphaelmerx_en_5.1.2_3.0_1694783693258.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_raphaelmerx","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_raphaelmerx", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_raphaelmerx| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/raphaelmerx/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_raulgdp_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_raulgdp_en.md new file mode 100644 index 00000000000000..78b12fc538a71a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_raulgdp_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_raulgdp DistilBertEmbeddings from raulgdp +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_raulgdp +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_raulgdp` is a English model originally trained by raulgdp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_raulgdp_en_5.1.2_3.0_1694777650610.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_raulgdp_en_5.1.2_3.0_1694777650610.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_raulgdp","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_raulgdp", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_raulgdp| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/raulgdp/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_ray2791_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_ray2791_en.md new file mode 100644 index 00000000000000..501b64bc880b82 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_ray2791_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_ray2791 DistilBertEmbeddings from Ray2791 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_ray2791 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_ray2791` is a English model originally trained by Ray2791. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_ray2791_en_5.1.2_3.0_1694777725820.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_ray2791_en_5.1.2_3.0_1694777725820.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_ray2791","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_ray2791", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_ray2791| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Ray2791/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_rayguo2023_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_rayguo2023_en.md new file mode 100644 index 00000000000000..73aa24abb3ba54 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_rayguo2023_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_rayguo2023 DistilBertEmbeddings from RayGuo2023 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_rayguo2023 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_rayguo2023` is a English model originally trained by RayGuo2023. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_rayguo2023_en_5.1.2_3.0_1694778200046.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_rayguo2023_en_5.1.2_3.0_1694778200046.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_rayguo2023","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_rayguo2023", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_rayguo2023| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/RayGuo2023/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_rd124_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_rd124_en.md new file mode 100644 index 00000000000000..84a7e06d499854 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_rd124_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_rd124 DistilBertEmbeddings from rd124 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_rd124 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_rd124` is a English model originally trained by rd124. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_rd124_en_5.1.2_3.0_1694790386344.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_rd124_en_5.1.2_3.0_1694790386344.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_rd124","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_rd124", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_rd124| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/rd124/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_rdvdsn_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_rdvdsn_en.md new file mode 100644 index 00000000000000..5708a4f1a88796 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_rdvdsn_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_rdvdsn DistilBertEmbeddings from rdvdsn +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_rdvdsn +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_rdvdsn` is a English model originally trained by rdvdsn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_rdvdsn_en_5.1.2_3.0_1694786547240.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_rdvdsn_en_5.1.2_3.0_1694786547240.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_rdvdsn","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_rdvdsn", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_rdvdsn| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/rdvdsn/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_renyulin_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_renyulin_en.md new file mode 100644 index 00000000000000..61b80ff542bdc8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_renyulin_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_renyulin DistilBertEmbeddings from renyulin +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_renyulin +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_renyulin` is a English model originally trained by renyulin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_renyulin_en_5.1.2_3.0_1694773717620.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_renyulin_en_5.1.2_3.0_1694773717620.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_renyulin","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_renyulin", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_renyulin| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/renyulin/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_replicate_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_replicate_en.md new file mode 100644 index 00000000000000..423b4eb65b7753 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_replicate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_replicate DistilBertEmbeddings from hxshen +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_replicate +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_replicate` is a English model originally trained by hxshen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_replicate_en_5.1.2_3.0_1694790283986.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_replicate_en_5.1.2_3.0_1694790283986.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_replicate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_replicate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_replicate| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/hxshen/distilbert-base-uncased-finetuned-imdb-replicate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_reza93v_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_reza93v_en.md new file mode 100644 index 00000000000000..1f4f6a5d579498 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_reza93v_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_reza93v DistilBertEmbeddings from reza93v +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_reza93v +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_reza93v` is a English model originally trained by reza93v. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_reza93v_en_5.1.2_3.0_1694791487598.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_reza93v_en_5.1.2_3.0_1694791487598.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_reza93v","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_reza93v", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_reza93v| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/reza93v/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_robkayinto_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_robkayinto_en.md new file mode 100644 index 00000000000000..bc850f49e0ee85 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_robkayinto_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_robkayinto DistilBertEmbeddings from robkayinto +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_robkayinto +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_robkayinto` is a English model originally trained by robkayinto. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_robkayinto_en_5.1.2_3.0_1694771947206.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_robkayinto_en_5.1.2_3.0_1694771947206.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_robkayinto","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_robkayinto", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_robkayinto| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/robkayinto/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_rugo_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_rugo_en.md new file mode 100644 index 00000000000000..23f2a3e442b287 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_rugo_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_rugo DistilBertEmbeddings from rugo +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_rugo +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_rugo` is a English model originally trained by rugo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_rugo_en_5.1.2_3.0_1694783203222.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_rugo_en_5.1.2_3.0_1694783203222.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_rugo","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_rugo", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_rugo| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/rugo/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_ryanlai_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_ryanlai_en.md new file mode 100644 index 00000000000000..867411875662ac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_ryanlai_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_ryanlai DistilBertEmbeddings from ryanlai +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_ryanlai +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_ryanlai` is a English model originally trained by ryanlai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_ryanlai_en_5.1.2_3.0_1694786984703.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_ryanlai_en_5.1.2_3.0_1694786984703.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_ryanlai","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_ryanlai", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_ryanlai| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ryanlai/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sabby_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sabby_en.md index 155b5987b5e13d..9ab0de2344b2ec 100644 --- a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sabby_en.md +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sabby_en.md @@ -24,8 +24,8 @@ Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sabby_en_5.1.2_3.0_1694736793793.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sabby_en_5.1.2_3.0_1694736793793.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sabby_en_5.1.2_3.0_1694770099489.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sabby_en_5.1.2_3.0_1694770099489.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sakaijun_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sakaijun_en.md new file mode 100644 index 00000000000000..878991606f333c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sakaijun_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_sakaijun DistilBertEmbeddings from SakaiJun +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_sakaijun +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_sakaijun` is a English model originally trained by SakaiJun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sakaijun_en_5.1.2_3.0_1694771049150.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sakaijun_en_5.1.2_3.0_1694771049150.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_sakaijun","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_sakaijun", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_sakaijun| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/SakaiJun/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_san94_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_san94_en.md new file mode 100644 index 00000000000000..d11174761785b4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_san94_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_san94 DistilBertEmbeddings from san94 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_san94 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_san94` is a English model originally trained by san94. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_san94_en_5.1.2_3.0_1694771416174.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_san94_en_5.1.2_3.0_1694771416174.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_san94","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_san94", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_san94| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/san94/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sarmila_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sarmila_en.md new file mode 100644 index 00000000000000..78661557f06760 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sarmila_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_sarmila DistilBertEmbeddings from Sarmila +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_sarmila +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_sarmila` is a English model originally trained by Sarmila. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sarmila_en_5.1.2_3.0_1694776735479.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sarmila_en_5.1.2_3.0_1694776735479.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_sarmila","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_sarmila", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_sarmila| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Sarmila/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sarthakc44_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sarthakc44_en.md new file mode 100644 index 00000000000000..12aa256cd8feb0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sarthakc44_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_sarthakc44 DistilBertEmbeddings from sarthakc44 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_sarthakc44 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_sarthakc44` is a English model originally trained by sarthakc44. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sarthakc44_en_5.1.2_3.0_1694773301034.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sarthakc44_en_5.1.2_3.0_1694773301034.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_sarthakc44","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_sarthakc44", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_sarthakc44| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/sarthakc44/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_satyashetty_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_satyashetty_en.md new file mode 100644 index 00000000000000..1ed1a185d38997 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_satyashetty_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_satyashetty DistilBertEmbeddings from satyashetty +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_satyashetty +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_satyashetty` is a English model originally trained by satyashetty. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_satyashetty_en_5.1.2_3.0_1694781764485.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_satyashetty_en_5.1.2_3.0_1694781764485.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_satyashetty","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_satyashetty", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_satyashetty| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/satyashetty/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sertemo_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sertemo_en.md new file mode 100644 index 00000000000000..d0970c40da7edd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sertemo_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_sertemo DistilBertEmbeddings from sertemo +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_sertemo +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_sertemo` is a English model originally trained by sertemo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sertemo_en_5.1.2_3.0_1694770457266.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sertemo_en_5.1.2_3.0_1694770457266.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_sertemo","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_sertemo", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_sertemo| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/sertemo/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sgasparorippa_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sgasparorippa_en.md new file mode 100644 index 00000000000000..2ecb6be829de77 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sgasparorippa_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_sgasparorippa DistilBertEmbeddings from sgasparorippa +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_sgasparorippa +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_sgasparorippa` is a English model originally trained by sgasparorippa. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sgasparorippa_en_5.1.2_3.0_1694776057551.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sgasparorippa_en_5.1.2_3.0_1694776057551.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_sgasparorippa","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_sgasparorippa", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_sgasparorippa| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/sgasparorippa/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_shahriarebrampour_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_shahriarebrampour_en.md new file mode 100644 index 00000000000000..3e3884debc4b28 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_shahriarebrampour_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_shahriarebrampour DistilBertEmbeddings from shahriarebrampour +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_shahriarebrampour +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_shahriarebrampour` is a English model originally trained by shahriarebrampour. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_shahriarebrampour_en_5.1.2_3.0_1694788371168.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_shahriarebrampour_en_5.1.2_3.0_1694788371168.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_shahriarebrampour","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_shahriarebrampour", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_shahriarebrampour| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/shahriarebrampour/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_shre_db_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_shre_db_en.md new file mode 100644 index 00000000000000..10467160eef45a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_shre_db_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_shre_db DistilBertEmbeddings from shre-db +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_shre_db +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_shre_db` is a English model originally trained by shre-db. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_shre_db_en_5.1.2_3.0_1694782000633.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_shre_db_en_5.1.2_3.0_1694782000633.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_shre_db","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_shre_db", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_shre_db| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/shre-db/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_shreyasdatar_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_shreyasdatar_en.md new file mode 100644 index 00000000000000..9460dd524f637f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_shreyasdatar_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_shreyasdatar DistilBertEmbeddings from shreyasdatar +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_shreyasdatar +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_shreyasdatar` is a English model originally trained by shreyasdatar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_shreyasdatar_en_5.1.2_3.0_1694776445411.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_shreyasdatar_en_5.1.2_3.0_1694776445411.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_shreyasdatar","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_shreyasdatar", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_shreyasdatar| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/shreyasdatar/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_smarquie_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_smarquie_en.md new file mode 100644 index 00000000000000..ed761d1542d162 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_smarquie_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_smarquie DistilBertEmbeddings from smarquie +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_smarquie +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_smarquie` is a English model originally trained by smarquie. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_smarquie_en_5.1.2_3.0_1694779920112.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_smarquie_en_5.1.2_3.0_1694779920112.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_smarquie","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_smarquie", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_smarquie| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/smarquie/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_snousias_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_snousias_en.md new file mode 100644 index 00000000000000..ecfa350dccffc4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_snousias_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_snousias DistilBertEmbeddings from snousias +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_snousias +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_snousias` is a English model originally trained by snousias. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_snousias_en_5.1.2_3.0_1694771960892.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_snousias_en_5.1.2_3.0_1694771960892.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_snousias","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_snousias", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_snousias| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/snousias/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sofa566_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sofa566_en.md new file mode 100644 index 00000000000000..02b2390b968550 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sofa566_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_sofa566 DistilBertEmbeddings from sofa566 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_sofa566 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_sofa566` is a English model originally trained by sofa566. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sofa566_en_5.1.2_3.0_1694780390044.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sofa566_en_5.1.2_3.0_1694780390044.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_sofa566","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_sofa566", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_sofa566| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/sofa566/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_solver_paul_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_solver_paul_en.md new file mode 100644 index 00000000000000..cd7424bfa94d9b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_solver_paul_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_solver_paul DistilBertEmbeddings from solver-paul +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_solver_paul +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_solver_paul` is a English model originally trained by solver-paul. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_solver_paul_en_5.1.2_3.0_1694781878267.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_solver_paul_en_5.1.2_3.0_1694781878267.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_solver_paul","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_solver_paul", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_solver_paul| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/solver-paul/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sonali_behera_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sonali_behera_en.md new file mode 100644 index 00000000000000..80d33229edbdb7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sonali_behera_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_sonali_behera DistilBertEmbeddings from Sonali-Behera +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_sonali_behera +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_sonali_behera` is a English model originally trained by Sonali-Behera. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sonali_behera_en_5.1.2_3.0_1694777089415.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sonali_behera_en_5.1.2_3.0_1694777089415.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_sonali_behera","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_sonali_behera", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_sonali_behera| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Sonali-Behera/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sumedha_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sumedha_en.md new file mode 100644 index 00000000000000..5dc9c50f7f2f47 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_sumedha_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_sumedha DistilBertEmbeddings from Sumedha +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_sumedha +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_sumedha` is a English model originally trained by Sumedha. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sumedha_en_5.1.2_3.0_1694772327267.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_sumedha_en_5.1.2_3.0_1694772327267.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_sumedha","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_sumedha", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_sumedha| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Sumedha/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_supersokol_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_supersokol_en.md new file mode 100644 index 00000000000000..91f40b986bcd2e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_supersokol_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_supersokol DistilBertEmbeddings from SUPERSOKOL +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_supersokol +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_supersokol` is a English model originally trained by SUPERSOKOL. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_supersokol_en_5.1.2_3.0_1694785118271.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_supersokol_en_5.1.2_3.0_1694785118271.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_supersokol","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_supersokol", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_supersokol| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/SUPERSOKOL/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_surjray_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_surjray_en.md new file mode 100644 index 00000000000000..f642086c54e41c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_surjray_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_surjray DistilBertEmbeddings from surjray +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_surjray +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_surjray` is a English model originally trained by surjray. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_surjray_en_5.1.2_3.0_1694778304075.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_surjray_en_5.1.2_3.0_1694778304075.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_surjray","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_surjray", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_surjray| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/surjray/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_susghosh_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_susghosh_en.md new file mode 100644 index 00000000000000..7d408a50f8418e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_susghosh_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_susghosh DistilBertEmbeddings from susghosh +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_susghosh +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_susghosh` is a English model originally trained by susghosh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_susghosh_en_5.1.2_3.0_1694781874762.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_susghosh_en_5.1.2_3.0_1694781874762.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_susghosh","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_susghosh", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_susghosh| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/susghosh/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_talha185_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_talha185_en.md new file mode 100644 index 00000000000000..bc36425da7d080 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_talha185_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_talha185 DistilBertEmbeddings from Talha185 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_talha185 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_talha185` is a English model originally trained by Talha185. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_talha185_en_5.1.2_3.0_1694772261015.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_talha185_en_5.1.2_3.0_1694772261015.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_talha185","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_talha185", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_talha185| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Talha185/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_techtank_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_techtank_en.md new file mode 100644 index 00000000000000..cae38c16a10a9b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_techtank_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_techtank DistilBertEmbeddings from techtank +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_techtank +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_techtank` is a English model originally trained by techtank. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_techtank_en_5.1.2_3.0_1694785949232.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_techtank_en_5.1.2_3.0_1694785949232.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_techtank","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_techtank", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_techtank| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/techtank/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_terps_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_terps_en.md new file mode 100644 index 00000000000000..6e26c7c64c3dda --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_terps_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_terps DistilBertEmbeddings from Terps +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_terps +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_terps` is a English model originally trained by Terps. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_terps_en_5.1.2_3.0_1694790485061.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_terps_en_5.1.2_3.0_1694790485061.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_terps","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_terps", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_terps| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Terps/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_thangvip_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_thangvip_en.md new file mode 100644 index 00000000000000..ac1647a221a16f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_thangvip_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_thangvip DistilBertEmbeddings from thangvip +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_thangvip +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_thangvip` is a English model originally trained by thangvip. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_thangvip_en_5.1.2_3.0_1694791230390.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_thangvip_en_5.1.2_3.0_1694791230390.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_thangvip","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_thangvip", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_thangvip| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/thangvip/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_thaophung_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_thaophung_en.md new file mode 100644 index 00000000000000..73c55c4da2c560 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_thaophung_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_thaophung DistilBertEmbeddings from thaophung +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_thaophung +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_thaophung` is a English model originally trained by thaophung. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_thaophung_en_5.1.2_3.0_1694772963814.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_thaophung_en_5.1.2_3.0_1694772963814.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_thaophung","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_thaophung", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_thaophung| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/thaophung/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_thetaphipsi_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_thetaphipsi_en.md new file mode 100644 index 00000000000000..54a2bea240c373 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_thetaphipsi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_thetaphipsi DistilBertEmbeddings from ThetaPhiPsi +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_thetaphipsi +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_thetaphipsi` is a English model originally trained by ThetaPhiPsi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_thetaphipsi_en_5.1.2_3.0_1694781139251.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_thetaphipsi_en_5.1.2_3.0_1694781139251.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_thetaphipsi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_thetaphipsi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_thetaphipsi| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ThetaPhiPsi/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_threite_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_threite_en.md new file mode 100644 index 00000000000000..7c340ddd3dd6b4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_threite_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_threite DistilBertEmbeddings from threite +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_threite +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_threite` is a English model originally trained by threite. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_threite_en_5.1.2_3.0_1694788185263.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_threite_en_5.1.2_3.0_1694788185263.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_threite","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_threite", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_threite| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/threite/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_thutrang_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_thutrang_en.md new file mode 100644 index 00000000000000..21b9dc45959db0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_thutrang_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_thutrang DistilBertEmbeddings from ThuTrang +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_thutrang +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_thutrang` is a English model originally trained by ThuTrang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_thutrang_en_5.1.2_3.0_1694789894015.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_thutrang_en_5.1.2_3.0_1694789894015.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_thutrang","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_thutrang", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_thutrang| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ThuTrang/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_timtl_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_timtl_en.md new file mode 100644 index 00000000000000..c35ee439580c6e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_timtl_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_timtl DistilBertEmbeddings from TimTL +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_timtl +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_timtl` is a English model originally trained by TimTL. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_timtl_en_5.1.2_3.0_1694784349104.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_timtl_en_5.1.2_3.0_1694784349104.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_timtl","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_timtl", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_timtl| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/TimTL/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_tkoyama_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_tkoyama_en.md new file mode 100644 index 00000000000000..d45409b3924e4d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_tkoyama_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_tkoyama DistilBertEmbeddings from tkoyama +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_tkoyama +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_tkoyama` is a English model originally trained by tkoyama. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_tkoyama_en_5.1.2_3.0_1694788619647.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_tkoyama_en_5.1.2_3.0_1694788619647.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_tkoyama","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_tkoyama", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_tkoyama| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/tkoyama/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_tlapusan_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_tlapusan_en.md new file mode 100644 index 00000000000000..e000090cfebd8a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_tlapusan_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_tlapusan DistilBertEmbeddings from tlapusan +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_tlapusan +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_tlapusan` is a English model originally trained by tlapusan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_tlapusan_en_5.1.2_3.0_1694789306473.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_tlapusan_en_5.1.2_3.0_1694789306473.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_tlapusan","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_tlapusan", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_tlapusan| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/tlapusan/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_tofunumber1_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_tofunumber1_en.md new file mode 100644 index 00000000000000..352b638c92fb6f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_tofunumber1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_tofunumber1 DistilBertEmbeddings from TofuNumber1 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_tofunumber1 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_tofunumber1` is a English model originally trained by TofuNumber1. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_tofunumber1_en_5.1.2_3.0_1694776635641.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_tofunumber1_en_5.1.2_3.0_1694776635641.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_tofunumber1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_tofunumber1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_tofunumber1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/TofuNumber1/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_tsahhi_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_tsahhi_en.md new file mode 100644 index 00000000000000..38674573a4d5a2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_tsahhi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_tsahhi DistilBertEmbeddings from Tsahhi +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_tsahhi +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_tsahhi` is a English model originally trained by Tsahhi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_tsahhi_en_5.1.2_3.0_1694788650908.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_tsahhi_en_5.1.2_3.0_1694788650908.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_tsahhi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_tsahhi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_tsahhi| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Tsahhi/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_tux_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_tux_en.md new file mode 100644 index 00000000000000..23da54dbc779d0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_tux_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_tux DistilBertEmbeddings from tux +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_tux +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_tux` is a English model originally trained by tux. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_tux_en_5.1.2_3.0_1694786698878.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_tux_en_5.1.2_3.0_1694786698878.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_tux","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_tux", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_tux| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/tux/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_tyson0420_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_tyson0420_en.md new file mode 100644 index 00000000000000..cc0f26202bbe37 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_tyson0420_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_tyson0420 DistilBertEmbeddings from tyson0420 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_tyson0420 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_tyson0420` is a English model originally trained by tyson0420. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_tyson0420_en_5.1.2_3.0_1694775228117.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_tyson0420_en_5.1.2_3.0_1694775228117.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_tyson0420","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_tyson0420", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_tyson0420| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/tyson0420/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_udoy_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_udoy_en.md new file mode 100644 index 00000000000000..e61ee669c6ab02 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_udoy_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_udoy DistilBertEmbeddings from Udoy +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_udoy +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_udoy` is a English model originally trained by Udoy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_udoy_en_5.1.2_3.0_1694786003499.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_udoy_en_5.1.2_3.0_1694786003499.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_udoy","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_udoy", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_udoy| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Udoy/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_v2_accelerate_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_v2_accelerate_en.md new file mode 100644 index 00000000000000..af4706e8fbe3ee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_v2_accelerate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_v2_accelerate DistilBertEmbeddings from kaiku03 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_v2_accelerate +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_v2_accelerate` is a English model originally trained by kaiku03. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_v2_accelerate_en_5.1.2_3.0_1694789614333.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_v2_accelerate_en_5.1.2_3.0_1694789614333.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_v2_accelerate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_v2_accelerate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_v2_accelerate| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/kaiku03/distilbert-base-uncased-finetuned-imdb_v2_accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_v2_francesco_a_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_v2_francesco_a_en.md new file mode 100644 index 00000000000000..21216c3d68f8ce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_v2_francesco_a_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_v2_francesco_a DistilBertEmbeddings from Francesco-A +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_v2_francesco_a +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_v2_francesco_a` is a English model originally trained by Francesco-A. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_v2_francesco_a_en_5.1.2_3.0_1694783237772.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_v2_francesco_a_en_5.1.2_3.0_1694783237772.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_v2_francesco_a","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_v2_francesco_a", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_v2_francesco_a| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Francesco-A/distilbert-base-uncased-finetuned-imdb-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_v2_rd124_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_v2_rd124_en.md new file mode 100644 index 00000000000000..9bbe40429ed6ce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_v2_rd124_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_v2_rd124 DistilBertEmbeddings from rd124 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_v2_rd124 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_v2_rd124` is a English model originally trained by rd124. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_v2_rd124_en_5.1.2_3.0_1694790490326.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_v2_rd124_en_5.1.2_3.0_1694790490326.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_v2_rd124","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_v2_rd124", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_v2_rd124| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/rd124/distilbert-base-uncased-finetuned-imdb-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_vanhoan_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_vanhoan_en.md new file mode 100644 index 00000000000000..d0beec3e4579d7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_vanhoan_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_vanhoan DistilBertEmbeddings from VanHoan +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_vanhoan +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_vanhoan` is a English model originally trained by VanHoan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_vanhoan_en_5.1.2_3.0_1694783996380.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_vanhoan_en_5.1.2_3.0_1694783996380.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_vanhoan","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_vanhoan", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_vanhoan| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/VanHoan/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_venkyz9_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_venkyz9_en.md new file mode 100644 index 00000000000000..a18874b7686d58 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_venkyz9_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_venkyz9 DistilBertEmbeddings from venkyz9 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_venkyz9 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_venkyz9` is a English model originally trained by venkyz9. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_venkyz9_en_5.1.2_3.0_1694777457894.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_venkyz9_en_5.1.2_3.0_1694777457894.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_venkyz9","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_venkyz9", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_venkyz9| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/venkyz9/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_vibharkchauhan_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_vibharkchauhan_en.md new file mode 100644 index 00000000000000..5d10c910439a5f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_vibharkchauhan_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_vibharkchauhan DistilBertEmbeddings from Vibharkchauhan +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_vibharkchauhan +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_vibharkchauhan` is a English model originally trained by Vibharkchauhan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_vibharkchauhan_en_5.1.2_3.0_1694770670699.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_vibharkchauhan_en_5.1.2_3.0_1694770670699.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_vibharkchauhan","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_vibharkchauhan", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_vibharkchauhan| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Vibharkchauhan/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_vives_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_vives_en.md new file mode 100644 index 00000000000000..0b575aff66f065 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_vives_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_vives DistilBertEmbeddings from vives +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_vives +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_vives` is a English model originally trained by vives. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_vives_en_5.1.2_3.0_1694772503575.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_vives_en_5.1.2_3.0_1694772503575.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_vives","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_vives", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_vives| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/vives/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_vsrinivas_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_vsrinivas_en.md new file mode 100644 index 00000000000000..0429da59fdcdfa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_vsrinivas_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_vsrinivas DistilBertEmbeddings from vsrinivas +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_vsrinivas +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_vsrinivas` is a English model originally trained by vsrinivas. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_vsrinivas_en_5.1.2_3.0_1694787983867.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_vsrinivas_en_5.1.2_3.0_1694787983867.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_vsrinivas","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_vsrinivas", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_vsrinivas| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/vsrinivas/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_whole_word_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_whole_word_en.md new file mode 100644 index 00000000000000..6998c4d2df35c7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_whole_word_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_whole_word DistilBertEmbeddings from PhysHunter +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_whole_word +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_whole_word` is a English model originally trained by PhysHunter. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_whole_word_en_5.1.2_3.0_1694773100332.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_whole_word_en_5.1.2_3.0_1694773100332.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_whole_word","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_whole_word", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_whole_word| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/PhysHunter/distilbert-base-uncased-finetuned-imdb-whole-word \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_wjbmattingly_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_wjbmattingly_en.md new file mode 100644 index 00000000000000..932dfdf7b1c789 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_wjbmattingly_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_wjbmattingly DistilBertEmbeddings from wjbmattingly +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_wjbmattingly +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_wjbmattingly` is a English model originally trained by wjbmattingly. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_wjbmattingly_en_5.1.2_3.0_1694786512902.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_wjbmattingly_en_5.1.2_3.0_1694786512902.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_wjbmattingly","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_wjbmattingly", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_wjbmattingly| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/wjbmattingly/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_y_haneji_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_y_haneji_en.md new file mode 100644 index 00000000000000..765c2547379625 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_y_haneji_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_y_haneji DistilBertEmbeddings from Y-Haneji +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_y_haneji +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_y_haneji` is a English model originally trained by Y-Haneji. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_y_haneji_en_5.1.2_3.0_1694779145507.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_y_haneji_en_5.1.2_3.0_1694779145507.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_y_haneji","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_y_haneji", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_y_haneji| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Y-Haneji/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_yangwooko_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_yangwooko_en.md new file mode 100644 index 00000000000000..4ff6d48920795c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_yangwooko_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_yangwooko DistilBertEmbeddings from yangwooko +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_yangwooko +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_yangwooko` is a English model originally trained by yangwooko. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_yangwooko_en_5.1.2_3.0_1694776915854.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_yangwooko_en_5.1.2_3.0_1694776915854.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_yangwooko","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_yangwooko", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_yangwooko| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/yangwooko/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_yuto01_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_yuto01_en.md new file mode 100644 index 00000000000000..54c7b28979c107 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_imdb_yuto01_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_imdb_yuto01 DistilBertEmbeddings from Yuto01 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_imdb_yuto01 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_imdb_yuto01` is a English model originally trained by Yuto01. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_yuto01_en_5.1.2_3.0_1694789487678.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_imdb_yuto01_en_5.1.2_3.0_1694789487678.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_imdb_yuto01","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_imdb_yuto01", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_imdb_yuto01| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Yuto01/distilbert-base-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_ivr_finetuned_ivr_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_ivr_finetuned_ivr_en.md new file mode 100644 index 00000000000000..5bb26cad9437d5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_ivr_finetuned_ivr_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_ivr_finetuned_ivr DistilBertEmbeddings from khubaib +author: John Snow Labs +name: distilbert_base_uncased_finetuned_ivr_finetuned_ivr +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_ivr_finetuned_ivr` is a English model originally trained by khubaib. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_ivr_finetuned_ivr_en_5.1.2_3.0_1694776652350.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_ivr_finetuned_ivr_en_5.1.2_3.0_1694776652350.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_ivr_finetuned_ivr","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_ivr_finetuned_ivr", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_ivr_finetuned_ivr| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/khubaib/distilbert-base-uncased-finetuned-ivr-finetuned-ivr \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_kintweetse_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_kintweetse_en.md new file mode 100644 index 00000000000000..28f11009faeec0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_kintweetse_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_kintweetse DistilBertEmbeddings from RogerB +author: John Snow Labs +name: distilbert_base_uncased_finetuned_kintweetse +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_kintweetse` is a English model originally trained by RogerB. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_kintweetse_en_5.1.2_3.0_1694773642871.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_kintweetse_en_5.1.2_3.0_1694773642871.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_kintweetse","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_kintweetse", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_kintweetse| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/RogerB/distilbert-base-uncased-finetuned-kintweetsE \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_mlm_1_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_mlm_1_en.md new file mode 100644 index 00000000000000..c9f7956e876fab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_mlm_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_mlm_1 DistilBertEmbeddings from aarroonn22 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_mlm_1 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_mlm_1` is a English model originally trained by aarroonn22. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_mlm_1_en_5.1.2_3.0_1694772083071.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_mlm_1_en_5.1.2_3.0_1694772083071.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_mlm_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_mlm_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_mlm_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/aarroonn22/distilbert-base-uncased-finetuned-mlm-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_mlm_2_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_mlm_2_en.md new file mode 100644 index 00000000000000..0b03bbfd1628c5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_mlm_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_mlm_2 DistilBertEmbeddings from aarroonn22 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_mlm_2 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_mlm_2` is a English model originally trained by aarroonn22. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_mlm_2_en_5.1.2_3.0_1694770824986.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_mlm_2_en_5.1.2_3.0_1694770824986.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_mlm_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_mlm_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_mlm_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/aarroonn22/distilbert-base-uncased-finetuned-mlm-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_nitro_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_nitro_en.md new file mode 100644 index 00000000000000..e0494c826755ce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_nitro_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_nitro DistilBertEmbeddings from dieexbr +author: John Snow Labs +name: distilbert_base_uncased_finetuned_nitro +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_nitro` is a English model originally trained by dieexbr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_nitro_en_5.1.2_3.0_1694771823890.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_nitro_en_5.1.2_3.0_1694771823890.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_nitro","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_nitro", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_nitro| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/dieexbr/distilbert-base-uncased-finetuned-nitro \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_outoh_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_outoh_en.md new file mode 100644 index 00000000000000..2f0bf97fda503e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_outoh_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_outoh DistilBertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_outoh +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_outoh` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_outoh_en_5.1.2_3.0_1694779309949.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_outoh_en_5.1.2_3.0_1694779309949.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_outoh","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_outoh", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_outoh| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/distilbert-base-uncased-finetuned-outoH \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_outop_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_outop_en.md new file mode 100644 index 00000000000000..d85783ca467698 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_outop_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_outop DistilBertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_outop +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_outop` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_outop_en_5.1.2_3.0_1694779414180.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_outop_en_5.1.2_3.0_1694779414180.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_outop","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_outop", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_outop| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/distilbert-base-uncased-finetuned-outop \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_outop_j_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_outop_j_en.md new file mode 100644 index 00000000000000..0a6350b9159fc5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_outop_j_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_outop_j DistilBertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_outop_j +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_outop_j` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_outop_j_en_5.1.2_3.0_1694779643765.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_outop_j_en_5.1.2_3.0_1694779643765.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_outop_j","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_outop_j", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_outop_j| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/distilbert-base-uncased-finetuned-outop-J \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_outop_y_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_outop_y_en.md new file mode 100644 index 00000000000000..7ac829c6411954 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_outop_y_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_outop_y DistilBertEmbeddings from himanimaheshwari3 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_outop_y +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_outop_y` is a English model originally trained by himanimaheshwari3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_outop_y_en_5.1.2_3.0_1694779519036.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_outop_y_en_5.1.2_3.0_1694779519036.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_outop_y","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_outop_y", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_outop_y| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/himanimaheshwari3/distilbert-base-uncased-finetuned-outop-y \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_preprint_full_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_preprint_full_en.md new file mode 100644 index 00000000000000..537f30f2a256de --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_preprint_full_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_preprint_full DistilBertEmbeddings from vamads +author: John Snow Labs +name: distilbert_base_uncased_finetuned_preprint_full +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_preprint_full` is a English model originally trained by vamads. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_preprint_full_en_5.1.2_3.0_1694780135347.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_preprint_full_en_5.1.2_3.0_1694780135347.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_preprint_full","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_preprint_full", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_preprint_full| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/vamads/distilbert-base-uncased-finetuned-preprint_full \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_provenances_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_provenances_en.md new file mode 100644 index 00000000000000..96df623755d907 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_provenances_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_provenances DistilBertEmbeddings from RiccardoGvn +author: John Snow Labs +name: distilbert_base_uncased_finetuned_provenances +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_provenances` is a English model originally trained by RiccardoGvn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_provenances_en_5.1.2_3.0_1694781872135.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_provenances_en_5.1.2_3.0_1694781872135.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_provenances","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_provenances", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_provenances| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/RiccardoGvn/distilbert-base-uncased-finetuned-provenances \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_provenances_finetuned_provenances_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_provenances_finetuned_provenances_en.md new file mode 100644 index 00000000000000..2e0bc5c4ca3205 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_provenances_finetuned_provenances_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_provenances_finetuned_provenances DistilBertEmbeddings from RiccardoGvn +author: John Snow Labs +name: distilbert_base_uncased_finetuned_provenances_finetuned_provenances +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_provenances_finetuned_provenances` is a English model originally trained by RiccardoGvn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_provenances_finetuned_provenances_en_5.1.2_3.0_1694781997921.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_provenances_finetuned_provenances_en_5.1.2_3.0_1694781997921.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_provenances_finetuned_provenances","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_provenances_finetuned_provenances", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_provenances_finetuned_provenances| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/RiccardoGvn/distilbert-base-uncased-finetuned-provenances-finetuned-provenances \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_rap_lyrics_v1_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_rap_lyrics_v1_en.md new file mode 100644 index 00000000000000..a6db78786dcad1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_rap_lyrics_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_rap_lyrics_v1 DistilBertEmbeddings from peteryushunli +author: John Snow Labs +name: distilbert_base_uncased_finetuned_rap_lyrics_v1 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_rap_lyrics_v1` is a English model originally trained by peteryushunli. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_rap_lyrics_v1_en_5.1.2_3.0_1694788210807.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_rap_lyrics_v1_en_5.1.2_3.0_1694788210807.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_rap_lyrics_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_rap_lyrics_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_rap_lyrics_v1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/peteryushunli/distilbert-base-uncased-finetuned-rap-lyrics-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_recipe_accelerate_1_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_recipe_accelerate_1_en.md new file mode 100644 index 00000000000000..2c0e27bcab0f19 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_recipe_accelerate_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_recipe_accelerate_1 DistilBertEmbeddings from CennetOguz +author: John Snow Labs +name: distilbert_base_uncased_finetuned_recipe_accelerate_1 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_recipe_accelerate_1` is a English model originally trained by CennetOguz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_recipe_accelerate_1_en_5.1.2_3.0_1694770672733.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_recipe_accelerate_1_en_5.1.2_3.0_1694770672733.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_recipe_accelerate_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_recipe_accelerate_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_recipe_accelerate_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_recipe_accelerate_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_recipe_accelerate_en.md new file mode 100644 index 00000000000000..d64ec039e6bc80 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_recipe_accelerate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_recipe_accelerate DistilBertEmbeddings from CennetOguz +author: John Snow Labs +name: distilbert_base_uncased_finetuned_recipe_accelerate +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_recipe_accelerate` is a English model originally trained by CennetOguz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_recipe_accelerate_en_5.1.2_3.0_1694770774708.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_recipe_accelerate_en_5.1.2_3.0_1694770774708.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_recipe_accelerate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_recipe_accelerate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_recipe_accelerate| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_speeches_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_speeches_en.md new file mode 100644 index 00000000000000..01ced0e38ca9fe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_speeches_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_speeches DistilBertEmbeddings from peterday +author: John Snow Labs +name: distilbert_base_uncased_finetuned_speeches +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_speeches` is a English model originally trained by peterday. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_speeches_en_5.1.2_3.0_1694770816356.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_speeches_en_5.1.2_3.0_1694770816356.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_speeches","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_speeches", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_speeches| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/peterday/distilbert-base-uncased-finetuned-speeches \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_batuhanyilmaz_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_batuhanyilmaz_en.md new file mode 100644 index 00000000000000..c76388f0a9c3c8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_batuhanyilmaz_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_batuhanyilmaz DistilBertEmbeddings from BatuhanYilmaz +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_batuhanyilmaz +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_batuhanyilmaz` is a English model originally trained by BatuhanYilmaz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_batuhanyilmaz_en_5.1.2_3.0_1694770351064.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_batuhanyilmaz_en_5.1.2_3.0_1694770351064.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_batuhanyilmaz","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_batuhanyilmaz", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_batuhanyilmaz| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/BatuhanYilmaz/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_bellawanggg_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_bellawanggg_en.md new file mode 100644 index 00000000000000..e50ed6892b1b27 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_bellawanggg_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_bellawanggg DistilBertEmbeddings from bellawanggg +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_bellawanggg +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_bellawanggg` is a English model originally trained by bellawanggg. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_bellawanggg_en_5.1.2_3.0_1694771192012.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_bellawanggg_en_5.1.2_3.0_1694771192012.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_bellawanggg","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_bellawanggg", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_bellawanggg| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/bellawanggg/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_coreyabs_db_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_coreyabs_db_en.md new file mode 100644 index 00000000000000..19254d4efb38ce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_coreyabs_db_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_coreyabs_db DistilBertEmbeddings from coreyabs-db +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_coreyabs_db +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_coreyabs_db` is a English model originally trained by coreyabs-db. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_coreyabs_db_en_5.1.2_3.0_1694781671387.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_coreyabs_db_en_5.1.2_3.0_1694781671387.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_coreyabs_db","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_coreyabs_db", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_coreyabs_db| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/coreyabs-db/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_dchung117_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_dchung117_en.md new file mode 100644 index 00000000000000..3e0cc2060164b7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_dchung117_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_dchung117 DistilBertEmbeddings from dchung117 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_dchung117 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_dchung117` is a English model originally trained by dchung117. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_dchung117_en_5.1.2_3.0_1694788118641.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_dchung117_en_5.1.2_3.0_1694788118641.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_dchung117","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_dchung117", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_dchung117| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/dchung117/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_dkimds_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_dkimds_en.md new file mode 100644 index 00000000000000..77e28d5e954cc1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_dkimds_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_dkimds DistilBertEmbeddings from dkimds +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_dkimds +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_dkimds` is a English model originally trained by dkimds. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_dkimds_en_5.1.2_3.0_1694786923235.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_dkimds_en_5.1.2_3.0_1694786923235.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_dkimds","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_dkimds", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_dkimds| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/dkimds/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_fadliaulawi_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_fadliaulawi_en.md new file mode 100644 index 00000000000000..27ef6d217bbcd7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_fadliaulawi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_fadliaulawi DistilBertEmbeddings from fadliaulawi +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_fadliaulawi +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_fadliaulawi` is a English model originally trained by fadliaulawi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_fadliaulawi_en_5.1.2_3.0_1694777976444.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_fadliaulawi_en_5.1.2_3.0_1694777976444.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_fadliaulawi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_fadliaulawi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_fadliaulawi| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/fadliaulawi/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_gautam1989_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_gautam1989_en.md new file mode 100644 index 00000000000000..f2312e27b7f8e3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_gautam1989_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_gautam1989 DistilBertEmbeddings from gautam1989 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_gautam1989 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_gautam1989` is a English model originally trained by gautam1989. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_gautam1989_en_5.1.2_3.0_1694773530136.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_gautam1989_en_5.1.2_3.0_1694773530136.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_gautam1989","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_gautam1989", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_gautam1989| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/gautam1989/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_gostrive_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_gostrive_en.md new file mode 100644 index 00000000000000..75b429d50c86ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_gostrive_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_gostrive DistilBertEmbeddings from gostrive +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_gostrive +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_gostrive` is a English model originally trained by gostrive. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_gostrive_en_5.1.2_3.0_1694782689611.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_gostrive_en_5.1.2_3.0_1694782689611.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_gostrive","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_gostrive", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_gostrive| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/gostrive/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_guoguo_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_guoguo_en.md new file mode 100644 index 00000000000000..5c932c1ae45321 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_guoguo_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_guoguo DistilBertEmbeddings from guoguo +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_guoguo +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_guoguo` is a English model originally trained by guoguo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_guoguo_en_5.1.2_3.0_1694784353246.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_guoguo_en_5.1.2_3.0_1694784353246.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_guoguo","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_guoguo", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_guoguo| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/guoguo/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_iotengtr_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_iotengtr_en.md new file mode 100644 index 00000000000000..6d0b52804d9bf6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_iotengtr_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_iotengtr DistilBertEmbeddings from iotengtr +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_iotengtr +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_iotengtr` is a English model originally trained by iotengtr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_iotengtr_en_5.1.2_3.0_1694774783675.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_iotengtr_en_5.1.2_3.0_1694774783675.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_iotengtr","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_iotengtr", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_iotengtr| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/iotengtr/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_juancopi81_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_juancopi81_en.md new file mode 100644 index 00000000000000..4707af5be751e6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_juancopi81_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_juancopi81 DistilBertEmbeddings from juancopi81 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_juancopi81 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_juancopi81` is a English model originally trained by juancopi81. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_juancopi81_en_5.1.2_3.0_1694779999978.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_juancopi81_en_5.1.2_3.0_1694779999978.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_juancopi81","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_juancopi81", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_juancopi81| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/juancopi81/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_jwlovetea_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_jwlovetea_en.md new file mode 100644 index 00000000000000..ca6d920b37cc4f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_jwlovetea_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_jwlovetea DistilBertEmbeddings from jwlovetea +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_jwlovetea +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_jwlovetea` is a English model originally trained by jwlovetea. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_jwlovetea_en_5.1.2_3.0_1694790258197.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_jwlovetea_en_5.1.2_3.0_1694790258197.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_jwlovetea","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_jwlovetea", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_jwlovetea| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/jwlovetea/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_lakecrimsonn_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_lakecrimsonn_en.md new file mode 100644 index 00000000000000..b799b235ed2df7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_lakecrimsonn_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_lakecrimsonn DistilBertEmbeddings from lakecrimsonn +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_lakecrimsonn +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_lakecrimsonn` is a English model originally trained by lakecrimsonn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_lakecrimsonn_en_5.1.2_3.0_1694775410500.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_lakecrimsonn_en_5.1.2_3.0_1694775410500.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_lakecrimsonn","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_lakecrimsonn", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_lakecrimsonn| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/lakecrimsonn/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_luzimu_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_luzimu_en.md new file mode 100644 index 00000000000000..201520baae29e2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_luzimu_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_luzimu DistilBertEmbeddings from luzimu +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_luzimu +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_luzimu` is a English model originally trained by luzimu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_luzimu_en_5.1.2_3.0_1694783536406.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_luzimu_en_5.1.2_3.0_1694783536406.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_luzimu","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_luzimu", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_luzimu| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/luzimu/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_maseiya_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_maseiya_en.md new file mode 100644 index 00000000000000..6aaf7db41b68e6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_maseiya_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_maseiya DistilBertEmbeddings from maseiya +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_maseiya +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_maseiya` is a English model originally trained by maseiya. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_maseiya_en_5.1.2_3.0_1694770328657.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_maseiya_en_5.1.2_3.0_1694770328657.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_maseiya","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_maseiya", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_maseiya| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/maseiya/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_mbateman_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_mbateman_en.md new file mode 100644 index 00000000000000..e117378fd6db5b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_mbateman_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_mbateman DistilBertEmbeddings from mbateman +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_mbateman +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_mbateman` is a English model originally trained by mbateman. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_mbateman_en_5.1.2_3.0_1694782120527.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_mbateman_en_5.1.2_3.0_1694782120527.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_mbateman","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_mbateman", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_mbateman| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/mbateman/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_miesnerjacob_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_miesnerjacob_en.md new file mode 100644 index 00000000000000..c947fab2d8e8ce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_miesnerjacob_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_miesnerjacob DistilBertEmbeddings from miesnerjacob +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_miesnerjacob +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_miesnerjacob` is a English model originally trained by miesnerjacob. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_miesnerjacob_en_5.1.2_3.0_1694774420966.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_miesnerjacob_en_5.1.2_3.0_1694774420966.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_miesnerjacob","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_miesnerjacob", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_miesnerjacob| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/miesnerjacob/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_nicolacandussi_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_nicolacandussi_en.md new file mode 100644 index 00000000000000..925e071a5e5068 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_nicolacandussi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_nicolacandussi DistilBertEmbeddings from nicolacandussi +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_nicolacandussi +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_nicolacandussi` is a English model originally trained by nicolacandussi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_nicolacandussi_en_5.1.2_3.0_1694792078563.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_nicolacandussi_en_5.1.2_3.0_1694792078563.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_nicolacandussi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_nicolacandussi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_nicolacandussi| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/nicolacandussi/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_nugget00_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_nugget00_en.md new file mode 100644 index 00000000000000..61660c170d082c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_nugget00_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_nugget00 DistilBertEmbeddings from nugget00 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_nugget00 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_nugget00` is a English model originally trained by nugget00. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_nugget00_en_5.1.2_3.0_1694788315308.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_nugget00_en_5.1.2_3.0_1694788315308.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_nugget00","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_nugget00", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_nugget00| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/nugget00/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_osanseviero_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_osanseviero_en.md new file mode 100644 index 00000000000000..32d5a19e816465 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_osanseviero_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_osanseviero DistilBertEmbeddings from osanseviero +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_osanseviero +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_osanseviero` is a English model originally trained by osanseviero. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_osanseviero_en_5.1.2_3.0_1694787328705.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_osanseviero_en_5.1.2_3.0_1694787328705.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_osanseviero","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_osanseviero", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_osanseviero| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/osanseviero/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_peterhsu_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_peterhsu_en.md new file mode 100644 index 00000000000000..93128e31c9cbaf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_peterhsu_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_peterhsu DistilBertEmbeddings from peterhsu +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_peterhsu +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_peterhsu` is a English model originally trained by peterhsu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_peterhsu_en_5.1.2_3.0_1694787941415.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_peterhsu_en_5.1.2_3.0_1694787941415.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_peterhsu","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_peterhsu", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_peterhsu| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/peterhsu/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_physhunter_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_physhunter_en.md new file mode 100644 index 00000000000000..a5dd3aaecc4b28 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_physhunter_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_physhunter DistilBertEmbeddings from PhysHunter +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_physhunter +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_physhunter` is a English model originally trained by PhysHunter. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_physhunter_en_5.1.2_3.0_1694776775566.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_physhunter_en_5.1.2_3.0_1694776775566.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_physhunter","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_physhunter", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_physhunter| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/PhysHunter/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_runningsnake_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_runningsnake_en.md new file mode 100644 index 00000000000000..6124c05c388d9f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_runningsnake_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_runningsnake DistilBertEmbeddings from runningsnake +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_runningsnake +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_runningsnake` is a English model originally trained by runningsnake. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_runningsnake_en_5.1.2_3.0_1694782116286.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_runningsnake_en_5.1.2_3.0_1694782116286.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_runningsnake","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_runningsnake", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_runningsnake| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/runningsnake/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_sayaendo_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_sayaendo_en.md new file mode 100644 index 00000000000000..a26a62436e8e37 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_sayaendo_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_sayaendo DistilBertEmbeddings from SayaEndo +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_sayaendo +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_sayaendo` is a English model originally trained by SayaEndo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_sayaendo_en_5.1.2_3.0_1694781746406.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_sayaendo_en_5.1.2_3.0_1694781746406.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_sayaendo","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_sayaendo", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_sayaendo| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/SayaEndo/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_sebastians_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_sebastians_en.md new file mode 100644 index 00000000000000..7880a9faf7bb14 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_sebastians_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_sebastians DistilBertEmbeddings from SebastianS +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_sebastians +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_sebastians` is a English model originally trained by SebastianS. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_sebastians_en_5.1.2_3.0_1694772917775.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_sebastians_en_5.1.2_3.0_1694772917775.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_sebastians","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_sebastians", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_sebastians| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/SebastianS/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_sgr23_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_sgr23_en.md new file mode 100644 index 00000000000000..efbac5bdd51fad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_sgr23_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_sgr23 DistilBertEmbeddings from sgr23 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_sgr23 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_sgr23` is a English model originally trained by sgr23. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_sgr23_en_5.1.2_3.0_1694784756753.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_sgr23_en_5.1.2_3.0_1694784756753.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_sgr23","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_sgr23", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_sgr23| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/sgr23/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_shadowtwin41_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_shadowtwin41_en.md new file mode 100644 index 00000000000000..6ca1c0b309f589 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_shadowtwin41_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_shadowtwin41 DistilBertEmbeddings from ShadowTwin41 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_shadowtwin41 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_shadowtwin41` is a English model originally trained by ShadowTwin41. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_shadowtwin41_en_5.1.2_3.0_1694788124987.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_shadowtwin41_en_5.1.2_3.0_1694788124987.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_shadowtwin41","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_shadowtwin41", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_shadowtwin41| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ShadowTwin41/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_soduhh_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_soduhh_en.md new file mode 100644 index 00000000000000..ba93a526c82f02 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_soduhh_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_soduhh DistilBertEmbeddings from soduhh +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_soduhh +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_soduhh` is a English model originally trained by soduhh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_soduhh_en_5.1.2_3.0_1694774128556.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_soduhh_en_5.1.2_3.0_1694774128556.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_soduhh","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_soduhh", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_soduhh| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/soduhh/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_sofa566_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_sofa566_en.md new file mode 100644 index 00000000000000..b830706876afd0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_sofa566_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_sofa566 DistilBertEmbeddings from sofa566 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_sofa566 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_sofa566` is a English model originally trained by sofa566. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_sofa566_en_5.1.2_3.0_1694781170986.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_sofa566_en_5.1.2_3.0_1694781170986.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_sofa566","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_sofa566", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_sofa566| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/sofa566/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_sonny_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_sonny_en.md new file mode 100644 index 00000000000000..ed7d934754d984 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_sonny_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_sonny DistilBertEmbeddings from Sonny +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_sonny +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_sonny` is a English model originally trained by Sonny. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_sonny_en_5.1.2_3.0_1694779487811.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_sonny_en_5.1.2_3.0_1694779487811.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_sonny","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_sonny", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_sonny| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Sonny/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_sophon_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_sophon_en.md new file mode 100644 index 00000000000000..8ae491e84cc761 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_sophon_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_sophon DistilBertEmbeddings from Sophon +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_sophon +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_sophon` is a English model originally trained by Sophon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_sophon_en_5.1.2_3.0_1694772344487.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_sophon_en_5.1.2_3.0_1694772344487.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_sophon","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_sophon", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_sophon| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Sophon/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_suzuki0829_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_suzuki0829_en.md new file mode 100644 index 00000000000000..b222b6dd23a283 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_suzuki0829_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_suzuki0829 DistilBertEmbeddings from suzuki0829 +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_suzuki0829 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_suzuki0829` is a English model originally trained by suzuki0829. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_suzuki0829_en_5.1.2_3.0_1694770510510.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_suzuki0829_en_5.1.2_3.0_1694770510510.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_suzuki0829","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_suzuki0829", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_suzuki0829| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/suzuki0829/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_thabet_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_thabet_en.md new file mode 100644 index 00000000000000..7a9acfa63cb213 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_thabet_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_squad_d5716d28_thabet DistilBertEmbeddings from Thabet +author: John Snow Labs +name: distilbert_base_uncased_finetuned_squad_d5716d28_thabet +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_squad_d5716d28_thabet` is a English model originally trained by Thabet. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_thabet_en_5.1.2_3.0_1694784973757.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_thabet_en_5.1.2_3.0_1694784973757.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_thabet","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_squad_d5716d28_thabet", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_squad_d5716d28_thabet| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Thabet/distilbert-base-uncased-finetuned-squad-d5716d28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_ysugawa_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_ysugawa_en.md index 1926befd573ad4..319879f624e735 100644 --- a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_ysugawa_en.md +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_squad_d5716d28_ysugawa_en.md @@ -24,8 +24,8 @@ Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_ysugawa_en_5.1.2_3.0_1694736922524.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_ysugawa_en_5.1.2_3.0_1694736922524.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_ysugawa_en_5.1.2_3.0_1694770212287.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_squad_d5716d28_ysugawa_en_5.1.2_3.0_1694770212287.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_test_headline_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_test_headline_en.md new file mode 100644 index 00000000000000..f01c79160c471c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_test_headline_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_test_headline DistilBertEmbeddings from lucypallent +author: John Snow Labs +name: distilbert_base_uncased_finetuned_test_headline +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_test_headline` is a English model originally trained by lucypallent. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_test_headline_en_5.1.2_3.0_1694790033469.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_test_headline_en_5.1.2_3.0_1694790033469.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_test_headline","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_test_headline", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_test_headline| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/lucypallent/distilbert-base-uncased-finetuned-test-headline \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_tweet_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_tweet_en.md new file mode 100644 index 00000000000000..cb54b422676481 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_tweet_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_tweet DistilBertEmbeddings from shreyasdatar +author: John Snow Labs +name: distilbert_base_uncased_finetuned_tweet +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_tweet` is a English model originally trained by shreyasdatar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_tweet_en_5.1.2_3.0_1694783838766.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_tweet_en_5.1.2_3.0_1694783838766.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_tweet","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_tweet", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_tweet| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/shreyasdatar/distilbert-base-uncased-finetuned-tweet \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_vk_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_vk_en.md new file mode 100644 index 00000000000000..b27a7654af3211 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_vk_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_vk DistilBertEmbeddings from bruhwalkk +author: John Snow Labs +name: distilbert_base_uncased_finetuned_vk +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_vk` is a English model originally trained by bruhwalkk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_vk_en_5.1.2_3.0_1694780625605.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_vk_en_5.1.2_3.0_1694780625605.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_vk","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_vk", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_vk| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.0 MB| + +## References + +https://huggingface.co/bruhwalkk/distilbert-base-uncased-finetuned-vk \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_wb_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_wb_en.md new file mode 100644 index 00000000000000..d2e6b25744d635 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_wb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_wb DistilBertEmbeddings from alexskrn +author: John Snow Labs +name: distilbert_base_uncased_finetuned_wb +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_wb` is a English model originally trained by alexskrn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_wb_en_5.1.2_3.0_1694782258713.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_wb_en_5.1.2_3.0_1694782258713.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_wb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_wb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_wb| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/alexskrn/distilbert-base-uncased-finetuned-wb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_wos_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_wos_en.md new file mode 100644 index 00000000000000..c6ede77dc6af52 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_finetuned_wos_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_finetuned_wos DistilBertEmbeddings from alvin-wen +author: John Snow Labs +name: distilbert_base_uncased_finetuned_wos +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_finetuned_wos` is a English model originally trained by alvin-wen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_wos_en_5.1.2_3.0_1694782853873.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_finetuned_wos_en_5.1.2_3.0_1694782853873.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_finetuned_wos","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_finetuned_wos", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_finetuned_wos| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/alvin-wen/distilbert-base-uncased-finetuned-wos \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_holocaust_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_holocaust_en.md new file mode 100644 index 00000000000000..39a447f9a36385 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_holocaust_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_holocaust DistilBertEmbeddings from wjbmattingly +author: John Snow Labs +name: distilbert_base_uncased_holocaust +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_holocaust` is a English model originally trained by wjbmattingly. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_holocaust_en_5.1.2_3.0_1694786616427.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_holocaust_en_5.1.2_3.0_1694786616427.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_holocaust","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_holocaust", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_holocaust| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/wjbmattingly/distilbert-base-uncased-holocaust \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_imdb_accelerate_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_imdb_accelerate_en.md new file mode 100644 index 00000000000000..13a0359616d1d0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_imdb_accelerate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_imdb_accelerate DistilBertEmbeddings from hieule +author: John Snow Labs +name: distilbert_base_uncased_imdb_accelerate +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_imdb_accelerate` is a English model originally trained by hieule. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_imdb_accelerate_en_5.1.2_3.0_1694784667459.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_imdb_accelerate_en_5.1.2_3.0_1694784667459.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_imdb_accelerate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_imdb_accelerate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_imdb_accelerate| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/hieule/distilbert-base-uncased-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_imdb_disbert1_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_imdb_disbert1_en.md new file mode 100644 index 00000000000000..698d45123a8c62 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_imdb_disbert1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_imdb_disbert1 DistilBertEmbeddings from Billwzl +author: John Snow Labs +name: distilbert_base_uncased_imdb_disbert1 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_imdb_disbert1` is a English model originally trained by Billwzl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_imdb_disbert1_en_5.1.2_3.0_1694783463877.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_imdb_disbert1_en_5.1.2_3.0_1694783463877.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_imdb_disbert1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_imdb_disbert1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_imdb_disbert1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.0 MB| + +## References + +https://huggingface.co/Billwzl/distilbert-base-uncased-IMDB_disbert1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_imdb_distilbert_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_imdb_distilbert_en.md new file mode 100644 index 00000000000000..02caca63fcd510 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_imdb_distilbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_imdb_distilbert DistilBertEmbeddings from Billwzl +author: John Snow Labs +name: distilbert_base_uncased_imdb_distilbert +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_imdb_distilbert` is a English model originally trained by Billwzl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_imdb_distilbert_en_5.1.2_3.0_1694782933769.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_imdb_distilbert_en_5.1.2_3.0_1694782933769.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_imdb_distilbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_imdb_distilbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_imdb_distilbert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Billwzl/distilbert-base-uncased-IMDB_distilbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_issues_128_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_issues_128_en.md new file mode 100644 index 00000000000000..c20f2d91d20f46 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_issues_128_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_issues_128 DistilBertEmbeddings from Chrispfield +author: John Snow Labs +name: distilbert_base_uncased_issues_128 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_issues_128` is a English model originally trained by Chrispfield. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_issues_128_en_5.1.2_3.0_1694784972786.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_issues_128_en_5.1.2_3.0_1694784972786.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_issues_128","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_issues_128", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_issues_128| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Chrispfield/distilbert-base-uncased-issues-128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_linkedin_domain_adaptation_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_linkedin_domain_adaptation_en.md new file mode 100644 index 00000000000000..d27eeedeb45238 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_linkedin_domain_adaptation_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_linkedin_domain_adaptation DistilBertEmbeddings from algiraldohe +author: John Snow Labs +name: distilbert_base_uncased_linkedin_domain_adaptation +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_linkedin_domain_adaptation` is a English model originally trained by algiraldohe. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_linkedin_domain_adaptation_en_5.1.2_3.0_1694772620399.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_linkedin_domain_adaptation_en_5.1.2_3.0_1694772620399.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_linkedin_domain_adaptation","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_linkedin_domain_adaptation", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_linkedin_domain_adaptation| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/algiraldohe/distilbert-base-uncased-linkedin-domain-adaptation \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_malayalam_arxiv_papers_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_malayalam_arxiv_papers_en.md new file mode 100644 index 00000000000000..4348333d106e19 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_malayalam_arxiv_papers_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_malayalam_arxiv_papers DistilBertEmbeddings from aalksii +author: John Snow Labs +name: distilbert_base_uncased_malayalam_arxiv_papers +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_malayalam_arxiv_papers` is a English model originally trained by aalksii. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_malayalam_arxiv_papers_en_5.1.2_3.0_1694783989229.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_malayalam_arxiv_papers_en_5.1.2_3.0_1694783989229.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_malayalam_arxiv_papers","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_malayalam_arxiv_papers", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_malayalam_arxiv_papers| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/aalksii/distilbert-base-uncased-ml-arxiv-papers \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_mask_accelerate_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_mask_accelerate_en.md new file mode 100644 index 00000000000000..5e083c5e694835 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_mask_accelerate_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_mask_accelerate DistilBertEmbeddings from xiannvhh +author: John Snow Labs +name: distilbert_base_uncased_mask_accelerate +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_mask_accelerate` is a English model originally trained by xiannvhh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_mask_accelerate_en_5.1.2_3.0_1694785625253.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_mask_accelerate_en_5.1.2_3.0_1694785625253.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_mask_accelerate","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_mask_accelerate", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_mask_accelerate| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/xiannvhh/distilbert-base-uncased-mask-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_mask_finetuned_imdb_v1_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_mask_finetuned_imdb_v1_en.md new file mode 100644 index 00000000000000..c88a0d0e3f6866 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_mask_finetuned_imdb_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_mask_finetuned_imdb_v1 DistilBertEmbeddings from kaiku03 +author: John Snow Labs +name: distilbert_base_uncased_mask_finetuned_imdb_v1 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_mask_finetuned_imdb_v1` is a English model originally trained by kaiku03. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_mask_finetuned_imdb_v1_en_5.1.2_3.0_1694788413645.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_mask_finetuned_imdb_v1_en_5.1.2_3.0_1694788413645.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_mask_finetuned_imdb_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_mask_finetuned_imdb_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_mask_finetuned_imdb_v1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/kaiku03/distilbert-base-uncased-mask-finetuned-imdb_v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_mlm_scirepeval_fos_chemistry_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_mlm_scirepeval_fos_chemistry_en.md new file mode 100644 index 00000000000000..990cf1b3846257 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_mlm_scirepeval_fos_chemistry_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_mlm_scirepeval_fos_chemistry DistilBertEmbeddings from jonas-luehrs +author: John Snow Labs +name: distilbert_base_uncased_mlm_scirepeval_fos_chemistry +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_mlm_scirepeval_fos_chemistry` is a English model originally trained by jonas-luehrs. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_mlm_scirepeval_fos_chemistry_en_5.1.2_3.0_1694784976059.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_mlm_scirepeval_fos_chemistry_en_5.1.2_3.0_1694784976059.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_mlm_scirepeval_fos_chemistry","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_mlm_scirepeval_fos_chemistry", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_mlm_scirepeval_fos_chemistry| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/jonas-luehrs/distilbert-base-uncased-MLM-scirepeval_fos_chemistry \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_mlm_tamil_local_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_mlm_tamil_local_en.md new file mode 100644 index 00000000000000..1a86dcf2ea1831 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_mlm_tamil_local_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_mlm_tamil_local DistilBertEmbeddings from medhabi +author: John Snow Labs +name: distilbert_base_uncased_mlm_tamil_local +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_mlm_tamil_local` is a English model originally trained by medhabi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_mlm_tamil_local_en_5.1.2_3.0_1694788852384.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_mlm_tamil_local_en_5.1.2_3.0_1694788852384.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_mlm_tamil_local","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_mlm_tamil_local", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_mlm_tamil_local| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/medhabi/distilbert-base-uncased-mlm-ta-local \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_scratch_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_scratch_en.md new file mode 100644 index 00000000000000..94c2c4fa2f0145 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_scratch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_scratch DistilBertEmbeddings from hieule +author: John Snow Labs +name: distilbert_base_uncased_scratch +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_scratch` is a English model originally trained by hieule. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_scratch_en_5.1.2_3.0_1694784563353.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_scratch_en_5.1.2_3.0_1694784563353.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_scratch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_scratch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_scratch| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/hieule/distilbert-base-uncased-scratch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_sparse_80_1x4_block_pruneofa_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_sparse_80_1x4_block_pruneofa_en.md new file mode 100644 index 00000000000000..d42b5dddc0ba91 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_sparse_80_1x4_block_pruneofa_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_sparse_80_1x4_block_pruneofa DistilBertEmbeddings from Intel +author: John Snow Labs +name: distilbert_base_uncased_sparse_80_1x4_block_pruneofa +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_sparse_80_1x4_block_pruneofa` is a English model originally trained by Intel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_sparse_80_1x4_block_pruneofa_en_5.1.2_3.0_1694785612750.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_sparse_80_1x4_block_pruneofa_en_5.1.2_3.0_1694785612750.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_sparse_80_1x4_block_pruneofa","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_sparse_80_1x4_block_pruneofa", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_sparse_80_1x4_block_pruneofa| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|136.5 MB| + +## References + +https://huggingface.co/Intel/distilbert-base-uncased-sparse-80-1x4-block-pruneofa \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_sparse_85_unstructured_pruneofa_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_sparse_85_unstructured_pruneofa_en.md new file mode 100644 index 00000000000000..a02bdabd658dce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_sparse_85_unstructured_pruneofa_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_sparse_85_unstructured_pruneofa DistilBertEmbeddings from Intel +author: John Snow Labs +name: distilbert_base_uncased_sparse_85_unstructured_pruneofa +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_sparse_85_unstructured_pruneofa` is a English model originally trained by Intel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_sparse_85_unstructured_pruneofa_en_5.1.2_3.0_1694778491947.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_sparse_85_unstructured_pruneofa_en_5.1.2_3.0_1694778491947.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_sparse_85_unstructured_pruneofa","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_sparse_85_unstructured_pruneofa", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_sparse_85_unstructured_pruneofa| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|132.5 MB| + +## References + +https://huggingface.co/Intel/distilbert-base-uncased-sparse-85-unstructured-pruneofa \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_sparse_90_unstructured_pruneofa_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_sparse_90_unstructured_pruneofa_en.md new file mode 100644 index 00000000000000..5a59934e1cf1d8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_sparse_90_unstructured_pruneofa_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_sparse_90_unstructured_pruneofa DistilBertEmbeddings from Intel +author: John Snow Labs +name: distilbert_base_uncased_sparse_90_unstructured_pruneofa +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_sparse_90_unstructured_pruneofa` is a English model originally trained by Intel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_sparse_90_unstructured_pruneofa_en_5.1.2_3.0_1694778619456.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_sparse_90_unstructured_pruneofa_en_5.1.2_3.0_1694778619456.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_sparse_90_unstructured_pruneofa","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_sparse_90_unstructured_pruneofa", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_sparse_90_unstructured_pruneofa| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|123.3 MB| + +## References + +https://huggingface.co/Intel/distilbert-base-uncased-sparse-90-unstructured-pruneofa \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_wholewordmasking_finetuned_imdb_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_wholewordmasking_finetuned_imdb_en.md new file mode 100644 index 00000000000000..809064db15d5be --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_base_uncased_wholewordmasking_finetuned_imdb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_base_uncased_wholewordmasking_finetuned_imdb DistilBertEmbeddings from VanHoan +author: John Snow Labs +name: distilbert_base_uncased_wholewordmasking_finetuned_imdb +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_uncased_wholewordmasking_finetuned_imdb` is a English model originally trained by VanHoan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_wholewordmasking_finetuned_imdb_en_5.1.2_3.0_1694784099518.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_uncased_wholewordmasking_finetuned_imdb_en_5.1.2_3.0_1694784099518.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_base_uncased_wholewordmasking_finetuned_imdb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_base_uncased_wholewordmasking_finetuned_imdb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_uncased_wholewordmasking_finetuned_imdb| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/VanHoan/distilbert-base-uncased-WholeWordMasking-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_classification_eplorer_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_classification_eplorer_en.md new file mode 100644 index 00000000000000..26a254a4a3a7b5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_classification_eplorer_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_classification_eplorer DistilBertEmbeddings from edanigoben +author: John Snow Labs +name: distilbert_classification_eplorer +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_classification_eplorer` is a English model originally trained by edanigoben. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_classification_eplorer_en_5.1.2_3.0_1694778754715.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_classification_eplorer_en_5.1.2_3.0_1694778754715.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_classification_eplorer","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_classification_eplorer", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_classification_eplorer| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/edanigoben/distilbert-classification-eplorer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_cmc_a8_h512_l4_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_cmc_a8_h512_l4_en.md new file mode 100644 index 00000000000000..744dc76b096790 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_cmc_a8_h512_l4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_cmc_a8_h512_l4 DistilBertEmbeddings from levuloihust +author: John Snow Labs +name: distilbert_cmc_a8_h512_l4 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_cmc_a8_h512_l4` is a English model originally trained by levuloihust. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_cmc_a8_h512_l4_en_5.1.2_3.0_1694782904245.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_cmc_a8_h512_l4_en_5.1.2_3.0_1694782904245.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_cmc_a8_h512_l4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_cmc_a8_h512_l4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_cmc_a8_h512_l4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|167.9 MB| + +## References + +https://huggingface.co/levuloihust/distilbert-cmc-A8-H512-L4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_domain_adapted_ecomm_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_domain_adapted_ecomm_en.md new file mode 100644 index 00000000000000..ecfa2e4d18f441 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_domain_adapted_ecomm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_domain_adapted_ecomm DistilBertEmbeddings from Kk2k +author: John Snow Labs +name: distilbert_domain_adapted_ecomm +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_domain_adapted_ecomm` is a English model originally trained by Kk2k. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_domain_adapted_ecomm_en_5.1.2_3.0_1694783099567.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_domain_adapted_ecomm_en_5.1.2_3.0_1694783099567.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_domain_adapted_ecomm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_domain_adapted_ecomm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_domain_adapted_ecomm| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.9 MB| + +## References + +https://huggingface.co/Kk2k/Distilbert_domain_adapted_ecomm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_base_uncased_continued_training_medqa_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_base_uncased_continued_training_medqa_en.md new file mode 100644 index 00000000000000..886624345f6f39 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_base_uncased_continued_training_medqa_en.md @@ -0,0 +1,101 @@ +--- +layout: model +title: English Bert Embeddings model (from Shaier) +author: John Snow Labs +name: distilbert_embeddings_base_uncased_continued_training_medqa +date: 2023-09-15 +tags: [open_source, distilbert, distilbert_embeddings, distilbertformaskedlm, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertForMaskedLM model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `distilbert-base-uncased-continued_training-medqa` is a English model originally trained by `Shaier`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_base_uncased_continued_training_medqa_en_5.1.2_3.0_1694777575763.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_base_uncased_continued_training_medqa_en_5.1.2_3.0_1694777575763.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_base_uncased_continued_training_medqa","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark-NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_base_uncased_continued_training_medqa","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark-NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_base_uncased_continued_training_medqa| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| +|Case sensitive:|false| + +## References + +References + +https://huggingface.co/Shaier/distilbert-base-uncased-continued_training-medqa \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_base_uncased_finetuned_imdb_accelerate_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_base_uncased_finetuned_imdb_accelerate_en.md new file mode 100644 index 00000000000000..3d3eba092e03a4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_base_uncased_finetuned_imdb_accelerate_en.md @@ -0,0 +1,101 @@ +--- +layout: model +title: English Bert Embeddings Cased model (from nrsmac) +author: John Snow Labs +name: distilbert_embeddings_base_uncased_finetuned_imdb_accelerate +date: 2023-09-15 +tags: [open_source, distilbert, distilbert_embeddings, distilbertformaskedlm, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertForMaskedLM model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `distilbert-base-uncased-finetuned-imdb-accelerate` is a English model originally trained by `nrsmac`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_base_uncased_finetuned_imdb_accelerate_en_5.1.2_3.0_1694779961580.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_base_uncased_finetuned_imdb_accelerate_en_5.1.2_3.0_1694779961580.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_base_uncased_finetuned_imdb_accelerate","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark-NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_base_uncased_finetuned_imdb_accelerate","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark-NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_base_uncased_finetuned_imdb_accelerate| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| +|Case sensitive:|false| + +## References + +References + +https://huggingface.co/nrsmac/distilbert-base-uncased-finetuned-imdb-accelerate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_bio_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_bio_cased_en.md new file mode 100644 index 00000000000000..9b96f3832a81fd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_bio_cased_en.md @@ -0,0 +1,101 @@ +--- +layout: model +title: English Bert Embeddings Cased model (from nlpie) +author: John Snow Labs +name: distilbert_embeddings_bio_cased +date: 2023-09-15 +tags: [open_source, distilbert, distilbert_embeddings, distilbertformaskedlm, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertForMaskedLM model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bio-distilbert-cased` is a English model originally trained by `nlpie`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_bio_cased_en_5.1.2_3.0_1694783708969.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_bio_cased_en_5.1.2_3.0_1694783708969.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_bio_cased","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark-NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_bio_cased","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark-NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_bio_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|243.7 MB| +|Case sensitive:|false| + +## References + +References + +https://huggingface.co/nlpie/bio-distilbert-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_bio_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_bio_uncased_en.md new file mode 100644 index 00000000000000..f692d7bf074a8d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_bio_uncased_en.md @@ -0,0 +1,101 @@ +--- +layout: model +title: English Bert Embeddings model (from nlpie) +author: John Snow Labs +name: distilbert_embeddings_bio_uncased +date: 2023-09-15 +tags: [open_source, distilbert, distilbert_embeddings, distilbertformaskedlm, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertForMaskedLM model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bio-distilbert-uncased` is a English model originally trained by `nlpie`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_bio_uncased_en_5.1.2_3.0_1694783869030.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_bio_uncased_en_5.1.2_3.0_1694783869030.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_bio_uncased","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark-NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_bio_uncased","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark-NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_bio_uncased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.3 MB| +|Case sensitive:|false| + +## References + +References + +https://huggingface.co/nlpie/bio-distilbert-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_clinical_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_clinical_en.md new file mode 100644 index 00000000000000..a0a9413550703a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_clinical_en.md @@ -0,0 +1,101 @@ +--- +layout: model +title: English Bert Embeddings model (from nlpie) +author: John Snow Labs +name: distilbert_embeddings_clinical +date: 2023-09-15 +tags: [open_source, distilbert, distilbert_embeddings, distilbertformaskedlm, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertForMaskedLM model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `clinical-distilbert` is a English model originally trained by `nlpie`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_clinical_en_5.1.2_3.0_1694771806781.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_clinical_en_5.1.2_3.0_1694771806781.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_clinical","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings]) + +data = spark.createDataFrame([["I love Spark-NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val embeddings = DistilBertEmbeddings.pretrained("distilbert_embeddings_clinical","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(True) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings)) + +val data = Seq("I love Spark-NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_clinical| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|243.6 MB| +|Case sensitive:|false| + +## References + +References + +https://huggingface.co/nlpie/clinical-distilbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_google_job_data_tuned_trial_8_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_google_job_data_tuned_trial_8_en.md new file mode 100644 index 00000000000000..1d3c9cefce2c49 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_google_job_data_tuned_trial_8_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_embeddings_google_job_data_tuned_trial_8 DistilBertEmbeddings from EslamAhmed +author: John Snow Labs +name: distilbert_embeddings_google_job_data_tuned_trial_8 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_embeddings_google_job_data_tuned_trial_8` is a English model originally trained by EslamAhmed. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_google_job_data_tuned_trial_8_en_5.1.2_3.0_1694773297076.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_google_job_data_tuned_trial_8_en_5.1.2_3.0_1694773297076.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_embeddings_google_job_data_tuned_trial_8","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_embeddings_google_job_data_tuned_trial_8", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_google_job_data_tuned_trial_8| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.3 MB| + +## References + +https://huggingface.co/EslamAhmed/google_Job_data_tuned_trial_8_11-2-2022 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_we4lkd_aml_1921_2017_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_we4lkd_aml_1921_2017_en.md new file mode 100644 index 00000000000000..f869068d5dd6da --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_embeddings_we4lkd_aml_1921_2017_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_embeddings_we4lkd_aml_1921_2017 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: distilbert_embeddings_we4lkd_aml_1921_2017 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_embeddings_we4lkd_aml_1921_2017` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_we4lkd_aml_1921_2017_en_5.1.2_3.0_1694779750303.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_embeddings_we4lkd_aml_1921_2017_en_5.1.2_3.0_1694779750303.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_embeddings_we4lkd_aml_1921_2017","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_embeddings_we4lkd_aml_1921_2017", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_embeddings_we4lkd_aml_1921_2017| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.1 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2017 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_finetuned_imdb_lyk0013_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_finetuned_imdb_lyk0013_en.md new file mode 100644 index 00000000000000..0b85162c5f5726 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_finetuned_imdb_lyk0013_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_finetuned_imdb_lyk0013 DistilBertEmbeddings from lyk0013 +author: John Snow Labs +name: distilbert_finetuned_imdb_lyk0013 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_finetuned_imdb_lyk0013` is a English model originally trained by lyk0013. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_finetuned_imdb_lyk0013_en_5.1.2_3.0_1694790297554.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_finetuned_imdb_lyk0013_en_5.1.2_3.0_1694790297554.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_finetuned_imdb_lyk0013","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_finetuned_imdb_lyk0013", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_finetuned_imdb_lyk0013| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/lyk0013/distilbert-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_finetuned_imdb_tsabing_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_finetuned_imdb_tsabing_en.md new file mode 100644 index 00000000000000..b35ecb267a931f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_finetuned_imdb_tsabing_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_finetuned_imdb_tsabing DistilBertEmbeddings from Tsabing +author: John Snow Labs +name: distilbert_finetuned_imdb_tsabing +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_finetuned_imdb_tsabing` is a English model originally trained by Tsabing. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_finetuned_imdb_tsabing_en_5.1.2_3.0_1694789241169.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_finetuned_imdb_tsabing_en_5.1.2_3.0_1694789241169.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_finetuned_imdb_tsabing","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_finetuned_imdb_tsabing", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_finetuned_imdb_tsabing| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Tsabing/distilbert-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_finetuned_spmlm_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_finetuned_spmlm_en.md new file mode 100644 index 00000000000000..41b6b0a5006381 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_finetuned_spmlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_finetuned_spmlm DistilBertEmbeddings from ashwathjadhav23 +author: John Snow Labs +name: distilbert_finetuned_spmlm +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_finetuned_spmlm` is a English model originally trained by ashwathjadhav23. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_finetuned_spmlm_en_5.1.2_3.0_1694773887957.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_finetuned_spmlm_en_5.1.2_3.0_1694773887957.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_finetuned_spmlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_finetuned_spmlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_finetuned_spmlm| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|243.7 MB| + +## References + +https://huggingface.co/ashwathjadhav23/DistilBert_Finetuned_SpMLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_french_explorer_classification_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_french_explorer_classification_en.md new file mode 100644 index 00000000000000..5d13d4d3aeccb5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_french_explorer_classification_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_french_explorer_classification DistilBertEmbeddings from factored +author: John Snow Labs +name: distilbert_french_explorer_classification +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_french_explorer_classification` is a English model originally trained by factored. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_french_explorer_classification_en_5.1.2_3.0_1694779552028.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_french_explorer_classification_en_5.1.2_3.0_1694779552028.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_french_explorer_classification","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_french_explorer_classification", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_french_explorer_classification| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.1 MB| + +## References + +https://huggingface.co/factored/distilbert-fr-explorer-classification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_hemingway_sar_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_hemingway_sar_en.md new file mode 100644 index 00000000000000..c6f43bb5523c7f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_hemingway_sar_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_hemingway_sar DistilBertEmbeddings from khazen2 +author: John Snow Labs +name: distilbert_hemingway_sar +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_hemingway_sar` is a English model originally trained by khazen2. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_hemingway_sar_en_5.1.2_3.0_1694786578751.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_hemingway_sar_en_5.1.2_3.0_1694786578751.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_hemingway_sar","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_hemingway_sar", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_hemingway_sar| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/khazen2/DistilBERT_Hemingway_SAR \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_hinglish_big_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_hinglish_big_en.md new file mode 100644 index 00000000000000..5b9789063aac23 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_hinglish_big_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_hinglish_big DistilBertEmbeddings from aditeyabaral +author: John Snow Labs +name: distilbert_hinglish_big +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_hinglish_big` is a English model originally trained by aditeyabaral. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_hinglish_big_en_5.1.2_3.0_1694779769717.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_hinglish_big_en_5.1.2_3.0_1694779769717.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_hinglish_big","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_hinglish_big", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_hinglish_big| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.0 MB| + +## References + +https://huggingface.co/aditeyabaral/distilbert-hinglish-big \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_hinglish_small_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_hinglish_small_en.md new file mode 100644 index 00000000000000..cb32a2ccc8ce17 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_hinglish_small_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_hinglish_small DistilBertEmbeddings from aditeyabaral +author: John Snow Labs +name: distilbert_hinglish_small +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_hinglish_small` is a English model originally trained by aditeyabaral. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_hinglish_small_en_5.1.2_3.0_1694779874373.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_hinglish_small_en_5.1.2_3.0_1694779874373.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_hinglish_small","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_hinglish_small", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_hinglish_small| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.0 MB| + +## References + +https://huggingface.co/aditeyabaral/distilbert-hinglish-small \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_imdb_negative_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_imdb_negative_en.md new file mode 100644 index 00000000000000..aa56d258ad77af --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_imdb_negative_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_imdb_negative DistilBertEmbeddings from michalwilk123 +author: John Snow Labs +name: distilbert_imdb_negative +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_imdb_negative` is a English model originally trained by michalwilk123. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_imdb_negative_en_5.1.2_3.0_1694782498235.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_imdb_negative_en_5.1.2_3.0_1694782498235.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_imdb_negative","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_imdb_negative", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_imdb_negative| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/michalwilk123/distilbert-imdb-negative \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_imdb_positive_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_imdb_positive_en.md new file mode 100644 index 00000000000000..ca0bd79a1b7f8d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_imdb_positive_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_imdb_positive DistilBertEmbeddings from michalwilk123 +author: John Snow Labs +name: distilbert_imdb_positive +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_imdb_positive` is a English model originally trained by michalwilk123. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_imdb_positive_en_5.1.2_3.0_1694782618191.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_imdb_positive_en_5.1.2_3.0_1694782618191.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_imdb_positive","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_imdb_positive", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_imdb_positive| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/michalwilk123/distilbert-imdb-positive \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_mlm_1000k_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_mlm_1000k_en.md new file mode 100644 index 00000000000000..c19330a9e22c6f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_mlm_1000k_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_mlm_1000k DistilBertEmbeddings from vocab-transformers +author: John Snow Labs +name: distilbert_mlm_1000k +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_mlm_1000k` is a English model originally trained by vocab-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_mlm_1000k_en_5.1.2_3.0_1694788526784.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_mlm_1000k_en_5.1.2_3.0_1694788526784.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_mlm_1000k","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_mlm_1000k", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_mlm_1000k| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|246.8 MB| + +## References + +https://huggingface.co/vocab-transformers/distilbert-mlm-1000k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_mlm_250k_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_mlm_250k_en.md new file mode 100644 index 00000000000000..17506d9d98ca7d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_mlm_250k_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_mlm_250k DistilBertEmbeddings from vocab-transformers +author: John Snow Labs +name: distilbert_mlm_250k +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_mlm_250k` is a English model originally trained by vocab-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_mlm_250k_en_5.1.2_3.0_1694788223580.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_mlm_250k_en_5.1.2_3.0_1694788223580.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_mlm_250k","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_mlm_250k", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_mlm_250k| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.0 MB| + +## References + +https://huggingface.co/vocab-transformers/distilbert-mlm-250k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_mlm_500k_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_mlm_500k_en.md new file mode 100644 index 00000000000000..09f5a95e3f3201 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_mlm_500k_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_mlm_500k DistilBertEmbeddings from vocab-transformers +author: John Snow Labs +name: distilbert_mlm_500k +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_mlm_500k` is a English model originally trained by vocab-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_mlm_500k_en_5.1.2_3.0_1694788324657.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_mlm_500k_en_5.1.2_3.0_1694788324657.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_mlm_500k","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_mlm_500k", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_mlm_500k| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|246.9 MB| + +## References + +https://huggingface.co/vocab-transformers/distilbert-mlm-500k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_mlm_750k_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_mlm_750k_en.md new file mode 100644 index 00000000000000..dcc0f4d7c33da6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_mlm_750k_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_mlm_750k DistilBertEmbeddings from vocab-transformers +author: John Snow Labs +name: distilbert_mlm_750k +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_mlm_750k` is a English model originally trained by vocab-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_mlm_750k_en_5.1.2_3.0_1694788420155.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_mlm_750k_en_5.1.2_3.0_1694788420155.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_mlm_750k","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_mlm_750k", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_mlm_750k| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|246.8 MB| + +## References + +https://huggingface.co/vocab-transformers/distilbert-mlm-750k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_mlm_best_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_mlm_best_en.md new file mode 100644 index 00000000000000..1eaec4ce82c3fa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_mlm_best_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_mlm_best DistilBertEmbeddings from vocab-transformers +author: John Snow Labs +name: distilbert_mlm_best +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_mlm_best` is a English model originally trained by vocab-transformers. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_mlm_best_en_5.1.2_3.0_1694788626661.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_mlm_best_en_5.1.2_3.0_1694788626661.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_mlm_best","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_mlm_best", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_mlm_best| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|246.9 MB| + +## References + +https://huggingface.co/vocab-transformers/distilbert-mlm-best \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_perigon_200k_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_perigon_200k_en.md new file mode 100644 index 00000000000000..6fcc0bad67de51 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_perigon_200k_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_perigon_200k DistilBertEmbeddings from judy93536 +author: John Snow Labs +name: distilbert_perigon_200k +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_perigon_200k` is a English model originally trained by judy93536. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_perigon_200k_en_5.1.2_3.0_1694785838237.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_perigon_200k_en_5.1.2_3.0_1694785838237.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_perigon_200k","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_perigon_200k", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_perigon_200k| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/judy93536/distilbert-perigon-200k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_pubmed_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_pubmed_mlm_en.md new file mode 100644 index 00000000000000..9b38b3d88bb013 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_pubmed_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_pubmed_mlm DistilBertEmbeddings from Gaborandi +author: John Snow Labs +name: distilbert_pubmed_mlm +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_pubmed_mlm` is a English model originally trained by Gaborandi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_pubmed_mlm_en_5.1.2_3.0_1694775368835.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_pubmed_mlm_en_5.1.2_3.0_1694775368835.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_pubmed_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_pubmed_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_pubmed_mlm| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.3 MB| + +## References + +https://huggingface.co/Gaborandi/distilbert-pubmed-MLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_ravenk_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_ravenk_en.md new file mode 100644 index 00000000000000..2db4d812e44890 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_ravenk_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_ravenk DistilBertEmbeddings from RavenK +author: John Snow Labs +name: distilbert_ravenk +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_ravenk` is a English model originally trained by RavenK. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_ravenk_en_5.1.2_3.0_1694786300721.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_ravenk_en_5.1.2_3.0_1694786300721.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_ravenk","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_ravenk", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_ravenk| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/RavenK/distilbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_sparsembed_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_sparsembed_en.md new file mode 100644 index 00000000000000..a7991ca7e186ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_sparsembed_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_sparsembed DistilBertEmbeddings from raphaelsty +author: John Snow Labs +name: distilbert_sparsembed +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_sparsembed` is a English model originally trained by raphaelsty. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_sparsembed_en_5.1.2_3.0_1694787885018.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_sparsembed_en_5.1.2_3.0_1694787885018.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_sparsembed","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_sparsembed", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_sparsembed| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/raphaelsty/distilbert-sparsembed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_splade_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_splade_en.md new file mode 100644 index 00000000000000..938bd19e5035ad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_splade_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_splade DistilBertEmbeddings from raphaelsty +author: John Snow Labs +name: distilbert_splade +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_splade` is a English model originally trained by raphaelsty. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_splade_en_5.1.2_3.0_1694787786344.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_splade_en_5.1.2_3.0_1694787786344.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_splade","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_splade", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_splade| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/raphaelsty/distilbert-splade \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_ugiugi_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_ugiugi_en.md new file mode 100644 index 00000000000000..4ca0440e6b3c1c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_ugiugi_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distilbert_ugiugi DistilBertEmbeddings from ugiugi +author: John Snow Labs +name: distilbert_ugiugi +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_ugiugi` is a English model originally trained by ugiugi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_ugiugi_en_5.1.2_3.0_1694785503613.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_ugiugi_en_5.1.2_3.0_1694785503613.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distilbert_ugiugi","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distilbert_ugiugi", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_ugiugi| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.3 MB| + +## References + +https://huggingface.co/ugiugi/distilbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distilbert_v1_en.md b/docs/_posts/ahmedlone127/2023-09-15-distilbert_v1_en.md index 661174247fb785..53d82d34f6bb8c 100644 --- a/docs/_posts/ahmedlone127/2023-09-15-distilbert_v1_en.md +++ b/docs/_posts/ahmedlone127/2023-09-15-distilbert_v1_en.md @@ -24,8 +24,8 @@ Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_v1_en_5.1.2_3.0_1694736931572.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_v1_en_5.1.2_3.0_1694736931572.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_v1_en_5.1.2_3.0_1694770244017.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_v1_en_5.1.2_3.0_1694770244017.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-09-15-distill_test_en.md b/docs/_posts/ahmedlone127/2023-09-15-distill_test_en.md new file mode 100644 index 00000000000000..b02c7b84a03c88 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distill_test_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distill_test DistilBertEmbeddings from domenicrosati +author: John Snow Labs +name: distill_test +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distill_test` is a English model originally trained by domenicrosati. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distill_test_en_5.1.2_3.0_1694778046009.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distill_test_en_5.1.2_3.0_1694778046009.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distill_test","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distill_test", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distill_test| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/domenicrosati/distill-test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distillbert_base_spanish_uncased_finetuned_imdb_en.md b/docs/_posts/ahmedlone127/2023-09-15-distillbert_base_spanish_uncased_finetuned_imdb_en.md new file mode 100644 index 00000000000000..67a6ad4c4cac72 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-distillbert_base_spanish_uncased_finetuned_imdb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English distillbert_base_spanish_uncased_finetuned_imdb DistilBertEmbeddings from franfram +author: John Snow Labs +name: distillbert_base_spanish_uncased_finetuned_imdb +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distillbert_base_spanish_uncased_finetuned_imdb` is a English model originally trained by franfram. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distillbert_base_spanish_uncased_finetuned_imdb_en_5.1.2_3.0_1694790663206.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distillbert_base_spanish_uncased_finetuned_imdb_en_5.1.2_3.0_1694790663206.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("distillbert_base_spanish_uncased_finetuned_imdb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("distillbert_base_spanish_uncased_finetuned_imdb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distillbert_base_spanish_uncased_finetuned_imdb| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|250.2 MB| + +## References + +https://huggingface.co/franfram/distillbert-base-spanish-uncased-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-distillbert_base_spanish_uncased_finetuned_spanish_corpus_en.md b/docs/_posts/ahmedlone127/2023-09-15-distillbert_base_spanish_uncased_finetuned_spanish_corpus_en.md index 29817fb96f563c..52ed7660530b68 100644 --- a/docs/_posts/ahmedlone127/2023-09-15-distillbert_base_spanish_uncased_finetuned_spanish_corpus_en.md +++ b/docs/_posts/ahmedlone127/2023-09-15-distillbert_base_spanish_uncased_finetuned_spanish_corpus_en.md @@ -24,8 +24,8 @@ Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distillbert_base_spanish_uncased_finetuned_spanish_corpus_en_5.1.2_3.0_1694736667906.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distillbert_base_spanish_uncased_finetuned_spanish_corpus_en_5.1.2_3.0_1694736667906.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distillbert_base_spanish_uncased_finetuned_spanish_corpus_en_5.1.2_3.0_1694769979577.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distillbert_base_spanish_uncased_finetuned_spanish_corpus_en_5.1.2_3.0_1694769979577.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-09-15-domain_adapted_arbert_goudma_en.md b/docs/_posts/ahmedlone127/2023-09-15-domain_adapted_arbert_goudma_en.md new file mode 100644 index 00000000000000..6f85457b85df97 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-domain_adapted_arbert_goudma_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English domain_adapted_arbert_goudma DistilBertEmbeddings from YassineToughrai +author: John Snow Labs +name: domain_adapted_arbert_goudma +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`domain_adapted_arbert_goudma` is a English model originally trained by YassineToughrai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/domain_adapted_arbert_goudma_en_5.1.2_3.0_1694782061367.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/domain_adapted_arbert_goudma_en_5.1.2_3.0_1694782061367.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("domain_adapted_arbert_goudma","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("domain_adapted_arbert_goudma", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|domain_adapted_arbert_goudma| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|606.8 MB| + +## References + +https://huggingface.co/YassineToughrai/Domain_adapted_ARBERT_GOUDMA \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-dummy_model_en.md b/docs/_posts/ahmedlone127/2023-09-15-dummy_model_en.md new file mode 100644 index 00000000000000..637b335a6645ef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-dummy_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English dummy_model DistilBertEmbeddings from luoweijie +author: John Snow Labs +name: dummy_model +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dummy_model` is a English model originally trained by luoweijie. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dummy_model_en_5.1.2_3.0_1694781796190.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dummy_model_en_5.1.2_3.0_1694781796190.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("dummy_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("dummy_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dummy_model| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/luoweijie/dummy-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-e4a_covid_distilbert_base_romanian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-e4a_covid_distilbert_base_romanian_cased_en.md new file mode 100644 index 00000000000000..03a4e43ee86fcb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-e4a_covid_distilbert_base_romanian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English e4a_covid_distilbert_base_romanian_cased DistilBertEmbeddings from racai +author: John Snow Labs +name: e4a_covid_distilbert_base_romanian_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`e4a_covid_distilbert_base_romanian_cased` is a English model originally trained by racai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e4a_covid_distilbert_base_romanian_cased_en_5.1.2_3.0_1694786227826.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e4a_covid_distilbert_base_romanian_cased_en_5.1.2_3.0_1694786227826.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("e4a_covid_distilbert_base_romanian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("e4a_covid_distilbert_base_romanian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e4a_covid_distilbert_base_romanian_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|305.9 MB| + +## References + +https://huggingface.co/racai/e4a-covid-distilbert-base-romanian-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-e4a_permits_distilbert_base_romanian_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-e4a_permits_distilbert_base_romanian_cased_en.md new file mode 100644 index 00000000000000..f223499ccfa25d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-e4a_permits_distilbert_base_romanian_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English e4a_permits_distilbert_base_romanian_cased DistilBertEmbeddings from racai +author: John Snow Labs +name: e4a_permits_distilbert_base_romanian_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`e4a_permits_distilbert_base_romanian_cased` is a English model originally trained by racai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/e4a_permits_distilbert_base_romanian_cased_en_5.1.2_3.0_1694786350915.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e4a_permits_distilbert_base_romanian_cased_en_5.1.2_3.0_1694786350915.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("e4a_permits_distilbert_base_romanian_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("e4a_permits_distilbert_base_romanian_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|e4a_permits_distilbert_base_romanian_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|305.9 MB| + +## References + +https://huggingface.co/racai/e4a-permits-distilbert-base-romanian-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-efficient_splade_v_large_doc_en.md b/docs/_posts/ahmedlone127/2023-09-15-efficient_splade_v_large_doc_en.md new file mode 100644 index 00000000000000..477278a7117682 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-efficient_splade_v_large_doc_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English efficient_splade_v_large_doc DistilBertEmbeddings from naver +author: John Snow Labs +name: efficient_splade_v_large_doc +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`efficient_splade_v_large_doc` is a English model originally trained by naver. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/efficient_splade_v_large_doc_en_5.1.2_3.0_1694778590163.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/efficient_splade_v_large_doc_en_5.1.2_3.0_1694778590163.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("efficient_splade_v_large_doc","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("efficient_splade_v_large_doc", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|efficient_splade_v_large_doc| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.3 MB| + +## References + +https://huggingface.co/naver/efficient-splade-V-large-doc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-efficient_splade_v_large_query_en.md b/docs/_posts/ahmedlone127/2023-09-15-efficient_splade_v_large_query_en.md new file mode 100644 index 00000000000000..bfc881dd2f0211 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-efficient_splade_v_large_query_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English efficient_splade_v_large_query DistilBertEmbeddings from naver +author: John Snow Labs +name: efficient_splade_v_large_query +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`efficient_splade_v_large_query` is a English model originally trained by naver. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/efficient_splade_v_large_query_en_5.1.2_3.0_1694778680533.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/efficient_splade_v_large_query_en_5.1.2_3.0_1694778680533.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("efficient_splade_v_large_query","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("efficient_splade_v_large_query", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|efficient_splade_v_large_query| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.4 MB| + +## References + +https://huggingface.co/naver/efficient-splade-V-large-query \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-efficient_splade_vi_bt_large_doc_en.md b/docs/_posts/ahmedlone127/2023-09-15-efficient_splade_vi_bt_large_doc_en.md new file mode 100644 index 00000000000000..12c67a2c71b8d5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-efficient_splade_vi_bt_large_doc_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English efficient_splade_vi_bt_large_doc DistilBertEmbeddings from naver +author: John Snow Labs +name: efficient_splade_vi_bt_large_doc +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`efficient_splade_vi_bt_large_doc` is a English model originally trained by naver. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/efficient_splade_vi_bt_large_doc_en_5.1.2_3.0_1694778781866.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/efficient_splade_vi_bt_large_doc_en_5.1.2_3.0_1694778781866.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("efficient_splade_vi_bt_large_doc","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("efficient_splade_vi_bt_large_doc", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|efficient_splade_vi_bt_large_doc| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.3 MB| + +## References + +https://huggingface.co/naver/efficient-splade-VI-BT-large-doc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-eighteenth_century_distilbert_en.md b/docs/_posts/ahmedlone127/2023-09-15-eighteenth_century_distilbert_en.md new file mode 100644 index 00000000000000..28896709b6a7aa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-eighteenth_century_distilbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English eighteenth_century_distilbert DistilBertEmbeddings from davanstrien +author: John Snow Labs +name: eighteenth_century_distilbert +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`eighteenth_century_distilbert` is a English model originally trained by davanstrien. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/eighteenth_century_distilbert_en_5.1.2_3.0_1694780486021.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/eighteenth_century_distilbert_en_5.1.2_3.0_1694780486021.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("eighteenth_century_distilbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("eighteenth_century_distilbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|eighteenth_century_distilbert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|244.4 MB| + +## References + +https://huggingface.co/davanstrien/eighteenth-century-distilbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-erwt_year_en.md b/docs/_posts/ahmedlone127/2023-09-15-erwt_year_en.md new file mode 100644 index 00000000000000..3c4d09ca41735d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-erwt_year_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English erwt_year DistilBertEmbeddings from Livingwithmachines +author: John Snow Labs +name: erwt_year +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`erwt_year` is a English model originally trained by Livingwithmachines. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/erwt_year_en_5.1.2_3.0_1694780244581.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/erwt_year_en_5.1.2_3.0_1694780244581.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("erwt_year","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("erwt_year", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|erwt_year| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Livingwithmachines/erwt-year \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-erwt_year_masked_25_en.md b/docs/_posts/ahmedlone127/2023-09-15-erwt_year_masked_25_en.md new file mode 100644 index 00000000000000..06371f700c903b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-erwt_year_masked_25_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English erwt_year_masked_25 DistilBertEmbeddings from Livingwithmachines +author: John Snow Labs +name: erwt_year_masked_25 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`erwt_year_masked_25` is a English model originally trained by Livingwithmachines. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/erwt_year_masked_25_en_5.1.2_3.0_1694780473232.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/erwt_year_masked_25_en_5.1.2_3.0_1694780473232.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("erwt_year_masked_25","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("erwt_year_masked_25", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|erwt_year_masked_25| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Livingwithmachines/erwt-year-masked-25 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-erwt_year_masked_75_en.md b/docs/_posts/ahmedlone127/2023-09-15-erwt_year_masked_75_en.md new file mode 100644 index 00000000000000..8c8bc839960829 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-erwt_year_masked_75_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English erwt_year_masked_75 DistilBertEmbeddings from Livingwithmachines +author: John Snow Labs +name: erwt_year_masked_75 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`erwt_year_masked_75` is a English model originally trained by Livingwithmachines. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/erwt_year_masked_75_en_5.1.2_3.0_1694780617608.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/erwt_year_masked_75_en_5.1.2_3.0_1694780617608.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("erwt_year_masked_75","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("erwt_year_masked_75", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|erwt_year_masked_75| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Livingwithmachines/erwt-year-masked-75 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-erwt_year_southern_sotho_en.md b/docs/_posts/ahmedlone127/2023-09-15-erwt_year_southern_sotho_en.md new file mode 100644 index 00000000000000..bf355fe105aac2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-erwt_year_southern_sotho_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English erwt_year_southern_sotho DistilBertEmbeddings from Livingwithmachines +author: John Snow Labs +name: erwt_year_southern_sotho +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`erwt_year_southern_sotho` is a English model originally trained by Livingwithmachines. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/erwt_year_southern_sotho_en_5.1.2_3.0_1694780351987.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/erwt_year_southern_sotho_en_5.1.2_3.0_1694780351987.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("erwt_year_southern_sotho","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("erwt_year_southern_sotho", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|erwt_year_southern_sotho| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.4 MB| + +## References + +https://huggingface.co/Livingwithmachines/erwt-year-st \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-experiment_en.md b/docs/_posts/ahmedlone127/2023-09-15-experiment_en.md new file mode 100644 index 00000000000000..b8c55b9fd506fa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-experiment_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English experiment DistilBertEmbeddings from apl33 +author: John Snow Labs +name: experiment +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`experiment` is a English model originally trained by apl33. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/experiment_en_5.1.2_3.0_1694779805982.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/experiment_en_5.1.2_3.0_1694779805982.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("experiment","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("experiment", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|experiment| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.3 MB| + +## References + +https://huggingface.co/apl33/experiment \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-few_mask_en.md b/docs/_posts/ahmedlone127/2023-09-15-few_mask_en.md new file mode 100644 index 00000000000000..ce492ff554f657 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-few_mask_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English few_mask DistilBertEmbeddings from Ondiet +author: John Snow Labs +name: few_mask +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`few_mask` is a English model originally trained by Ondiet. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/few_mask_en_5.1.2_3.0_1694788941452.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/few_mask_en_5.1.2_3.0_1694788941452.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("few_mask","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("few_mask", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|few_mask| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Ondiet/few_mask \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-film20000distilbert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-15-film20000distilbert_base_uncased_en.md index 74d1ca7c856994..8cc80dd7d0cead 100644 --- a/docs/_posts/ahmedlone127/2023-09-15-film20000distilbert_base_uncased_en.md +++ b/docs/_posts/ahmedlone127/2023-09-15-film20000distilbert_base_uncased_en.md @@ -24,8 +24,8 @@ Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/film20000distilbert_base_uncased_en_5.1.2_3.0_1694736242255.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/film20000distilbert_base_uncased_en_5.1.2_3.0_1694736242255.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/film20000distilbert_base_uncased_en_5.1.2_3.0_1694774514582.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/film20000distilbert_base_uncased_en_5.1.2_3.0_1694774514582.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-09-15-film20000film20000distilbert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-15-film20000film20000distilbert_base_uncased_en.md new file mode 100644 index 00000000000000..cae359671ffaae --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-film20000film20000distilbert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English film20000film20000distilbert_base_uncased DistilBertEmbeddings from AmaiaSolaun +author: John Snow Labs +name: film20000film20000distilbert_base_uncased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`film20000film20000distilbert_base_uncased` is a English model originally trained by AmaiaSolaun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/film20000film20000distilbert_base_uncased_en_5.1.2_3.0_1694775693003.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/film20000film20000distilbert_base_uncased_en_5.1.2_3.0_1694775693003.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("film20000film20000distilbert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("film20000film20000distilbert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|film20000film20000distilbert_base_uncased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.3 MB| + +## References + +https://huggingface.co/AmaiaSolaun/film20000film20000distilbert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-film95000distilbert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-15-film95000distilbert_base_uncased_en.md new file mode 100644 index 00000000000000..1a617a1bdd3510 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-film95000distilbert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English film95000distilbert_base_uncased DistilBertEmbeddings from AmaiaSolaun +author: John Snow Labs +name: film95000distilbert_base_uncased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`film95000distilbert_base_uncased` is a English model originally trained by AmaiaSolaun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/film95000distilbert_base_uncased_en_5.1.2_3.0_1694776512015.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/film95000distilbert_base_uncased_en_5.1.2_3.0_1694776512015.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("film95000distilbert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("film95000distilbert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|film95000distilbert_base_uncased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.3 MB| + +## References + +https://huggingface.co/AmaiaSolaun/film95000distilbert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-film98984distilbert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-15-film98984distilbert_base_uncased_en.md new file mode 100644 index 00000000000000..2d5bbcbe0fd425 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-film98984distilbert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English film98984distilbert_base_uncased DistilBertEmbeddings from AmaiaSolaun +author: John Snow Labs +name: film98984distilbert_base_uncased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`film98984distilbert_base_uncased` is a English model originally trained by AmaiaSolaun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/film98984distilbert_base_uncased_en_5.1.2_3.0_1694775585695.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/film98984distilbert_base_uncased_en_5.1.2_3.0_1694775585695.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("film98984distilbert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("film98984distilbert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|film98984distilbert_base_uncased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.3 MB| + +## References + +https://huggingface.co/AmaiaSolaun/film98984distilbert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-fine_tuned_distilbert_nosql_injection_en.md b/docs/_posts/ahmedlone127/2023-09-15-fine_tuned_distilbert_nosql_injection_en.md new file mode 100644 index 00000000000000..4f609c5a04c68a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-fine_tuned_distilbert_nosql_injection_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English fine_tuned_distilbert_nosql_injection DistilBertEmbeddings from ankush-003 +author: John Snow Labs +name: fine_tuned_distilbert_nosql_injection +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tuned_distilbert_nosql_injection` is a English model originally trained by ankush-003. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tuned_distilbert_nosql_injection_en_5.1.2_3.0_1694775832410.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tuned_distilbert_nosql_injection_en_5.1.2_3.0_1694775832410.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("fine_tuned_distilbert_nosql_injection","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("fine_tuned_distilbert_nosql_injection", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tuned_distilbert_nosql_injection| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.8 MB| + +## References + +https://huggingface.co/ankush-003/fine-tuned-distilbert-nosql-injection \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-first_try_4_en.md b/docs/_posts/ahmedlone127/2023-09-15-first_try_4_en.md new file mode 100644 index 00000000000000..a26be8582a30fa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-first_try_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English first_try_4 DistilBertEmbeddings from disanda +author: John Snow Labs +name: first_try_4 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`first_try_4` is a English model originally trained by disanda. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/first_try_4_en_5.1.2_3.0_1694773416430.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/first_try_4_en_5.1.2_3.0_1694773416430.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("first_try_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("first_try_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|first_try_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/disanda/first_try_4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-flang_distilbert_en.md b/docs/_posts/ahmedlone127/2023-09-15-flang_distilbert_en.md new file mode 100644 index 00000000000000..f6ef0f490e70cf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-flang_distilbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English flang_distilbert DistilBertEmbeddings from SALT-NLP +author: John Snow Labs +name: flang_distilbert +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`flang_distilbert` is a English model originally trained by SALT-NLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/flang_distilbert_en_5.1.2_3.0_1694777676228.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/flang_distilbert_en_5.1.2_3.0_1694777676228.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("flang_distilbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("flang_distilbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|flang_distilbert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/SALT-NLP/FLANG-DistilBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-german_poetry_distilbert_en.md b/docs/_posts/ahmedlone127/2023-09-15-german_poetry_distilbert_en.md new file mode 100644 index 00000000000000..8d51fc0905638c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-german_poetry_distilbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English german_poetry_distilbert DistilBertEmbeddings from Anjoe +author: John Snow Labs +name: german_poetry_distilbert +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`german_poetry_distilbert` is a English model originally trained by Anjoe. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/german_poetry_distilbert_en_5.1.2_3.0_1694774320800.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/german_poetry_distilbert_en_5.1.2_3.0_1694774320800.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("german_poetry_distilbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("german_poetry_distilbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|german_poetry_distilbert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|250.3 MB| + +## References + +https://huggingface.co/Anjoe/german-poetry-distilbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-google_job_data_tuned_trial_1_en.md b/docs/_posts/ahmedlone127/2023-09-15-google_job_data_tuned_trial_1_en.md new file mode 100644 index 00000000000000..6a6806eaa9bf20 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-google_job_data_tuned_trial_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English google_job_data_tuned_trial_1 DistilBertEmbeddings from EslamAhmed +author: John Snow Labs +name: google_job_data_tuned_trial_1 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`google_job_data_tuned_trial_1` is a English model originally trained by EslamAhmed. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/google_job_data_tuned_trial_1_en_5.1.2_3.0_1694770671424.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/google_job_data_tuned_trial_1_en_5.1.2_3.0_1694770671424.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("google_job_data_tuned_trial_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("google_job_data_tuned_trial_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|google_job_data_tuned_trial_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.3 MB| + +## References + +https://huggingface.co/EslamAhmed/google_Job_data_tuned_trial_1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-google_job_data_tuned_trial_2_11_2_2022_en.md b/docs/_posts/ahmedlone127/2023-09-15-google_job_data_tuned_trial_2_11_2_2022_en.md new file mode 100644 index 00000000000000..09bd2241a5d469 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-google_job_data_tuned_trial_2_11_2_2022_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English google_job_data_tuned_trial_2_11_2_2022 DistilBertEmbeddings from EslamAhmed +author: John Snow Labs +name: google_job_data_tuned_trial_2_11_2_2022 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`google_job_data_tuned_trial_2_11_2_2022` is a English model originally trained by EslamAhmed. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/google_job_data_tuned_trial_2_11_2_2022_en_5.1.2_3.0_1694772782812.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/google_job_data_tuned_trial_2_11_2_2022_en_5.1.2_3.0_1694772782812.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("google_job_data_tuned_trial_2_11_2_2022","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("google_job_data_tuned_trial_2_11_2_2022", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|google_job_data_tuned_trial_2_11_2_2022| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|402.3 MB| + +## References + +https://huggingface.co/EslamAhmed/google_Job_data_tuned_trial_2_11-2-2022 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-hf_distilbert_imdb_mlm_cosine_en.md b/docs/_posts/ahmedlone127/2023-09-15-hf_distilbert_imdb_mlm_cosine_en.md index f1b4ae3231f99c..321a26b9575858 100644 --- a/docs/_posts/ahmedlone127/2023-09-15-hf_distilbert_imdb_mlm_cosine_en.md +++ b/docs/_posts/ahmedlone127/2023-09-15-hf_distilbert_imdb_mlm_cosine_en.md @@ -24,8 +24,8 @@ Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to {:.btn-box} -[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hf_distilbert_imdb_mlm_cosine_en_5.1.2_3.0_1694736657876.zip){:.button.button-orange.button-orange-trans.arr.button-icon} -[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hf_distilbert_imdb_mlm_cosine_en_5.1.2_3.0_1694736657876.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hf_distilbert_imdb_mlm_cosine_en_5.1.2_3.0_1694769976827.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hf_distilbert_imdb_mlm_cosine_en_5.1.2_3.0_1694769976827.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} ## How to use diff --git a/docs/_posts/ahmedlone127/2023-09-15-hf_distilbert_imdb_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-15-hf_distilbert_imdb_mlm_en.md new file mode 100644 index 00000000000000..598fef3dfcef95 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-hf_distilbert_imdb_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English hf_distilbert_imdb_mlm DistilBertEmbeddings from nos1de +author: John Snow Labs +name: hf_distilbert_imdb_mlm +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hf_distilbert_imdb_mlm` is a English model originally trained by nos1de. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hf_distilbert_imdb_mlm_en_5.1.2_3.0_1694791148509.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hf_distilbert_imdb_mlm_en_5.1.2_3.0_1694791148509.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("hf_distilbert_imdb_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("hf_distilbert_imdb_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hf_distilbert_imdb_mlm| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/nos1de/hf-distilbert-imdb-mlm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-hinglish_distilbert_en.md b/docs/_posts/ahmedlone127/2023-09-15-hinglish_distilbert_en.md new file mode 100644 index 00000000000000..c571c046c03da8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-hinglish_distilbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English hinglish_distilbert DistilBertEmbeddings from meghanabhange +author: John Snow Labs +name: hinglish_distilbert +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hinglish_distilbert` is a English model originally trained by meghanabhange. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hinglish_distilbert_en_5.1.2_3.0_1694782372500.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hinglish_distilbert_en_5.1.2_3.0_1694782372500.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("hinglish_distilbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("hinglish_distilbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hinglish_distilbert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|243.7 MB| + +## References + +https://huggingface.co/meghanabhange/Hinglish-DistilBert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-indic_transformers_hindi_distilbert_en.md b/docs/_posts/ahmedlone127/2023-09-15-indic_transformers_hindi_distilbert_en.md new file mode 100644 index 00000000000000..ae1d2a7f6cb8bc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-indic_transformers_hindi_distilbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English indic_transformers_hindi_distilbert DistilBertEmbeddings from neuralspace +author: John Snow Labs +name: indic_transformers_hindi_distilbert +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indic_transformers_hindi_distilbert` is a English model originally trained by neuralspace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indic_transformers_hindi_distilbert_en_5.1.2_3.0_1694783192541.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indic_transformers_hindi_distilbert_en_5.1.2_3.0_1694783192541.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("indic_transformers_hindi_distilbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("indic_transformers_hindi_distilbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indic_transformers_hindi_distilbert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/neuralspace/indic-transformers-hi-distilbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_adagrad_en.md b/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_adagrad_en.md new file mode 100644 index 00000000000000..324774f9db35d9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_adagrad_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English inisw08_distilbert_mlm_adagrad DistilBertEmbeddings from ugiugi +author: John Snow Labs +name: inisw08_distilbert_mlm_adagrad +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`inisw08_distilbert_mlm_adagrad` is a English model originally trained by ugiugi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/inisw08_distilbert_mlm_adagrad_en_5.1.2_3.0_1694788039538.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/inisw08_distilbert_mlm_adagrad_en_5.1.2_3.0_1694788039538.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("inisw08_distilbert_mlm_adagrad","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("inisw08_distilbert_mlm_adagrad", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|inisw08_distilbert_mlm_adagrad| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ugiugi/inisw08-DistilBERT-mlm-adagrad \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_adamw_hf_en.md b/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_adamw_hf_en.md new file mode 100644 index 00000000000000..d36fb1f1702e99 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_adamw_hf_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English inisw08_distilbert_mlm_adamw_hf DistilBertEmbeddings from ugiugi +author: John Snow Labs +name: inisw08_distilbert_mlm_adamw_hf +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`inisw08_distilbert_mlm_adamw_hf` is a English model originally trained by ugiugi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/inisw08_distilbert_mlm_adamw_hf_en_5.1.2_3.0_1694787638535.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/inisw08_distilbert_mlm_adamw_hf_en_5.1.2_3.0_1694787638535.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("inisw08_distilbert_mlm_adamw_hf","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("inisw08_distilbert_mlm_adamw_hf", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|inisw08_distilbert_mlm_adamw_hf| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.3 MB| + +## References + +https://huggingface.co/ugiugi/inisw08-DistilBERT-mlm-adamw_hf \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_adamw_torch_0608_en.md b/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_adamw_torch_0608_en.md new file mode 100644 index 00000000000000..6f7fa1991c81dd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_adamw_torch_0608_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English inisw08_distilbert_mlm_adamw_torch_0608 DistilBertEmbeddings from ugiugi +author: John Snow Labs +name: inisw08_distilbert_mlm_adamw_torch_0608 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`inisw08_distilbert_mlm_adamw_torch_0608` is a English model originally trained by ugiugi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/inisw08_distilbert_mlm_adamw_torch_0608_en_5.1.2_3.0_1694788142265.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/inisw08_distilbert_mlm_adamw_torch_0608_en_5.1.2_3.0_1694788142265.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("inisw08_distilbert_mlm_adamw_torch_0608","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("inisw08_distilbert_mlm_adamw_torch_0608", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|inisw08_distilbert_mlm_adamw_torch_0608| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.7 MB| + +## References + +https://huggingface.co/ugiugi/inisw08-DistilBERT-mlm-adamw_torch_0608 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_adamw_torch_en.md b/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_adamw_torch_en.md new file mode 100644 index 00000000000000..c93842c9710865 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_adamw_torch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English inisw08_distilbert_mlm_adamw_torch DistilBertEmbeddings from ugiugi +author: John Snow Labs +name: inisw08_distilbert_mlm_adamw_torch +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`inisw08_distilbert_mlm_adamw_torch` is a English model originally trained by ugiugi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/inisw08_distilbert_mlm_adamw_torch_en_5.1.2_3.0_1694787825797.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/inisw08_distilbert_mlm_adamw_torch_en_5.1.2_3.0_1694787825797.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("inisw08_distilbert_mlm_adamw_torch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("inisw08_distilbert_mlm_adamw_torch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|inisw08_distilbert_mlm_adamw_torch| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.3 MB| + +## References + +https://huggingface.co/ugiugi/inisw08-DistilBERT-mlm-adamw_torch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_adamw_torch_fused_en.md b/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_adamw_torch_fused_en.md new file mode 100644 index 00000000000000..1063741d4d1b59 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_adamw_torch_fused_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English inisw08_distilbert_mlm_adamw_torch_fused DistilBertEmbeddings from ugiugi +author: John Snow Labs +name: inisw08_distilbert_mlm_adamw_torch_fused +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`inisw08_distilbert_mlm_adamw_torch_fused` is a English model originally trained by ugiugi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/inisw08_distilbert_mlm_adamw_torch_fused_en_5.1.2_3.0_1694788244866.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/inisw08_distilbert_mlm_adamw_torch_fused_en_5.1.2_3.0_1694788244866.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("inisw08_distilbert_mlm_adamw_torch_fused","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("inisw08_distilbert_mlm_adamw_torch_fused", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|inisw08_distilbert_mlm_adamw_torch_fused| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.3 MB| + +## References + +https://huggingface.co/ugiugi/inisw08-DistilBERT-mlm-adamw_torch_fused \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_lion_32bit_en.md b/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_lion_32bit_en.md new file mode 100644 index 00000000000000..776b5189ddfcba --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_lion_32bit_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English inisw08_distilbert_mlm_lion_32bit DistilBertEmbeddings from ugiugi +author: John Snow Labs +name: inisw08_distilbert_mlm_lion_32bit +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`inisw08_distilbert_mlm_lion_32bit` is a English model originally trained by ugiugi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/inisw08_distilbert_mlm_lion_32bit_en_5.1.2_3.0_1694788344252.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/inisw08_distilbert_mlm_lion_32bit_en_5.1.2_3.0_1694788344252.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("inisw08_distilbert_mlm_lion_32bit","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("inisw08_distilbert_mlm_lion_32bit", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|inisw08_distilbert_mlm_lion_32bit| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|245.9 MB| + +## References + +https://huggingface.co/ugiugi/inisw08-DistilBERT-mlm-lion_32bit \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_lion_32bit_test_en.md b/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_lion_32bit_test_en.md new file mode 100644 index 00000000000000..0672005b36e2aa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_lion_32bit_test_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English inisw08_distilbert_mlm_lion_32bit_test DistilBertEmbeddings from ugiugi +author: John Snow Labs +name: inisw08_distilbert_mlm_lion_32bit_test +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`inisw08_distilbert_mlm_lion_32bit_test` is a English model originally trained by ugiugi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/inisw08_distilbert_mlm_lion_32bit_test_en_5.1.2_3.0_1694788450083.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/inisw08_distilbert_mlm_lion_32bit_test_en_5.1.2_3.0_1694788450083.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("inisw08_distilbert_mlm_lion_32bit_test","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("inisw08_distilbert_mlm_lion_32bit_test", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|inisw08_distilbert_mlm_lion_32bit_test| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|245.9 MB| + +## References + +https://huggingface.co/ugiugi/inisw08-DistilBERT-mlm-lion_32bit_test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_sgd_en.md b/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_sgd_en.md new file mode 100644 index 00000000000000..c1dee01844e52a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-inisw08_distilbert_mlm_sgd_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English inisw08_distilbert_mlm_sgd DistilBertEmbeddings from ugiugi +author: John Snow Labs +name: inisw08_distilbert_mlm_sgd +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`inisw08_distilbert_mlm_sgd` is a English model originally trained by ugiugi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/inisw08_distilbert_mlm_sgd_en_5.1.2_3.0_1694787934386.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/inisw08_distilbert_mlm_sgd_en_5.1.2_3.0_1694787934386.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("inisw08_distilbert_mlm_sgd","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("inisw08_distilbert_mlm_sgd", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|inisw08_distilbert_mlm_sgd| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ugiugi/inisw08-DistilBERT-mlm-sgd \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-inisw08_robert_mlm_adamw_torch_test_en.md b/docs/_posts/ahmedlone127/2023-09-15-inisw08_robert_mlm_adamw_torch_test_en.md new file mode 100644 index 00000000000000..dfff7490abf030 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-inisw08_robert_mlm_adamw_torch_test_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English inisw08_robert_mlm_adamw_torch_test DistilBertEmbeddings from ugiugi +author: John Snow Labs +name: inisw08_robert_mlm_adamw_torch_test +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`inisw08_robert_mlm_adamw_torch_test` is a English model originally trained by ugiugi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/inisw08_robert_mlm_adamw_torch_test_en_5.1.2_3.0_1694789322009.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/inisw08_robert_mlm_adamw_torch_test_en_5.1.2_3.0_1694789322009.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("inisw08_robert_mlm_adamw_torch_test","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("inisw08_robert_mlm_adamw_torch_test", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|inisw08_robert_mlm_adamw_torch_test| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.3 MB| + +## References + +https://huggingface.co/ugiugi/inisw08-RoBERT-mlm-adamw_torch_test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-javanese_distilbert_small_imdb_jv.md b/docs/_posts/ahmedlone127/2023-09-15-javanese_distilbert_small_imdb_jv.md new file mode 100644 index 00000000000000..7954b2cb6667a5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-javanese_distilbert_small_imdb_jv.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Javanese javanese_distilbert_small_imdb DistilBertEmbeddings from w11wo +author: John Snow Labs +name: javanese_distilbert_small_imdb +date: 2023-09-15 +tags: [distilbert, jv, open_source, fill_mask, onnx] +task: Embeddings +language: jv +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`javanese_distilbert_small_imdb` is a Javanese model originally trained by w11wo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/javanese_distilbert_small_imdb_jv_5.1.2_3.0_1694785464605.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/javanese_distilbert_small_imdb_jv_5.1.2_3.0_1694785464605.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("javanese_distilbert_small_imdb","jv") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("javanese_distilbert_small_imdb", "jv") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|javanese_distilbert_small_imdb| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|jv| +|Size:|247.6 MB| + +## References + +https://huggingface.co/w11wo/javanese-distilbert-small-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-javanese_distilbert_small_jv.md b/docs/_posts/ahmedlone127/2023-09-15-javanese_distilbert_small_jv.md new file mode 100644 index 00000000000000..3374867b783e48 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-javanese_distilbert_small_jv.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Javanese javanese_distilbert_small DistilBertEmbeddings from w11wo +author: John Snow Labs +name: javanese_distilbert_small +date: 2023-09-15 +tags: [distilbert, jv, open_source, fill_mask, onnx] +task: Embeddings +language: jv +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`javanese_distilbert_small` is a Javanese model originally trained by w11wo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/javanese_distilbert_small_jv_5.1.2_3.0_1694785574408.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/javanese_distilbert_small_jv_5.1.2_3.0_1694785574408.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("javanese_distilbert_small","jv") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("javanese_distilbert_small", "jv") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|javanese_distilbert_small| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|jv| +|Size:|247.6 MB| + +## References + +https://huggingface.co/w11wo/javanese-distilbert-small \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-kaz_legal_distilbert_full_corpus_10.0_en.md b/docs/_posts/ahmedlone127/2023-09-15-kaz_legal_distilbert_full_corpus_10.0_en.md new file mode 100644 index 00000000000000..16a4bef397b6e4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-kaz_legal_distilbert_full_corpus_10.0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kaz_legal_distilbert_full_corpus_10.0 DistilBertEmbeddings from kaisar-barlybay-sse +author: John Snow Labs +name: kaz_legal_distilbert_full_corpus_10.0 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kaz_legal_distilbert_full_corpus_10.0` is a English model originally trained by kaisar-barlybay-sse. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kaz_legal_distilbert_full_corpus_10.0_en_5.1.2_3.0_1694781030169.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kaz_legal_distilbert_full_corpus_10.0_en_5.1.2_3.0_1694781030169.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("kaz_legal_distilbert_full_corpus_10.0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("kaz_legal_distilbert_full_corpus_10.0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kaz_legal_distilbert_full_corpus_10.0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.9 MB| + +## References + +https://huggingface.co/kaisar-barlybay-sse/kaz_legal_distilbert_full_corpus_10.0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-kaz_legal_distilbert_full_corpus_3.0_en.md b/docs/_posts/ahmedlone127/2023-09-15-kaz_legal_distilbert_full_corpus_3.0_en.md new file mode 100644 index 00000000000000..316e5e5bcf3328 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-kaz_legal_distilbert_full_corpus_3.0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kaz_legal_distilbert_full_corpus_3.0 DistilBertEmbeddings from kaisar-barlybay-sse +author: John Snow Labs +name: kaz_legal_distilbert_full_corpus_3.0 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kaz_legal_distilbert_full_corpus_3.0` is a English model originally trained by kaisar-barlybay-sse. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kaz_legal_distilbert_full_corpus_3.0_en_5.1.2_3.0_1694780769803.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kaz_legal_distilbert_full_corpus_3.0_en_5.1.2_3.0_1694780769803.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("kaz_legal_distilbert_full_corpus_3.0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("kaz_legal_distilbert_full_corpus_3.0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kaz_legal_distilbert_full_corpus_3.0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.8 MB| + +## References + +https://huggingface.co/kaisar-barlybay-sse/kaz_legal_distilbert_full_corpus_3.0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-kaz_legal_distilbert_legal_corpus_312818008_words_4.945454545454545_en.md b/docs/_posts/ahmedlone127/2023-09-15-kaz_legal_distilbert_legal_corpus_312818008_words_4.945454545454545_en.md new file mode 100644 index 00000000000000..7bd19e82e8bdf7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-kaz_legal_distilbert_legal_corpus_312818008_words_4.945454545454545_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English kaz_legal_distilbert_legal_corpus_312818008_words_4.945454545454545 DistilBertEmbeddings from kaisar-barlybay-sse +author: John Snow Labs +name: kaz_legal_distilbert_legal_corpus_312818008_words_4.945454545454545 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kaz_legal_distilbert_legal_corpus_312818008_words_4.945454545454545` is a English model originally trained by kaisar-barlybay-sse. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kaz_legal_distilbert_legal_corpus_312818008_words_4.945454545454545_en_5.1.2_3.0_1694782306602.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kaz_legal_distilbert_legal_corpus_312818008_words_4.945454545454545_en_5.1.2_3.0_1694782306602.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("kaz_legal_distilbert_legal_corpus_312818008_words_4.945454545454545","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("kaz_legal_distilbert_legal_corpus_312818008_words_4.945454545454545", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kaz_legal_distilbert_legal_corpus_312818008_words_4.945454545454545| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.0 MB| + +## References + +https://huggingface.co/kaisar-barlybay-sse/kaz_legal_distilbert_legal_corpus_312818008_words_4.945454545454545 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-lsg_distilbert_base_uncased_4096_en.md b/docs/_posts/ahmedlone127/2023-09-15-lsg_distilbert_base_uncased_4096_en.md new file mode 100644 index 00000000000000..376789eb00edb9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-lsg_distilbert_base_uncased_4096_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English lsg_distilbert_base_uncased_4096 DistilBertEmbeddings from ccdv +author: John Snow Labs +name: lsg_distilbert_base_uncased_4096 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lsg_distilbert_base_uncased_4096` is a English model originally trained by ccdv. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lsg_distilbert_base_uncased_4096_en_5.1.2_3.0_1694786927231.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lsg_distilbert_base_uncased_4096_en_5.1.2_3.0_1694786927231.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("lsg_distilbert_base_uncased_4096","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("lsg_distilbert_base_uncased_4096", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lsg_distilbert_base_uncased_4096| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|257.5 MB| + +## References + +https://huggingface.co/ccdv/lsg-distilbert-base-uncased-4096 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-ltrc_distilbert_en.md b/docs/_posts/ahmedlone127/2023-09-15-ltrc_distilbert_en.md new file mode 100644 index 00000000000000..8437b1fe1fab69 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-ltrc_distilbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ltrc_distilbert DistilBertEmbeddings from ltrctelugu +author: John Snow Labs +name: ltrc_distilbert +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ltrc_distilbert` is a English model originally trained by ltrctelugu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ltrc_distilbert_en_5.1.2_3.0_1694781693527.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ltrc_distilbert_en_5.1.2_3.0_1694781693527.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("ltrc_distilbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("ltrc_distilbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ltrc_distilbert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|243.6 MB| + +## References + +https://huggingface.co/ltrctelugu/ltrc-distilbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-m4_mlm_en.md b/docs/_posts/ahmedlone127/2023-09-15-m4_mlm_en.md new file mode 100644 index 00000000000000..a445e59cb23558 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-m4_mlm_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m4_mlm DistilBertEmbeddings from S2312dal +author: John Snow Labs +name: m4_mlm +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m4_mlm` is a English model originally trained by S2312dal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m4_mlm_en_5.1.2_3.0_1694776951864.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m4_mlm_en_5.1.2_3.0_1694776951864.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("m4_mlm","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("m4_mlm", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m4_mlm| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/S2312dal/M4_MLM \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-malaysian_distilbert_small_ms.md b/docs/_posts/ahmedlone127/2023-09-15-malaysian_distilbert_small_ms.md new file mode 100644 index 00000000000000..4517c842fa1a98 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-malaysian_distilbert_small_ms.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Malay (macrolanguage) malaysian_distilbert_small DistilBertEmbeddings from w11wo +author: John Snow Labs +name: malaysian_distilbert_small +date: 2023-09-15 +tags: [distilbert, ms, open_source, fill_mask, onnx] +task: Embeddings +language: ms +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`malaysian_distilbert_small` is a Malay (macrolanguage) model originally trained by w11wo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/malaysian_distilbert_small_ms_5.1.2_3.0_1694785690565.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/malaysian_distilbert_small_ms_5.1.2_3.0_1694785690565.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("malaysian_distilbert_small","ms") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("malaysian_distilbert_small", "ms") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|malaysian_distilbert_small| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|ms| +|Size:|248.1 MB| + +## References + +https://huggingface.co/w11wo/malaysian-distilbert-small \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-marathi_distilbert_mr.md b/docs/_posts/ahmedlone127/2023-09-15-marathi_distilbert_mr.md new file mode 100644 index 00000000000000..c5a3ed8a1bd254 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-marathi_distilbert_mr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Marathi marathi_distilbert DistilBertEmbeddings from DarshanDeshpande +author: John Snow Labs +name: marathi_distilbert +date: 2023-09-15 +tags: [distilbert, mr, open_source, fill_mask, onnx] +task: Embeddings +language: mr +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`marathi_distilbert` is a Marathi model originally trained by DarshanDeshpande. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/marathi_distilbert_mr_5.1.2_3.0_1694771200340.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/marathi_distilbert_mr_5.1.2_3.0_1694771200340.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("marathi_distilbert","mr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("marathi_distilbert", "mr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|marathi_distilbert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|mr| +|Size:|247.5 MB| + +## References + +https://huggingface.co/DarshanDeshpande/marathi-distilbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-marathi_distilbert_pretrained_mr.md b/docs/_posts/ahmedlone127/2023-09-15-marathi_distilbert_pretrained_mr.md new file mode 100644 index 00000000000000..7c83b8dce548c1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-marathi_distilbert_pretrained_mr.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Marathi marathi_distilbert_pretrained DistilBertEmbeddings from boleshirish +author: John Snow Labs +name: marathi_distilbert_pretrained +date: 2023-09-15 +tags: [distilbert, mr, open_source, fill_mask, onnx] +task: Embeddings +language: mr +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`marathi_distilbert_pretrained` is a Marathi model originally trained by boleshirish. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/marathi_distilbert_pretrained_mr_5.1.2_3.0_1694788553079.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/marathi_distilbert_pretrained_mr_5.1.2_3.0_1694788553079.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("marathi_distilbert_pretrained","mr") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("marathi_distilbert_pretrained", "mr") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|marathi_distilbert_pretrained| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|mr| +|Size:|248.7 MB| + +## References + +https://huggingface.co/boleshirish/Marathi_DistilBert_Pretrained \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-masked_language_finetuned_model_en.md b/docs/_posts/ahmedlone127/2023-09-15-masked_language_finetuned_model_en.md new file mode 100644 index 00000000000000..40192a38512713 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-masked_language_finetuned_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English masked_language_finetuned_model DistilBertEmbeddings from benlehrburger +author: John Snow Labs +name: masked_language_finetuned_model +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`masked_language_finetuned_model` is a English model originally trained by benlehrburger. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/masked_language_finetuned_model_en_5.1.2_3.0_1694786097156.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/masked_language_finetuned_model_en_5.1.2_3.0_1694786097156.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("masked_language_finetuned_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("masked_language_finetuned_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|masked_language_finetuned_model| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/benlehrburger/masked-language-finetuned-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-masked_language_model_en.md b/docs/_posts/ahmedlone127/2023-09-15-masked_language_model_en.md new file mode 100644 index 00000000000000..adcef53f7f409b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-masked_language_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English masked_language_model DistilBertEmbeddings from ayoolaolafenwa +author: John Snow Labs +name: masked_language_model +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`masked_language_model` is a English model originally trained by ayoolaolafenwa. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/masked_language_model_en_5.1.2_3.0_1694771656379.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/masked_language_model_en_5.1.2_3.0_1694771656379.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("masked_language_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("masked_language_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|masked_language_model| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/ayoolaolafenwa/Masked-Language-Model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-mdistilbertv2.1_en.md b/docs/_posts/ahmedlone127/2023-09-15-mdistilbertv2.1_en.md new file mode 100644 index 00000000000000..b23b5c99e69dd9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-mdistilbertv2.1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mdistilbertv2.1 DistilBertEmbeddings from bongsoo +author: John Snow Labs +name: mdistilbertv2.1 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mdistilbertv2.1` is a English model originally trained by bongsoo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mdistilbertv2.1_en_5.1.2_3.0_1694787507745.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mdistilbertv2.1_en_5.1.2_3.0_1694787507745.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("mdistilbertv2.1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("mdistilbertv2.1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mdistilbertv2.1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|602.3 MB| + +## References + +https://huggingface.co/bongsoo/mdistilbertV2.1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-mdistilbertv3.1_en.md b/docs/_posts/ahmedlone127/2023-09-15-mdistilbertv3.1_en.md new file mode 100644 index 00000000000000..15d7d294a616f8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-mdistilbertv3.1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mdistilbertv3.1 DistilBertEmbeddings from bongsoo +author: John Snow Labs +name: mdistilbertv3.1 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mdistilbertv3.1` is a English model originally trained by bongsoo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mdistilbertv3.1_en_5.1.2_3.0_1694771042552.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mdistilbertv3.1_en_5.1.2_3.0_1694771042552.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("mdistilbertv3.1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("mdistilbertv3.1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mdistilbertv3.1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|621.1 MB| + +## References + +https://huggingface.co/bongsoo/mdistilbertV3.1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-medicaldistilbert_en.md b/docs/_posts/ahmedlone127/2023-09-15-medicaldistilbert_en.md new file mode 100644 index 00000000000000..e6123d11546197 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-medicaldistilbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English medicaldistilbert DistilBertEmbeddings from Gaborandi +author: John Snow Labs +name: medicaldistilbert +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`medicaldistilbert` is a English model originally trained by Gaborandi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/medicaldistilbert_en_5.1.2_3.0_1694770810957.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/medicaldistilbert_en_5.1.2_3.0_1694770810957.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("medicaldistilbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("medicaldistilbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|medicaldistilbert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Gaborandi/Medicaldistilbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-mlm_distilbert_en.md b/docs/_posts/ahmedlone127/2023-09-15-mlm_distilbert_en.md new file mode 100644 index 00000000000000..ece88dcd338735 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-mlm_distilbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_distilbert DistilBertEmbeddings from dsoum +author: John Snow Labs +name: mlm_distilbert +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_distilbert` is a English model originally trained by dsoum. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_distilbert_en_5.1.2_3.0_1694787195352.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_distilbert_en_5.1.2_3.0_1694787195352.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("mlm_distilbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("mlm_distilbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_distilbert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/dsoum/mlm-distilbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-mlm_model_en.md b/docs/_posts/ahmedlone127/2023-09-15-mlm_model_en.md new file mode 100644 index 00000000000000..8a610ed40f2187 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-mlm_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mlm_model DistilBertEmbeddings from amkorba +author: John Snow Labs +name: mlm_model +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mlm_model` is a English model originally trained by amkorba. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mlm_model_en_5.1.2_3.0_1694772762718.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mlm_model_en_5.1.2_3.0_1694772762718.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("mlm_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("mlm_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mlm_model| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/amkorba/mlm-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-mtl_distilbert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-15-mtl_distilbert_base_uncased_en.md new file mode 100644 index 00000000000000..4b67a1557223d5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-mtl_distilbert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mtl_distilbert_base_uncased DistilBertEmbeddings from jgammack +author: John Snow Labs +name: mtl_distilbert_base_uncased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mtl_distilbert_base_uncased` is a English model originally trained by jgammack. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mtl_distilbert_base_uncased_en_5.1.2_3.0_1694781088162.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mtl_distilbert_base_uncased_en_5.1.2_3.0_1694781088162.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("mtl_distilbert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("mtl_distilbert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mtl_distilbert_base_uncased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/jgammack/MTL-distilbert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-output_en.md b/docs/_posts/ahmedlone127/2023-09-15-output_en.md new file mode 100644 index 00000000000000..6a86ca14ebdedf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-output_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English output DistilBertEmbeddings from soyisauce +author: John Snow Labs +name: output +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`output` is a English model originally trained by soyisauce. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/output_en_5.1.2_3.0_1694790179928.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/output_en_5.1.2_3.0_1694790179928.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("output","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("output", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|output| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/soyisauce/output \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-predreambert_en.md b/docs/_posts/ahmedlone127/2023-09-15-predreambert_en.md new file mode 100644 index 00000000000000..72253567f3f796 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-predreambert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English predreambert DistilBertEmbeddings from asheads +author: John Snow Labs +name: predreambert +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`predreambert` is a English model originally trained by asheads. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/predreambert_en_5.1.2_3.0_1694780049333.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/predreambert_en_5.1.2_3.0_1694780049333.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("predreambert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("predreambert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|predreambert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/asheads/PredreamBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-pt_distilbert_base_en.md b/docs/_posts/ahmedlone127/2023-09-15-pt_distilbert_base_en.md new file mode 100644 index 00000000000000..c51f4eec9ad63d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-pt_distilbert_base_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English pt_distilbert_base DistilBertEmbeddings from SharedBailii +author: John Snow Labs +name: pt_distilbert_base +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pt_distilbert_base` is a English model originally trained by SharedBailii. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pt_distilbert_base_en_5.1.2_3.0_1694782385724.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pt_distilbert_base_en_5.1.2_3.0_1694782385724.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("pt_distilbert_base","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("pt_distilbert_base", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pt_distilbert_base| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/SharedBailii/PT-distilbert-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-remote_sensing_distilbert_cased_en.md b/docs/_posts/ahmedlone127/2023-09-15-remote_sensing_distilbert_cased_en.md new file mode 100644 index 00000000000000..0707313250a5a5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-remote_sensing_distilbert_cased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English remote_sensing_distilbert_cased DistilBertEmbeddings from Chramer +author: John Snow Labs +name: remote_sensing_distilbert_cased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`remote_sensing_distilbert_cased` is a English model originally trained by Chramer. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/remote_sensing_distilbert_cased_en_5.1.2_3.0_1694772072848.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/remote_sensing_distilbert_cased_en_5.1.2_3.0_1694772072848.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("remote_sensing_distilbert_cased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("remote_sensing_distilbert_cased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|remote_sensing_distilbert_cased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|243.7 MB| + +## References + +https://huggingface.co/Chramer/remote-sensing-distilbert-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-sae_distilbert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-09-15-sae_distilbert_base_uncased_en.md new file mode 100644 index 00000000000000..0f3157aeda9ed5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-sae_distilbert_base_uncased_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sae_distilbert_base_uncased DistilBertEmbeddings from jgammack +author: John Snow Labs +name: sae_distilbert_base_uncased +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sae_distilbert_base_uncased` is a English model originally trained by jgammack. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sae_distilbert_base_uncased_en_5.1.2_3.0_1694781193775.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sae_distilbert_base_uncased_en_5.1.2_3.0_1694781193775.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("sae_distilbert_base_uncased","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("sae_distilbert_base_uncased", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sae_distilbert_base_uncased| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/jgammack/SAE-distilbert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-skc_mlm_english_torch_en.md b/docs/_posts/ahmedlone127/2023-09-15-skc_mlm_english_torch_en.md new file mode 100644 index 00000000000000..17177818563154 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-skc_mlm_english_torch_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English skc_mlm_english_torch DistilBertEmbeddings from Tobias +author: John Snow Labs +name: skc_mlm_english_torch +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`skc_mlm_english_torch` is a English model originally trained by Tobias. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/skc_mlm_english_torch_en_5.1.2_3.0_1694782805554.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/skc_mlm_english_torch_en_5.1.2_3.0_1694782805554.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("skc_mlm_english_torch","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("skc_mlm_english_torch", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|skc_mlm_english_torch| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Tobias/skc_MLM_English_torch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-sparsembed_max_en.md b/docs/_posts/ahmedlone127/2023-09-15-sparsembed_max_en.md new file mode 100644 index 00000000000000..fa8707c7a1768b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-sparsembed_max_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sparsembed_max DistilBertEmbeddings from raphaelsty +author: John Snow Labs +name: sparsembed_max +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sparsembed_max` is a English model originally trained by raphaelsty. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sparsembed_max_en_5.1.2_3.0_1694787581861.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sparsembed_max_en_5.1.2_3.0_1694787581861.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("sparsembed_max","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("sparsembed_max", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sparsembed_max| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.3 MB| + +## References + +https://huggingface.co/raphaelsty/sparsembed-max \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-splade_max_en.md b/docs/_posts/ahmedlone127/2023-09-15-splade_max_en.md new file mode 100644 index 00000000000000..1c920ce3fce2db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-splade_max_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English splade_max DistilBertEmbeddings from raphaelsty +author: John Snow Labs +name: splade_max +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`splade_max` is a English model originally trained by raphaelsty. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/splade_max_en_5.1.2_3.0_1694787692631.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/splade_max_en_5.1.2_3.0_1694787692631.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("splade_max","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("splade_max", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|splade_max| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.3 MB| + +## References + +https://huggingface.co/raphaelsty/splade-max \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-splade_v2_distil_en.md b/docs/_posts/ahmedlone127/2023-09-15-splade_v2_distil_en.md new file mode 100644 index 00000000000000..43c1b804c422ff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-splade_v2_distil_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English splade_v2_distil DistilBertEmbeddings from naver +author: John Snow Labs +name: splade_v2_distil +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`splade_v2_distil` is a English model originally trained by naver. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/splade_v2_distil_en_5.1.2_3.0_1694779382367.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/splade_v2_distil_en_5.1.2_3.0_1694779382367.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("splade_v2_distil","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("splade_v2_distil", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|splade_v2_distil| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/naver/splade_v2_distil \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-splade_v2_max_en.md b/docs/_posts/ahmedlone127/2023-09-15-splade_v2_max_en.md new file mode 100644 index 00000000000000..d042ebb8c632d6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-splade_v2_max_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English splade_v2_max DistilBertEmbeddings from naver +author: John Snow Labs +name: splade_v2_max +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`splade_v2_max` is a English model originally trained by naver. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/splade_v2_max_en_5.1.2_3.0_1694779276923.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/splade_v2_max_en_5.1.2_3.0_1694779276923.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("splade_v2_max","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("splade_v2_max", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|splade_v2_max| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.3 MB| + +## References + +https://huggingface.co/naver/splade_v2_max \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-spladex_tt_spanish_en.md b/docs/_posts/ahmedlone127/2023-09-15-spladex_tt_spanish_en.md new file mode 100644 index 00000000000000..31891ab0708eab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-spladex_tt_spanish_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English spladex_tt_spanish DistilBertEmbeddings from JAWCF +author: John Snow Labs +name: spladex_tt_spanish +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`spladex_tt_spanish` is a English model originally trained by JAWCF. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/spladex_tt_spanish_en_5.1.2_3.0_1694789904459.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/spladex_tt_spanish_en_5.1.2_3.0_1694789904459.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("spladex_tt_spanish","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("spladex_tt_spanish", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|spladex_tt_spanish| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|505.4 MB| + +## References + +https://huggingface.co/JAWCF/spladeX-TT-es \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-spladex_zs_en.md b/docs/_posts/ahmedlone127/2023-09-15-spladex_zs_en.md new file mode 100644 index 00000000000000..e79f094bcad8ac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-spladex_zs_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English spladex_zs DistilBertEmbeddings from JAWCF +author: John Snow Labs +name: spladex_zs +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`spladex_zs` is a English model originally trained by JAWCF. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/spladex_zs_en_5.1.2_3.0_1694790065534.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/spladex_zs_en_5.1.2_3.0_1694790065534.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("spladex_zs","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("spladex_zs", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|spladex_zs| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|505.4 MB| + +## References + +https://huggingface.co/JAWCF/spladeX-ZS \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-tapt_distillbert_en.md b/docs/_posts/ahmedlone127/2023-09-15-tapt_distillbert_en.md new file mode 100644 index 00000000000000..69937e84661fd4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-tapt_distillbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tapt_distillbert DistilBertEmbeddings from minhdang241 +author: John Snow Labs +name: tapt_distillbert +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tapt_distillbert` is a English model originally trained by minhdang241. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tapt_distillbert_en_5.1.2_3.0_1694782808771.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tapt_distillbert_en_5.1.2_3.0_1694782808771.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("tapt_distillbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("tapt_distillbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tapt_distillbert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/minhdang241/TAPT_distillBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-test_text_en.md b/docs/_posts/ahmedlone127/2023-09-15-test_text_en.md new file mode 100644 index 00000000000000..d2be0e53c78ec8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-test_text_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English test_text DistilBertEmbeddings from joaogante +author: John Snow Labs +name: test_text +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`test_text` is a English model originally trained by joaogante. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/test_text_en_5.1.2_3.0_1694774637956.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/test_text_en_5.1.2_3.0_1694774637956.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("test_text","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("test_text", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|test_text| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/joaogante/test_text \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-tod_distilbert_jnt_v1_en.md b/docs/_posts/ahmedlone127/2023-09-15-tod_distilbert_jnt_v1_en.md new file mode 100644 index 00000000000000..861e565ff3c87f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-tod_distilbert_jnt_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tod_distilbert_jnt_v1 DistilBertEmbeddings from TODBERT +author: John Snow Labs +name: tod_distilbert_jnt_v1 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tod_distilbert_jnt_v1` is a English model originally trained by TODBERT. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tod_distilbert_jnt_v1_en_5.1.2_3.0_1694779640585.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tod_distilbert_jnt_v1_en_5.1.2_3.0_1694779640585.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("tod_distilbert_jnt_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("tod_distilbert_jnt_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tod_distilbert_jnt_v1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|246.8 MB| + +## References + +https://huggingface.co/TODBERT/TOD-DistilBERT-JNT-V1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-train_mask_language_model_en.md b/docs/_posts/ahmedlone127/2023-09-15-train_mask_language_model_en.md new file mode 100644 index 00000000000000..f0bff3435fbd59 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-train_mask_language_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English train_mask_language_model DistilBertEmbeddings from hubert10 +author: John Snow Labs +name: train_mask_language_model +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`train_mask_language_model` is a English model originally trained by hubert10. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/train_mask_language_model_en_5.1.2_3.0_1694772588866.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/train_mask_language_model_en_5.1.2_3.0_1694772588866.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("train_mask_language_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("train_mask_language_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|train_mask_language_model| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/hubert10/train_mask_language_model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-ukri_distilbert_en.md b/docs/_posts/ahmedlone127/2023-09-15-ukri_distilbert_en.md new file mode 100644 index 00000000000000..5902d7ce08ac0b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-ukri_distilbert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English ukri_distilbert DistilBertEmbeddings from Brawl +author: John Snow Labs +name: ukri_distilbert +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ukri_distilbert` is a English model originally trained by Brawl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ukri_distilbert_en_5.1.2_3.0_1694770523162.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ukri_distilbert_en_5.1.2_3.0_1694770523162.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("ukri_distilbert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("ukri_distilbert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ukri_distilbert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|247.2 MB| + +## References + +https://huggingface.co/Brawl/UKRI_DistilBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1963_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1963_en.md new file mode 100644 index 00000000000000..e800a194851297 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1963_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1963 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1963 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1963` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1963_en_5.1.2_3.0_1694773487647.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1963_en_5.1.2_3.0_1694773487647.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1963","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1963", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1963| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1963 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1964_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1964_en.md new file mode 100644 index 00000000000000..a38a2a04e59245 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1964_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1964 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1964 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1964` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1964_en_5.1.2_3.0_1694773586947.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1964_en_5.1.2_3.0_1694773586947.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1964","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1964", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1964| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1964 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1965_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1965_en.md new file mode 100644 index 00000000000000..820c72ba116f6d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1965_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1965 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1965 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1965` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1965_en_5.1.2_3.0_1694773687356.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1965_en_5.1.2_3.0_1694773687356.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1965","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1965", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1965| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1965 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1966_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1966_en.md new file mode 100644 index 00000000000000..e18fa86cff9a13 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1966_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1966 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1966 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1966` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1966_en_5.1.2_3.0_1694773798546.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1966_en_5.1.2_3.0_1694773798546.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1966","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1966", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1966| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1966 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1967_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1967_en.md new file mode 100644 index 00000000000000..fb9ab2afc4d963 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1967_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1967 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1967 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1967` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1967_en_5.1.2_3.0_1694773929268.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1967_en_5.1.2_3.0_1694773929268.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1967","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1967", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1967| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1967 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1968_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1968_en.md new file mode 100644 index 00000000000000..60f33b9d3132b4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1968_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1968 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1968 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1968` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1968_en_5.1.2_3.0_1694774027847.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1968_en_5.1.2_3.0_1694774027847.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1968","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1968", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1968| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1968 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1969_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1969_en.md new file mode 100644 index 00000000000000..ca4f8ff89ed8d4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1969_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1969 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1969 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1969` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1969_en_5.1.2_3.0_1694774142310.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1969_en_5.1.2_3.0_1694774142310.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1969","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1969", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1969| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1969 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1970_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1970_en.md new file mode 100644 index 00000000000000..5ab2bc2ce8618c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1970_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1970 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1970 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1970` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1970_en_5.1.2_3.0_1694774244194.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1970_en_5.1.2_3.0_1694774244194.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1970","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1970", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1970| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1970 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1971_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1971_en.md new file mode 100644 index 00000000000000..71a3e6bcf0c16b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1971_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1971 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1971 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1971` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1971_en_5.1.2_3.0_1694774357133.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1971_en_5.1.2_3.0_1694774357133.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1971","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1971", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1971| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1971 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1972_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1972_en.md new file mode 100644 index 00000000000000..5f986da2d19b2a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1972_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1972 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1972 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1972` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1972_en_5.1.2_3.0_1694774463133.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1972_en_5.1.2_3.0_1694774463133.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1972","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1972", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1972| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1972 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1973_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1973_en.md new file mode 100644 index 00000000000000..f14431f32ff89d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1973_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1973 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1973 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1973` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1973_en_5.1.2_3.0_1694774590358.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1973_en_5.1.2_3.0_1694774590358.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1973","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1973", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1973| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1973 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1974_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1974_en.md new file mode 100644 index 00000000000000..95844ea70c7824 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1974_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1974 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1974 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1974` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1974_en_5.1.2_3.0_1694774693818.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1974_en_5.1.2_3.0_1694774693818.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1974","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1974", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1974| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1974 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1975_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1975_en.md new file mode 100644 index 00000000000000..7f5251a5778c40 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1975_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1975 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1975 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1975` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1975_en_5.1.2_3.0_1694774827264.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1975_en_5.1.2_3.0_1694774827264.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1975","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1975", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1975| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1975 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1976_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1976_en.md new file mode 100644 index 00000000000000..68783c132c6dfe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1976_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1976 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1976 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1976` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1976_en_5.1.2_3.0_1694774929781.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1976_en_5.1.2_3.0_1694774929781.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1976","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1976", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1976| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1976 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1977_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1977_en.md new file mode 100644 index 00000000000000..01808de95bdc50 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1977_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1977 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1977 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1977` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1977_en_5.1.2_3.0_1694775021489.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1977_en_5.1.2_3.0_1694775021489.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1977","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1977", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1977| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1977 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1978_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1978_en.md new file mode 100644 index 00000000000000..8c2d1c452b4aae --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1978_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1978 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1978 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1978` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1978_en_5.1.2_3.0_1694775150025.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1978_en_5.1.2_3.0_1694775150025.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1978","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1978", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1978| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1978 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1979_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1979_en.md new file mode 100644 index 00000000000000..5c775e52179fa9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1979_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1979 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1979 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1979` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1979_en_5.1.2_3.0_1694775255167.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1979_en_5.1.2_3.0_1694775255167.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1979","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1979", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1979| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1979 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1980_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1980_en.md new file mode 100644 index 00000000000000..394924fa6c7408 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1980_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1980 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1980 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1980` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1980_en_5.1.2_3.0_1694775377281.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1980_en_5.1.2_3.0_1694775377281.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1980","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1980", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1980| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1980 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1981_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1981_en.md new file mode 100644 index 00000000000000..2fccf80b9b85f3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1981_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1981 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1981 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1981` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1981_en_5.1.2_3.0_1694775483982.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1981_en_5.1.2_3.0_1694775483982.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1981","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1981", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1981| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1981 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1982_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1982_en.md new file mode 100644 index 00000000000000..d27b0f38611287 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1982_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1982 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1982 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1982` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1982_en_5.1.2_3.0_1694775591119.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1982_en_5.1.2_3.0_1694775591119.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1982","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1982", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1982| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.8 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1982 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1983_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1983_en.md new file mode 100644 index 00000000000000..d2641597f55424 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1983_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1983 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1983 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1983` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1983_en_5.1.2_3.0_1694775699603.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1983_en_5.1.2_3.0_1694775699603.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1983","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1983", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1983| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.8 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1983 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1984_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1984_en.md new file mode 100644 index 00000000000000..314e351c6b685d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1984_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1984 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1984 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1984` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1984_en_5.1.2_3.0_1694775808317.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1984_en_5.1.2_3.0_1694775808317.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1984","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1984", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1984| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1984 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1985_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1985_en.md new file mode 100644 index 00000000000000..d99d6f3e6cdd94 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1985_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1985 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1985 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1985` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1985_en_5.1.2_3.0_1694775921031.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1985_en_5.1.2_3.0_1694775921031.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1985","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1985", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1985| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1985 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1986_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1986_en.md new file mode 100644 index 00000000000000..607337889bb71e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1986_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1986 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1986 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1986` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1986_en_5.1.2_3.0_1694776020069.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1986_en_5.1.2_3.0_1694776020069.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1986","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1986", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1986| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1986 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1987_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1987_en.md new file mode 100644 index 00000000000000..dba252f85cddde --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1987_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1987 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1987 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1987` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1987_en_5.1.2_3.0_1694776123514.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1987_en_5.1.2_3.0_1694776123514.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1987","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1987", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1987| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1987 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1988_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1988_en.md new file mode 100644 index 00000000000000..2a33f8a19c4b0e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1988_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1988 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1988 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1988` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1988_en_5.1.2_3.0_1694776243529.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1988_en_5.1.2_3.0_1694776243529.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1988","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1988", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1988| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1988 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1989_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1989_en.md new file mode 100644 index 00000000000000..ffe9c4ab164292 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1989_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1989 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1989 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1989` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1989_en_5.1.2_3.0_1694776373157.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1989_en_5.1.2_3.0_1694776373157.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1989","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1989", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1989| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1989 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1990_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1990_en.md new file mode 100644 index 00000000000000..ea391432fdae25 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1990_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1990 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1990 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1990` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1990_en_5.1.2_3.0_1694776502740.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1990_en_5.1.2_3.0_1694776502740.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1990","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1990", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1990| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1990 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1991_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1991_en.md new file mode 100644 index 00000000000000..a6f9d10d89716b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1991_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1991 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1991 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1991` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1991_en_5.1.2_3.0_1694776614990.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1991_en_5.1.2_3.0_1694776614990.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1991","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1991", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1991| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.7 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1991 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1992_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1992_en.md new file mode 100644 index 00000000000000..591d008d964723 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1992_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1992 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1992 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1992` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1992_en_5.1.2_3.0_1694776717629.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1992_en_5.1.2_3.0_1694776717629.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1992","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1992", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1992| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.8 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1992 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1993_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1993_en.md new file mode 100644 index 00000000000000..393ce0853add0d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1993_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1993 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1993 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1993` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1993_en_5.1.2_3.0_1694776835046.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1993_en_5.1.2_3.0_1694776835046.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1993","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1993", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1993| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.8 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1993 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1994_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1994_en.md new file mode 100644 index 00000000000000..1568d8f62ac121 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1994_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1994 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1994 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1994` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1994_en_5.1.2_3.0_1694776961741.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1994_en_5.1.2_3.0_1694776961741.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1994","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1994", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1994| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.8 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1994 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1995_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1995_en.md new file mode 100644 index 00000000000000..f8405218b93ec2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1995_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1995 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1995 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1995` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1995_en_5.1.2_3.0_1694777092432.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1995_en_5.1.2_3.0_1694777092432.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1995","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1995", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1995| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.9 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1995 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1996_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1996_en.md new file mode 100644 index 00000000000000..8b7b3e69ac754a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1996_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1996 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1996 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1996` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1996_en_5.1.2_3.0_1694777218916.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1996_en_5.1.2_3.0_1694777218916.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1996","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1996", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1996| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.9 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1996 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1997_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1997_en.md new file mode 100644 index 00000000000000..74e5da87a82a1b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1997_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1997 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1997 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1997` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1997_en_5.1.2_3.0_1694777344228.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1997_en_5.1.2_3.0_1694777344228.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1997","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1997", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1997| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.9 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1997 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1998_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1998_en.md new file mode 100644 index 00000000000000..180aa19d38bda3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1998_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1998 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1998 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1998` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1998_en_5.1.2_3.0_1694777463917.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1998_en_5.1.2_3.0_1694777463917.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1998","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1998", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1998| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.0 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1998 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1999_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1999_en.md new file mode 100644 index 00000000000000..a72f9d8336cf72 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_1999_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_1999 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_1999 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_1999` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1999_en_5.1.2_3.0_1694777589682.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_1999_en_5.1.2_3.0_1694777589682.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_1999","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_1999", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_1999| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.0 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_1999 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2000_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2000_en.md new file mode 100644 index 00000000000000..c19707e701a55a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2000_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2000 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2000 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2000` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2000_en_5.1.2_3.0_1694777690320.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2000_en_5.1.2_3.0_1694777690320.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2000","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2000", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2000| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.0 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2000 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2001_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2001_en.md new file mode 100644 index 00000000000000..a5e3f7c2ad4ec8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2001_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2001 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2001 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2001` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2001_en_5.1.2_3.0_1694777805767.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2001_en_5.1.2_3.0_1694777805767.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2001","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2001", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2001| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.1 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2001 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2002_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2002_en.md new file mode 100644 index 00000000000000..413ea42da61b12 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2002_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2002 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2002 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2002` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2002_en_5.1.2_3.0_1694777907120.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2002_en_5.1.2_3.0_1694777907120.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2002","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2002", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2002| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.1 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2002 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2003_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2003_en.md new file mode 100644 index 00000000000000..398c4ecae0fbd1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2003_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2003 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2003 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2003` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2003_en_5.1.2_3.0_1694778027263.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2003_en_5.1.2_3.0_1694778027263.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2003","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2003", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2003| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.1 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2003 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2004_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2004_en.md new file mode 100644 index 00000000000000..e5a2eabc09dae3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2004_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2004 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2004 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2004` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2004_en_5.1.2_3.0_1694778149385.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2004_en_5.1.2_3.0_1694778149385.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2004","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2004", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2004| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.1 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2004 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2005_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2005_en.md new file mode 100644 index 00000000000000..c51cb72b77b2eb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2005_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2005 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2005 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2005` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2005_en_5.1.2_3.0_1694778259096.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2005_en_5.1.2_3.0_1694778259096.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2005","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2005", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2005| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.1 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2005 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2006_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2006_en.md new file mode 100644 index 00000000000000..0fed6891c3d96e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2006_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2006 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2006 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2006` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2006_en_5.1.2_3.0_1694778368628.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2006_en_5.1.2_3.0_1694778368628.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2006","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2006", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2006| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.1 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2006 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2007_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2007_en.md new file mode 100644 index 00000000000000..c95f16af9274d2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2007_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2007 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2007 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2007` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2007_en_5.1.2_3.0_1694778485519.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2007_en_5.1.2_3.0_1694778485519.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2007","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2007", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2007| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.1 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2007 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2008_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2008_en.md new file mode 100644 index 00000000000000..67d6fdd800fa49 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2008_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2008 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2008 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2008` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2008_en_5.1.2_3.0_1694778619433.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2008_en_5.1.2_3.0_1694778619433.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2008","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2008", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2008| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.1 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2008 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2009_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2009_en.md new file mode 100644 index 00000000000000..8010cbeac8f448 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2009_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2009 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2009 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2009` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2009_en_5.1.2_3.0_1694778757644.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2009_en_5.1.2_3.0_1694778757644.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2009","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2009", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2009| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.1 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2009 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2010_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2010_en.md new file mode 100644 index 00000000000000..8cf2ab3b538bcd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2010_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2010 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2010 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2010` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2010_en_5.1.2_3.0_1694778898284.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2010_en_5.1.2_3.0_1694778898284.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2010","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2010", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2010| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.1 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2010 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2011_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2011_en.md new file mode 100644 index 00000000000000..5b1cef748e41a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2011_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2011 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2011 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2011` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2011_en_5.1.2_3.0_1694779011031.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2011_en_5.1.2_3.0_1694779011031.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2011","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2011", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2011| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.1 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2011 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2012_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2012_en.md new file mode 100644 index 00000000000000..a7b5f2e4d2874b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2012_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2012 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2012 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2012` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2012_en_5.1.2_3.0_1694779142430.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2012_en_5.1.2_3.0_1694779142430.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2012","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2012", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2012| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.1 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2012 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2013_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2013_en.md new file mode 100644 index 00000000000000..2e8545fe1d7c3a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2013_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2013 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2013 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2013` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2013_en_5.1.2_3.0_1694779243866.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2013_en_5.1.2_3.0_1694779243866.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2013","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2013", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2013| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.1 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2013 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2014_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2014_en.md new file mode 100644 index 00000000000000..adca6b77377c91 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2014_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2014 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2014 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2014` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2014_en_5.1.2_3.0_1694779355175.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2014_en_5.1.2_3.0_1694779355175.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2014","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2014", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2014| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.1 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2014 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2015_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2015_en.md new file mode 100644 index 00000000000000..9a0790e9d5b221 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2015_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2015 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2015 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2015` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2015_en_5.1.2_3.0_1694779488434.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2015_en_5.1.2_3.0_1694779488434.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2015","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2015", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2015| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.1 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2015 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2016_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2016_en.md new file mode 100644 index 00000000000000..a3f53f8b44e445 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2016_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2016 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2016 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2016` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2016_en_5.1.2_3.0_1694779636190.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2016_en_5.1.2_3.0_1694779636190.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2016","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2016", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2016| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|249.1 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2016 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2018_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2018_en.md new file mode 100644 index 00000000000000..6592a4d95a20f9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2018_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2018 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2018 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2018` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2018_en_5.1.2_3.0_1694785478792.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2018_en_5.1.2_3.0_1694785478792.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2018","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2018", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2018| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.8 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2018 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2019_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2019_en.md new file mode 100644 index 00000000000000..22cc6b73354023 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2019_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2019 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2019 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2019` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2019_en_5.1.2_3.0_1694785606974.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2019_en_5.1.2_3.0_1694785606974.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2019","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2019", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2019| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.8 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2019 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2020_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2020_en.md new file mode 100644 index 00000000000000..65676e6d915678 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2020_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2020 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2020 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2020` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2020_en_5.1.2_3.0_1694785723737.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2020_en_5.1.2_3.0_1694785723737.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2020","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2020", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2020| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.9 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2020 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2021_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2021_en.md new file mode 100644 index 00000000000000..237d768698138d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2021_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2021 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2021 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2021` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2021_en_5.1.2_3.0_1694785831607.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2021_en_5.1.2_3.0_1694785831607.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2021","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2021", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2021| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.9 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2021 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2022_en.md b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2022_en.md new file mode 100644 index 00000000000000..2b3a06b24d23cd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-we4lkd_aml_distilbert_1921_2022_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English we4lkd_aml_distilbert_1921_2022 DistilBertEmbeddings from matheusvolpon +author: John Snow Labs +name: we4lkd_aml_distilbert_1921_2022 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`we4lkd_aml_distilbert_1921_2022` is a English model originally trained by matheusvolpon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2022_en_5.1.2_3.0_1694785944436.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/we4lkd_aml_distilbert_1921_2022_en_5.1.2_3.0_1694785944436.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("we4lkd_aml_distilbert_1921_2022","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("we4lkd_aml_distilbert_1921_2022", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|we4lkd_aml_distilbert_1921_2022| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|248.9 MB| + +## References + +https://huggingface.co/matheusvolpon/WE4LKD_AML_distilbert_1921_2022 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-yolochess_mlm_azure_cloud_35_en.md b/docs/_posts/ahmedlone127/2023-09-15-yolochess_mlm_azure_cloud_35_en.md new file mode 100644 index 00000000000000..602f37319c98d7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-yolochess_mlm_azure_cloud_35_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English yolochess_mlm_azure_cloud_35 DistilBertEmbeddings from jrahn +author: John Snow Labs +name: yolochess_mlm_azure_cloud_35 +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`yolochess_mlm_azure_cloud_35` is a English model originally trained by jrahn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/yolochess_mlm_azure_cloud_35_en_5.1.2_3.0_1694790564963.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/yolochess_mlm_azure_cloud_35_en_5.1.2_3.0_1694790564963.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("yolochess_mlm_azure_cloud_35","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("yolochess_mlm_azure_cloud_35", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|yolochess_mlm_azure_cloud_35| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|244.3 MB| + +## References + +https://huggingface.co/jrahn/yolochess_mlm_azure-cloud-35 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-15-zero_shot_cross_lingual_transfer_demo_masked_en.md b/docs/_posts/ahmedlone127/2023-09-15-zero_shot_cross_lingual_transfer_demo_masked_en.md new file mode 100644 index 00000000000000..b36a9314ab6920 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-15-zero_shot_cross_lingual_transfer_demo_masked_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English zero_shot_cross_lingual_transfer_demo_masked DistilBertEmbeddings from zzzotop +author: John Snow Labs +name: zero_shot_cross_lingual_transfer_demo_masked +date: 2023-09-15 +tags: [distilbert, en, open_source, fill_mask, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: DistilBertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained DistilBertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`zero_shot_cross_lingual_transfer_demo_masked` is a English model originally trained by zzzotop. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/zero_shot_cross_lingual_transfer_demo_masked_en_5.1.2_3.0_1694784022746.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/zero_shot_cross_lingual_transfer_demo_masked_en_5.1.2_3.0_1694784022746.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +embeddings =DistilBertEmbeddings.pretrained("zero_shot_cross_lingual_transfer_demo_masked","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val embeddings = DistilBertEmbeddings + .pretrained("zero_shot_cross_lingual_transfer_demo_masked", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|zero_shot_cross_lingual_transfer_demo_masked| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[embeddings]| +|Language:|en| +|Size:|505.4 MB| + +## References + +https://huggingface.co/zzzotop/zero-shot-cross-lingual-transfer-demo-masked \ No newline at end of file From 6ee008b17efae5ad79faf1bbb5dec862e21a66f1 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Wed, 20 Sep 2023 14:10:04 +0700 Subject: [PATCH 46/55] 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_4_en (#13992) * Add model 2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_1_en * Add model 2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_embedding_3_en * Add model 2023-09-18-m3_experiment_albert_base_v2_rct_sample_eda_2_en * Add model 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2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_synonym_2_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_eda_1_en * Add model 2023-09-18-albert_base_v2_finetuned_filtered_0609_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_add_v2_en * Add model 2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_0_en * Add model 2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_synonym_4_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_2_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_0_en * Add model 2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_synonym_1_en * Add model 2023-09-18-albert_offensive_lm_tapt_finetuned_en * Add model 2023-09-18-albert_small_kor_cross_encoder_v1_ko * Add model 2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_random_1_en * Add model 2023-09-18-m3_experiment_albert_base_v2_rct_sample_eda_3_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_eda_3_en * Add model 2023-09-18-m3_experiment_albert_base_v2_amcd_eda_3_en * Add model 2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_0_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_eda_0_en * Add model 2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_1_en * Add model 2023-09-18-albert_goodnotes_reddit_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_2_en * Add model 2023-09-18-m3_experiment_albert_base_v2_sciie_back_translation_en * Add model 2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_0_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_1_en * Add model 2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_random_0_en * Add model 2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_2_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_add_v4_en * Add model 2023-09-18-m3_experiment_albert_base_v2_amcd_back_translation_en * Add model 2023-09-18-albert_base_vitaminc_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_0_en * Add model 2023-09-18-m3_experiment_albert_base_v2_sciie_eda_2_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_add_v3_greedy_en * Add model 2023-09-18-m3_experiment_albert_base_v2_amcd_add_v4_en * Add model 2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_random_1_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_add_v4_en * Add model 2023-09-18-m3_experiment_albert_base_v2_amcd_vanilla_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_0_en * Add model 2023-09-18-albert_base_v2_mnli_tehrannlp_en * Add model 2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_random_2_en * Add model 2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_2_en * Add model 2023-09-18-albert_base_v2_imdb_textattack_en * Add model 2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_3_en * Add model 2023-09-18-albert_base_v2_wnli_en * Add model 2023-09-18-m3_experiment_albert_base_v2_citation_intent_eda_0_en * Add model 2023-09-18-m3_experiment_albert_base_v2_sciie_eda_0_en * Add model 2023-09-18-m3_experiment_albert_base_v2_citation_intent_eda_4_en * Add model 2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_random_2_en * Add model 2023-09-18-albert_large_v2_hoax_classifier_def_v1_en * Add model 2023-09-18-albert_large_v2_qqp_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_1_en * Add model 2023-09-18-albert_tiny_spanish_fakenews_en * Add model 2023-09-18-m3_experiment_albert_base_v2_rct_sample_add_v5_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_1_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_vanilla_en * Add model 2023-09-18-m3_experiment_albert_base_v2_citation_intent_eda_3_en * Add model 2023-09-18-albert_base_v2_sst_2_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_3_en * Add model 2023-09-18-albert_for_math_arabic_base_ft_en * Add model 2023-09-18-albert_base_v2_mnli_prajjwal1_en * Add model 2023-09-18-m3_experiment_albert_base_v2_chemprot_add_v3_greedy_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_add_v3_greedy_en * Add model 2023-09-18-m3_experiment_albert_base_v2_rct_sample_add_v3_greedy_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_vanilla_en * Add model 2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_embedding_1_en * Add model 2023-09-18-albert_model_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_3_en * Add model 2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_4_en * Add model 2023-09-18-m3_experiment_albert_base_v2_citation_intent_back_translation_en * Add model 2023-09-18-m3_experiment_albert_base_v2_chemprot_eda_1_en * Add model 2023-09-18-m3_experiment_albert_base_v2_chemprot_eda_3_en * Add model 2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_embedding_2_en * Add model 2023-09-18-m3_experiment_albert_base_v2_rct_sample_back_translation_en * Add model 2023-09-18-albert_base_v2_tweet_about_disaster_or_not_en * Add model 2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_0_en * Add model 2023-09-18-albert_base_quora_classifier_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_back_translation_en * Add model 2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_random_2_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_2_en * Add model 2023-09-18-albert_base_vitaminc_wnei_fever_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_add_v4_en * Add model 2023-09-18-albert_base_v2_qqp_en * Add model 2023-09-18-albert_xlarge_vitaminc_fever_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_0_en * Add model 2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_random_4_en * Add model 2023-09-18-albert_base_v2_hoax_classifier_def_v1_en * Add model 2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_0_en * Add model 2023-09-18-albert_large_v2_mnli_en * Add model 2023-09-18-albert_base_v2_finetuned_mrpc_en * Add model 2023-09-18-albert_large_v2_stsb_en * Add model 2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_2_en * Add model 2023-09-18-albert_large_v2_mrpc_en * Add model 2023-09-18-m3_experiment_albert_base_v2_citation_intent_eda_2_en * Add model 2023-09-18-albert_large_v2_qnli_en * Add model 2023-09-18-m3_experiment_albert_base_v2_amcd_add_v5_en * Add model 2023-09-18-albert_base_v2_ag_news_en * Add model 2023-09-18-m3_experiment_albert_base_v2_sciie_add_v2_en * Add model 2023-09-18-albert_base_vitaminc_fever_en * Add model 2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_1_en * Add model 2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_embedding_0_en * Add model 2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_3_en * Add model 2023-09-18-albert_xlarge_vitaminc_mnli_en * Add model 2023-09-18-albert_xlarge_arabic_finetuned_emotion_aetd_en * Add model 2023-09-18-m3_experiment_albert_base_v2_chemprot_eda_2_en * Add model 2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_synonym_4_en * Add model 2023-09-19-albert_persian_farsi_zwnj_base_v2_ner_fa * Add model 2023-09-19-albert_persian_farsi_zwnj_base_v2_ner_fa * Add model 2023-09-19-lodosalberttr_en * Add model 2023-09-19-albert_large_v2_ner_wnut_17_en * Add model 2023-09-19-albert_persian_farsi_base_v2_ner_arman_fa * Add model 2023-09-19-albert_large_v2_ner_conll2003_en * Add model 2023-09-19-albert_persian_farsi_base_v2_ner_peyma_fa * Add model 2023-09-19-albert_base_v2_en * Add model 2023-09-19-albert_large_v2_ner_wikiann_en * Add model 2023-09-19-tiny_albert_en * Add model 2023-09-19-albert_for_question_answering_en * Add model 2023-09-19-albert_xlarge_squad_finetuned_en * Add model 2023-09-19-albert_xxlarge_squad_finetuned_en * Add model 2023-09-19-albert_qa_squad_slp_en * Add model 2023-09-19-albert_base_finetuned_recipeqa_modified_en * Add model 2023-09-19-albert_qa_slp_en * Add model 2023-09-19-albert_v2_base_finetuned_recipeqa_modified_en * Add model 2023-09-19-albert_base_v2_finetuned_squad_attempt_1_en * Add model 2023-09-19-m_albert_qa_model_en * Add model 2023-09-19-albert_large_v2_spoken_squad_en * Add model 2023-09-19-albert_qa_xxlargev1_squad2_512_en * Add model 2023-09-19-albert_qa_xxlarge_v2_squad2_covid_deepset_en * Add model 2023-09-19-albert_qa_xlarge_finetuned_en * Add model 2023-09-19-albert_qa_cs224n_squad2.0_base_v2_en * Add model 2023-09-19-albert_xl_v2_finetuned_squad_en * Add model 2023-09-19-albert_qa_cs224n_squad2.0_large_v2_en * Add model 2023-09-19-albert_xxl_v2_finetuned_squad_en * Add model 2023-09-19-albert_qa_cs224n_squad2.0_xxlarge_v1_en * Add model 2023-09-19-albert_persian_farsi_base_v2_finetuned_squad_en * Add model 2023-09-19-albert_persian_farsi_base_v2_pquad_en * Add model 2023-09-19-albert_qa_xlarge_v2_squad_v2_en * Add model 2023-09-19-albert_persian_farsi_base_v2_persian_qa_en * Add model 2023-09-19-albert_qa_base_v2_squad_en * Add model 2023-09-19-albert_persian_farsi_base_v2_parsquad_en * Add model 2023-09-19-albert_persian_farsi_base_v2_pquad_and_persian_qa_en * Add model 2023-09-19-albert_qa_xxlarge_v2_squad2_en * Add model 2023-09-19-albert_qa_nlpunibo_en * Add model 2023-09-19-albert_qa_xxlarge_v1_finetuned_squad2_en * Add model 2023-09-19-albert_qa_xxlarge_tweetqa_en * Add model 2023-09-19-albert_base_qa_squad2_en * Add model 2023-09-20-albert_qa_biom_xxlarge_squad2_en * Add model 2023-09-20-albert_qa_ai_club_inductions_21_nlp_en * Add model 2023-09-20-albert_qa_qa_1e_en --------- Co-authored-by: ahmedlone127 --- ...18-albert_base_climate_claim_related_en.md | 93 ++++++++++++++++ ...9-18-albert_base_fever_claim_related_en.md | 93 ++++++++++++++++ .../2023-09-18-albert_base_mnli_en.md | 93 ++++++++++++++++ ...3-09-18-albert_base_quora_classifier_en.md | 93 ++++++++++++++++ .../2023-09-18-albert_base_v1_mnli_en.md | 93 ++++++++++++++++ ...09-18-albert_base_v1_prop_16_labeled_en.md | 93 ++++++++++++++++ .../2023-09-18-albert_base_v2_ag_news_en.md | 93 ++++++++++++++++ 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++++++++++++++++ ...09-18-albert_for_math_arabic_base_ft_en.md | 93 ++++++++++++++++ ...r_multilabel_sentence_classification_en.md | 93 ++++++++++++++++ .../2023-09-18-albert_goodnotes_reddit_en.md | 93 ++++++++++++++++ .../2023-09-18-albert_large_v2_cls_sst2_en.md | 93 ++++++++++++++++ .../2023-09-18-albert_large_v2_cls_subj_en.md | 93 ++++++++++++++++ .../2023-09-18-albert_large_v2_cola_en.md | 93 ++++++++++++++++ ...bert_large_v2_hoax_classifier_def_v1_en.md | 93 ++++++++++++++++ .../2023-09-18-albert_large_v2_mnli_en.md | 93 ++++++++++++++++ .../2023-09-18-albert_large_v2_mrpc_en.md | 93 ++++++++++++++++ .../2023-09-18-albert_large_v2_qnli_en.md | 93 ++++++++++++++++ .../2023-09-18-albert_large_v2_qqp_en.md | 93 ++++++++++++++++ .../2023-09-18-albert_large_v2_rte_en.md | 93 ++++++++++++++++ .../2023-09-18-albert_large_v2_sst2_en.md | 93 ++++++++++++++++ .../2023-09-18-albert_large_v2_stsb_en.md | 93 ++++++++++++++++ .../2023-09-18-albert_model_en.md | 93 ++++++++++++++++ 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b/docs/_posts/ahmedlone127/2023-09-18-albert_base_climate_claim_related_en.md new file mode 100644 index 00000000000000..a6212234777de3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_climate_claim_related_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_climate_claim_related AlbertForSequenceClassification from mwong +author: John Snow Labs +name: albert_base_climate_claim_related +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_climate_claim_related` is a English model originally trained by mwong. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_climate_claim_related_en_5.1.2_3.0_1695061577335.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_climate_claim_related_en_5.1.2_3.0_1695061577335.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_climate_claim_related","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_climate_claim_related", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_climate_claim_related| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/mwong/albert-base-climate-claim-related \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_fever_claim_related_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_fever_claim_related_en.md new file mode 100644 index 00000000000000..505171c013ff19 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_fever_claim_related_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_fever_claim_related AlbertForSequenceClassification from mwong +author: John Snow Labs +name: albert_base_fever_claim_related +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_fever_claim_related` is a English model originally trained by mwong. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_fever_claim_related_en_5.1.2_3.0_1695068402270.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_fever_claim_related_en_5.1.2_3.0_1695068402270.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_fever_claim_related","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_fever_claim_related", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_fever_claim_related| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/mwong/albert-base-fever-claim-related \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_mnli_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_mnli_en.md new file mode 100644 index 00000000000000..5278d63f1353da --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_mnli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_mnli AlbertForSequenceClassification from tals +author: John Snow Labs +name: albert_base_mnli +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_mnli` is a English model originally trained by tals. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_mnli_en_5.1.2_3.0_1695066395250.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_mnli_en_5.1.2_3.0_1695066395250.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_mnli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_mnli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_mnli| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/tals/albert-base-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_quora_classifier_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_quora_classifier_en.md new file mode 100644 index 00000000000000..c7bc53ba3d193c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_quora_classifier_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_quora_classifier AlbertForSequenceClassification from pertschuk +author: John Snow Labs +name: albert_base_quora_classifier +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_quora_classifier` is a English model originally trained by pertschuk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_quora_classifier_en_5.1.2_3.0_1695065865157.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_quora_classifier_en_5.1.2_3.0_1695065865157.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_quora_classifier","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_quora_classifier", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_quora_classifier| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/pertschuk/albert-base-quora-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v1_mnli_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v1_mnli_en.md new file mode 100644 index 00000000000000..90eeea4f8c5447 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v1_mnli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v1_mnli AlbertForSequenceClassification from prajjwal1 +author: John Snow Labs +name: albert_base_v1_mnli +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v1_mnli` is a English model originally trained by prajjwal1. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v1_mnli_en_5.1.2_3.0_1695066228309.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v1_mnli_en_5.1.2_3.0_1695066228309.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v1_mnli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v1_mnli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v1_mnli| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/prajjwal1/albert-base-v1-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v1_prop_16_labeled_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v1_prop_16_labeled_en.md new file mode 100644 index 00000000000000..ac660de2078629 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v1_prop_16_labeled_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v1_prop_16_labeled AlbertForSequenceClassification from kellyz +author: John Snow Labs +name: albert_base_v1_prop_16_labeled +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v1_prop_16_labeled` is a English model originally trained by kellyz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v1_prop_16_labeled_en_5.1.2_3.0_1695062747273.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v1_prop_16_labeled_en_5.1.2_3.0_1695062747273.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v1_prop_16_labeled","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v1_prop_16_labeled", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v1_prop_16_labeled| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/kellyz/albert-base-v1-prop-16-labeled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_ag_news_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_ag_news_en.md new file mode 100644 index 00000000000000..a3630af6aa4e34 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_ag_news_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_ag_news AlbertForSequenceClassification from textattack +author: John Snow Labs +name: albert_base_v2_ag_news +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_ag_news` is a English model originally trained by textattack. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_ag_news_en_5.1.2_3.0_1695067426273.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_ag_news_en_5.1.2_3.0_1695067426273.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_ag_news","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_ag_news", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_ag_news| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/textattack/albert-base-v2-ag-news \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_finetuned_filtered_0609_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_finetuned_filtered_0609_en.md new file mode 100644 index 00000000000000..708b2f022eb8dc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_finetuned_filtered_0609_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_finetuned_filtered_0609 AlbertForSequenceClassification from YeRyeongLee +author: John Snow Labs +name: albert_base_v2_finetuned_filtered_0609 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_finetuned_filtered_0609` is a English model originally trained by YeRyeongLee. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_finetuned_filtered_0609_en_5.1.2_3.0_1695061973284.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_finetuned_filtered_0609_en_5.1.2_3.0_1695061973284.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_finetuned_filtered_0609","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_finetuned_filtered_0609", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_finetuned_filtered_0609| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/YeRyeongLee/albert-base-v2-finetuned-filtered-0609 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_finetuned_mrpc_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_finetuned_mrpc_en.md new file mode 100644 index 00000000000000..2364320eee458f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_finetuned_mrpc_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_finetuned_mrpc AlbertForSequenceClassification from VitaliiVrublevskyi +author: John Snow Labs +name: albert_base_v2_finetuned_mrpc +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_finetuned_mrpc` is a English model originally trained by VitaliiVrublevskyi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_finetuned_mrpc_en_5.1.2_3.0_1695069182875.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_finetuned_mrpc_en_5.1.2_3.0_1695069182875.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_finetuned_mrpc","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_finetuned_mrpc", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_finetuned_mrpc| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/VitaliiVrublevskyi/albert-base-v2-finetuned-mrpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_finetuned_tweets_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_finetuned_tweets_en.md new file mode 100644 index 00000000000000..66bd3c8cf19e7b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_finetuned_tweets_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_finetuned_tweets AlbertForSequenceClassification from minimax123 +author: John Snow Labs +name: albert_base_v2_finetuned_tweets +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_finetuned_tweets` is a English model originally trained by minimax123. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_finetuned_tweets_en_5.1.2_3.0_1695063210980.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_finetuned_tweets_en_5.1.2_3.0_1695063210980.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_finetuned_tweets","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_finetuned_tweets", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_finetuned_tweets| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/minimax123/albert-base-v2-finetuned-tweets \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_hoax_classifier_def_v1_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_hoax_classifier_def_v1_en.md new file mode 100644 index 00000000000000..9bc9e5d70541fa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_hoax_classifier_def_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_hoax_classifier_def_v1 AlbertForSequenceClassification from research-dump +author: John Snow Labs +name: albert_base_v2_hoax_classifier_def_v1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_hoax_classifier_def_v1` is a English model originally trained by research-dump. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_hoax_classifier_def_v1_en_5.1.2_3.0_1695068148950.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_hoax_classifier_def_v1_en_5.1.2_3.0_1695068148950.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_hoax_classifier_def_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_hoax_classifier_def_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_hoax_classifier_def_v1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/research-dump/albert-base-v2_hoax_classifier_def_v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_hoax_classifier_fulltext_v1_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_hoax_classifier_fulltext_v1_en.md new file mode 100644 index 00000000000000..ce942473420558 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_hoax_classifier_fulltext_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_hoax_classifier_fulltext_v1 AlbertForSequenceClassification from research-dump +author: John Snow Labs +name: albert_base_v2_hoax_classifier_fulltext_v1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_hoax_classifier_fulltext_v1` is a English model originally trained by research-dump. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_hoax_classifier_fulltext_v1_en_5.1.2_3.0_1695068548526.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_hoax_classifier_fulltext_v1_en_5.1.2_3.0_1695068548526.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_hoax_classifier_fulltext_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_hoax_classifier_fulltext_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_hoax_classifier_fulltext_v1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/research-dump/albert-base-v2_hoax_classifier_fulltext_v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_hoax_classifier_v1_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_hoax_classifier_v1_en.md new file mode 100644 index 00000000000000..74f6a6013e89b1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_hoax_classifier_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_hoax_classifier_v1 AlbertForSequenceClassification from research-dump +author: John Snow Labs +name: albert_base_v2_hoax_classifier_v1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_hoax_classifier_v1` is a English model originally trained by research-dump. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_hoax_classifier_v1_en_5.1.2_3.0_1695067713824.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_hoax_classifier_v1_en_5.1.2_3.0_1695067713824.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_hoax_classifier_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_hoax_classifier_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_hoax_classifier_v1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/research-dump/albert-base-v2_hoax_classifier_v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_imdb_textattack_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_imdb_textattack_en.md new file mode 100644 index 00000000000000..4dd408aec3a812 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_imdb_textattack_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_imdb_textattack AlbertForSequenceClassification from textattack +author: John Snow Labs +name: albert_base_v2_imdb_textattack +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_imdb_textattack` is a English model originally trained by textattack. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_imdb_textattack_en_5.1.2_3.0_1695067513920.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_imdb_textattack_en_5.1.2_3.0_1695067513920.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_imdb_textattack","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_imdb_textattack", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_imdb_textattack| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/textattack/albert-base-v2-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_jomart_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_jomart_en.md new file mode 100644 index 00000000000000..e72ab43d003cfa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_jomart_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_jomart AlbertForSequenceClassification from JoMart +author: John Snow Labs +name: albert_base_v2_jomart +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_jomart` is a English model originally trained by JoMart. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_jomart_en_5.1.2_3.0_1695061760946.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_jomart_en_5.1.2_3.0_1695061760946.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_jomart","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_jomart", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_jomart| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/JoMart/albert-base-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_mbti_classification_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_mbti_classification_en.md new file mode 100644 index 00000000000000..b1bf2342231e4d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_mbti_classification_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_mbti_classification AlbertForSequenceClassification from JanSt +author: John Snow Labs +name: albert_base_v2_mbti_classification +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_mbti_classification` is a English model originally trained by JanSt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_mbti_classification_en_5.1.2_3.0_1695067863364.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_mbti_classification_en_5.1.2_3.0_1695067863364.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_mbti_classification","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_mbti_classification", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_mbti_classification| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/JanSt/albert-base-v2_mbti-classification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_mnli_alireza1044_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_mnli_alireza1044_en.md new file mode 100644 index 00000000000000..8b5a92aa26cf2a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_mnli_alireza1044_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_mnli_alireza1044 AlbertForSequenceClassification from Alireza1044 +author: John Snow Labs +name: albert_base_v2_mnli_alireza1044 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_mnli_alireza1044` is a English model originally trained by Alireza1044. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_mnli_alireza1044_en_5.1.2_3.0_1695061648302.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_mnli_alireza1044_en_5.1.2_3.0_1695061648302.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_mnli_alireza1044","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_mnli_alireza1044", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_mnli_alireza1044| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/Alireza1044/albert-base-v2-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_mnli_prajjwal1_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_mnli_prajjwal1_en.md new file mode 100644 index 00000000000000..f53a0ecc6afe12 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_mnli_prajjwal1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_mnli_prajjwal1 AlbertForSequenceClassification from prajjwal1 +author: John Snow Labs +name: albert_base_v2_mnli_prajjwal1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_mnli_prajjwal1` is a English model originally trained by prajjwal1. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_mnli_prajjwal1_en_5.1.2_3.0_1695066316371.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_mnli_prajjwal1_en_5.1.2_3.0_1695066316371.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_mnli_prajjwal1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_mnli_prajjwal1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_mnli_prajjwal1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/prajjwal1/albert-base-v2-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_mnli_tehrannlp_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_mnli_tehrannlp_en.md new file mode 100644 index 00000000000000..c5fd01aa5c62b4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_mnli_tehrannlp_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_mnli_tehrannlp AlbertForSequenceClassification from TehranNLP +author: John Snow Labs +name: albert_base_v2_mnli_tehrannlp +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_mnli_tehrannlp` is a English model originally trained by TehranNLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_mnli_tehrannlp_en_5.1.2_3.0_1695063412933.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_mnli_tehrannlp_en_5.1.2_3.0_1695063412933.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_mnli_tehrannlp","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_mnli_tehrannlp", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_mnli_tehrannlp| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/TehranNLP/albert-base-v2-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_offenseval2019_downsample_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_offenseval2019_downsample_en.md new file mode 100644 index 00000000000000..e8239559ae34aa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_offenseval2019_downsample_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_offenseval2019_downsample AlbertForSequenceClassification from mohsenfayyaz +author: John Snow Labs +name: albert_base_v2_offenseval2019_downsample +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_offenseval2019_downsample` is a English model originally trained by mohsenfayyaz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_offenseval2019_downsample_en_5.1.2_3.0_1695065586767.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_offenseval2019_downsample_en_5.1.2_3.0_1695065586767.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_offenseval2019_downsample","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_offenseval2019_downsample", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_offenseval2019_downsample| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/mohsenfayyaz/albert-base-v2-offenseval2019-downsample \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_pub_section_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_pub_section_en.md new file mode 100644 index 00000000000000..f49e48c3917cd0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_pub_section_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_pub_section AlbertForSequenceClassification from ml4pubmed +author: John Snow Labs +name: albert_base_v2_pub_section +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_pub_section` is a English model originally trained by ml4pubmed. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_pub_section_en_5.1.2_3.0_1695061672080.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_pub_section_en_5.1.2_3.0_1695061672080.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_pub_section","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_pub_section", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_pub_section| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|25.1 MB| + +## References + +https://huggingface.co/ml4pubmed/albert-base-v2_pub_section \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_qnli_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_qnli_en.md new file mode 100644 index 00000000000000..63d14a145614d4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_qnli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_qnli AlbertForSequenceClassification from Alireza1044 +author: John Snow Labs +name: albert_base_v2_qnli +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_qnli` is a English model originally trained by Alireza1044. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_qnli_en_5.1.2_3.0_1695061776604.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_qnli_en_5.1.2_3.0_1695061776604.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_qnli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_qnli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_qnli| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/Alireza1044/albert-base-v2-qnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_qqp_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_qqp_en.md new file mode 100644 index 00000000000000..7922dd26527313 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_qqp_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_qqp AlbertForSequenceClassification from Alireza1044 +author: John Snow Labs +name: albert_base_v2_qqp +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_qqp` is a English model originally trained by Alireza1044. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_qqp_en_5.1.2_3.0_1695061839213.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_qqp_en_5.1.2_3.0_1695061839213.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_qqp","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_qqp", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_qqp| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/Alireza1044/albert-base-v2-qqp \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_rotten_tomatoes_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_rotten_tomatoes_en.md new file mode 100644 index 00000000000000..73b5e05dfda613 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_rotten_tomatoes_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_rotten_tomatoes AlbertForSequenceClassification from textattack +author: John Snow Labs +name: albert_base_v2_rotten_tomatoes +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_rotten_tomatoes` is a English model originally trained by textattack. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_rotten_tomatoes_en_5.1.2_3.0_1695067594715.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_rotten_tomatoes_en_5.1.2_3.0_1695067594715.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_rotten_tomatoes","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_rotten_tomatoes", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_rotten_tomatoes| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/textattack/albert-base-v2-rotten-tomatoes \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_snli_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_snli_en.md new file mode 100644 index 00000000000000..b81362e9aac630 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_snli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_snli AlbertForSequenceClassification from textattack +author: John Snow Labs +name: albert_base_v2_snli +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_snli` is a English model originally trained by textattack. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_snli_en_5.1.2_3.0_1695067672178.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_snli_en_5.1.2_3.0_1695067672178.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_snli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_snli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_snli| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/textattack/albert-base-v2-snli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_sst_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_sst_2_en.md new file mode 100644 index 00000000000000..a73efaa1d2ff3c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_sst_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_sst_2 AlbertForSequenceClassification from textattack +author: John Snow Labs +name: albert_base_v2_sst_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_sst_2` is a English model originally trained by textattack. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_sst_2_en_5.1.2_3.0_1695067258811.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_sst_2_en_5.1.2_3.0_1695067258811.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_sst_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_sst_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_sst_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/textattack/albert-base-v2-SST-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_sts_b_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_sts_b_en.md new file mode 100644 index 00000000000000..861685872206fd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_sts_b_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_sts_b AlbertForSequenceClassification from textattack +author: John Snow Labs +name: albert_base_v2_sts_b +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_sts_b` is a English model originally trained by textattack. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_sts_b_en_5.1.2_3.0_1695067330382.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_sts_b_en_5.1.2_3.0_1695067330382.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_sts_b","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_sts_b", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_sts_b| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/textattack/albert-base-v2-STS-B \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_toxicity_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_toxicity_en.md new file mode 100644 index 00000000000000..a532f37e60c9d6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_toxicity_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_toxicity AlbertForSequenceClassification from mohsenfayyaz +author: John Snow Labs +name: albert_base_v2_toxicity +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_toxicity` is a English model originally trained by mohsenfayyaz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_toxicity_en_5.1.2_3.0_1695065658829.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_toxicity_en_5.1.2_3.0_1695065658829.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_toxicity","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_toxicity", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_toxicity| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/mohsenfayyaz/albert-base-v2-toxicity \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_tweet_about_disaster_or_not_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_tweet_about_disaster_or_not_en.md new file mode 100644 index 00000000000000..7ce224b039e5b7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_tweet_about_disaster_or_not_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_tweet_about_disaster_or_not AlbertForSequenceClassification from DunnBC22 +author: John Snow Labs +name: albert_base_v2_tweet_about_disaster_or_not +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_tweet_about_disaster_or_not` is a English model originally trained by DunnBC22. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_tweet_about_disaster_or_not_en_5.1.2_3.0_1695067141950.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_tweet_about_disaster_or_not_en_5.1.2_3.0_1695067141950.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_tweet_about_disaster_or_not","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_tweet_about_disaster_or_not", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_tweet_about_disaster_or_not| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/DunnBC22/albert-base-v2-Tweet_About_Disaster_Or_Not \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_wnli_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_wnli_en.md new file mode 100644 index 00000000000000..7ff34fca41aca3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_wnli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_wnli AlbertForSequenceClassification from Alireza1044 +author: John Snow Labs +name: albert_base_v2_wnli +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_wnli` is a English model originally trained by Alireza1044. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_wnli_en_5.1.2_3.0_1695062146368.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_wnli_en_5.1.2_3.0_1695062146368.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_wnli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_wnli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_wnli| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/Alireza1044/albert-base-v2-wnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_yelp_polarity_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_yelp_polarity_en.md new file mode 100644 index 00000000000000..47822a118654d4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_v2_yelp_polarity_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_yelp_polarity AlbertForSequenceClassification from textattack +author: John Snow Labs +name: albert_base_v2_yelp_polarity +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_yelp_polarity` is a English model originally trained by textattack. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_yelp_polarity_en_5.1.2_3.0_1695067750262.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_yelp_polarity_en_5.1.2_3.0_1695067750262.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_v2_yelp_polarity","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_v2_yelp_polarity", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_yelp_polarity| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/textattack/albert-base-v2-yelp-polarity \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_vitaminc_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_vitaminc_en.md new file mode 100644 index 00000000000000..085b308008be09 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_vitaminc_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_vitaminc AlbertForSequenceClassification from tals +author: John Snow Labs +name: albert_base_vitaminc +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_vitaminc` is a English model originally trained by tals. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_vitaminc_en_5.1.2_3.0_1695066643732.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_vitaminc_en_5.1.2_3.0_1695066643732.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_vitaminc","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_vitaminc", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_vitaminc| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/tals/albert-base-vitaminc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_vitaminc_fever_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_vitaminc_fever_en.md new file mode 100644 index 00000000000000..995f314cc2a6ac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_vitaminc_fever_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_vitaminc_fever AlbertForSequenceClassification from tals +author: John Snow Labs +name: albert_base_vitaminc_fever +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_vitaminc_fever` is a English model originally trained by tals. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_vitaminc_fever_en_5.1.2_3.0_1695066483165.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_vitaminc_fever_en_5.1.2_3.0_1695066483165.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_vitaminc_fever","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_vitaminc_fever", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_vitaminc_fever| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/tals/albert-base-vitaminc-fever \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_vitaminc_flagging_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_vitaminc_flagging_en.md new file mode 100644 index 00000000000000..f74aecf4d3aa1c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_vitaminc_flagging_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_vitaminc_flagging AlbertForSequenceClassification from tals +author: John Snow Labs +name: albert_base_vitaminc_flagging +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_vitaminc_flagging` is a English model originally trained by tals. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_vitaminc_flagging_en_5.1.2_3.0_1695066739098.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_vitaminc_flagging_en_5.1.2_3.0_1695066739098.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_vitaminc_flagging","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_vitaminc_flagging", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_vitaminc_flagging| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/tals/albert-base-vitaminc_flagging \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_vitaminc_mnli_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_vitaminc_mnli_en.md new file mode 100644 index 00000000000000..63b6b3099ca3bc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_vitaminc_mnli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_vitaminc_mnli AlbertForSequenceClassification from tals +author: John Snow Labs +name: albert_base_vitaminc_mnli +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_vitaminc_mnli` is a English model originally trained by tals. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_vitaminc_mnli_en_5.1.2_3.0_1695066560291.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_vitaminc_mnli_en_5.1.2_3.0_1695066560291.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_vitaminc_mnli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_vitaminc_mnli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_vitaminc_mnli| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/tals/albert-base-vitaminc-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_base_vitaminc_wnei_fever_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_base_vitaminc_wnei_fever_en.md new file mode 100644 index 00000000000000..eb49e55fe2fd70 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_base_vitaminc_wnei_fever_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_vitaminc_wnei_fever AlbertForSequenceClassification from tals +author: John Snow Labs +name: albert_base_vitaminc_wnei_fever +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_vitaminc_wnei_fever` is a English model originally trained by tals. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_vitaminc_wnei_fever_en_5.1.2_3.0_1695066829743.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_vitaminc_wnei_fever_en_5.1.2_3.0_1695066829743.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_base_vitaminc_wnei_fever","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_base_vitaminc_wnei_fever", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_vitaminc_wnei_fever| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/tals/albert-base-vitaminc_wnei-fever \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_dnd_intents_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_dnd_intents_en.md new file mode 100644 index 00000000000000..ed0c8f541ef5dc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_dnd_intents_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_dnd_intents AlbertForSequenceClassification from neurae +author: John Snow Labs +name: albert_dnd_intents +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_dnd_intents` is a English model originally trained by neurae. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_dnd_intents_en_5.1.2_3.0_1695065350093.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_dnd_intents_en_5.1.2_3.0_1695065350093.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_dnd_intents","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_dnd_intents", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_dnd_intents| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/neurae/albert-dnd-intents \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_for_math_arabic_base_ft_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_for_math_arabic_base_ft_en.md new file mode 100644 index 00000000000000..307744326961ae --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_for_math_arabic_base_ft_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_for_math_arabic_base_ft AlbertForSequenceClassification from AnReu +author: John Snow Labs +name: albert_for_math_arabic_base_ft +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_for_math_arabic_base_ft` is a English model originally trained by AnReu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_for_math_arabic_base_ft_en_5.1.2_3.0_1695068267186.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_for_math_arabic_base_ft_en_5.1.2_3.0_1695068267186.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_for_math_arabic_base_ft","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_for_math_arabic_base_ft", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_for_math_arabic_base_ft| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/AnReu/albert-for-math-ar-base-ft \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_for_multilabel_sentence_classification_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_for_multilabel_sentence_classification_en.md new file mode 100644 index 00000000000000..8bd4402b2f3e3c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_for_multilabel_sentence_classification_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_for_multilabel_sentence_classification AlbertForSequenceClassification from Zamachi +author: John Snow Labs +name: albert_for_multilabel_sentence_classification +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_for_multilabel_sentence_classification` is a English model originally trained by Zamachi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_for_multilabel_sentence_classification_en_5.1.2_3.0_1695062063382.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_for_multilabel_sentence_classification_en_5.1.2_3.0_1695062063382.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_for_multilabel_sentence_classification","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_for_multilabel_sentence_classification", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_for_multilabel_sentence_classification| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/Zamachi/albert-for-multilabel-sentence-classification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_goodnotes_reddit_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_goodnotes_reddit_en.md new file mode 100644 index 00000000000000..a64b93b23e9df6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_goodnotes_reddit_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_goodnotes_reddit AlbertForSequenceClassification from vionwinnie +author: John Snow Labs +name: albert_goodnotes_reddit +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_goodnotes_reddit` is a English model originally trained by vionwinnie. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_goodnotes_reddit_en_5.1.2_3.0_1695067820028.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_goodnotes_reddit_en_5.1.2_3.0_1695067820028.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_goodnotes_reddit","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_goodnotes_reddit", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_goodnotes_reddit| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/vionwinnie/albert-goodnotes-reddit \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_cls_sst2_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_cls_sst2_en.md new file mode 100644 index 00000000000000..cff887daeee16c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_cls_sst2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_large_v2_cls_sst2 AlbertForSequenceClassification from ghatgetanuj +author: John Snow Labs +name: albert_large_v2_cls_sst2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_large_v2_cls_sst2` is a English model originally trained by ghatgetanuj. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_v2_cls_sst2_en_5.1.2_3.0_1695064661881.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_v2_cls_sst2_en_5.1.2_3.0_1695064661881.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_large_v2_cls_sst2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_large_v2_cls_sst2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_v2_cls_sst2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|66.7 MB| + +## References + +https://huggingface.co/ghatgetanuj/albert-large-v2_cls_sst2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_cls_subj_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_cls_subj_en.md new file mode 100644 index 00000000000000..0d1919fddb32dd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_cls_subj_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_large_v2_cls_subj AlbertForSequenceClassification from ghatgetanuj +author: John Snow Labs +name: albert_large_v2_cls_subj +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_large_v2_cls_subj` is a English model originally trained by ghatgetanuj. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_v2_cls_subj_en_5.1.2_3.0_1695064394094.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_v2_cls_subj_en_5.1.2_3.0_1695064394094.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_large_v2_cls_subj","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_large_v2_cls_subj", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_v2_cls_subj| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|66.7 MB| + +## References + +https://huggingface.co/ghatgetanuj/albert-large-v2_cls_subj \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_cola_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_cola_en.md new file mode 100644 index 00000000000000..719b957bc3223e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_cola_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_large_v2_cola AlbertForSequenceClassification from WeightWatcher +author: John Snow Labs +name: albert_large_v2_cola +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_large_v2_cola` is a English model originally trained by WeightWatcher. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_v2_cola_en_5.1.2_3.0_1695066562573.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_v2_cola_en_5.1.2_3.0_1695066562573.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_large_v2_cola","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_large_v2_cola", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_v2_cola| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|66.7 MB| + +## References + +https://huggingface.co/WeightWatcher/albert-large-v2-cola \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_hoax_classifier_def_v1_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_hoax_classifier_def_v1_en.md new file mode 100644 index 00000000000000..3834e314d84f97 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_hoax_classifier_def_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_large_v2_hoax_classifier_def_v1 AlbertForSequenceClassification from research-dump +author: John Snow Labs +name: albert_large_v2_hoax_classifier_def_v1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_large_v2_hoax_classifier_def_v1` is a English model originally trained by research-dump. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_v2_hoax_classifier_def_v1_en_5.1.2_3.0_1695068224689.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_v2_hoax_classifier_def_v1_en_5.1.2_3.0_1695068224689.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_large_v2_hoax_classifier_def_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_large_v2_hoax_classifier_def_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_v2_hoax_classifier_def_v1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|66.7 MB| + +## References + +https://huggingface.co/research-dump/albert-large-v2_hoax_classifier_def_v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_mnli_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_mnli_en.md new file mode 100644 index 00000000000000..02af71cabd98a8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_mnli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_large_v2_mnli AlbertForSequenceClassification from WeightWatcher +author: John Snow Labs +name: albert_large_v2_mnli +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_large_v2_mnli` is a English model originally trained by WeightWatcher. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_v2_mnli_en_5.1.2_3.0_1695066654869.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_v2_mnli_en_5.1.2_3.0_1695066654869.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_large_v2_mnli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_large_v2_mnli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_v2_mnli| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|66.7 MB| + +## References + +https://huggingface.co/WeightWatcher/albert-large-v2-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_mrpc_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_mrpc_en.md new file mode 100644 index 00000000000000..48766658afa8c7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_mrpc_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_large_v2_mrpc AlbertForSequenceClassification from WeightWatcher +author: John Snow Labs +name: albert_large_v2_mrpc +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_large_v2_mrpc` is a English model originally trained by WeightWatcher. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_v2_mrpc_en_5.1.2_3.0_1695066472886.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_v2_mrpc_en_5.1.2_3.0_1695066472886.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_large_v2_mrpc","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_large_v2_mrpc", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_v2_mrpc| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|66.7 MB| + +## References + +https://huggingface.co/WeightWatcher/albert-large-v2-mrpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_qnli_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_qnli_en.md new file mode 100644 index 00000000000000..9e1b7bb4ba4201 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_qnli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_large_v2_qnli AlbertForSequenceClassification from WeightWatcher +author: John Snow Labs +name: albert_large_v2_qnli +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_large_v2_qnli` is a English model originally trained by WeightWatcher. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_v2_qnli_en_5.1.2_3.0_1695066763969.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_v2_qnli_en_5.1.2_3.0_1695066763969.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_large_v2_qnli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_large_v2_qnli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_v2_qnli| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|66.7 MB| + +## References + +https://huggingface.co/WeightWatcher/albert-large-v2-qnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_qqp_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_qqp_en.md new file mode 100644 index 00000000000000..c3f4f62c4aa2e4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_qqp_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_large_v2_qqp AlbertForSequenceClassification from WeightWatcher +author: John Snow Labs +name: albert_large_v2_qqp +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_large_v2_qqp` is a English model originally trained by WeightWatcher. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_v2_qqp_en_5.1.2_3.0_1695066850082.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_v2_qqp_en_5.1.2_3.0_1695066850082.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_large_v2_qqp","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_large_v2_qqp", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_v2_qqp| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|66.7 MB| + +## References + +https://huggingface.co/WeightWatcher/albert-large-v2-qqp \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_rte_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_rte_en.md new file mode 100644 index 00000000000000..0ac44be6017663 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_rte_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_large_v2_rte AlbertForSequenceClassification from WeightWatcher +author: John Snow Labs +name: albert_large_v2_rte +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_large_v2_rte` is a English model originally trained by WeightWatcher. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_v2_rte_en_5.1.2_3.0_1695066936099.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_v2_rte_en_5.1.2_3.0_1695066936099.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_large_v2_rte","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_large_v2_rte", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_v2_rte| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|66.7 MB| + +## References + +https://huggingface.co/WeightWatcher/albert-large-v2-rte \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_sst2_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_sst2_en.md new file mode 100644 index 00000000000000..d8a7921ccca498 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_sst2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_large_v2_sst2 AlbertForSequenceClassification from WeightWatcher +author: John Snow Labs +name: albert_large_v2_sst2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_large_v2_sst2` is a English model originally trained by WeightWatcher. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_v2_sst2_en_5.1.2_3.0_1695067014193.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_v2_sst2_en_5.1.2_3.0_1695067014193.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_large_v2_sst2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_large_v2_sst2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_v2_sst2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|66.7 MB| + +## References + +https://huggingface.co/WeightWatcher/albert-large-v2-sst2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_stsb_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_stsb_en.md new file mode 100644 index 00000000000000..32364433ef5cc6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_large_v2_stsb_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_large_v2_stsb AlbertForSequenceClassification from WeightWatcher +author: John Snow Labs +name: albert_large_v2_stsb +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_large_v2_stsb` is a English model originally trained by WeightWatcher. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_v2_stsb_en_5.1.2_3.0_1695067083135.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_v2_stsb_en_5.1.2_3.0_1695067083135.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_large_v2_stsb","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_large_v2_stsb", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_v2_stsb| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|66.7 MB| + +## References + +https://huggingface.co/WeightWatcher/albert-large-v2-stsb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_model_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_model_en.md new file mode 100644 index 00000000000000..22aed4749bd8a1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_model AlbertForSequenceClassification from fffffly +author: John Snow Labs +name: albert_model +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_model` is a English model originally trained by fffffly. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_model_en_5.1.2_3.0_1695065474100.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_model_en_5.1.2_3.0_1695065474100.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_model","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_model", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_model| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/fffffly/albert_model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_offensive_lm_tapt_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_offensive_lm_tapt_finetuned_en.md new file mode 100644 index 00000000000000..e723a430573b6d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_offensive_lm_tapt_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_offensive_lm_tapt_finetuned AlbertForSequenceClassification from k4black +author: John Snow Labs +name: albert_offensive_lm_tapt_finetuned +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_offensive_lm_tapt_finetuned` is a English model originally trained by k4black. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_offensive_lm_tapt_finetuned_en_5.1.2_3.0_1695064045496.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_offensive_lm_tapt_finetuned_en_5.1.2_3.0_1695064045496.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_offensive_lm_tapt_finetuned","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_offensive_lm_tapt_finetuned", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_offensive_lm_tapt_finetuned| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/k4black/albert-offensive-lm-tapt-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_small_kor_cross_encoder_v1_ko.md b/docs/_posts/ahmedlone127/2023-09-18-albert_small_kor_cross_encoder_v1_ko.md new file mode 100644 index 00000000000000..3fe906680d4eb8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_small_kor_cross_encoder_v1_ko.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Korean albert_small_kor_cross_encoder_v1 AlbertForSequenceClassification from bongsoo +author: John Snow Labs +name: albert_small_kor_cross_encoder_v1 +date: 2023-09-18 +tags: [albert, ko, open_source, sequence_classification, onnx] +task: Text Classification +language: ko +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_small_kor_cross_encoder_v1` is a Korean model originally trained by bongsoo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_small_kor_cross_encoder_v1_ko_5.1.2_3.0_1695065204321.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_small_kor_cross_encoder_v1_ko_5.1.2_3.0_1695065204321.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_small_kor_cross_encoder_v1","ko") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_small_kor_cross_encoder_v1", "ko") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_small_kor_cross_encoder_v1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|ko| +|Size:|44.0 MB| + +## References + +https://huggingface.co/bongsoo/albert-small-kor-cross-encoder-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_tiny_spanish_fakenews_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_tiny_spanish_fakenews_en.md new file mode 100644 index 00000000000000..6a00a693140698 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_tiny_spanish_fakenews_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_tiny_spanish_fakenews AlbertForSequenceClassification from natsanchezc +author: John Snow Labs +name: albert_tiny_spanish_fakenews +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_tiny_spanish_fakenews` is a English model originally trained by natsanchezc. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_tiny_spanish_fakenews_en_5.1.2_3.0_1695066230540.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_tiny_spanish_fakenews_en_5.1.2_3.0_1695066230540.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_tiny_spanish_fakenews","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_tiny_spanish_fakenews", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_tiny_spanish_fakenews| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|20.4 MB| + +## References + +https://huggingface.co/natsanchezc/albert-tiny-spanish-fakenews \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_xlarge_arabic_finetuned_emotion_aetd_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_xlarge_arabic_finetuned_emotion_aetd_en.md new file mode 100644 index 00000000000000..dbf7d97a6a1387 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_xlarge_arabic_finetuned_emotion_aetd_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_xlarge_arabic_finetuned_emotion_aetd AlbertForSequenceClassification from MahaJar +author: John Snow Labs +name: albert_xlarge_arabic_finetuned_emotion_aetd +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_xlarge_arabic_finetuned_emotion_aetd` is a English model originally trained by MahaJar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_xlarge_arabic_finetuned_emotion_aetd_en_5.1.2_3.0_1695066084097.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_xlarge_arabic_finetuned_emotion_aetd_en_5.1.2_3.0_1695066084097.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_xlarge_arabic_finetuned_emotion_aetd","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_xlarge_arabic_finetuned_emotion_aetd", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_xlarge_arabic_finetuned_emotion_aetd| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|220.4 MB| + +## References + +https://huggingface.co/MahaJar/albert-xlarge-arabic-finetuned-emotion_AETD \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_xlarge_vitaminc_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_xlarge_vitaminc_en.md new file mode 100644 index 00000000000000..f3511431daa5a1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_xlarge_vitaminc_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_xlarge_vitaminc AlbertForSequenceClassification from tals +author: John Snow Labs +name: albert_xlarge_vitaminc +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_xlarge_vitaminc` is a English model originally trained by tals. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_xlarge_vitaminc_en_5.1.2_3.0_1695067188597.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_xlarge_vitaminc_en_5.1.2_3.0_1695067188597.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_xlarge_vitaminc","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_xlarge_vitaminc", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_xlarge_vitaminc| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|220.5 MB| + +## References + +https://huggingface.co/tals/albert-xlarge-vitaminc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_xlarge_vitaminc_fever_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_xlarge_vitaminc_fever_en.md new file mode 100644 index 00000000000000..1a6d168e2cf9ba --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_xlarge_vitaminc_fever_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_xlarge_vitaminc_fever AlbertForSequenceClassification from tals +author: John Snow Labs +name: albert_xlarge_vitaminc_fever +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_xlarge_vitaminc_fever` is a English model originally trained by tals. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_xlarge_vitaminc_fever_en_5.1.2_3.0_1695066951128.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_xlarge_vitaminc_fever_en_5.1.2_3.0_1695066951128.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_xlarge_vitaminc_fever","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_xlarge_vitaminc_fever", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_xlarge_vitaminc_fever| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|220.5 MB| + +## References + +https://huggingface.co/tals/albert-xlarge-vitaminc-fever \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_xlarge_vitaminc_mnli_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_xlarge_vitaminc_mnli_en.md new file mode 100644 index 00000000000000..79ebdfa39f2e7e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_xlarge_vitaminc_mnli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_xlarge_vitaminc_mnli AlbertForSequenceClassification from tals +author: John Snow Labs +name: albert_xlarge_vitaminc_mnli +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_xlarge_vitaminc_mnli` is a English model originally trained by tals. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_xlarge_vitaminc_mnli_en_5.1.2_3.0_1695067066498.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_xlarge_vitaminc_mnli_en_5.1.2_3.0_1695067066498.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_xlarge_vitaminc_mnli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_xlarge_vitaminc_mnli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_xlarge_vitaminc_mnli| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|220.5 MB| + +## References + +https://huggingface.co/tals/albert-xlarge-vitaminc-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_xxlarge_v2_hoax_classifier_def_v1_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_xxlarge_v2_hoax_classifier_def_v1_en.md new file mode 100644 index 00000000000000..acf8f72aa47050 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_xxlarge_v2_hoax_classifier_def_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_xxlarge_v2_hoax_classifier_def_v1 AlbertForSequenceClassification from research-dump +author: John Snow Labs +name: albert_xxlarge_v2_hoax_classifier_def_v1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_xxlarge_v2_hoax_classifier_def_v1` is a English model originally trained by research-dump. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_xxlarge_v2_hoax_classifier_def_v1_en_5.1.2_3.0_1695068451848.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_xxlarge_v2_hoax_classifier_def_v1_en_5.1.2_3.0_1695068451848.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_xxlarge_v2_hoax_classifier_def_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_xxlarge_v2_hoax_classifier_def_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_xxlarge_v2_hoax_classifier_def_v1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|833.9 MB| + +## References + +https://huggingface.co/research-dump/albert-xxlarge-v2_hoax_classifier_def_v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_xxlarge_v2_hoax_classifier_v1_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_xxlarge_v2_hoax_classifier_v1_en.md new file mode 100644 index 00000000000000..9fd569fcb93dd9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_xxlarge_v2_hoax_classifier_v1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_xxlarge_v2_hoax_classifier_v1 AlbertForSequenceClassification from research-dump +author: John Snow Labs +name: albert_xxlarge_v2_hoax_classifier_v1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_xxlarge_v2_hoax_classifier_v1` is a English model originally trained by research-dump. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_xxlarge_v2_hoax_classifier_v1_en_5.1.2_3.0_1695067937484.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_xxlarge_v2_hoax_classifier_v1_en_5.1.2_3.0_1695067937484.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_xxlarge_v2_hoax_classifier_v1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_xxlarge_v2_hoax_classifier_v1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_xxlarge_v2_hoax_classifier_v1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|833.9 MB| + +## References + +https://huggingface.co/research-dump/albert-xxlarge-v2_hoax_classifier_v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-albert_xxlarge_v2_snli_mnli_fever_anli_r1_r2_r3_nli_en.md b/docs/_posts/ahmedlone127/2023-09-18-albert_xxlarge_v2_snli_mnli_fever_anli_r1_r2_r3_nli_en.md new file mode 100644 index 00000000000000..0182c7a8413685 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-albert_xxlarge_v2_snli_mnli_fever_anli_r1_r2_r3_nli_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_xxlarge_v2_snli_mnli_fever_anli_r1_r2_r3_nli AlbertForSequenceClassification from ynie +author: John Snow Labs +name: albert_xxlarge_v2_snli_mnli_fever_anli_r1_r2_r3_nli +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_xxlarge_v2_snli_mnli_fever_anli_r1_r2_r3_nli` is a English model originally trained by ynie. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_xxlarge_v2_snli_mnli_fever_anli_r1_r2_r3_nli_en_5.1.2_3.0_1695068066328.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_xxlarge_v2_snli_mnli_fever_anli_r1_r2_r3_nli_en_5.1.2_3.0_1695068066328.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("albert_xxlarge_v2_snli_mnli_fever_anli_r1_r2_r3_nli","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("albert_xxlarge_v2_snli_mnli_fever_anli_r1_r2_r3_nli", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_xxlarge_v2_snli_mnli_fever_anli_r1_r2_r3_nli| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|833.9 MB| + +## References + +https://huggingface.co/ynie/albert-xxlarge-v2-snli_mnli_fever_anli_R1_R2_R3-nli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-alberta_fact_checking_en.md b/docs/_posts/ahmedlone127/2023-09-18-alberta_fact_checking_en.md new file mode 100644 index 00000000000000..f248e79332dfee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-alberta_fact_checking_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English alberta_fact_checking AlbertForSequenceClassification from Dzeniks +author: John Snow Labs +name: alberta_fact_checking +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`alberta_fact_checking` is a English model originally trained by Dzeniks. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/alberta_fact_checking_en_5.1.2_3.0_1695065294318.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/alberta_fact_checking_en_5.1.2_3.0_1695065294318.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("alberta_fact_checking","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("alberta_fact_checking", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|alberta_fact_checking| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/Dzeniks/alberta_fact_checking \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-autotrain_app_review_train_albert_1314550196_en.md b/docs/_posts/ahmedlone127/2023-09-18-autotrain_app_review_train_albert_1314550196_en.md new file mode 100644 index 00000000000000..329f39cd4ac8ef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-autotrain_app_review_train_albert_1314550196_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English autotrain_app_review_train_albert_1314550196 AlbertForSequenceClassification from noob123 +author: John Snow Labs +name: autotrain_app_review_train_albert_1314550196 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`autotrain_app_review_train_albert_1314550196` is a English model originally trained by noob123. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/autotrain_app_review_train_albert_1314550196_en_5.1.2_3.0_1695063882846.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/autotrain_app_review_train_albert_1314550196_en_5.1.2_3.0_1695063882846.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("autotrain_app_review_train_albert_1314550196","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("autotrain_app_review_train_albert_1314550196", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|autotrain_app_review_train_albert_1314550196| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/noob123/autotrain-app_review_train_albert-1314550196 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-autotrain_security_text_classification_albert_688320769_en.md b/docs/_posts/ahmedlone127/2023-09-18-autotrain_security_text_classification_albert_688320769_en.md new file mode 100644 index 00000000000000..2615a6b3544b24 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-autotrain_security_text_classification_albert_688320769_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English autotrain_security_text_classification_albert_688320769 AlbertForSequenceClassification from vlsb +author: John Snow Labs +name: autotrain_security_text_classification_albert_688320769 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`autotrain_security_text_classification_albert_688320769` is a English model originally trained by vlsb. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/autotrain_security_text_classification_albert_688320769_en_5.1.2_3.0_1695068193893.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/autotrain_security_text_classification_albert_688320769_en_5.1.2_3.0_1695068193893.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("autotrain_security_text_classification_albert_688320769","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("autotrain_security_text_classification_albert_688320769", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|autotrain_security_text_classification_albert_688320769| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/vlsb/autotrain-security-text-classification-albert-688320769 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-edos_2023_baseline_albert_base_v2_label_vector_en.md b/docs/_posts/ahmedlone127/2023-09-18-edos_2023_baseline_albert_base_v2_label_vector_en.md new file mode 100644 index 00000000000000..5fe6d2003705ef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-edos_2023_baseline_albert_base_v2_label_vector_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English edos_2023_baseline_albert_base_v2_label_vector AlbertForSequenceClassification from lct-rug-2022 +author: John Snow Labs +name: edos_2023_baseline_albert_base_v2_label_vector +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`edos_2023_baseline_albert_base_v2_label_vector` is a English model originally trained by lct-rug-2022. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/edos_2023_baseline_albert_base_v2_label_vector_en_5.1.2_3.0_1695064267131.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/edos_2023_baseline_albert_base_v2_label_vector_en_5.1.2_3.0_1695064267131.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("edos_2023_baseline_albert_base_v2_label_vector","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("edos_2023_baseline_albert_base_v2_label_vector", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|edos_2023_baseline_albert_base_v2_label_vector| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|28.4 MB| + +## References + +https://huggingface.co/lct-rug-2022/edos-2023-baseline-albert-base-v2-label_vector \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-finetuned_albert_base_v2_en.md b/docs/_posts/ahmedlone127/2023-09-18-finetuned_albert_base_v2_en.md new file mode 100644 index 00000000000000..1bcbb2abc9afb2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-finetuned_albert_base_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English finetuned_albert_base_v2 AlbertForSequenceClassification from Queensly +author: John Snow Labs +name: finetuned_albert_base_v2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finetuned_albert_base_v2` is a English model originally trained by Queensly. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finetuned_albert_base_v2_en_5.1.2_3.0_1695063297167.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finetuned_albert_base_v2_en_5.1.2_3.0_1695063297167.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("finetuned_albert_base_v2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("finetuned_albert_base_v2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finetuned_albert_base_v2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/Queensly/finetuned_albert_base_v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_add_v2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_add_v2_en.md new file mode 100644 index 00000000000000..5f51bcbdc270ad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_add_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_add_v2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_add_v2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_add_v2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_add_v2_en_5.1.2_3.0_1695062951241.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_add_v2_en_5.1.2_3.0_1695062951241.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_add_v2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_add_v2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_add_v2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-add-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_add_v3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_add_v3_en.md new file mode 100644 index 00000000000000..c7fb3a0de3c6b3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_add_v3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_add_v3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_add_v3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_add_v3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_add_v3_en_5.1.2_3.0_1695063387122.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_add_v3_en_5.1.2_3.0_1695063387122.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_add_v3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_add_v3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_add_v3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-add-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_add_v3_greedy_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_add_v3_greedy_en.md new file mode 100644 index 00000000000000..1df16c5be025b1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_add_v3_greedy_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_add_v3_greedy AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_add_v3_greedy +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_add_v3_greedy` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_add_v3_greedy_en_5.1.2_3.0_1695066398520.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_add_v3_greedy_en_5.1.2_3.0_1695066398520.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_add_v3_greedy","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_add_v3_greedy", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_add_v3_greedy| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-add-v3-greedy \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_add_v4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_add_v4_en.md new file mode 100644 index 00000000000000..4502399a39199e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_add_v4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_add_v4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_add_v4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_add_v4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_add_v4_en_5.1.2_3.0_1695063060870.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_add_v4_en_5.1.2_3.0_1695063060870.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_add_v4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_add_v4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_add_v4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-add-v4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_add_v5_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_add_v5_en.md new file mode 100644 index 00000000000000..d6fbba35b24085 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_add_v5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_add_v5 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_add_v5 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_add_v5` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_add_v5_en_5.1.2_3.0_1695063482602.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_add_v5_en_5.1.2_3.0_1695063482602.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_add_v5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_add_v5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_add_v5| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-add-v5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_back_translation_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_back_translation_en.md new file mode 100644 index 00000000000000..2beca52fe818eb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_back_translation_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_back_translation AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_back_translation +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_back_translation` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_back_translation_en_5.1.2_3.0_1695064863291.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_back_translation_en_5.1.2_3.0_1695064863291.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_back_translation","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_back_translation", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_back_translation| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-back-translation \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_eda_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_eda_0_en.md new file mode 100644 index 00000000000000..17a38c895ba624 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_eda_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_eda_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_eda_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_eda_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_eda_0_en_5.1.2_3.0_1695064112411.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_eda_0_en_5.1.2_3.0_1695064112411.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_eda_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_eda_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_eda_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-eda-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_eda_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_eda_1_en.md new file mode 100644 index 00000000000000..587e5a67c6dd10 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_eda_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_eda_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_eda_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_eda_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_eda_1_en_5.1.2_3.0_1695064189599.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_eda_1_en_5.1.2_3.0_1695064189599.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_eda_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_eda_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_eda_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-eda-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_eda_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_eda_2_en.md new file mode 100644 index 00000000000000..747b83ac4611d2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_eda_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_eda_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_eda_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_eda_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_eda_2_en_5.1.2_3.0_1695064273251.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_eda_2_en_5.1.2_3.0_1695064273251.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_eda_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_eda_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_eda_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-eda-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_eda_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_eda_3_en.md new file mode 100644 index 00000000000000..0cec80db71bc85 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_eda_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_eda_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_eda_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_eda_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_eda_3_en_5.1.2_3.0_1695064359403.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_eda_3_en_5.1.2_3.0_1695064359403.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_eda_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_eda_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_eda_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-eda-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_eda_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_eda_4_en.md new file mode 100644 index 00000000000000..1740a9aedc3641 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_eda_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_eda_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_eda_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_eda_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_eda_4_en_5.1.2_3.0_1695064430928.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_eda_4_en_5.1.2_3.0_1695064430928.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_eda_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_eda_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_eda_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-eda-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_vanilla_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_vanilla_en.md new file mode 100644 index 00000000000000..bb5c45bc9b13f0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_vanilla_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_vanilla AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_vanilla +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_vanilla` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_vanilla_en_5.1.2_3.0_1695067142580.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_vanilla_en_5.1.2_3.0_1695067142580.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_vanilla","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_vanilla", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_vanilla| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-vanilla \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_embedding_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_embedding_0_en.md new file mode 100644 index 00000000000000..833ae6622534b1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_embedding_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_word_swapping_embedding_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_word_swapping_embedding_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_word_swapping_embedding_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_embedding_0_en_5.1.2_3.0_1695063121298.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_embedding_0_en_5.1.2_3.0_1695063121298.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_embedding_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_embedding_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_word_swapping_embedding_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-word-swapping-embedding-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_embedding_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_embedding_1_en.md new file mode 100644 index 00000000000000..7af2303453ef59 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_embedding_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_word_swapping_embedding_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_word_swapping_embedding_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_word_swapping_embedding_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_embedding_1_en_5.1.2_3.0_1695063314340.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_embedding_1_en_5.1.2_3.0_1695063314340.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_embedding_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_embedding_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_word_swapping_embedding_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-word-swapping-embedding-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_embedding_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_embedding_2_en.md new file mode 100644 index 00000000000000..1e7f4dd1d48155 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_embedding_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_word_swapping_embedding_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_word_swapping_embedding_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_word_swapping_embedding_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_embedding_2_en_5.1.2_3.0_1695063475776.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_embedding_2_en_5.1.2_3.0_1695063475776.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_embedding_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_embedding_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_word_swapping_embedding_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-word-swapping-embedding-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_embedding_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_embedding_3_en.md new file mode 100644 index 00000000000000..69d503027fed20 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_embedding_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_word_swapping_embedding_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_word_swapping_embedding_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_word_swapping_embedding_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_embedding_3_en_5.1.2_3.0_1695063663201.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_embedding_3_en_5.1.2_3.0_1695063663201.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_embedding_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_embedding_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_word_swapping_embedding_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-word-swapping-embedding-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_embedding_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_embedding_4_en.md new file mode 100644 index 00000000000000..a55f8468d1ff12 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_embedding_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_word_swapping_embedding_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_word_swapping_embedding_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_word_swapping_embedding_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_embedding_4_en_5.1.2_3.0_1695063826582.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_embedding_4_en_5.1.2_3.0_1695063826582.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_embedding_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_embedding_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_word_swapping_embedding_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-word-swapping-embedding-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_random_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_random_0_en.md new file mode 100644 index 00000000000000..2d182d51f3eeb5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_random_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_word_swapping_random_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_word_swapping_random_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_word_swapping_random_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_random_0_en_5.1.2_3.0_1695065909533.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_random_0_en_5.1.2_3.0_1695065909533.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_random_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_random_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_word_swapping_random_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-word-swapping-random-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_random_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_random_1_en.md new file mode 100644 index 00000000000000..1f5141396ebd0f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_random_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_word_swapping_random_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_word_swapping_random_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_word_swapping_random_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_random_1_en_5.1.2_3.0_1695066252422.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_random_1_en_5.1.2_3.0_1695066252422.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_random_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_random_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_word_swapping_random_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-word-swapping-random-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_random_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_random_2_en.md new file mode 100644 index 00000000000000..2382f8e817eb35 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_random_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_word_swapping_random_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_word_swapping_random_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_word_swapping_random_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_random_2_en_5.1.2_3.0_1695066408329.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_random_2_en_5.1.2_3.0_1695066408329.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_random_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_random_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_word_swapping_random_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-word-swapping-random-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_random_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_random_3_en.md new file mode 100644 index 00000000000000..4bfb0d9538f2a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_random_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_word_swapping_random_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_word_swapping_random_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_word_swapping_random_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_random_3_en_5.1.2_3.0_1695066574474.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_random_3_en_5.1.2_3.0_1695066574474.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_random_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_random_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_word_swapping_random_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-word-swapping-random-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_random_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_random_4_en.md new file mode 100644 index 00000000000000..21d501e4d559d1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_random_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_word_swapping_random_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_word_swapping_random_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_word_swapping_random_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_random_4_en_5.1.2_3.0_1695062699768.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_random_4_en_5.1.2_3.0_1695062699768.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_random_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_random_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_word_swapping_random_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-word-swapping-random-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_synonym_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_synonym_0_en.md new file mode 100644 index 00000000000000..76b59faded6777 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_synonym_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_word_swapping_synonym_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_word_swapping_synonym_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_word_swapping_synonym_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_synonym_0_en_5.1.2_3.0_1695065844297.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_synonym_0_en_5.1.2_3.0_1695065844297.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_synonym_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_synonym_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_word_swapping_synonym_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-word-swapping-synonym-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_synonym_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_synonym_1_en.md new file mode 100644 index 00000000000000..84146381888d65 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_synonym_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_word_swapping_synonym_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_word_swapping_synonym_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_word_swapping_synonym_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_synonym_1_en_5.1.2_3.0_1695066182334.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_synonym_1_en_5.1.2_3.0_1695066182334.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_synonym_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_synonym_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_word_swapping_synonym_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-word-swapping-synonym-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_synonym_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_synonym_2_en.md new file mode 100644 index 00000000000000..53666c6ca26306 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_synonym_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_word_swapping_synonym_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_word_swapping_synonym_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_word_swapping_synonym_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_synonym_2_en_5.1.2_3.0_1695066326686.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_synonym_2_en_5.1.2_3.0_1695066326686.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_synonym_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_synonym_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_word_swapping_synonym_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-word-swapping-synonym-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_synonym_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_synonym_3_en.md new file mode 100644 index 00000000000000..d90d74586106ce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_synonym_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_word_swapping_synonym_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_word_swapping_synonym_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_word_swapping_synonym_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_synonym_3_en_5.1.2_3.0_1695066489127.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_synonym_3_en_5.1.2_3.0_1695066489127.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_synonym_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_synonym_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_word_swapping_synonym_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-word-swapping-synonym-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_synonym_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_synonym_4_en.md new file mode 100644 index 00000000000000..fdee1129e28b7f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_amcd_word_swapping_synonym_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_amcd_word_swapping_synonym_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_amcd_word_swapping_synonym_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_amcd_word_swapping_synonym_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_synonym_4_en_5.1.2_3.0_1695062991788.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_amcd_word_swapping_synonym_4_en_5.1.2_3.0_1695062991788.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_synonym_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_amcd_word_swapping_synonym_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_amcd_word_swapping_synonym_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-amcd-word-swapping-synonym-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_add_v2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_add_v2_en.md new file mode 100644 index 00000000000000..11a08dbeb2d352 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_add_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_add_v2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_add_v2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_add_v2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_add_v2_en_5.1.2_3.0_1695063091310.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_add_v2_en_5.1.2_3.0_1695063091310.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_add_v2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_add_v2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_add_v2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-add-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_add_v3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_add_v3_en.md new file mode 100644 index 00000000000000..3f834190e0ad85 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_add_v3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_add_v3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_add_v3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_add_v3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_add_v3_en_5.1.2_3.0_1695063560807.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_add_v3_en_5.1.2_3.0_1695063560807.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_add_v3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_add_v3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_add_v3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-add-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_add_v3_greedy_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_add_v3_greedy_en.md new file mode 100644 index 00000000000000..4d58b42f3024b8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_add_v3_greedy_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_add_v3_greedy AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_add_v3_greedy +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_add_v3_greedy` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_add_v3_greedy_en_5.1.2_3.0_1695066486815.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_add_v3_greedy_en_5.1.2_3.0_1695066486815.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_add_v3_greedy","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_add_v3_greedy", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_add_v3_greedy| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-add-v3-greedy \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_back_translation_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_back_translation_en.md new file mode 100644 index 00000000000000..97df6d059169b7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_back_translation_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_back_translation AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_back_translation +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_back_translation` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_back_translation_en_5.1.2_3.0_1695064948971.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_back_translation_en_5.1.2_3.0_1695064948971.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_back_translation","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_back_translation", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_back_translation| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-back-translation \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_eda_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_eda_0_en.md new file mode 100644 index 00000000000000..91ace9c97b9bbb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_eda_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_eda_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_eda_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_eda_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_eda_0_en_5.1.2_3.0_1695068902480.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_eda_0_en_5.1.2_3.0_1695068902480.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_eda_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_eda_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_eda_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-eda-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_eda_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_eda_1_en.md new file mode 100644 index 00000000000000..1755cda356aead --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_eda_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_eda_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_eda_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_eda_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_eda_1_en_5.1.2_3.0_1695069027405.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_eda_1_en_5.1.2_3.0_1695069027405.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_eda_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_eda_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_eda_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-eda-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_eda_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_eda_2_en.md new file mode 100644 index 00000000000000..3ec9a538dc6161 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_eda_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_eda_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_eda_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_eda_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_eda_2_en_5.1.2_3.0_1695069246806.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_eda_2_en_5.1.2_3.0_1695069246806.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_eda_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_eda_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_eda_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-eda-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_eda_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_eda_3_en.md new file mode 100644 index 00000000000000..21122daf0d4be6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_eda_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_eda_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_eda_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_eda_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_eda_3_en_5.1.2_3.0_1695069360213.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_eda_3_en_5.1.2_3.0_1695069360213.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_eda_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_eda_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_eda_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-eda-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_eda_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_eda_4_en.md new file mode 100644 index 00000000000000..b06c6214d1afa0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_eda_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_eda_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_eda_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_eda_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_eda_4_en_5.1.2_3.0_1695069414782.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_eda_4_en_5.1.2_3.0_1695069414782.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_eda_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_eda_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_eda_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-eda-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_vanilla_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_vanilla_en.md new file mode 100644 index 00000000000000..76319a7f984c60 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_vanilla_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_vanilla AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_vanilla +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_vanilla` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_vanilla_en_5.1.2_3.0_1695067318954.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_vanilla_en_5.1.2_3.0_1695067318954.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_vanilla","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_vanilla", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_vanilla| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-vanilla \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_0_en.md new file mode 100644 index 00000000000000..7ef60552100b65 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_0_en_5.1.2_3.0_1695063897321.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_0_en_5.1.2_3.0_1695063897321.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-word-swapping-embedding-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_1_en.md new file mode 100644 index 00000000000000..6fc67cbad9356f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_1_en_5.1.2_3.0_1695064057062.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_1_en_5.1.2_3.0_1695064057062.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-word-swapping-embedding-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_2_en.md new file mode 100644 index 00000000000000..cf2f6d37fa29cd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_2_en_5.1.2_3.0_1695064200809.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_2_en_5.1.2_3.0_1695064200809.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-word-swapping-embedding-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_3_en.md new file mode 100644 index 00000000000000..fd562b86add36f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_3_en_5.1.2_3.0_1695064370410.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_3_en_5.1.2_3.0_1695064370410.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-word-swapping-embedding-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_4_en.md new file mode 100644 index 00000000000000..a798480ff00953 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_4_en_5.1.2_3.0_1695064462661.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_4_en_5.1.2_3.0_1695064462661.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_word_swapping_embedding_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-word-swapping-embedding-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_random_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_random_0_en.md new file mode 100644 index 00000000000000..7cec0ef9e29d0b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_random_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_word_swapping_random_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_word_swapping_random_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_word_swapping_random_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_random_0_en_5.1.2_3.0_1695066662585.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_random_0_en_5.1.2_3.0_1695066662585.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_random_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_random_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_word_swapping_random_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-word-swapping-random-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_random_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_random_3_en.md new file mode 100644 index 00000000000000..76c3ec735254ed --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_random_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_word_swapping_random_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_word_swapping_random_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_word_swapping_random_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_random_3_en_5.1.2_3.0_1695067140590.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_random_3_en_5.1.2_3.0_1695067140590.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_random_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_random_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_word_swapping_random_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-word-swapping-random-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_random_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_random_4_en.md new file mode 100644 index 00000000000000..598010d1e2a3f7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_random_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_word_swapping_random_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_word_swapping_random_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_word_swapping_random_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_random_4_en_5.1.2_3.0_1695067322469.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_random_4_en_5.1.2_3.0_1695067322469.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_random_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_random_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_word_swapping_random_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-word-swapping-random-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_0_en.md new file mode 100644 index 00000000000000..fb3b62b1c961e1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_0_en_5.1.2_3.0_1695066756459.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_0_en_5.1.2_3.0_1695066756459.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-word-swapping-synonym-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_1_en.md new file mode 100644 index 00000000000000..5bdb0cc293c0f7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_1_en_5.1.2_3.0_1695066976098.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_1_en_5.1.2_3.0_1695066976098.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-word-swapping-synonym-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_2_en.md new file mode 100644 index 00000000000000..b98dce2520d857 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_2_en_5.1.2_3.0_1695067237324.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_2_en_5.1.2_3.0_1695067237324.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-word-swapping-synonym-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_3_en.md new file mode 100644 index 00000000000000..b685fad9a04cf7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_3_en_5.1.2_3.0_1695067412841.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_3_en_5.1.2_3.0_1695067412841.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-word-swapping-synonym-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_4_en.md new file mode 100644 index 00000000000000..6e35cf45bcdd8c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_4_en_5.1.2_3.0_1695067744764.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_4_en_5.1.2_3.0_1695067744764.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_chemprot_word_swapping_synonym_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.3 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-chemprot-word-swapping-synonym-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_add_v2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_add_v2_en.md new file mode 100644 index 00000000000000..e566ae171ded90 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_add_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_add_v2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_add_v2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_add_v2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_add_v2_en_5.1.2_3.0_1695062819521.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_add_v2_en_5.1.2_3.0_1695062819521.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_add_v2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_add_v2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_add_v2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-add-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_add_v3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_add_v3_en.md new file mode 100644 index 00000000000000..7e3d46fc31f9b0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_add_v3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_add_v3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_add_v3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_add_v3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_add_v3_en_5.1.2_3.0_1695063312027.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_add_v3_en_5.1.2_3.0_1695063312027.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_add_v3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_add_v3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_add_v3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-add-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_add_v3_greedy_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_add_v3_greedy_en.md new file mode 100644 index 00000000000000..7ebb02ab78ee36 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_add_v3_greedy_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_add_v3_greedy AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_add_v3_greedy +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_add_v3_greedy` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_add_v3_greedy_en_5.1.2_3.0_1695066570491.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_add_v3_greedy_en_5.1.2_3.0_1695066570491.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_add_v3_greedy","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_add_v3_greedy", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_add_v3_greedy| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-add-v3-greedy \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_add_v4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_add_v4_en.md new file mode 100644 index 00000000000000..2182ac0f5de444 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_add_v4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_add_v4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_add_v4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_add_v4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_add_v4_en_5.1.2_3.0_1695063125626.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_add_v4_en_5.1.2_3.0_1695063125626.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_add_v4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_add_v4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_add_v4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-add-v4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_add_v5_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_add_v5_en.md new file mode 100644 index 00000000000000..3a2c1b64429a1a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_add_v5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_add_v5 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_add_v5 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_add_v5` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_add_v5_en_5.1.2_3.0_1695063569950.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_add_v5_en_5.1.2_3.0_1695063569950.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_add_v5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_add_v5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_add_v5| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-add-v5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_back_translation_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_back_translation_en.md new file mode 100644 index 00000000000000..3318ec1a178415 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_back_translation_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_back_translation AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_back_translation +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_back_translation` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_back_translation_en_5.1.2_3.0_1695065043736.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_back_translation_en_5.1.2_3.0_1695065043736.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_back_translation","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_back_translation", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_back_translation| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-back-translation \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_eda_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_eda_0_en.md new file mode 100644 index 00000000000000..15fabf0fca2af2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_eda_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_eda_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_eda_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_eda_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_eda_0_en_5.1.2_3.0_1695064493620.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_eda_0_en_5.1.2_3.0_1695064493620.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_eda_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_eda_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_eda_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-eda-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_eda_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_eda_1_en.md new file mode 100644 index 00000000000000..a4cab28ae5335f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_eda_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_eda_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_eda_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_eda_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_eda_1_en_5.1.2_3.0_1695064569477.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_eda_1_en_5.1.2_3.0_1695064569477.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_eda_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_eda_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_eda_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-eda-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_eda_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_eda_2_en.md new file mode 100644 index 00000000000000..1a040d9af1796d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_eda_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_eda_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_eda_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_eda_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_eda_2_en_5.1.2_3.0_1695064658768.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_eda_2_en_5.1.2_3.0_1695064658768.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_eda_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_eda_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_eda_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-eda-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_eda_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_eda_3_en.md new file mode 100644 index 00000000000000..ccf9eb3c74621c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_eda_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_eda_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_eda_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_eda_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_eda_3_en_5.1.2_3.0_1695064758210.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_eda_3_en_5.1.2_3.0_1695064758210.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_eda_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_eda_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_eda_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-eda-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_eda_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_eda_4_en.md new file mode 100644 index 00000000000000..e3861a6eabb66b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_eda_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_eda_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_eda_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_eda_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_eda_4_en_5.1.2_3.0_1695065447398.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_eda_4_en_5.1.2_3.0_1695065447398.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_eda_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_eda_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_eda_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-eda-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_vanilla_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_vanilla_en.md new file mode 100644 index 00000000000000..64b75717f8ff6b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_vanilla_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_vanilla AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_vanilla +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_vanilla` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_vanilla_en_5.1.2_3.0_1695067394522.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_vanilla_en_5.1.2_3.0_1695067394522.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_vanilla","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_vanilla", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_vanilla| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-vanilla \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_0_en.md new file mode 100644 index 00000000000000..c2a617c8433bfc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_0_en_5.1.2_3.0_1695064640923.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_0_en_5.1.2_3.0_1695064640923.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-word-swapping-embedding-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_1_en.md new file mode 100644 index 00000000000000..fafcc952797526 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_1_en_5.1.2_3.0_1695064751933.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_1_en_5.1.2_3.0_1695064751933.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-word-swapping-embedding-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_2_en.md new file mode 100644 index 00000000000000..03c907d1678016 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_2_en_5.1.2_3.0_1695064945303.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_2_en_5.1.2_3.0_1695064945303.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-word-swapping-embedding-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_3_en.md new file mode 100644 index 00000000000000..ac63c3912458f6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_3_en_5.1.2_3.0_1695065035312.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_3_en_5.1.2_3.0_1695065035312.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-word-swapping-embedding-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_4_en.md new file mode 100644 index 00000000000000..53a5d3d19e826f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_4_en_5.1.2_3.0_1695065198692.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_4_en_5.1.2_3.0_1695065198692.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_word_swapping_embedding_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-word-swapping-embedding-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_random_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_random_0_en.md new file mode 100644 index 00000000000000..542937125942ef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_random_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_word_swapping_random_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_word_swapping_random_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_word_swapping_random_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_random_0_en_5.1.2_3.0_1695068329153.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_random_0_en_5.1.2_3.0_1695068329153.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_random_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_random_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_word_swapping_random_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-word-swapping-random-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_random_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_random_1_en.md new file mode 100644 index 00000000000000..166c3053d872d6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_random_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_word_swapping_random_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_word_swapping_random_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_word_swapping_random_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_random_1_en_5.1.2_3.0_1695068474188.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_random_1_en_5.1.2_3.0_1695068474188.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_random_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_random_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_word_swapping_random_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-word-swapping-random-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_random_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_random_2_en.md new file mode 100644 index 00000000000000..2300a84081b4cd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_random_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_word_swapping_random_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_word_swapping_random_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_word_swapping_random_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_random_2_en_5.1.2_3.0_1695068552306.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_random_2_en_5.1.2_3.0_1695068552306.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_random_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_random_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_word_swapping_random_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-word-swapping-random-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_random_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_random_3_en.md new file mode 100644 index 00000000000000..cd93393f7193fa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_random_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_word_swapping_random_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_word_swapping_random_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_word_swapping_random_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_random_3_en_5.1.2_3.0_1695068640615.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_random_3_en_5.1.2_3.0_1695068640615.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_random_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_random_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_word_swapping_random_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-word-swapping-random-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_random_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_random_4_en.md new file mode 100644 index 00000000000000..bfbb8a8e82e962 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_random_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_word_swapping_random_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_word_swapping_random_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_word_swapping_random_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_random_4_en_5.1.2_3.0_1695068733768.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_random_4_en_5.1.2_3.0_1695068733768.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_random_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_random_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_word_swapping_random_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-word-swapping-random-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_0_en.md new file mode 100644 index 00000000000000..01159c40fb4155 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_0_en_5.1.2_3.0_1695067815473.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_0_en_5.1.2_3.0_1695067815473.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-word-swapping-synonym-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_1_en.md new file mode 100644 index 00000000000000..2b9a4f483058de --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_1_en_5.1.2_3.0_1695067896321.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_1_en_5.1.2_3.0_1695067896321.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-word-swapping-synonym-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_2_en.md new file mode 100644 index 00000000000000..93045bec21b032 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_2_en_5.1.2_3.0_1695068045326.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_2_en_5.1.2_3.0_1695068045326.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-word-swapping-synonym-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_3_en.md new file mode 100644 index 00000000000000..d0a684fc6b7171 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_3_en_5.1.2_3.0_1695068117515.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_3_en_5.1.2_3.0_1695068117515.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-word-swapping-synonym-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_4_en.md new file mode 100644 index 00000000000000..8c4367d41053da --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_4_en_5.1.2_3.0_1695068253653.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_4_en_5.1.2_3.0_1695068253653.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_citation_intent_word_swapping_synonym_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-citation-intent-word-swapping-synonym-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_add_v2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_add_v2_en.md new file mode 100644 index 00000000000000..d01cde44543022 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_add_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_add_v2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_add_v2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_add_v2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_add_v2_en_5.1.2_3.0_1695063955500.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_add_v2_en_5.1.2_3.0_1695063955500.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_add_v2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_add_v2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_add_v2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-add-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_add_v3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_add_v3_en.md new file mode 100644 index 00000000000000..952370d77ab6c5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_add_v3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_add_v3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_add_v3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_add_v3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_add_v3_en_5.1.2_3.0_1695064029052.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_add_v3_en_5.1.2_3.0_1695064029052.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_add_v3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_add_v3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_add_v3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-add-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_add_v3_greedy_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_add_v3_greedy_en.md new file mode 100644 index 00000000000000..c5f6ac07fbcbd7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_add_v3_greedy_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_add_v3_greedy AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_add_v3_greedy +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_add_v3_greedy` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_add_v3_greedy_en_5.1.2_3.0_1695066653173.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_add_v3_greedy_en_5.1.2_3.0_1695066653173.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_add_v3_greedy","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_add_v3_greedy", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_add_v3_greedy| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-add-v3-greedy \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_add_v4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_add_v4_en.md new file mode 100644 index 00000000000000..b57ccc55ad810d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_add_v4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_add_v4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_add_v4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_add_v4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_add_v4_en_5.1.2_3.0_1695063191334.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_add_v4_en_5.1.2_3.0_1695063191334.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_add_v4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_add_v4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_add_v4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-add-v4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_add_v5_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_add_v5_en.md new file mode 100644 index 00000000000000..64bd8d0e0e6992 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_add_v5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_add_v5 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_add_v5 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_add_v5` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_add_v5_en_5.1.2_3.0_1695063666282.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_add_v5_en_5.1.2_3.0_1695063666282.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_add_v5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_add_v5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_add_v5| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-add-v5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_back_translation_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_back_translation_en.md new file mode 100644 index 00000000000000..068746474fc8a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_back_translation_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_back_translation AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_back_translation +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_back_translation` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_back_translation_en_5.1.2_3.0_1695065123975.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_back_translation_en_5.1.2_3.0_1695065123975.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_back_translation","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_back_translation", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_back_translation| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-back-translation \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_eda_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_eda_0_en.md new file mode 100644 index 00000000000000..d919640e6ea9c9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_eda_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_eda_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_eda_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_eda_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_eda_0_en_5.1.2_3.0_1695061656434.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_eda_0_en_5.1.2_3.0_1695061656434.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_eda_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_eda_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_eda_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-eda-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_eda_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_eda_1_en.md new file mode 100644 index 00000000000000..090af9128c9dee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_eda_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_eda_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_eda_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_eda_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_eda_1_en_5.1.2_3.0_1695061806670.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_eda_1_en_5.1.2_3.0_1695061806670.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_eda_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_eda_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_eda_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-eda-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_eda_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_eda_2_en.md new file mode 100644 index 00000000000000..eb1d89161c9ce4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_eda_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_eda_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_eda_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_eda_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_eda_2_en_5.1.2_3.0_1695061974716.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_eda_2_en_5.1.2_3.0_1695061974716.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_eda_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_eda_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_eda_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-eda-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_eda_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_eda_3_en.md new file mode 100644 index 00000000000000..5106cc040c63a3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_eda_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_eda_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_eda_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_eda_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_eda_3_en_5.1.2_3.0_1695062165896.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_eda_3_en_5.1.2_3.0_1695062165896.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_eda_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_eda_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_eda_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-eda-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_eda_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_eda_4_en.md new file mode 100644 index 00000000000000..efaa5db5af2eaa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_eda_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_eda_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_eda_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_eda_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_eda_4_en_5.1.2_3.0_1695062453783.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_eda_4_en_5.1.2_3.0_1695062453783.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_eda_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_eda_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_eda_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-eda-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_vanilla_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_vanilla_en.md new file mode 100644 index 00000000000000..175da967e887fc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_vanilla_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_vanilla AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_vanilla +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_vanilla` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_vanilla_en_5.1.2_3.0_1695067563736.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_vanilla_en_5.1.2_3.0_1695067563736.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_vanilla","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_vanilla", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_vanilla| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-vanilla \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_0_en.md new file mode 100644 index 00000000000000..4897610141e9fd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_0_en_5.1.2_3.0_1695065942415.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_0_en_5.1.2_3.0_1695065942415.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-word-swapping-embedding-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_1_en.md new file mode 100644 index 00000000000000..58594d379000fe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_1_en_5.1.2_3.0_1695066011521.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_1_en_5.1.2_3.0_1695066011521.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-word-swapping-embedding-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_2_en.md new file mode 100644 index 00000000000000..2cf355a4fe7dbe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_2_en_5.1.2_3.0_1695065648058.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_2_en_5.1.2_3.0_1695065648058.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-word-swapping-embedding-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_3_en.md new file mode 100644 index 00000000000000..103f740961c1ed --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_3_en_5.1.2_3.0_1695066841795.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_3_en_5.1.2_3.0_1695066841795.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-word-swapping-embedding-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_4_en.md new file mode 100644 index 00000000000000..47ed5deb92ae95 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_4_en_5.1.2_3.0_1695067047043.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_4_en_5.1.2_3.0_1695067047043.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_word_swapping_embedding_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-word-swapping-embedding-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_random_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_random_0_en.md new file mode 100644 index 00000000000000..bec891833832e2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_random_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_word_swapping_random_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_word_swapping_random_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_word_swapping_random_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_random_0_en_5.1.2_3.0_1695066079195.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_random_0_en_5.1.2_3.0_1695066079195.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_random_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_random_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_word_swapping_random_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-word-swapping-random-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_random_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_random_1_en.md new file mode 100644 index 00000000000000..62d35f3b10bc5a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_random_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_word_swapping_random_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_word_swapping_random_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_word_swapping_random_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_random_1_en_5.1.2_3.0_1695066143519.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_random_1_en_5.1.2_3.0_1695066143519.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_random_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_random_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_word_swapping_random_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-word-swapping-random-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_random_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_random_2_en.md new file mode 100644 index 00000000000000..09b71afd20913f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_random_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_word_swapping_random_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_word_swapping_random_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_word_swapping_random_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_random_2_en_5.1.2_3.0_1695062777040.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_random_2_en_5.1.2_3.0_1695062777040.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_random_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_random_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_word_swapping_random_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-word-swapping-random-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_random_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_random_3_en.md new file mode 100644 index 00000000000000..a36f6a94deeda3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_random_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_word_swapping_random_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_word_swapping_random_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_word_swapping_random_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_random_3_en_5.1.2_3.0_1695062856364.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_random_3_en_5.1.2_3.0_1695062856364.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_random_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_random_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_word_swapping_random_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-word-swapping-random-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_random_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_random_4_en.md new file mode 100644 index 00000000000000..3c72290327a19f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_random_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_word_swapping_random_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_word_swapping_random_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_word_swapping_random_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_random_4_en_5.1.2_3.0_1695062920631.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_random_4_en_5.1.2_3.0_1695062920631.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_random_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_random_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_word_swapping_random_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-word-swapping-random-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_0_en.md new file mode 100644 index 00000000000000..ff0146f9276b3c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_0_en_5.1.2_3.0_1695065820899.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_0_en_5.1.2_3.0_1695065820899.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-word-swapping-synonym-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_1_en.md new file mode 100644 index 00000000000000..83263e30d659e6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_1_en_5.1.2_3.0_1695065882137.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_1_en_5.1.2_3.0_1695065882137.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-word-swapping-synonym-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_2_en.md new file mode 100644 index 00000000000000..9cf02e6fa0632a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_2_en_5.1.2_3.0_1695068412804.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_2_en_5.1.2_3.0_1695068412804.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-word-swapping-synonym-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_3_en.md new file mode 100644 index 00000000000000..71bcf3b9beb309 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_3_en_5.1.2_3.0_1695068815311.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_3_en_5.1.2_3.0_1695068815311.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-word-swapping-synonym-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_4_en.md new file mode 100644 index 00000000000000..6dedbe20c197cf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_4_en_5.1.2_3.0_1695068971021.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_4_en_5.1.2_3.0_1695068971021.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_rct_sample_word_swapping_synonym_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-rct-sample-word-swapping-synonym-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_add_v2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_add_v2_en.md new file mode 100644 index 00000000000000..3d11ef71efa4fb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_add_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_add_v2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_add_v2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_add_v2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_add_v2_en_5.1.2_3.0_1695062883529.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_add_v2_en_5.1.2_3.0_1695062883529.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_add_v2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_add_v2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_add_v2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-add-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_add_v3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_add_v3_en.md new file mode 100644 index 00000000000000..423b456b259d82 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_add_v3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_add_v3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_add_v3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_add_v3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_add_v3_en_5.1.2_3.0_1695063472364.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_add_v3_en_5.1.2_3.0_1695063472364.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_add_v3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_add_v3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_add_v3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-add-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_add_v3_greedy_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_add_v3_greedy_en.md new file mode 100644 index 00000000000000..dbf29d55db23c9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_add_v3_greedy_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_add_v3_greedy AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_add_v3_greedy +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_add_v3_greedy` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_add_v3_greedy_en_5.1.2_3.0_1695066755333.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_add_v3_greedy_en_5.1.2_3.0_1695066755333.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_add_v3_greedy","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_add_v3_greedy", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_add_v3_greedy| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-add-v3-greedy \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_back_translation_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_back_translation_en.md new file mode 100644 index 00000000000000..20b210e27fa13f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_back_translation_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_back_translation AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_back_translation +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_back_translation` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_back_translation_en_5.1.2_3.0_1695065208298.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_back_translation_en_5.1.2_3.0_1695065208298.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_back_translation","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_back_translation", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_back_translation| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-back-translation \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_eda_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_eda_0_en.md new file mode 100644 index 00000000000000..7d838319e59033 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_eda_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_eda_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_eda_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_eda_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_eda_0_en_5.1.2_3.0_1695062601285.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_eda_0_en_5.1.2_3.0_1695062601285.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_eda_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_eda_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_eda_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-eda-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_eda_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_eda_1_en.md new file mode 100644 index 00000000000000..2bf2f456484ebe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_eda_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_eda_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_eda_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_eda_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_eda_1_en_5.1.2_3.0_1695062683576.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_eda_1_en_5.1.2_3.0_1695062683576.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_eda_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_eda_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_eda_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-eda-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_eda_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_eda_2_en.md new file mode 100644 index 00000000000000..ae37966808d6b6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_eda_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_eda_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_eda_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_eda_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_eda_2_en_5.1.2_3.0_1695062761915.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_eda_2_en_5.1.2_3.0_1695062761915.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_eda_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_eda_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_eda_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-eda-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_eda_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_eda_3_en.md new file mode 100644 index 00000000000000..0ccb2c61e72fbd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_eda_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_eda_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_eda_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_eda_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_eda_3_en_5.1.2_3.0_1695062915099.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_eda_3_en_5.1.2_3.0_1695062915099.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_eda_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_eda_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_eda_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-eda-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_eda_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_eda_4_en.md new file mode 100644 index 00000000000000..a414c8fed903d7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_eda_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_eda_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_eda_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_eda_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_eda_4_en_5.1.2_3.0_1695065361942.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_eda_4_en_5.1.2_3.0_1695065361942.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_eda_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_eda_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_eda_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-eda-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_vanilla_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_vanilla_en.md new file mode 100644 index 00000000000000..e5af7f0ac519df --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_vanilla_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_vanilla AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_vanilla +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_vanilla` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_vanilla_en_5.1.2_3.0_1695067737806.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_vanilla_en_5.1.2_3.0_1695067737806.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_vanilla","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_vanilla", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_vanilla| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-vanilla \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_embedding_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_embedding_0_en.md new file mode 100644 index 00000000000000..e4d7e2028c25a1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_embedding_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_word_swapping_embedding_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_word_swapping_embedding_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_word_swapping_embedding_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_embedding_0_en_5.1.2_3.0_1695065711115.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_embedding_0_en_5.1.2_3.0_1695065711115.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_embedding_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_embedding_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_word_swapping_embedding_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-word-swapping-embedding-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_embedding_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_embedding_1_en.md new file mode 100644 index 00000000000000..1da91f90b667dd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_embedding_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_word_swapping_embedding_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_word_swapping_embedding_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_word_swapping_embedding_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_embedding_1_en_5.1.2_3.0_1695065780276.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_embedding_1_en_5.1.2_3.0_1695065780276.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_embedding_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_embedding_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_word_swapping_embedding_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-word-swapping-embedding-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_embedding_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_embedding_2_en.md new file mode 100644 index 00000000000000..b021c22d14cfca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_embedding_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_word_swapping_embedding_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_word_swapping_embedding_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_word_swapping_embedding_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_embedding_2_en_5.1.2_3.0_1695065979772.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_embedding_2_en_5.1.2_3.0_1695065979772.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_embedding_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_embedding_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_word_swapping_embedding_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-word-swapping-embedding-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_embedding_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_embedding_3_en.md new file mode 100644 index 00000000000000..1aa26b12b9fa63 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_embedding_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_word_swapping_embedding_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_word_swapping_embedding_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_word_swapping_embedding_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_embedding_3_en_5.1.2_3.0_1695066049985.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_embedding_3_en_5.1.2_3.0_1695066049985.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_embedding_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_embedding_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_word_swapping_embedding_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-word-swapping-embedding-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_embedding_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_embedding_4_en.md new file mode 100644 index 00000000000000..1b470555f4cc62 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_embedding_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_word_swapping_embedding_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_word_swapping_embedding_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_word_swapping_embedding_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_embedding_4_en_5.1.2_3.0_1695066115015.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_embedding_4_en_5.1.2_3.0_1695066115015.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_embedding_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_embedding_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_word_swapping_embedding_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-word-swapping-embedding-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_random_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_random_0_en.md new file mode 100644 index 00000000000000..bd2eb6f1daab94 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_random_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_word_swapping_random_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_word_swapping_random_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_word_swapping_random_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_random_0_en_5.1.2_3.0_1695067506121.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_random_0_en_5.1.2_3.0_1695067506121.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_random_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_random_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_word_swapping_random_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-word-swapping-random-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_random_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_random_1_en.md new file mode 100644 index 00000000000000..cf68eb7e85c88a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_random_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_word_swapping_random_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_word_swapping_random_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_word_swapping_random_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_random_1_en_5.1.2_3.0_1695067589057.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_random_1_en_5.1.2_3.0_1695067589057.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_random_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_random_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_word_swapping_random_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-word-swapping-random-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_random_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_random_2_en.md new file mode 100644 index 00000000000000..a0e90e2d448c70 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_random_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_word_swapping_random_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_word_swapping_random_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_word_swapping_random_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_random_2_en_5.1.2_3.0_1695067665091.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_random_2_en_5.1.2_3.0_1695067665091.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_random_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_random_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_word_swapping_random_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-word-swapping-random-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_random_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_random_3_en.md new file mode 100644 index 00000000000000..d5ff4a97afe495 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_random_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_word_swapping_random_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_word_swapping_random_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_word_swapping_random_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_random_3_en_5.1.2_3.0_1695067959147.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_random_3_en_5.1.2_3.0_1695067959147.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_random_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_random_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_word_swapping_random_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-word-swapping-random-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_random_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_random_4_en.md new file mode 100644 index 00000000000000..7dee7787fe746d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_random_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_word_swapping_random_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_word_swapping_random_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_word_swapping_random_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_random_4_en_5.1.2_3.0_1695068182594.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_random_4_en_5.1.2_3.0_1695068182594.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_random_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_random_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_word_swapping_random_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-word-swapping-random-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_synonym_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_synonym_0_en.md new file mode 100644 index 00000000000000..feb622894c946a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_synonym_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_word_swapping_synonym_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_word_swapping_synonym_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_word_swapping_synonym_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_synonym_0_en_5.1.2_3.0_1695065753378.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_synonym_0_en_5.1.2_3.0_1695065753378.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_synonym_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_synonym_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_word_swapping_synonym_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-word-swapping-synonym-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_synonym_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_synonym_1_en.md new file mode 100644 index 00000000000000..48d3b6e2fcb6ad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_synonym_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_word_swapping_synonym_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_word_swapping_synonym_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_word_swapping_synonym_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_synonym_1_en_5.1.2_3.0_1695069332405.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_synonym_1_en_5.1.2_3.0_1695069332405.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_synonym_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_synonym_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_word_swapping_synonym_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-word-swapping-synonym-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_synonym_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_synonym_2_en.md new file mode 100644 index 00000000000000..ec46f1776484e0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_synonym_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_word_swapping_synonym_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_word_swapping_synonym_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_word_swapping_synonym_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_synonym_2_en_5.1.2_3.0_1695061662249.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_synonym_2_en_5.1.2_3.0_1695061662249.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_synonym_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_synonym_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_word_swapping_synonym_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-word-swapping-synonym-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_synonym_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_synonym_3_en.md new file mode 100644 index 00000000000000..66e79f8f50cf86 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_synonym_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_word_swapping_synonym_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_word_swapping_synonym_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_word_swapping_synonym_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_synonym_3_en_5.1.2_3.0_1695061817028.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_synonym_3_en_5.1.2_3.0_1695061817028.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_synonym_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_synonym_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_word_swapping_synonym_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-word-swapping-synonym-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_synonym_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_synonym_4_en.md new file mode 100644 index 00000000000000..9409c0cf1c9786 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_sciie_word_swapping_synonym_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_sciie_word_swapping_synonym_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_sciie_word_swapping_synonym_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_sciie_word_swapping_synonym_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_synonym_4_en_5.1.2_3.0_1695061987104.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_sciie_word_swapping_synonym_4_en_5.1.2_3.0_1695061987104.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_synonym_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_sciie_word_swapping_synonym_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_sciie_word_swapping_synonym_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-sciie-word-swapping-synonym-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_add_v2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_add_v2_en.md new file mode 100644 index 00000000000000..796205a6ea86d4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_add_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_add_v2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_add_v2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_add_v2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_add_v2_en_5.1.2_3.0_1695063017835.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_add_v2_en_5.1.2_3.0_1695063017835.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_add_v2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_add_v2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_add_v2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-add-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3_en.md new file mode 100644 index 00000000000000..4e439c50ebb443 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3_en_5.1.2_3.0_1695063651684.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3_en_5.1.2_3.0_1695063651684.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-add-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3_greedy_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3_greedy_en.md new file mode 100644 index 00000000000000..42c75f082ca8a8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3_greedy_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3_greedy AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3_greedy +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3_greedy` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3_greedy_en_5.1.2_3.0_1695066988320.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3_greedy_en_5.1.2_3.0_1695066988320.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3_greedy","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3_greedy", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_add_v3_greedy| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-add-v3-greedy \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_add_v4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_add_v4_en.md new file mode 100644 index 00000000000000..718ba3a88993bf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_add_v4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_add_v4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_add_v4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_add_v4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_add_v4_en_5.1.2_3.0_1695063402223.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_add_v4_en_5.1.2_3.0_1695063402223.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_add_v4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_add_v4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_add_v4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-add-v4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_add_v5_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_add_v5_en.md new file mode 100644 index 00000000000000..bda2574b91d482 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_add_v5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_add_v5 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_add_v5 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_add_v5` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_add_v5_en_5.1.2_3.0_1695063908626.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_add_v5_en_5.1.2_3.0_1695063908626.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_add_v5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_add_v5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_add_v5| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-add-v5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_back_translation_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_back_translation_en.md new file mode 100644 index 00000000000000..b001f181a9ea2d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_back_translation_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_back_translation AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_back_translation +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_back_translation` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_back_translation_en_5.1.2_3.0_1695065600854.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_back_translation_en_5.1.2_3.0_1695065600854.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_back_translation","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_back_translation", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_back_translation| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-back-translation \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_eda_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_eda_0_en.md new file mode 100644 index 00000000000000..b227fe0b30d0a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_eda_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_eda_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_eda_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_eda_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_eda_0_en_5.1.2_3.0_1695064543714.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_eda_0_en_5.1.2_3.0_1695064543714.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_eda_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_eda_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_eda_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-eda-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_eda_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_eda_1_en.md new file mode 100644 index 00000000000000..7f8262682f1d9c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_eda_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_eda_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_eda_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_eda_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_eda_1_en_5.1.2_3.0_1695065376115.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_eda_1_en_5.1.2_3.0_1695065376115.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_eda_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_eda_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_eda_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-eda-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_eda_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_eda_2_en.md new file mode 100644 index 00000000000000..3339d5618b9b17 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_eda_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_eda_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_eda_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_eda_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_eda_2_en_5.1.2_3.0_1695064863104.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_eda_2_en_5.1.2_3.0_1695064863104.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_eda_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_eda_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_eda_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-eda-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_eda_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_eda_3_en.md new file mode 100644 index 00000000000000..99924af8436e22 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_eda_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_eda_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_eda_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_eda_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_eda_3_en_5.1.2_3.0_1695065115894.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_eda_3_en_5.1.2_3.0_1695065115894.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_eda_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_eda_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_eda_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-eda-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_eda_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_eda_4_en.md new file mode 100644 index 00000000000000..c947119de7f3a0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_eda_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_eda_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_eda_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_eda_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_eda_4_en_5.1.2_3.0_1695065287936.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_eda_4_en_5.1.2_3.0_1695065287936.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_eda_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_eda_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_eda_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-eda-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_vanilla_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_vanilla_en.md new file mode 100644 index 00000000000000..c3a15cbced256c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_vanilla_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_vanilla AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_vanilla +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_vanilla` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_vanilla_en_5.1.2_3.0_1695068173633.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_vanilla_en_5.1.2_3.0_1695068173633.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_vanilla","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_vanilla", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_vanilla| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-vanilla \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_0_en.md new file mode 100644 index 00000000000000..e28f83545e3efe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_0_en_5.1.2_3.0_1695065442742.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_0_en_5.1.2_3.0_1695065442742.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-word-swapping-embedding-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_1_en.md new file mode 100644 index 00000000000000..685e51c6a2978b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_1_en_5.1.2_3.0_1695065505528.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_1_en_5.1.2_3.0_1695065505528.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-word-swapping-embedding-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_2_en.md new file mode 100644 index 00000000000000..d8a4a52a45fa8e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_2_en_5.1.2_3.0_1695065580077.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_2_en_5.1.2_3.0_1695065580077.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-word-swapping-embedding-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_3_en.md new file mode 100644 index 00000000000000..60d3f74a9fe59c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_3_en_5.1.2_3.0_1695062334761.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_3_en_5.1.2_3.0_1695062334761.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-word-swapping-embedding-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_4_en.md new file mode 100644 index 00000000000000..6ea9fe74ed5ee6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_4_en_5.1.2_3.0_1695062479803.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_4_en_5.1.2_3.0_1695062479803.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_embedding_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-word-swapping-embedding-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_0_en.md new file mode 100644 index 00000000000000..a574f427c2d027 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_0_en_5.1.2_3.0_1695069388140.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_0_en_5.1.2_3.0_1695069388140.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-word-swapping-random-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_1_en.md new file mode 100644 index 00000000000000..0552195b65312b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_1_en_5.1.2_3.0_1695061745442.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_1_en_5.1.2_3.0_1695061745442.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-word-swapping-random-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_2_en.md new file mode 100644 index 00000000000000..44c2f1f5821f49 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_2_en_5.1.2_3.0_1695061904359.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_2_en_5.1.2_3.0_1695061904359.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-word-swapping-random-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_3_en.md new file mode 100644 index 00000000000000..8e057f6a906d9c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_3_en_5.1.2_3.0_1695062074718.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_3_en_5.1.2_3.0_1695062074718.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-word-swapping-random-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_4_en.md new file mode 100644 index 00000000000000..33b1ff921632e3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_4_en_5.1.2_3.0_1695062164729.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_4_en_5.1.2_3.0_1695062164729.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_random_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-word-swapping-random-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_0_en.md new file mode 100644 index 00000000000000..7fb59dabf972da --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_0_en_5.1.2_3.0_1695066231533.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_0_en_5.1.2_3.0_1695066231533.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-word-swapping-synonym-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_1_en.md new file mode 100644 index 00000000000000..7d1cd475286ccd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_1_en_5.1.2_3.0_1695062254406.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_1_en_5.1.2_3.0_1695062254406.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-word-swapping-synonym-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_2_en.md new file mode 100644 index 00000000000000..5a1f651a5a5012 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_2_en_5.1.2_3.0_1695062413251.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_2_en_5.1.2_3.0_1695062413251.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-word-swapping-synonym-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_3_en.md new file mode 100644 index 00000000000000..a4764cfe5805e2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_3_en_5.1.2_3.0_1695062547268.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_3_en_5.1.2_3.0_1695062547268.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-word-swapping-synonym-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_4_en.md new file mode 100644 index 00000000000000..690639267d00a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_4_en_5.1.2_3.0_1695062628121.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_4_en_5.1.2_3.0_1695062628121.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_emotion_word_swapping_synonym_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-emotion-word-swapping-synonym-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_add_v2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_add_v2_en.md new file mode 100644 index 00000000000000..f5dc984b96b5b0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_add_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_add_v2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_add_v2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_add_v2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_add_v2_en_5.1.2_3.0_1695063238031.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_add_v2_en_5.1.2_3.0_1695063238031.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_add_v2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_add_v2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_add_v2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-add-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_add_v3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_add_v3_en.md new file mode 100644 index 00000000000000..63c4193415d0a1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_add_v3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_add_v3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_add_v3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_add_v3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_add_v3_en_5.1.2_3.0_1695063733087.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_add_v3_en_5.1.2_3.0_1695063733087.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_add_v3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_add_v3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_add_v3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-add-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_add_v3_greedy_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_add_v3_greedy_en.md new file mode 100644 index 00000000000000..147e35662a654d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_add_v3_greedy_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_add_v3_greedy AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_add_v3_greedy +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_add_v3_greedy` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_add_v3_greedy_en_5.1.2_3.0_1695066918422.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_add_v3_greedy_en_5.1.2_3.0_1695066918422.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_add_v3_greedy","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_add_v3_greedy", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_add_v3_greedy| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-add-v3-greedy \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_add_v4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_add_v4_en.md new file mode 100644 index 00000000000000..ff1dffb437b48b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_add_v4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_add_v4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_add_v4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_add_v4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_add_v4_en_5.1.2_3.0_1695063329939.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_add_v4_en_5.1.2_3.0_1695063329939.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_add_v4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_add_v4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_add_v4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-add-v4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_add_v5_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_add_v5_en.md new file mode 100644 index 00000000000000..5fa6af16876097 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_add_v5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_add_v5 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_add_v5 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_add_v5` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_add_v5_en_5.1.2_3.0_1695063831720.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_add_v5_en_5.1.2_3.0_1695063831720.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_add_v5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_add_v5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_add_v5| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-add-v5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_back_translation_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_back_translation_en.md new file mode 100644 index 00000000000000..ddecc90433c0b3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_back_translation_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_back_translation AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_back_translation +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_back_translation` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_back_translation_en_5.1.2_3.0_1695065521901.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_back_translation_en_5.1.2_3.0_1695065521901.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_back_translation","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_back_translation", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_back_translation| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-back-translation \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_eda_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_eda_0_en.md new file mode 100644 index 00000000000000..3eaff3c18355aa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_eda_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_eda_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_eda_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_eda_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_eda_0_en_5.1.2_3.0_1695063567675.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_eda_0_en_5.1.2_3.0_1695063567675.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_eda_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_eda_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_eda_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-eda-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_eda_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_eda_1_en.md new file mode 100644 index 00000000000000..d8af0fd892b4c0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_eda_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_eda_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_eda_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_eda_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_eda_1_en_5.1.2_3.0_1695063744010.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_eda_1_en_5.1.2_3.0_1695063744010.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_eda_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_eda_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_eda_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-eda-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_eda_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_eda_2_en.md new file mode 100644 index 00000000000000..a167176e0f5af2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_eda_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_eda_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_eda_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_eda_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_eda_2_en_5.1.2_3.0_1695063981698.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_eda_2_en_5.1.2_3.0_1695063981698.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_eda_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_eda_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_eda_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-eda-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_eda_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_eda_3_en.md new file mode 100644 index 00000000000000..d12216d819b9e9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_eda_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_eda_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_eda_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_eda_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_eda_3_en_5.1.2_3.0_1695064129822.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_eda_3_en_5.1.2_3.0_1695064129822.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_eda_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_eda_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_eda_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-eda-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_eda_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_eda_4_en.md new file mode 100644 index 00000000000000..e0e4f461eb7954 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_eda_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_eda_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_eda_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_eda_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_eda_4_en_5.1.2_3.0_1695064283114.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_eda_4_en_5.1.2_3.0_1695064283114.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_eda_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_eda_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_eda_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-eda-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_vanilla_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_vanilla_en.md new file mode 100644 index 00000000000000..52378655a34fac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_vanilla_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_vanilla AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_vanilla +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_vanilla` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_vanilla_en_5.1.2_3.0_1695068105957.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_vanilla_en_5.1.2_3.0_1695068105957.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_vanilla","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_vanilla", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_vanilla| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-vanilla \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_0_en.md new file mode 100644 index 00000000000000..1e6d137f32f6a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_0_en_5.1.2_3.0_1695061735581.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_0_en_5.1.2_3.0_1695061735581.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-word-swapping-embedding-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_1_en.md new file mode 100644 index 00000000000000..6c71b0b2cdffd7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_1_en_5.1.2_3.0_1695061884317.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_1_en_5.1.2_3.0_1695061884317.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-word-swapping-embedding-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_2_en.md new file mode 100644 index 00000000000000..f47ec888b13609 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_2_en_5.1.2_3.0_1695062075224.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_2_en_5.1.2_3.0_1695062075224.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-word-swapping-embedding-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_3_en.md new file mode 100644 index 00000000000000..50bcfd5b21a393 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_3_en_5.1.2_3.0_1695062319093.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_3_en_5.1.2_3.0_1695062319093.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-word-swapping-embedding-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_4_en.md new file mode 100644 index 00000000000000..e178ea76dd316c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_4_en_5.1.2_3.0_1695062839881.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_4_en_5.1.2_3.0_1695062839881.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_embedding_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-word-swapping-embedding-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_0_en.md new file mode 100644 index 00000000000000..d3191be1953fb9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_0_en_5.1.2_3.0_1695068905936.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_0_en_5.1.2_3.0_1695068905936.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-word-swapping-random-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_1_en.md new file mode 100644 index 00000000000000..e883c895a62b2d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_1_en_5.1.2_3.0_1695069032975.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_1_en_5.1.2_3.0_1695069032975.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-word-swapping-random-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_2_en.md new file mode 100644 index 00000000000000..36a8fa8c2cf672 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_2_en_5.1.2_3.0_1695069156917.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_2_en_5.1.2_3.0_1695069156917.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-word-swapping-random-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_3_en.md new file mode 100644 index 00000000000000..3360e5d25b7f92 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_3_en_5.1.2_3.0_1695069218917.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_3_en_5.1.2_3.0_1695069218917.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-word-swapping-random-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_4_en.md new file mode 100644 index 00000000000000..df5fe7179812a2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_4_en_5.1.2_3.0_1695069277201.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_4_en_5.1.2_3.0_1695069277201.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_random_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-word-swapping-random-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_0_en.md new file mode 100644 index 00000000000000..8bd508e5d2b95a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_0_en_5.1.2_3.0_1695068812882.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_0_en_5.1.2_3.0_1695068812882.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-word-swapping-synonym-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_1_en.md new file mode 100644 index 00000000000000..5e734019c7a282 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_1_en_5.1.2_3.0_1695068964030.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_1_en_5.1.2_3.0_1695068964030.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-word-swapping-synonym-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_2_en.md new file mode 100644 index 00000000000000..c7a8bbb0973c83 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_2_en_5.1.2_3.0_1695069189667.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_2_en_5.1.2_3.0_1695069189667.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-word-swapping-synonym-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_3_en.md new file mode 100644 index 00000000000000..5d72aaacfa5f0e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_3_en_5.1.2_3.0_1695069302240.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_3_en_5.1.2_3.0_1695069302240.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-word-swapping-synonym-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_4_en.md new file mode 100644 index 00000000000000..4e695f67932e81 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_4_en_5.1.2_3.0_1695061559983.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_4_en_5.1.2_3.0_1695061559983.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_hate_word_swapping_synonym_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-hate-word-swapping-synonym-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_add_v2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_add_v2_en.md new file mode 100644 index 00000000000000..53c4e45f0435fc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_add_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_add_v2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_add_v2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_add_v2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_add_v2_en_5.1.2_3.0_1695063168425.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_add_v2_en_5.1.2_3.0_1695063168425.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_add_v2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_add_v2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_add_v2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-add-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_add_v3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_add_v3_en.md new file mode 100644 index 00000000000000..918c57afe16b15 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_add_v3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_add_v3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_add_v3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_add_v3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_add_v3_en_5.1.2_3.0_1695063815484.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_add_v3_en_5.1.2_3.0_1695063815484.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_add_v3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_add_v3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_add_v3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-add-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_add_v3_greedy_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_add_v3_greedy_en.md new file mode 100644 index 00000000000000..9da0c7e8cef7fd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_add_v3_greedy_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_add_v3_greedy AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_add_v3_greedy +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_add_v3_greedy` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_add_v3_greedy_en_5.1.2_3.0_1695066841376.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_add_v3_greedy_en_5.1.2_3.0_1695066841376.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_add_v3_greedy","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_add_v3_greedy", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_add_v3_greedy| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-add-v3-greedy \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_add_v4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_add_v4_en.md new file mode 100644 index 00000000000000..a7183e23724b24 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_add_v4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_add_v4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_add_v4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_add_v4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_add_v4_en_5.1.2_3.0_1695063264391.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_add_v4_en_5.1.2_3.0_1695063264391.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_add_v4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_add_v4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_add_v4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-add-v4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_add_v5_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_add_v5_en.md new file mode 100644 index 00000000000000..a00f318ceff60e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_add_v5_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_add_v5 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_add_v5 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_add_v5` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_add_v5_en_5.1.2_3.0_1695063751751.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_add_v5_en_5.1.2_3.0_1695063751751.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_add_v5","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_add_v5", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_add_v5| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-add-v5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_back_translation_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_back_translation_en.md new file mode 100644 index 00000000000000..0dc5e66b2eeaf8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_back_translation_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_back_translation AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_back_translation +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_back_translation` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_back_translation_en_5.1.2_3.0_1695065295611.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_back_translation_en_5.1.2_3.0_1695065295611.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_back_translation","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_back_translation", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_back_translation| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-back-translation \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_eda_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_eda_0_en.md new file mode 100644 index 00000000000000..44897f858303a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_eda_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_eda_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_eda_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_eda_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_eda_0_en_5.1.2_3.0_1695062983180.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_eda_0_en_5.1.2_3.0_1695062983180.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_eda_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_eda_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_eda_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-eda-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_eda_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_eda_1_en.md new file mode 100644 index 00000000000000..562edfd99994ed --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_eda_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_eda_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_eda_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_eda_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_eda_1_en_5.1.2_3.0_1695063053385.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_eda_1_en_5.1.2_3.0_1695063053385.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_eda_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_eda_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_eda_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-eda-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_eda_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_eda_2_en.md new file mode 100644 index 00000000000000..12625144b03a92 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_eda_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_eda_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_eda_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_eda_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_eda_2_en_5.1.2_3.0_1695063183295.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_eda_2_en_5.1.2_3.0_1695063183295.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_eda_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_eda_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_eda_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-eda-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_eda_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_eda_3_en.md new file mode 100644 index 00000000000000..04fd93108ddf34 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_eda_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_eda_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_eda_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_eda_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_eda_3_en_5.1.2_3.0_1695063247843.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_eda_3_en_5.1.2_3.0_1695063247843.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_eda_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_eda_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_eda_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-eda-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_eda_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_eda_4_en.md new file mode 100644 index 00000000000000..0980685e45b782 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_eda_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_eda_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_eda_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_eda_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_eda_4_en_5.1.2_3.0_1695063390616.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_eda_4_en_5.1.2_3.0_1695063390616.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_eda_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_eda_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_eda_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-eda-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_vanilla_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_vanilla_en.md new file mode 100644 index 00000000000000..dcae8e0298dbca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_vanilla_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_vanilla AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_vanilla +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_vanilla` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_vanilla_en_5.1.2_3.0_1695067812718.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_vanilla_en_5.1.2_3.0_1695067812718.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_vanilla","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_vanilla", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_vanilla| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-vanilla \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_0_en.md new file mode 100644 index 00000000000000..03e1b745a614af --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_0_en_5.1.2_3.0_1695067652571.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_0_en_5.1.2_3.0_1695067652571.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-word-swapping-embedding-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_1_en.md new file mode 100644 index 00000000000000..e6a23553e8df1b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_1_en_5.1.2_3.0_1695067887532.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_1_en_5.1.2_3.0_1695067887532.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-word-swapping-embedding-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_2_en.md new file mode 100644 index 00000000000000..417a99c46e5cbd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_2_en_5.1.2_3.0_1695067953535.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_2_en_5.1.2_3.0_1695067953535.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-word-swapping-embedding-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_3_en.md new file mode 100644 index 00000000000000..4bf749b4530064 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_3_en_5.1.2_3.0_1695068239907.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_3_en_5.1.2_3.0_1695068239907.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-word-swapping-embedding-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_4_en.md new file mode 100644 index 00000000000000..a7c7b15ec34230 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_4_en_5.1.2_3.0_1695068320901.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_4_en_5.1.2_3.0_1695068320901.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_embedding_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-word-swapping-embedding-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_0_en.md new file mode 100644 index 00000000000000..1dfa404b2be8bb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_0_en_5.1.2_3.0_1695068406014.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_0_en_5.1.2_3.0_1695068406014.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-word-swapping-random-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_1_en.md new file mode 100644 index 00000000000000..1cbd216e5592c3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_1_en_5.1.2_3.0_1695068465760.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_1_en_5.1.2_3.0_1695068465760.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-word-swapping-random-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_2_en.md new file mode 100644 index 00000000000000..fc039f4e347847 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_2_en_5.1.2_3.0_1695068549948.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_2_en_5.1.2_3.0_1695068549948.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-word-swapping-random-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_3_en.md new file mode 100644 index 00000000000000..762fc9e9a8e416 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_3_en_5.1.2_3.0_1695068636878.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_3_en_5.1.2_3.0_1695068636878.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-word-swapping-random-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_4_en.md new file mode 100644 index 00000000000000..8194ba5ec31329 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_4_en_5.1.2_3.0_1695068733629.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_4_en_5.1.2_3.0_1695068733629.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_random_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-word-swapping-random-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_0_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_0_en.md new file mode 100644 index 00000000000000..478eaf9524ae7b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_0_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_0 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_0 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_0` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_0_en_5.1.2_3.0_1695065669939.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_0_en_5.1.2_3.0_1695065669939.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_0","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_0", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_0| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-word-swapping-synonym-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_1_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_1_en.md new file mode 100644 index 00000000000000..3e17025c6f63be --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_1 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_1 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_1` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_1_en_5.1.2_3.0_1695066320040.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_1_en_5.1.2_3.0_1695066320040.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_1","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_1", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-word-swapping-synonym-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_2_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_2_en.md new file mode 100644 index 00000000000000..29e327a48f9ad6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_2 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_2` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_2_en_5.1.2_3.0_1695067055008.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_2_en_5.1.2_3.0_1695067055008.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-word-swapping-synonym-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_3_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_3_en.md new file mode 100644 index 00000000000000..6f137756072323 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_3_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_3 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_3 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_3` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_3_en_5.1.2_3.0_1695067235586.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_3_en_5.1.2_3.0_1695067235586.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_3","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_3", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_3| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-word-swapping-synonym-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_4_en.md b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_4_en.md new file mode 100644 index 00000000000000..a757614ada68db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_4_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_4 AlbertForSequenceClassification from m3 +author: John Snow Labs +name: m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_4 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_4` is a English model originally trained by m3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_4_en_5.1.2_3.0_1695067485305.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_4_en_5.1.2_3.0_1695067485305.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_4","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_4", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m3_experiment_albert_base_v2_tweet_eval_irony_word_swapping_synonym_4| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/m3/m3-experiment-albert-base-v2-tweet-eval-irony-word-swapping-synonym-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-mnli_albert_base_v2_en.md b/docs/_posts/ahmedlone127/2023-09-18-mnli_albert_base_v2_en.md new file mode 100644 index 00000000000000..29e71daaf10d5c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-mnli_albert_base_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English mnli_albert_base_v2 AlbertForSequenceClassification from boychaboy +author: John Snow Labs +name: mnli_albert_base_v2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mnli_albert_base_v2` is a English model originally trained by boychaboy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mnli_albert_base_v2_en_5.1.2_3.0_1695065135816.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mnli_albert_base_v2_en_5.1.2_3.0_1695065135816.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("mnli_albert_base_v2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("mnli_albert_base_v2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mnli_albert_base_v2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/boychaboy/MNLI_albert-base-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-nosql_identifier_albert_en.md b/docs/_posts/ahmedlone127/2023-09-18-nosql_identifier_albert_en.md new file mode 100644 index 00000000000000..c6d44038539605 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-nosql_identifier_albert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English nosql_identifier_albert AlbertForSequenceClassification from ankush-003 +author: John Snow Labs +name: nosql_identifier_albert +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nosql_identifier_albert` is a English model originally trained by ankush-003. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nosql_identifier_albert_en_5.1.2_3.0_1695066381574.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nosql_identifier_albert_en_5.1.2_3.0_1695066381574.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("nosql_identifier_albert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("nosql_identifier_albert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nosql_identifier_albert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/ankush-003/nosql-identifier-albert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-sst2_eda_albert_en.md b/docs/_posts/ahmedlone127/2023-09-18-sst2_eda_albert_en.md new file mode 100644 index 00000000000000..51ab1bdb5f807f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-sst2_eda_albert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English sst2_eda_albert AlbertForSequenceClassification from o2poi +author: John Snow Labs +name: sst2_eda_albert +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sst2_eda_albert` is a English model originally trained by o2poi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sst2_eda_albert_en_5.1.2_3.0_1695065782859.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sst2_eda_albert_en_5.1.2_3.0_1695065782859.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("sst2_eda_albert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("sst2_eda_albert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sst2_eda_albert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/o2poi/sst2-eda-albert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-trecdl22_crossencoder_albert_en.md b/docs/_posts/ahmedlone127/2023-09-18-trecdl22_crossencoder_albert_en.md new file mode 100644 index 00000000000000..5cf75f40893e24 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-trecdl22_crossencoder_albert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English trecdl22_crossencoder_albert AlbertForSequenceClassification from naver +author: John Snow Labs +name: trecdl22_crossencoder_albert +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`trecdl22_crossencoder_albert` is a English model originally trained by naver. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/trecdl22_crossencoder_albert_en_5.1.2_3.0_1695067000082.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/trecdl22_crossencoder_albert_en_5.1.2_3.0_1695067000082.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("trecdl22_crossencoder_albert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("trecdl22_crossencoder_albert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|trecdl22_crossencoder_albert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|425.6 MB| + +## References + +https://huggingface.co/naver/trecdl22-crossencoder-albert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-18-yhi_message_type_paraphrase_albert_small_v2_en.md b/docs/_posts/ahmedlone127/2023-09-18-yhi_message_type_paraphrase_albert_small_v2_en.md new file mode 100644 index 00000000000000..d8468214f0640f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-18-yhi_message_type_paraphrase_albert_small_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English yhi_message_type_paraphrase_albert_small_v2 AlbertForSequenceClassification from cvapict +author: John Snow Labs +name: yhi_message_type_paraphrase_albert_small_v2 +date: 2023-09-18 +tags: [albert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`yhi_message_type_paraphrase_albert_small_v2` is a English model originally trained by cvapict. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/yhi_message_type_paraphrase_albert_small_v2_en_5.1.2_3.0_1695068956108.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/yhi_message_type_paraphrase_albert_small_v2_en_5.1.2_3.0_1695068956108.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForSequenceClassification.pretrained("yhi_message_type_paraphrase_albert_small_v2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForSequenceClassification + .pretrained("yhi_message_type_paraphrase_albert_small_v2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|yhi_message_type_paraphrase_albert_small_v2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|44.2 MB| + +## References + +https://huggingface.co/cvapict/yhi-message-type-paraphrase-albert-small-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_base_finetuned_recipeqa_modified_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_base_finetuned_recipeqa_modified_en.md new file mode 100644 index 00000000000000..705840f9b3c83b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_base_finetuned_recipeqa_modified_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_finetuned_recipeqa_modified AlbertForQuestionAnswering from tamhuynh27 +author: John Snow Labs +name: albert_base_finetuned_recipeqa_modified +date: 2023-09-19 +tags: [albert, en, open_source, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_finetuned_recipeqa_modified` is a English model originally trained by tamhuynh27. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_finetuned_recipeqa_modified_en_5.1.2_3.0_1695096558497.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_finetuned_recipeqa_modified_en_5.1.2_3.0_1695096558497.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = MultiDocumentAssembler() \ + .setInputCol(["question", "context"]) \ + .setOutputCol(["document_question", "document_context"]) + + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_base_finetuned_recipeqa_modified","en") \ + .setInputCols(["document_question","document_context"]) \ + .setOutputCol("answer") + +pipeline = Pipeline().setStages([document_assembler, spanClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new MultiDocumentAssembler() + .setInputCol(Array("question", "context")) + .setOutputCol(Array("document_question", "document_context")) + +val spanClassifier = AlbertForQuestionAnswering + .pretrained("albert_base_finetuned_recipeqa_modified", "en") + .setInputCols(Array("document_question","document_context")) + .setOutputCol("answer") + +val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_finetuned_recipeqa_modified| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|42.0 MB| + +## References + +https://huggingface.co/tamhuynh27/albert-base-finetuned-recipeqa-modified \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_base_qa_squad2_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_base_qa_squad2_en.md new file mode 100644 index 00000000000000..5d87cc00b0df8d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_base_qa_squad2_en.md @@ -0,0 +1,98 @@ +--- +layout: model +title: English AlbertForQuestionAnswering model (from twmkn9) +author: John Snow Labs +name: albert_base_qa_squad2 +date: 2023-09-19 +tags: [open_source, albert, question_answering, en, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `albert-base-v2-squad2` is a English model originally trained by `twmkn9`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_qa_squad2_en_5.1.2_3.0_1695099216275.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_qa_squad2_en_5.1.2_3.0_1695099216275.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = MultiDocumentAssembler() \ +.setInputCols(["question", "context"]) \ +.setOutputCols(["document_question", "document_context"]) + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_base_qa_squad2","en") \ +.setInputCols(["document_question", "document_context"]) \ +.setOutputCol("answer")\ +.setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, spanClassifier]) + +data = spark.createDataFrame([["What is my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new MultiDocumentAssembler() +.setInputCols(Array("question", "context")) +.setOutputCols(Array("document_question", "document_context")) + +val spanClassifer = AlbertForQuestionAnswering.pretrained("albert_base_qa_squad2","en") +.setInputCols(Array("document", "token")) +.setOutputCol("answer") +.setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier)) + +val data = Seq("What is my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.span_question.albert").predict("""What is my name?|||"My name is Clara and I live in Berkeley.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_qa_squad2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|42.0 MB| + +## References + +References + +https://huggingface.co/twmkn9/albert-base-v2-squad2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_base_v2_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_base_v2_en.md new file mode 100644 index 00000000000000..8d2056a3f57e0c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_base_v2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2 AlbertForTokenClassification from vumichien +author: John Snow Labs +name: albert_base_v2 +date: 2023-09-19 +tags: [albert, en, open_source, token_classification, onnx] +task: Named Entity Recognition +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForTokenClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2` is a English model originally trained by vumichien. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_en_5.1.2_3.0_1695087778578.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_en_5.1.2_3.0_1695087778578.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForTokenClassification.pretrained("albert_base_v2","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("ner") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForTokenClassification + .pretrained("albert_base_v2", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[ner]| +|Language:|en| +|Size:|42.0 MB| + +## References + +https://huggingface.co/vumichien/albert-base-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_base_v2_finetuned_squad_attempt_1_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_base_v2_finetuned_squad_attempt_1_en.md new file mode 100644 index 00000000000000..52b55dde7ac724 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_base_v2_finetuned_squad_attempt_1_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_base_v2_finetuned_squad_attempt_1 AlbertForQuestionAnswering from tyuukau +author: John Snow Labs +name: albert_base_v2_finetuned_squad_attempt_1 +date: 2023-09-19 +tags: [albert, en, open_source, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_v2_finetuned_squad_attempt_1` is a English model originally trained by tyuukau. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_v2_finetuned_squad_attempt_1_en_5.1.2_3.0_1695096771216.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_v2_finetuned_squad_attempt_1_en_5.1.2_3.0_1695096771216.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = MultiDocumentAssembler() \ + .setInputCol(["question", "context"]) \ + .setOutputCol(["document_question", "document_context"]) + + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_base_v2_finetuned_squad_attempt_1","en") \ + .setInputCols(["document_question","document_context"]) \ + .setOutputCol("answer") + +pipeline = Pipeline().setStages([document_assembler, spanClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new MultiDocumentAssembler() + .setInputCol(Array("question", "context")) + .setOutputCol(Array("document_question", "document_context")) + +val spanClassifier = AlbertForQuestionAnswering + .pretrained("albert_base_v2_finetuned_squad_attempt_1", "en") + .setInputCols(Array("document_question","document_context")) + .setOutputCol("answer") + +val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_base_v2_finetuned_squad_attempt_1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|42.0 MB| + +## References + +https://huggingface.co/tyuukau/albert-base-v2-finetuned-squad-attempt-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_for_question_answering_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_for_question_answering_en.md new file mode 100644 index 00000000000000..593133c6ae3854 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_for_question_answering_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_for_question_answering AlbertForQuestionAnswering from Zamachi +author: John Snow Labs +name: albert_for_question_answering +date: 2023-09-19 +tags: [albert, en, open_source, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_for_question_answering` is a English model originally trained by Zamachi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_for_question_answering_en_5.1.2_3.0_1695096011865.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_for_question_answering_en_5.1.2_3.0_1695096011865.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = MultiDocumentAssembler() \ + .setInputCol(["question", "context"]) \ + .setOutputCol(["document_question", "document_context"]) + + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_for_question_answering","en") \ + .setInputCols(["document_question","document_context"]) \ + .setOutputCol("answer") + +pipeline = Pipeline().setStages([document_assembler, spanClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new MultiDocumentAssembler() + .setInputCol(Array("question", "context")) + .setOutputCol(Array("document_question", "document_context")) + +val spanClassifier = AlbertForQuestionAnswering + .pretrained("albert_for_question_answering", "en") + .setInputCols(Array("document_question","document_context")) + .setOutputCol("answer") + +val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_for_question_answering| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|41.9 MB| + +## References + +https://huggingface.co/Zamachi/albert-for-question-answering \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_large_v2_ner_conll2003_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_large_v2_ner_conll2003_en.md new file mode 100644 index 00000000000000..03b26b4339a408 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_large_v2_ner_conll2003_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_large_v2_ner_conll2003 AlbertForTokenClassification from Gladiator +author: John Snow Labs +name: albert_large_v2_ner_conll2003 +date: 2023-09-19 +tags: [albert, en, open_source, token_classification, onnx] +task: Named Entity Recognition +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForTokenClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_large_v2_ner_conll2003` is a English model originally trained by Gladiator. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_v2_ner_conll2003_en_5.1.2_3.0_1695087646804.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_v2_ner_conll2003_en_5.1.2_3.0_1695087646804.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForTokenClassification.pretrained("albert_large_v2_ner_conll2003","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("ner") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForTokenClassification + .pretrained("albert_large_v2_ner_conll2003", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_v2_ner_conll2003| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[ner]| +|Language:|en| +|Size:|62.8 MB| + +## References + +https://huggingface.co/Gladiator/albert-large-v2_ner_conll2003 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_large_v2_ner_wikiann_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_large_v2_ner_wikiann_en.md new file mode 100644 index 00000000000000..c90b0b6ecf37f4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_large_v2_ner_wikiann_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_large_v2_ner_wikiann AlbertForTokenClassification from Gladiator +author: John Snow Labs +name: albert_large_v2_ner_wikiann +date: 2023-09-19 +tags: [albert, en, open_source, token_classification, onnx] +task: Named Entity Recognition +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForTokenClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_large_v2_ner_wikiann` is a English model originally trained by Gladiator. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_v2_ner_wikiann_en_5.1.2_3.0_1695087727017.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_v2_ner_wikiann_en_5.1.2_3.0_1695087727017.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForTokenClassification.pretrained("albert_large_v2_ner_wikiann","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("ner") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForTokenClassification + .pretrained("albert_large_v2_ner_wikiann", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_v2_ner_wikiann| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[ner]| +|Language:|en| +|Size:|62.8 MB| + +## References + +https://huggingface.co/Gladiator/albert-large-v2_ner_wikiann \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_large_v2_ner_wnut_17_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_large_v2_ner_wnut_17_en.md new file mode 100644 index 00000000000000..eab66a127c975f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_large_v2_ner_wnut_17_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_large_v2_ner_wnut_17 AlbertForTokenClassification from Gladiator +author: John Snow Labs +name: albert_large_v2_ner_wnut_17 +date: 2023-09-19 +tags: [albert, en, open_source, token_classification, onnx] +task: Named Entity Recognition +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForTokenClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_large_v2_ner_wnut_17` is a English model originally trained by Gladiator. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_v2_ner_wnut_17_en_5.1.2_3.0_1695087559225.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_v2_ner_wnut_17_en_5.1.2_3.0_1695087559225.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForTokenClassification.pretrained("albert_large_v2_ner_wnut_17","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("ner") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForTokenClassification + .pretrained("albert_large_v2_ner_wnut_17", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_v2_ner_wnut_17| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[ner]| +|Language:|en| +|Size:|62.8 MB| + +## References + +https://huggingface.co/Gladiator/albert-large-v2_ner_wnut_17 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_large_v2_spoken_squad_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_large_v2_spoken_squad_en.md new file mode 100644 index 00000000000000..2cbc7eabd28291 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_large_v2_spoken_squad_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_large_v2_spoken_squad AlbertForQuestionAnswering from AshtonIsNotHere +author: John Snow Labs +name: albert_large_v2_spoken_squad +date: 2023-09-19 +tags: [albert, en, open_source, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_large_v2_spoken_squad` is a English model originally trained by AshtonIsNotHere. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_v2_spoken_squad_en_5.1.2_3.0_1695096969866.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_v2_spoken_squad_en_5.1.2_3.0_1695096969866.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = MultiDocumentAssembler() \ + .setInputCol(["question", "context"]) \ + .setOutputCol(["document_question", "document_context"]) + + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_large_v2_spoken_squad","en") \ + .setInputCols(["document_question","document_context"]) \ + .setOutputCol("answer") + +pipeline = Pipeline().setStages([document_assembler, spanClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new MultiDocumentAssembler() + .setInputCol(Array("question", "context")) + .setOutputCol(Array("document_question", "document_context")) + +val spanClassifier = AlbertForQuestionAnswering + .pretrained("albert_large_v2_spoken_squad", "en") + .setInputCols(Array("document_question","document_context")) + .setOutputCol("answer") + +val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_large_v2_spoken_squad| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|62.7 MB| + +## References + +https://huggingface.co/AshtonIsNotHere/albert-large-v2-spoken-squad \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_finetuned_squad_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_finetuned_squad_en.md new file mode 100644 index 00000000000000..0b9e09e9519397 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_finetuned_squad_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_persian_farsi_base_v2_finetuned_squad AlbertForQuestionAnswering from mhmsadegh +author: John Snow Labs +name: albert_persian_farsi_base_v2_finetuned_squad +date: 2023-09-19 +tags: [albert, en, open_source, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_persian_farsi_base_v2_finetuned_squad` is a English model originally trained by mhmsadegh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_persian_farsi_base_v2_finetuned_squad_en_5.1.2_3.0_1695098037048.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_persian_farsi_base_v2_finetuned_squad_en_5.1.2_3.0_1695098037048.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = MultiDocumentAssembler() \ + .setInputCol(["question", "context"]) \ + .setOutputCol(["document_question", "document_context"]) + + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_persian_farsi_base_v2_finetuned_squad","en") \ + .setInputCols(["document_question","document_context"]) \ + .setOutputCol("answer") + +pipeline = Pipeline().setStages([document_assembler, spanClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new MultiDocumentAssembler() + .setInputCol(Array("question", "context")) + .setOutputCol(Array("document_question", "document_context")) + +val spanClassifier = AlbertForQuestionAnswering + .pretrained("albert_persian_farsi_base_v2_finetuned_squad", "en") + .setInputCols(Array("document_question","document_context")) + .setOutputCol("answer") + +val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_persian_farsi_base_v2_finetuned_squad| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|66.3 MB| + +## References + +https://huggingface.co/mhmsadegh/albert-fa-base-v2-finetuned-squad \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_ner_arman_fa.md b/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_ner_arman_fa.md new file mode 100644 index 00000000000000..499823e497a777 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_ner_arman_fa.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Persian albert_persian_farsi_base_v2_ner_arman AlbertForTokenClassification from m3hrdadfi +author: John Snow Labs +name: albert_persian_farsi_base_v2_ner_arman +date: 2023-09-19 +tags: [albert, fa, open_source, token_classification, onnx] +task: Named Entity Recognition +language: fa +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForTokenClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_persian_farsi_base_v2_ner_arman` is a Persian model originally trained by m3hrdadfi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_persian_farsi_base_v2_ner_arman_fa_5.1.2_3.0_1695087624458.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_persian_farsi_base_v2_ner_arman_fa_5.1.2_3.0_1695087624458.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForTokenClassification.pretrained("albert_persian_farsi_base_v2_ner_arman","fa") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("ner") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForTokenClassification + .pretrained("albert_persian_farsi_base_v2_ner_arman", "fa") + .setInputCols(Array("documents","token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_persian_farsi_base_v2_ner_arman| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[ner]| +|Language:|fa| +|Size:|66.4 MB| + +## References + +https://huggingface.co/m3hrdadfi/albert-fa-base-v2-ner-arman \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_ner_peyma_fa.md b/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_ner_peyma_fa.md new file mode 100644 index 00000000000000..5319deb4092215 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_ner_peyma_fa.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Persian albert_persian_farsi_base_v2_ner_peyma AlbertForTokenClassification from m3hrdadfi +author: John Snow Labs +name: albert_persian_farsi_base_v2_ner_peyma +date: 2023-09-19 +tags: [albert, fa, open_source, token_classification, onnx] +task: Named Entity Recognition +language: fa +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForTokenClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_persian_farsi_base_v2_ner_peyma` is a Persian model originally trained by m3hrdadfi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_persian_farsi_base_v2_ner_peyma_fa_5.1.2_3.0_1695087715650.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_persian_farsi_base_v2_ner_peyma_fa_5.1.2_3.0_1695087715650.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForTokenClassification.pretrained("albert_persian_farsi_base_v2_ner_peyma","fa") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("ner") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForTokenClassification + .pretrained("albert_persian_farsi_base_v2_ner_peyma", "fa") + .setInputCols(Array("documents","token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_persian_farsi_base_v2_ner_peyma| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[ner]| +|Language:|fa| +|Size:|66.4 MB| + +## References + +https://huggingface.co/m3hrdadfi/albert-fa-base-v2-ner-peyma \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_parsquad_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_parsquad_en.md new file mode 100644 index 00000000000000..1ca217c2832ed5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_parsquad_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_persian_farsi_base_v2_parsquad AlbertForQuestionAnswering from mohsenfayyaz +author: John Snow Labs +name: albert_persian_farsi_base_v2_parsquad +date: 2023-09-19 +tags: [albert, en, open_source, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_persian_farsi_base_v2_parsquad` is a English model originally trained by mohsenfayyaz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_persian_farsi_base_v2_parsquad_en_5.1.2_3.0_1695098252255.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_persian_farsi_base_v2_parsquad_en_5.1.2_3.0_1695098252255.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = MultiDocumentAssembler() \ + .setInputCol(["question", "context"]) \ + .setOutputCol(["document_question", "document_context"]) + + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_persian_farsi_base_v2_parsquad","en") \ + .setInputCols(["document_question","document_context"]) \ + .setOutputCol("answer") + +pipeline = Pipeline().setStages([document_assembler, spanClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new MultiDocumentAssembler() + .setInputCol(Array("question", "context")) + .setOutputCol(Array("document_question", "document_context")) + +val spanClassifier = AlbertForQuestionAnswering + .pretrained("albert_persian_farsi_base_v2_parsquad", "en") + .setInputCols(Array("document_question","document_context")) + .setOutputCol("answer") + +val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_persian_farsi_base_v2_parsquad| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|66.3 MB| + +## References + +https://huggingface.co/mohsenfayyaz/albert-fa-base-v2_parsquad \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_persian_qa_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_persian_qa_en.md new file mode 100644 index 00000000000000..ede1f6eb70f62d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_persian_qa_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_persian_farsi_base_v2_persian_qa AlbertForQuestionAnswering from mohsenfayyaz +author: John Snow Labs +name: albert_persian_farsi_base_v2_persian_qa +date: 2023-09-19 +tags: [albert, en, open_source, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_persian_farsi_base_v2_persian_qa` is a English model originally trained by mohsenfayyaz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_persian_farsi_base_v2_persian_qa_en_5.1.2_3.0_1695098180715.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_persian_farsi_base_v2_persian_qa_en_5.1.2_3.0_1695098180715.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = MultiDocumentAssembler() \ + .setInputCol(["question", "context"]) \ + .setOutputCol(["document_question", "document_context"]) + + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_persian_farsi_base_v2_persian_qa","en") \ + .setInputCols(["document_question","document_context"]) \ + .setOutputCol("answer") + +pipeline = Pipeline().setStages([document_assembler, spanClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new MultiDocumentAssembler() + .setInputCol(Array("question", "context")) + .setOutputCol(Array("document_question", "document_context")) + +val spanClassifier = AlbertForQuestionAnswering + .pretrained("albert_persian_farsi_base_v2_persian_qa", "en") + .setInputCols(Array("document_question","document_context")) + .setOutputCol("answer") + +val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_persian_farsi_base_v2_persian_qa| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|66.3 MB| + +## References + +https://huggingface.co/mohsenfayyaz/albert-fa-base-v2_persian_qa \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_pquad_and_persian_qa_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_pquad_and_persian_qa_en.md new file mode 100644 index 00000000000000..00570a14d286bc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_pquad_and_persian_qa_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_persian_farsi_base_v2_pquad_and_persian_qa AlbertForQuestionAnswering from mohsenfayyaz +author: John Snow Labs +name: albert_persian_farsi_base_v2_pquad_and_persian_qa +date: 2023-09-19 +tags: [albert, en, open_source, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_persian_farsi_base_v2_pquad_and_persian_qa` is a English model originally trained by mohsenfayyaz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_persian_farsi_base_v2_pquad_and_persian_qa_en_5.1.2_3.0_1695098320355.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_persian_farsi_base_v2_pquad_and_persian_qa_en_5.1.2_3.0_1695098320355.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = MultiDocumentAssembler() \ + .setInputCol(["question", "context"]) \ + .setOutputCol(["document_question", "document_context"]) + + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_persian_farsi_base_v2_pquad_and_persian_qa","en") \ + .setInputCols(["document_question","document_context"]) \ + .setOutputCol("answer") + +pipeline = Pipeline().setStages([document_assembler, spanClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new MultiDocumentAssembler() + .setInputCol(Array("question", "context")) + .setOutputCol(Array("document_question", "document_context")) + +val spanClassifier = AlbertForQuestionAnswering + .pretrained("albert_persian_farsi_base_v2_pquad_and_persian_qa", "en") + .setInputCols(Array("document_question","document_context")) + .setOutputCol("answer") + +val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_persian_farsi_base_v2_pquad_and_persian_qa| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|66.3 MB| + +## References + +https://huggingface.co/mohsenfayyaz/albert-fa-base-v2_pquad_and_persian_qa \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_pquad_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_pquad_en.md new file mode 100644 index 00000000000000..220f0ba4c0d54b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_base_v2_pquad_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_persian_farsi_base_v2_pquad AlbertForQuestionAnswering from mohsenfayyaz +author: John Snow Labs +name: albert_persian_farsi_base_v2_pquad +date: 2023-09-19 +tags: [albert, en, open_source, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_persian_farsi_base_v2_pquad` is a English model originally trained by mohsenfayyaz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_persian_farsi_base_v2_pquad_en_5.1.2_3.0_1695098109290.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_persian_farsi_base_v2_pquad_en_5.1.2_3.0_1695098109290.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = MultiDocumentAssembler() \ + .setInputCol(["question", "context"]) \ + .setOutputCol(["document_question", "document_context"]) + + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_persian_farsi_base_v2_pquad","en") \ + .setInputCols(["document_question","document_context"]) \ + .setOutputCol("answer") + +pipeline = Pipeline().setStages([document_assembler, spanClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new MultiDocumentAssembler() + .setInputCol(Array("question", "context")) + .setOutputCol(Array("document_question", "document_context")) + +val spanClassifier = AlbertForQuestionAnswering + .pretrained("albert_persian_farsi_base_v2_pquad", "en") + .setInputCols(Array("document_question","document_context")) + .setOutputCol("answer") + +val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_persian_farsi_base_v2_pquad| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|66.3 MB| + +## References + +https://huggingface.co/mohsenfayyaz/albert-fa-base-v2_pquad \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_zwnj_base_v2_ner_fa.md b/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_zwnj_base_v2_ner_fa.md new file mode 100644 index 00000000000000..958dd83bf9889e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_persian_farsi_zwnj_base_v2_ner_fa.md @@ -0,0 +1,93 @@ +--- +layout: model +title: Persian albert_persian_farsi_zwnj_base_v2_ner AlbertForTokenClassification from HooshvareLab +author: John Snow Labs +name: albert_persian_farsi_zwnj_base_v2_ner +date: 2023-09-19 +tags: [albert, fa, open_source, token_classification, onnx] +task: Named Entity Recognition +language: fa +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForTokenClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_persian_farsi_zwnj_base_v2_ner` is a Persian model originally trained by HooshvareLab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_persian_farsi_zwnj_base_v2_ner_fa_5.1.2_3.0_1695087008371.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_persian_farsi_zwnj_base_v2_ner_fa_5.1.2_3.0_1695087008371.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForTokenClassification.pretrained("albert_persian_farsi_zwnj_base_v2_ner","fa") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("ner") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForTokenClassification + .pretrained("albert_persian_farsi_zwnj_base_v2_ner", "fa") + .setInputCols(Array("documents","token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_persian_farsi_zwnj_base_v2_ner| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[ner]| +|Language:|fa| +|Size:|42.0 MB| + +## References + +https://huggingface.co/HooshvareLab/albert-fa-zwnj-base-v2-ner \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_qa_base_v2_squad_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_base_v2_squad_en.md new file mode 100644 index 00000000000000..1677838e86281e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_base_v2_squad_en.md @@ -0,0 +1,99 @@ +--- +layout: model +title: English AlbertForQuestionAnswering model (from madlag) +author: John Snow Labs +name: albert_qa_base_v2_squad +date: 2023-09-19 +tags: [en, open_source, albert, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `albert-base-v2-squad` is a English model originally trained by `madlag`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_qa_base_v2_squad_en_5.1.2_3.0_1695098215184.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_qa_base_v2_squad_en_5.1.2_3.0_1695098215184.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = MultiDocumentAssembler() \ +.setInputCols(["question", "context"]) \ +.setOutputCols(["document_question", "document_context"]) + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_qa_base_v2_squad","en") \ +.setInputCols(["document_question", "document_context"]) \ +.setOutputCol("answer")\ +.setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, spanClassifier]) + +data = spark.createDataFrame([["What is my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new MultiDocumentAssembler() +.setInputCols(Array("question", "context")) +.setOutputCols(Array("document_question", "document_context")) + +val spanClassifer = AlbertForQuestionAnswering.pretrained("albert_qa_base_v2_squad","en") +.setInputCols(Array("document", "token")) +.setOutputCol("answer") +.setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier)) + +val data = Seq("What is my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.answer_question.squad.albert.base_v2").predict("""What is my name?|||"My name is Clara and I live in Berkeley.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_qa_base_v2_squad| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|42.0 MB| + +## References + +References + +- https://huggingface.co/madlag/albert-base-v2-squad +- https://github.com/google-research/albert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_qa_cs224n_squad2.0_base_v2_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_cs224n_squad2.0_base_v2_en.md new file mode 100644 index 00000000000000..34b2c420f80759 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_cs224n_squad2.0_base_v2_en.md @@ -0,0 +1,101 @@ +--- +layout: model +title: English AlbertForQuestionAnswering Base model (from elgeish) +author: John Snow Labs +name: albert_qa_cs224n_squad2.0_base_v2 +date: 2023-09-19 +tags: [en, open_source, albert, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `cs224n-squad2.0-albert-base-v2` is a English model originally trained by `elgeish`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_qa_cs224n_squad2.0_base_v2_en_5.1.2_3.0_1695097578480.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_qa_cs224n_squad2.0_base_v2_en_5.1.2_3.0_1695097578480.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = MultiDocumentAssembler() \ +.setInputCols(["question", "context"]) \ +.setOutputCols(["document_question", "document_context"]) + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_qa_cs224n_squad2.0_base_v2","en") \ +.setInputCols(["document_question", "document_context"]) \ +.setOutputCol("answer")\ +.setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, spanClassifier]) + +data = spark.createDataFrame([["What is my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new MultiDocumentAssembler() +.setInputCols(Array("question", "context")) +.setOutputCols(Array("document_question", "document_context")) + +val spanClassifer = AlbertForQuestionAnswering.pretrained("albert_qa_cs224n_squad2.0_base_v2","en") +.setInputCols(Array("document", "token")) +.setOutputCol("answer") +.setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier)) + +val data = Seq("What is my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.answer_question.squadv2.albert.base_v2.by_elgeish").predict("""What is my name?|||"My name is Clara and I live in Berkeley.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_qa_cs224n_squad2.0_base_v2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|42.0 MB| + +## References + +References + +- https://huggingface.co/elgeish/cs224n-squad2.0-albert-base-v2 +- http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf +- https://rajpurkar.github.io/SQuAD-explorer/ +- https://github.com/elgeish/squad/tree/master/data \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_qa_cs224n_squad2.0_large_v2_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_cs224n_squad2.0_large_v2_en.md new file mode 100644 index 00000000000000..0d62800a8ea89b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_cs224n_squad2.0_large_v2_en.md @@ -0,0 +1,101 @@ +--- +layout: model +title: English AlbertForQuestionAnswering Large model (from elgeish) +author: John Snow Labs +name: albert_qa_cs224n_squad2.0_large_v2 +date: 2023-09-19 +tags: [en, open_source, albert, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `cs224n-squad2.0-albert-large-v2` is a English model originally trained by `elgeish`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_qa_cs224n_squad2.0_large_v2_en_5.1.2_3.0_1695097676421.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_qa_cs224n_squad2.0_large_v2_en_5.1.2_3.0_1695097676421.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = MultiDocumentAssembler() \ +.setInputCols(["question", "context"]) \ +.setOutputCols(["document_question", "document_context"]) + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_qa_cs224n_squad2.0_large_v2","en") \ +.setInputCols(["document_question", "document_context"]) \ +.setOutputCol("answer")\ +.setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, spanClassifier]) + +data = spark.createDataFrame([["What is my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new MultiDocumentAssembler() +.setInputCols(Array("question", "context")) +.setOutputCols(Array("document_question", "document_context")) + +val spanClassifer = AlbertForQuestionAnswering.pretrained("albert_qa_cs224n_squad2.0_large_v2","en") +.setInputCols(Array("document", "token")) +.setOutputCol("answer") +.setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier)) + +val data = Seq("What is my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.answer_question.squadv2.albert.large_v2").predict("""What is my name?|||"My name is Clara and I live in Berkeley.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_qa_cs224n_squad2.0_large_v2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|62.7 MB| + +## References + +References + +- https://huggingface.co/elgeish/cs224n-squad2.0-albert-large-v2 +- http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf +- https://rajpurkar.github.io/SQuAD-explorer/ +- https://github.com/elgeish/squad/tree/master/data \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_qa_cs224n_squad2.0_xxlarge_v1_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_cs224n_squad2.0_xxlarge_v1_en.md new file mode 100644 index 00000000000000..4551ac8f6ac268 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_cs224n_squad2.0_xxlarge_v1_en.md @@ -0,0 +1,101 @@ +--- +layout: model +title: English AlbertForQuestionAnswering XXLarge model (from elgeish) +author: John Snow Labs +name: albert_qa_cs224n_squad2.0_xxlarge_v1 +date: 2023-09-19 +tags: [en, open_source, albert, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `cs224n-squad2.0-albert-xxlarge-v1` is a English model originally trained by `elgeish`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_qa_cs224n_squad2.0_xxlarge_v1_en_5.1.2_3.0_1695097965207.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_qa_cs224n_squad2.0_xxlarge_v1_en_5.1.2_3.0_1695097965207.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = MultiDocumentAssembler() \ +.setInputCols(["question", "context"]) \ +.setOutputCols(["document_question", "document_context"]) + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_qa_cs224n_squad2.0_xxlarge_v1","en") \ +.setInputCols(["document_question", "document_context"]) \ +.setOutputCol("answer")\ +.setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, spanClassifier]) + +data = spark.createDataFrame([["What is my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new MultiDocumentAssembler() +.setInputCols(Array("question", "context")) +.setOutputCols(Array("document_question", "document_context")) + +val spanClassifer = AlbertForQuestionAnswering.pretrained("albert_qa_cs224n_squad2.0_xxlarge_v1","en") +.setInputCols(Array("document", "token")) +.setOutputCol("answer") +.setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier)) + +val data = Seq("What is my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.answer_question.squadv2.albert.xxl.by_elgeish").predict("""What is my name?|||"My name is Clara and I live in Berkeley.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_qa_cs224n_squad2.0_xxlarge_v1| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|771.7 MB| + +## References + +References + +- https://huggingface.co/elgeish/cs224n-squad2.0-albert-xxlarge-v1 +- http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf +- https://rajpurkar.github.io/SQuAD-explorer/ +- https://github.com/elgeish/squad/tree/master/data \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_qa_nlpunibo_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_nlpunibo_en.md new file mode 100644 index 00000000000000..98c6b1e9e230e3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_nlpunibo_en.md @@ -0,0 +1,98 @@ +--- +layout: model +title: English AlbertForQuestionAnswering model (from nlpunibo) +author: John Snow Labs +name: albert_qa_nlpunibo +date: 2023-09-19 +tags: [en, open_source, albert, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `albert` is a English model originally trained by `nlpunibo`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_qa_nlpunibo_en_5.1.2_3.0_1695098552119.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_qa_nlpunibo_en_5.1.2_3.0_1695098552119.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = MultiDocumentAssembler() \ +.setInputCols(["question", "context"]) \ +.setOutputCols(["document_question", "document_context"]) + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_qa_nlpunibo","en") \ +.setInputCols(["document_question", "document_context"]) \ +.setOutputCol("answer")\ +.setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, spanClassifier]) + +data = spark.createDataFrame([["What is my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new MultiDocumentAssembler() +.setInputCols(Array("question", "context")) +.setOutputCols(Array("document_question", "document_context")) + +val spanClassifer = AlbertForQuestionAnswering.pretrained("albert_qa_nlpunibo","en") +.setInputCols(Array("document", "token")) +.setOutputCol("answer") +.setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier)) + +val data = Seq("What is my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.answer_question.albert.by_nlpunibo").predict("""What is my name?|||"My name is Clara and I live in Berkeley.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_qa_nlpunibo| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|42.0 MB| + +## References + +References + +- https://huggingface.co/nlpunibo/albert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_qa_slp_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_slp_en.md new file mode 100644 index 00000000000000..e6340e88813afe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_slp_en.md @@ -0,0 +1,98 @@ +--- +layout: model +title: English AlbertForQuestionAnswering model (from rowan1224) +author: John Snow Labs +name: albert_qa_slp +date: 2023-09-19 +tags: [en, open_source, albert, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `albert-slp` is a English model originally trained by `rowan1224`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_qa_slp_en_5.1.2_3.0_1695096583068.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_qa_slp_en_5.1.2_3.0_1695096583068.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = MultiDocumentAssembler() \ +.setInputCols(["question", "context"]) \ +.setOutputCols(["document_question", "document_context"]) + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_qa_slp","en") \ +.setInputCols(["document_question", "document_context"]) \ +.setOutputCol("answer")\ +.setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, spanClassifier]) + +data = spark.createDataFrame([["What is my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new MultiDocumentAssembler() +.setInputCols(Array("question", "context")) +.setOutputCols(Array("document_question", "document_context")) + +val spanClassifer = AlbertForQuestionAnswering.pretrained("albert_qa_slp","en") +.setInputCols(Array("document", "token")) +.setOutputCol("answer") +.setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier)) + +val data = Seq("What is my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.answer_question.albert.by_rowan1224").predict("""What is my name?|||"My name is Clara and I live in Berkeley.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_qa_slp| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|42.0 MB| + +## References + +References + +- https://huggingface.co/rowan1224/albert-slp \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_qa_squad_slp_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_squad_slp_en.md new file mode 100644 index 00000000000000..166f7655453766 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_squad_slp_en.md @@ -0,0 +1,98 @@ +--- +layout: model +title: English AlbertForQuestionAnswering model (from rowan1224) Squad +author: John Snow Labs +name: albert_qa_squad_slp +date: 2023-09-19 +tags: [en, open_source, albert, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `albert-squad-slp` is a English model originally trained by `rowan1224`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_qa_squad_slp_en_5.1.2_3.0_1695096478522.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_qa_squad_slp_en_5.1.2_3.0_1695096478522.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = MultiDocumentAssembler() \ +.setInputCols(["question", "context"]) \ +.setOutputCols(["document_question", "document_context"]) + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_qa_squad_slp","en") \ +.setInputCols(["document_question", "document_context"]) \ +.setOutputCol("answer")\ +.setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, spanClassifier]) + +data = spark.createDataFrame([["What is my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new MultiDocumentAssembler() +.setInputCols(Array("question", "context")) +.setOutputCols(Array("document_question", "document_context")) + +val spanClassifer = AlbertForQuestionAnswering.pretrained("albert_qa_squad_slp","en") +.setInputCols(Array("document", "token")) +.setOutputCol("answer") +.setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier)) + +val data = Seq("What is my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.answer_question.squad.albert.by_rowan1224").predict("""What is my name?|||"My name is Clara and I live in Berkeley.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_qa_squad_slp| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|42.0 MB| + +## References + +References + +- https://huggingface.co/rowan1224/albert-squad-slp \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xlarge_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xlarge_finetuned_en.md new file mode 100644 index 00000000000000..7184538489c9ad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xlarge_finetuned_en.md @@ -0,0 +1,99 @@ +--- +layout: model +title: English AlbertForQuestionAnswering model (from 123tarunanand) +author: John Snow Labs +name: albert_qa_xlarge_finetuned +date: 2023-09-19 +tags: [en, open_source, albert, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `albert-xlarge-finetuned` is a English model originally trained by `123tarunanand`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_qa_xlarge_finetuned_en_5.1.2_3.0_1695097465378.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_qa_xlarge_finetuned_en_5.1.2_3.0_1695097465378.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = MultiDocumentAssembler() \ +.setInputCols(["question", "context"]) \ +.setOutputCols(["document_question", "document_context"]) + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_qa_xlarge_finetuned","en") \ +.setInputCols(["document_question", "document_context"]) \ +.setOutputCol("answer")\ +.setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, spanClassifier]) + +data = spark.createDataFrame([["What is my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new MultiDocumentAssembler() +.setInputCols(Array("question", "context")) +.setOutputCols(Array("document_question", "document_context")) + +val spanClassifer = AlbertForQuestionAnswering.pretrained("albert_qa_xlarge_finetuned","en") +.setInputCols(Array("document", "token")) +.setOutputCol("answer") +.setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier)) + +val data = Seq("What is my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.answer_question.albert.xl").predict("""What is my name?|||"My name is Clara and I live in Berkeley.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_qa_xlarge_finetuned| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|204.7 MB| + +## References + +References + +- https://huggingface.co/123tarunanand/albert-xlarge-finetuned +- https://rajpurkar.github.io/SQuAD-explorer/ \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xlarge_v2_squad_v2_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xlarge_v2_squad_v2_en.md new file mode 100644 index 00000000000000..9d930ff6604448 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xlarge_v2_squad_v2_en.md @@ -0,0 +1,99 @@ +--- +layout: model +title: English AlbertForQuestionAnswering model (from ktrapeznikov) +author: John Snow Labs +name: albert_qa_xlarge_v2_squad_v2 +date: 2023-09-19 +tags: [en, open_source, albert, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `albert-xlarge-v2-squad-v2` is a English model originally trained by `ktrapeznikov`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_qa_xlarge_v2_squad_v2_en_5.1.2_3.0_1695098142567.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_qa_xlarge_v2_squad_v2_en_5.1.2_3.0_1695098142567.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = MultiDocumentAssembler() \ +.setInputCols(["question", "context"]) \ +.setOutputCols(["document_question", "document_context"]) + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_qa_xlarge_v2_squad_v2","en") \ +.setInputCols(["document_question", "document_context"]) \ +.setOutputCol("answer")\ +.setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, spanClassifier]) + +data = spark.createDataFrame([["What is my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new MultiDocumentAssembler() +.setInputCols(Array("question", "context")) +.setOutputCols(Array("document_question", "document_context")) + +val spanClassifer = AlbertForQuestionAnswering.pretrained("albert_qa_xlarge_v2_squad_v2","en") +.setInputCols(Array("document", "token")) +.setOutputCol("answer") +.setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier)) + +val data = Seq("What is my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.answer_question.squadv2.albert.xl_v2").predict("""What is my name?|||"My name is Clara and I live in Berkeley.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_qa_xlarge_v2_squad_v2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|204.7 MB| + +## References + +References + +- https://huggingface.co/ktrapeznikov/albert-xlarge-v2-squad-v2 +- https://rajpurkar.github.io/SQuAD-explorer/ \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xxlarge_tweetqa_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xxlarge_tweetqa_en.md new file mode 100644 index 00000000000000..a4586ff1274957 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xxlarge_tweetqa_en.md @@ -0,0 +1,98 @@ +--- +layout: model +title: English AlbertForQuestionAnswering model (from saburbutt) TweetQA +author: John Snow Labs +name: albert_qa_xxlarge_tweetqa +date: 2023-09-19 +tags: [en, open_source, albert, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `albert_xxlarge_tweetqa` is a English model originally trained by `saburbutt`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_qa_xxlarge_tweetqa_en_5.1.2_3.0_1695099069331.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_qa_xxlarge_tweetqa_en_5.1.2_3.0_1695099069331.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = MultiDocumentAssembler() \ +.setInputCols(["question", "context"]) \ +.setOutputCols(["document_question", "document_context"]) + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_qa_xxlarge_tweetqa","en") \ +.setInputCols(["document_question", "document_context"]) \ +.setOutputCol("answer")\ +.setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, spanClassifier]) + +data = spark.createDataFrame([["What is my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new MultiDocumentAssembler() +.setInputCols(Array("question", "context")) +.setOutputCols(Array("document_question", "document_context")) + +val spanClassifer = AlbertForQuestionAnswering.pretrained("albert_qa_xxlarge_tweetqa","en") +.setInputCols(Array("document", "token")) +.setOutputCol("answer") +.setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier)) + +val data = Seq("What is my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.answer_question.trivia.albert.xxl").predict("""What is my name?|||"My name is Clara and I live in Berkeley.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_qa_xxlarge_tweetqa| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|771.0 MB| + +## References + +References + +- https://huggingface.co/saburbutt/albert_xxlarge_tweetqa \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xxlarge_v1_finetuned_squad2_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xxlarge_v1_finetuned_squad2_en.md new file mode 100644 index 00000000000000..47363f974f87cc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xxlarge_v1_finetuned_squad2_en.md @@ -0,0 +1,98 @@ +--- +layout: model +title: English AlbertForQuestionAnswering model (from replydotai) +author: John Snow Labs +name: albert_qa_xxlarge_v1_finetuned_squad2 +date: 2023-09-19 +tags: [en, open_source, albert, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `albert-xxlarge-v1-finetuned-squad2` is a English model originally trained by `replydotai`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_qa_xxlarge_v1_finetuned_squad2_en_5.1.2_3.0_1695098816257.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_qa_xxlarge_v1_finetuned_squad2_en_5.1.2_3.0_1695098816257.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = MultiDocumentAssembler() \ +.setInputCols(["question", "context"]) \ +.setOutputCols(["document_question", "document_context"]) + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_qa_xxlarge_v1_finetuned_squad2","en") \ +.setInputCols(["document_question", "document_context"]) \ +.setOutputCol("answer")\ +.setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, spanClassifier]) + +data = spark.createDataFrame([["What is my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new MultiDocumentAssembler() +.setInputCols(Array("question", "context")) +.setOutputCols(Array("document_question", "document_context")) + +val spanClassifer = AlbertForQuestionAnswering.pretrained("albert_qa_xxlarge_v1_finetuned_squad2","en") +.setInputCols(Array("document", "token")) +.setOutputCol("answer") +.setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier)) + +val data = Seq("What is my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.answer_question.squadv2.albert.xxl").predict("""What is my name?|||"My name is Clara and I live in Berkeley.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_qa_xxlarge_v1_finetuned_squad2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|771.7 MB| + +## References + +References + +- https://huggingface.co/replydotai/albert-xxlarge-v1-finetuned-squad2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xxlarge_v2_squad2_covid_deepset_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xxlarge_v2_squad2_covid_deepset_en.md new file mode 100644 index 00000000000000..9f17fa07455d41 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xxlarge_v2_squad2_covid_deepset_en.md @@ -0,0 +1,98 @@ +--- +layout: model +title: English AlbertForQuestionAnswering model (from armageddon) +author: John Snow Labs +name: albert_qa_xxlarge_v2_squad2_covid_deepset +date: 2023-09-19 +tags: [en, open_source, albert, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `albert-xxlarge-v2-squad2-covid-qa-deepset` is a English model originally trained by `armageddon`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_qa_xxlarge_v2_squad2_covid_deepset_en_5.1.2_3.0_1695097330861.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_qa_xxlarge_v2_squad2_covid_deepset_en_5.1.2_3.0_1695097330861.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = MultiDocumentAssembler() \ +.setInputCols(["question", "context"]) \ +.setOutputCols(["document_question", "document_context"]) + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_qa_xxlarge_v2_squad2_covid_deepset","en") \ +.setInputCols(["document_question", "document_context"]) \ +.setOutputCol("answer")\ +.setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, spanClassifier]) + +data = spark.createDataFrame([["What is my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new MultiDocumentAssembler() +.setInputCols(Array("question", "context")) +.setOutputCols(Array("document_question", "document_context")) + +val spanClassifer = AlbertForQuestionAnswering.pretrained("albert_qa_xxlarge_v2_squad2_covid_deepset","en") +.setInputCols(Array("document", "token")) +.setOutputCol("answer") +.setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier)) + +val data = Seq("What is my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.answer_question.squadv2_covid.albert.xxl_v2").predict("""What is my name?|||"My name is Clara and I live in Berkeley.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_qa_xxlarge_v2_squad2_covid_deepset| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|771.0 MB| + +## References + +References + +- https://huggingface.co/armageddon/albert-xxlarge-v2-squad2-covid-qa-deepset \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xxlarge_v2_squad2_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xxlarge_v2_squad2_en.md new file mode 100644 index 00000000000000..95a5a7d77c667c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xxlarge_v2_squad2_en.md @@ -0,0 +1,98 @@ +--- +layout: model +title: English AlbertForQuestionAnswering model (from mfeb) +author: John Snow Labs +name: albert_qa_xxlarge_v2_squad2 +date: 2023-09-19 +tags: [en, open_source, albert, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `albert-xxlarge-v2-squad2` is a English model originally trained by `mfeb`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_qa_xxlarge_v2_squad2_en_5.1.2_3.0_1695098470262.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_qa_xxlarge_v2_squad2_en_5.1.2_3.0_1695098470262.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = MultiDocumentAssembler() \ +.setInputCols(["question", "context"]) \ +.setOutputCols(["document_question", "document_context"]) + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_qa_xxlarge_v2_squad2","en") \ +.setInputCols(["document_question", "document_context"]) \ +.setOutputCol("answer")\ +.setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, spanClassifier]) + +data = spark.createDataFrame([["What is my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new MultiDocumentAssembler() +.setInputCols(Array("question", "context")) +.setOutputCols(Array("document_question", "document_context")) + +val spanClassifer = AlbertForQuestionAnswering.pretrained("albert_qa_xxlarge_v2_squad2","en") +.setInputCols(Array("document", "token")) +.setOutputCol("answer") +.setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier)) + +val data = Seq("What is my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.answer_question.squadv2.albert.xxl_v2").predict("""What is my name?|||"My name is Clara and I live in Berkeley.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_qa_xxlarge_v2_squad2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|771.0 MB| + +## References + +References + +- https://huggingface.co/mfeb/albert-xxlarge-v2-squad2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xxlargev1_squad2_512_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xxlargev1_squad2_512_en.md new file mode 100644 index 00000000000000..fb4aafad120fee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_qa_xxlargev1_squad2_512_en.md @@ -0,0 +1,98 @@ +--- +layout: model +title: English AlbertForQuestionAnswering model (from ahotrod) +author: John Snow Labs +name: albert_qa_xxlargev1_squad2_512 +date: 2023-09-19 +tags: [en, open_source, albert, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `albert_xxlargev1_squad2_512` is a English model originally trained by `ahotrod`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_qa_xxlargev1_squad2_512_en_5.1.2_3.0_1695097007309.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_qa_xxlargev1_squad2_512_en_5.1.2_3.0_1695097007309.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = MultiDocumentAssembler() \ +.setInputCols(["question", "context"]) \ +.setOutputCols(["document_question", "document_context"]) + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_qa_xxlargev1_squad2_512","en") \ +.setInputCols(["document_question", "document_context"]) \ +.setOutputCol("answer")\ +.setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, spanClassifier]) + +data = spark.createDataFrame([["What is my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new MultiDocumentAssembler() +.setInputCols(Array("question", "context")) +.setOutputCols(Array("document_question", "document_context")) + +val spanClassifer = AlbertForQuestionAnswering.pretrained("albert_qa_xxlargev1_squad2_512","en") +.setInputCols(Array("document", "token")) +.setOutputCol("answer") +.setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier)) + +val data = Seq("What is my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.answer_question.squadv2.albert.xxl_512d").predict("""What is my name?|||"My name is Clara and I live in Berkeley.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_qa_xxlargev1_squad2_512| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|771.7 MB| + +## References + +References + +- https://huggingface.co/ahotrod/albert_xxlargev1_squad2_512 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_v2_base_finetuned_recipeqa_modified_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_v2_base_finetuned_recipeqa_modified_en.md new file mode 100644 index 00000000000000..89e1963a6e38e5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_v2_base_finetuned_recipeqa_modified_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_v2_base_finetuned_recipeqa_modified AlbertForQuestionAnswering from tamhuynh27 +author: John Snow Labs +name: albert_v2_base_finetuned_recipeqa_modified +date: 2023-09-19 +tags: [albert, en, open_source, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_v2_base_finetuned_recipeqa_modified` is a English model originally trained by tamhuynh27. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_v2_base_finetuned_recipeqa_modified_en_5.1.2_3.0_1695096628837.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_v2_base_finetuned_recipeqa_modified_en_5.1.2_3.0_1695096628837.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = MultiDocumentAssembler() \ + .setInputCol(["question", "context"]) \ + .setOutputCol(["document_question", "document_context"]) + + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_v2_base_finetuned_recipeqa_modified","en") \ + .setInputCols(["document_question","document_context"]) \ + .setOutputCol("answer") + +pipeline = Pipeline().setStages([document_assembler, spanClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new MultiDocumentAssembler() + .setInputCol(Array("question", "context")) + .setOutputCol(Array("document_question", "document_context")) + +val spanClassifier = AlbertForQuestionAnswering + .pretrained("albert_v2_base_finetuned_recipeqa_modified", "en") + .setInputCols(Array("document_question","document_context")) + .setOutputCol("answer") + +val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_v2_base_finetuned_recipeqa_modified| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|42.0 MB| + +## References + +https://huggingface.co/tamhuynh27/albert-v2-base-finetuned-recipeqa-modified \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_xl_v2_finetuned_squad_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_xl_v2_finetuned_squad_en.md new file mode 100644 index 00000000000000..75feb480116802 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_xl_v2_finetuned_squad_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_xl_v2_finetuned_squad AlbertForQuestionAnswering from anas-awadalla +author: John Snow Labs +name: albert_xl_v2_finetuned_squad +date: 2023-09-19 +tags: [albert, en, open_source, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_xl_v2_finetuned_squad` is a English model originally trained by anas-awadalla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_xl_v2_finetuned_squad_en_5.1.2_3.0_1695097658112.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_xl_v2_finetuned_squad_en_5.1.2_3.0_1695097658112.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = MultiDocumentAssembler() \ + .setInputCol(["question", "context"]) \ + .setOutputCol(["document_question", "document_context"]) + + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_xl_v2_finetuned_squad","en") \ + .setInputCols(["document_question","document_context"]) \ + .setOutputCol("answer") + +pipeline = Pipeline().setStages([document_assembler, spanClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new MultiDocumentAssembler() + .setInputCol(Array("question", "context")) + .setOutputCol(Array("document_question", "document_context")) + +val spanClassifier = AlbertForQuestionAnswering + .pretrained("albert_xl_v2_finetuned_squad", "en") + .setInputCols(Array("document_question","document_context")) + .setOutputCol("answer") + +val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_xl_v2_finetuned_squad| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|204.7 MB| + +## References + +https://huggingface.co/anas-awadalla/albert-xl-v2-finetuned-squad \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_xlarge_squad_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_xlarge_squad_finetuned_en.md new file mode 100644 index 00000000000000..cfa74cae3399f3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_xlarge_squad_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_xlarge_squad_finetuned AlbertForQuestionAnswering from rahulchakwate +author: John Snow Labs +name: albert_xlarge_squad_finetuned +date: 2023-09-19 +tags: [albert, en, open_source, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_xlarge_squad_finetuned` is a English model originally trained by rahulchakwate. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_xlarge_squad_finetuned_en_5.1.2_3.0_1695096239337.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_xlarge_squad_finetuned_en_5.1.2_3.0_1695096239337.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = MultiDocumentAssembler() \ + .setInputCol(["question", "context"]) \ + .setOutputCol(["document_question", "document_context"]) + + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_xlarge_squad_finetuned","en") \ + .setInputCols(["document_question","document_context"]) \ + .setOutputCol("answer") + +pipeline = Pipeline().setStages([document_assembler, spanClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new MultiDocumentAssembler() + .setInputCol(Array("question", "context")) + .setOutputCol(Array("document_question", "document_context")) + +val spanClassifier = AlbertForQuestionAnswering + .pretrained("albert_xlarge_squad_finetuned", "en") + .setInputCols(Array("document_question","document_context")) + .setOutputCol("answer") + +val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_xlarge_squad_finetuned| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|204.7 MB| + +## References + +https://huggingface.co/rahulchakwate/albert-xlarge-squad-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_xxl_v2_finetuned_squad_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_xxl_v2_finetuned_squad_en.md new file mode 100644 index 00000000000000..60fc5639835bf5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_xxl_v2_finetuned_squad_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_xxl_v2_finetuned_squad AlbertForQuestionAnswering from anas-awadalla +author: John Snow Labs +name: albert_xxl_v2_finetuned_squad +date: 2023-09-19 +tags: [albert, en, open_source, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_xxl_v2_finetuned_squad` is a English model originally trained by anas-awadalla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_xxl_v2_finetuned_squad_en_5.1.2_3.0_1695097862908.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_xxl_v2_finetuned_squad_en_5.1.2_3.0_1695097862908.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = MultiDocumentAssembler() \ + .setInputCol(["question", "context"]) \ + .setOutputCol(["document_question", "document_context"]) + + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_xxl_v2_finetuned_squad","en") \ + .setInputCols(["document_question","document_context"]) \ + .setOutputCol("answer") + +pipeline = Pipeline().setStages([document_assembler, spanClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new MultiDocumentAssembler() + .setInputCol(Array("question", "context")) + .setOutputCol(Array("document_question", "document_context")) + +val spanClassifier = AlbertForQuestionAnswering + .pretrained("albert_xxl_v2_finetuned_squad", "en") + .setInputCols(Array("document_question","document_context")) + .setOutputCol("answer") + +val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_xxl_v2_finetuned_squad| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|771.0 MB| + +## References + +https://huggingface.co/anas-awadalla/albert-xxl-v2-finetuned-squad \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-albert_xxlarge_squad_finetuned_en.md b/docs/_posts/ahmedlone127/2023-09-19-albert_xxlarge_squad_finetuned_en.md new file mode 100644 index 00000000000000..cda270324178f8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-albert_xxlarge_squad_finetuned_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_xxlarge_squad_finetuned AlbertForQuestionAnswering from rahulchakwate +author: John Snow Labs +name: albert_xxlarge_squad_finetuned +date: 2023-09-19 +tags: [albert, en, open_source, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_xxlarge_squad_finetuned` is a English model originally trained by rahulchakwate. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_xxlarge_squad_finetuned_en_5.1.2_3.0_1695096458329.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_xxlarge_squad_finetuned_en_5.1.2_3.0_1695096458329.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = MultiDocumentAssembler() \ + .setInputCol(["question", "context"]) \ + .setOutputCol(["document_question", "document_context"]) + + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_xxlarge_squad_finetuned","en") \ + .setInputCols(["document_question","document_context"]) \ + .setOutputCol("answer") + +pipeline = Pipeline().setStages([document_assembler, spanClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new MultiDocumentAssembler() + .setInputCol(Array("question", "context")) + .setOutputCol(Array("document_question", "document_context")) + +val spanClassifier = AlbertForQuestionAnswering + .pretrained("albert_xxlarge_squad_finetuned", "en") + .setInputCols(Array("document_question","document_context")) + .setOutputCol("answer") + +val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_xxlarge_squad_finetuned| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|771.7 MB| + +## References + +https://huggingface.co/rahulchakwate/albert-xxlarge-squad-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-lodosalberttr_en.md b/docs/_posts/ahmedlone127/2023-09-19-lodosalberttr_en.md new file mode 100644 index 00000000000000..cc0cfa143bb41e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-lodosalberttr_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English lodosalberttr AlbertForTokenClassification from bgk +author: John Snow Labs +name: lodosalberttr +date: 2023-09-19 +tags: [albert, en, open_source, token_classification, onnx] +task: Named Entity Recognition +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForTokenClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`lodosalberttr` is a English model originally trained by bgk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/lodosalberttr_en_5.1.2_3.0_1695087490256.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/lodosalberttr_en_5.1.2_3.0_1695087490256.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForTokenClassification.pretrained("lodosalberttr","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("ner") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForTokenClassification + .pretrained("lodosalberttr", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|lodosalberttr| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[ner]| +|Language:|en| +|Size:|42.9 MB| + +## References + +https://huggingface.co/bgk/lodosalberttr \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-m_albert_qa_model_en.md b/docs/_posts/ahmedlone127/2023-09-19-m_albert_qa_model_en.md new file mode 100644 index 00000000000000..47b6835e0c85d3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-m_albert_qa_model_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English m_albert_qa_model AlbertForQuestionAnswering from Chetna19 +author: John Snow Labs +name: m_albert_qa_model +date: 2023-09-19 +tags: [albert, en, open_source, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`m_albert_qa_model` is a English model originally trained by Chetna19. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/m_albert_qa_model_en_5.1.2_3.0_1695096857164.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/m_albert_qa_model_en_5.1.2_3.0_1695096857164.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = MultiDocumentAssembler() \ + .setInputCol(["question", "context"]) \ + .setOutputCol(["document_question", "document_context"]) + + +spanClassifier = AlbertForQuestionAnswering.pretrained("m_albert_qa_model","en") \ + .setInputCols(["document_question","document_context"]) \ + .setOutputCol("answer") + +pipeline = Pipeline().setStages([document_assembler, spanClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new MultiDocumentAssembler() + .setInputCol(Array("question", "context")) + .setOutputCol(Array("document_question", "document_context")) + +val spanClassifier = AlbertForQuestionAnswering + .pretrained("m_albert_qa_model", "en") + .setInputCols(Array("document_question","document_context")) + .setOutputCol("answer") + +val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|m_albert_qa_model| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|42.0 MB| + +## References + +https://huggingface.co/Chetna19/m_albert_qa_model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-19-tiny_albert_en.md b/docs/_posts/ahmedlone127/2023-09-19-tiny_albert_en.md new file mode 100644 index 00000000000000..2fbf97a169407b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-19-tiny_albert_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English tiny_albert AlbertForTokenClassification from vumichien +author: John Snow Labs +name: tiny_albert +date: 2023-09-19 +tags: [albert, en, open_source, token_classification, onnx] +task: Named Entity Recognition +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForTokenClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_albert` is a English model originally trained by vumichien. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_albert_en_5.1.2_3.0_1695087950750.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_albert_en_5.1.2_3.0_1695087950750.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + + +sequenceClassifier = AlbertForTokenClassification.pretrained("tiny_albert","en") \ + .setInputCols(["documents","token"]) \ + .setOutputCol("ner") + +pipeline = Pipeline().setStages([document_assembler, sequenceClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("embeddings") + +val sequenceClassifier = AlbertForTokenClassification + .pretrained("tiny_albert", "en") + .setInputCols(Array("documents","token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sequenceClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_albert| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[ner]| +|Language:|en| +|Size:|1.0 MB| + +## References + +https://huggingface.co/vumichien/tiny-albert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-20-albert_qa_ai_club_inductions_21_nlp_en.md b/docs/_posts/ahmedlone127/2023-09-20-albert_qa_ai_club_inductions_21_nlp_en.md new file mode 100644 index 00000000000000..bbe2774b3c3c79 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-20-albert_qa_ai_club_inductions_21_nlp_en.md @@ -0,0 +1,98 @@ +--- +layout: model +title: English AlbertForQuestionAnswering model (from AyushPJ) +author: John Snow Labs +name: albert_qa_ai_club_inductions_21_nlp +date: 2023-09-20 +tags: [en, open_source, albert, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `ai-club-inductions-21-nlp-ALBERT` is a English model originally trained by `AyushPJ`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_qa_ai_club_inductions_21_nlp_en_5.1.2_3.0_1695188557360.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_qa_ai_club_inductions_21_nlp_en_5.1.2_3.0_1695188557360.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = MultiDocumentAssembler() \ +.setInputCols(["question", "context"]) \ +.setOutputCols(["document_question", "document_context"]) + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_qa_ai_club_inductions_21_nlp","en") \ +.setInputCols(["document_question", "document_context"]) \ +.setOutputCol("answer")\ +.setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, spanClassifier]) + +data = spark.createDataFrame([["What is my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new MultiDocumentAssembler() +.setInputCols(Array("question", "context")) +.setOutputCols(Array("document_question", "document_context")) + +val spanClassifer = AlbertForQuestionAnswering.pretrained("albert_qa_ai_club_inductions_21_nlp","en") +.setInputCols(Array("document", "token")) +.setOutputCol("answer") +.setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier)) + +val data = Seq("What is my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.answer_question.albert.by_AyushPJ").predict("""What is my name?|||"My name is Clara and I live in Berkeley.""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_qa_ai_club_inductions_21_nlp| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|42.0 MB| + +## References + +References + +- https://huggingface.co/AyushPJ/ai-club-inductions-21-nlp-ALBERT \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-20-albert_qa_biom_xxlarge_squad2_en.md b/docs/_posts/ahmedlone127/2023-09-20-albert_qa_biom_xxlarge_squad2_en.md new file mode 100644 index 00000000000000..b59b0ed635bcc3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-20-albert_qa_biom_xxlarge_squad2_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_qa_biom_xxlarge_squad2 AlbertForQuestionAnswering from sultan +author: John Snow Labs +name: albert_qa_biom_xxlarge_squad2 +date: 2023-09-20 +tags: [albert, en, open_source, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_qa_biom_xxlarge_squad2` is a English model originally trained by sultan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_qa_biom_xxlarge_squad2_en_5.1.2_3.0_1695188434641.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_qa_biom_xxlarge_squad2_en_5.1.2_3.0_1695188434641.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = MultiDocumentAssembler() \ + .setInputCol(["question", "context"]) \ + .setOutputCol(["document_question", "document_context"]) + + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_qa_biom_xxlarge_squad2","en") \ + .setInputCols(["document_question","document_context"]) \ + .setOutputCol("answer") + +pipeline = Pipeline().setStages([document_assembler, spanClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new MultiDocumentAssembler() + .setInputCol(Array("question", "context")) + .setOutputCol(Array("document_question", "document_context")) + +val spanClassifier = AlbertForQuestionAnswering + .pretrained("albert_qa_biom_xxlarge_squad2", "en") + .setInputCols(Array("document_question","document_context")) + .setOutputCol("answer") + +val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_qa_biom_xxlarge_squad2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|771.3 MB| + +## References + +https://huggingface.co/sultan/BioM-ALBERT-xxlarge-SQuAD2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-20-albert_qa_qa_1e_en.md b/docs/_posts/ahmedlone127/2023-09-20-albert_qa_qa_1e_en.md new file mode 100644 index 00000000000000..27b603e1afbc75 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-20-albert_qa_qa_1e_en.md @@ -0,0 +1,93 @@ +--- +layout: model +title: English albert_qa_qa_1e AlbertForQuestionAnswering from SalmanMo +author: John Snow Labs +name: albert_qa_qa_1e +date: 2023-09-20 +tags: [albert, en, open_source, question_answering, onnx] +task: Question Answering +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: onnx +annotator: AlbertForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained AlbertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_qa_qa_1e` is a English model originally trained by SalmanMo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_qa_qa_1e_en_5.1.2_3.0_1695188622456.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_qa_qa_1e_en_5.1.2_3.0_1695188622456.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = MultiDocumentAssembler() \ + .setInputCol(["question", "context"]) \ + .setOutputCol(["document_question", "document_context"]) + + +spanClassifier = AlbertForQuestionAnswering.pretrained("albert_qa_qa_1e","en") \ + .setInputCols(["document_question","document_context"]) \ + .setOutputCol("answer") + +pipeline = Pipeline().setStages([document_assembler, spanClassifier]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new MultiDocumentAssembler() + .setInputCol(Array("question", "context")) + .setOutputCol(Array("document_question", "document_context")) + +val spanClassifier = AlbertForQuestionAnswering + .pretrained("albert_qa_qa_1e", "en") + .setInputCols(Array("document_question","document_context")) + .setOutputCol("answer") + +val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|albert_qa_qa_1e| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|42.0 MB| + +## References + +https://huggingface.co/SalmanMo/ALBERT_QA_1e \ No newline at end of file From 577ecb674de8144438e904ee953dc5a7874f0ba8 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Mon, 25 Sep 2023 21:49:21 +0700 Subject: [PATCH 47/55] Add model 2023-09-18-AtgxRobertaBaseSquad2_en (#13988) Co-authored-by: LIN-Yu-Ting --- .../2023-09-18-AtgxRobertaBaseSquad2_en.md | 61 +++++++++++++++++++ 1 file changed, 61 insertions(+) create mode 100644 docs/_posts/LIN-Yu-Ting/2023-09-18-AtgxRobertaBaseSquad2_en.md diff --git a/docs/_posts/LIN-Yu-Ting/2023-09-18-AtgxRobertaBaseSquad2_en.md b/docs/_posts/LIN-Yu-Ting/2023-09-18-AtgxRobertaBaseSquad2_en.md new file mode 100644 index 00000000000000..09f07830994d02 --- /dev/null +++ b/docs/_posts/LIN-Yu-Ting/2023-09-18-AtgxRobertaBaseSquad2_en.md @@ -0,0 +1,61 @@ +--- +layout: model +title: Atgenomix Testing QA Model +author: LIN-Yu-Ting +name: AtgxRobertaBaseSquad2 +date: 2023-09-18 +tags: [en, open_source, tensorflow] +task: Question Answering +language: en +edition: Spark NLP 4.4.3 +spark_version: 3.4 +supported: false +engine: tensorflow +annotator: RoBertaForQuestionAnswering +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Testing Question Answering model for Atgenomix usage + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/community.johnsnowlabs.com/LIN-Yu-Ting/AtgxRobertaBaseSquad2_en_4.4.3_3.4_1695000774804.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://community.johnsnowlabs.com/LIN-Yu-Ting/AtgxRobertaBaseSquad2_en_4.4.3_3.4_1695000774804.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +import sparknlp +spark = sparknlp.start() +``` + +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|AtgxRobertaBaseSquad2| +|Compatibility:|Spark NLP 4.4.3+| +|License:|Open Source| +|Edition:|Community| +|Input Labels:|[document_question, document_context]| +|Output Labels:|[answer]| +|Language:|en| +|Size:|460.0 MB| +|Case sensitive:|true| +|Max sentence length:|512| \ No newline at end of file From d4bd5508d9a21df00220bf9ae3a58dfbbd17ecb1 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Mon, 25 Sep 2023 21:56:49 +0700 Subject: [PATCH 48/55] 2023-09-20-image_captioning_vit_gpt2_en (#13999) * Add model 2023-09-20-image_captioning_vit_gpt2_en * Update 2023-09-20-image_captioning_vit_gpt2_en.md make sure spark_version is 3.0 --------- Co-authored-by: DevinTDHa Co-authored-by: Maziyar Panahi --- ...2023-09-20-image_captioning_vit_gpt2_en.md | 125 ++++++++++++++++++ 1 file changed, 125 insertions(+) create mode 100644 docs/_posts/DevinTDHa/2023-09-20-image_captioning_vit_gpt2_en.md diff --git a/docs/_posts/DevinTDHa/2023-09-20-image_captioning_vit_gpt2_en.md b/docs/_posts/DevinTDHa/2023-09-20-image_captioning_vit_gpt2_en.md new file mode 100644 index 00000000000000..f7573201263d99 --- /dev/null +++ b/docs/_posts/DevinTDHa/2023-09-20-image_captioning_vit_gpt2_en.md @@ -0,0 +1,125 @@ +--- +layout: model +title: Image Caption with VisionEncoderDecoder ViT GPT2 +author: John Snow Labs +name: image_captioning_vit_gpt2 +date: 2023-09-20 +tags: [en, vit, gpt2, image, captioning, open_source, tensorflow] +task: Image Captioning +language: en +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: VisionEncoderDecoderForImageCaptioning +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This is an image captioning model using ViT to encode images and GPT2 to generate captions. Original model from https://huggingface.co/nlpconnect/vit-gpt2-image-captioning + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/image_captioning_vit_gpt2_en_5.1.2_3.0_1695215721202.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/image_captioning_vit_gpt2_en_5.1.2_3.0_1695215721202.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +import sparknlp +from sparknlp.base import * +from sparknlp.annotator import * +from pyspark.ml import Pipeline +imageDF = spark.read \ + .format("image") \ + .option("dropInvalid", value = True) \ + .load("src/test/resources/image/") +imageAssembler = ImageAssembler() \ + .setInputCol("image") \ + .setOutputCol("image_assembler") +imageCaptioning = VisionEncoderDecoderForImageCaptioning \ + .pretrained() \ + .setBeamSize(2) \ + .setDoSample(False) \ + .setInputCols(["image_assembler"]) \ + .setOutputCol("caption") +pipeline = Pipeline().setStages([imageAssembler, imageCaptioning]) +pipelineDF = pipeline.fit(imageDF).transform(imageDF) +pipelineDF \ + .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "caption.result") \ + .show(truncate = False) +``` +```scala +import com.johnsnowlabs.nlp.annotator._ +import com.johnsnowlabs.nlp.ImageAssembler +import org.apache.spark.ml.Pipeline + +val imageDF: DataFrame = spark.read + .format("image") + .option("dropInvalid", value = true) + .load("src/test/resources/image/") + +val imageCaptioning = new ImageAssembler() + .setInputCol("image") + .setOutputCol("image_assembler") + +val imageClassifier = VisionEncoderDecoderForImageCaptioning + .pretrained() + .setBeamSize(2) + .setDoSample(false) + .setInputCols("image_assembler") + .setOutputCol("caption") + +val pipeline = new Pipeline().setStages(Array(imageAssembler, imageCaptioning)) +val pipelineDF = pipeline.fit(imageDF).transform(imageDF) + +pipelineDF + .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "caption.result") + .show(truncate = false) +``` +
+ +## Results + +```bash ++-----------------+---------------------------------------------------------+ +|image_name |result | ++-----------------+---------------------------------------------------------+ +|palace.JPEG |[a large room filled with furniture and a large window] | +|egyptian_cat.jpeg|[a cat laying on a couch next to another cat] | +|hippopotamus.JPEG|[a brown bear in a body of water] | +|hen.JPEG |[a flock of chickens standing next to each other] | +|ostrich.JPEG |[a large bird standing on top of a lush green field] | +|junco.JPEG |[a small bird standing on a wet ground] | +|bluetick.jpg |[a small dog standing on a wooden floor] | +|chihuahua.jpg |[a small brown dog wearing a blue sweater] | +|tractor.JPEG |[a man is standing in a field with a tractor] | +|ox.JPEG |[a large brown cow standing on top of a lush green field]| ++-----------------+---------------------------------------------------------+ +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|image_captioning_vit_gpt2| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[image_assembler]| +|Output Labels:|[caption]| +|Language:|en| +|Size:|890.3 MB| From 00e0a8d39aae7eaa32c22010e8cb79290df5cee0 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Mon, 25 Sep 2023 21:57:14 +0700 Subject: [PATCH 49/55] 2023-09-21-multilingual_e5_base_xx (#14002) * Add model 2023-09-21-multilingual_e5_base_xx * Add model 2023-09-21-multilingual_e5_large_xx * Add model 2023-09-21-multilingual_e5_small_xx --------- Co-authored-by: ahmedlone127 --- .../2023-09-21-multilingual_e5_base_xx.md | 100 ++++++++++++++++++ .../2023-09-21-multilingual_e5_large_xx.md | 100 ++++++++++++++++++ .../2023-09-21-multilingual_e5_small_xx.md | 100 ++++++++++++++++++ 3 files changed, 300 insertions(+) create mode 100644 docs/_posts/ahmedlone127/2023-09-21-multilingual_e5_base_xx.md create mode 100644 docs/_posts/ahmedlone127/2023-09-21-multilingual_e5_large_xx.md create mode 100644 docs/_posts/ahmedlone127/2023-09-21-multilingual_e5_small_xx.md diff --git a/docs/_posts/ahmedlone127/2023-09-21-multilingual_e5_base_xx.md b/docs/_posts/ahmedlone127/2023-09-21-multilingual_e5_base_xx.md new file mode 100644 index 00000000000000..fa531cfdfd1f9a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-21-multilingual_e5_base_xx.md @@ -0,0 +1,100 @@ +--- +layout: model +title: Multilingual multilingual_e5_base XlmRoBertaSentenceEmbeddings from intfloat +author: John Snow Labs +name: multilingual_e5_base +date: 2023-09-21 +tags: [xlm_roberta, xx, open_source, tensorflow] +task: Embeddings +language: xx +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: XlmRoBertaSentenceEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained XlmRoBertaSentenceEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multilingual_e5_base` is a Multilingual model originally trained by intfloat. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/multilingual_e5_base_xx_5.1.2_3.0_1695314944172.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multilingual_e5_base_xx_5.1.2_3.0_1695314944172.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +sentencerDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")\ + .setInputCols(["document"])\ + .setOutputCol("sentence") + +embeddings =XlmRoBertaSentenceEmbeddings.pretrained("multilingual_e5_base","xx") \ + .setInputCols(["sentence"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, sentencerDL, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val sentencerDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx") + .setInputCols(["document"]) + .setOutputCol("sentence") + +val embeddings = XlmRoBertaSentenceEmbeddings + .pretrained("multilingual_e5_base", "xx") + .setInputCols(Array("sentence")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sentencerDL, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|multilingual_e5_base| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|667.1 MB| + +## References + +https://huggingface.co/intfloat/multilingual-e5-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-21-multilingual_e5_large_xx.md b/docs/_posts/ahmedlone127/2023-09-21-multilingual_e5_large_xx.md new file mode 100644 index 00000000000000..68f15a5f8e283f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-21-multilingual_e5_large_xx.md @@ -0,0 +1,100 @@ +--- +layout: model +title: Multilingual multilingual_e5_large XlmRoBertaSentenceEmbeddings from intfloat +author: John Snow Labs +name: multilingual_e5_large +date: 2023-09-21 +tags: [xlm_roberta, xx, open_source, tensorflow] +task: Embeddings +language: xx +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: XlmRoBertaSentenceEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained XlmRoBertaSentenceEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multilingual_e5_large` is a Multilingual model originally trained by intfloat. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/multilingual_e5_large_xx_5.1.2_3.0_1695315223158.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multilingual_e5_large_xx_5.1.2_3.0_1695315223158.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +sentencerDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")\ + .setInputCols(["document"])\ + .setOutputCol("sentence") + +embeddings =XlmRoBertaSentenceEmbeddings.pretrained("multilingual_e5_large","xx") \ + .setInputCols(["sentence"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, sentencerDL, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val sentencerDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx") + .setInputCols(["document"]) + .setOutputCol("sentence") + +val embeddings = XlmRoBertaSentenceEmbeddings + .pretrained("multilingual_e5_large", "xx") + .setInputCols(Array("sentence")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sentencerDL, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|multilingual_e5_large| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|1.3 GB| + +## References + +https://huggingface.co/intfloat/multilingual-e5-large \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-09-21-multilingual_e5_small_xx.md b/docs/_posts/ahmedlone127/2023-09-21-multilingual_e5_small_xx.md new file mode 100644 index 00000000000000..c2760613db9832 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-09-21-multilingual_e5_small_xx.md @@ -0,0 +1,100 @@ +--- +layout: model +title: Multilingual multilingual_e5_small XlmRoBertaSentenceEmbeddings from intfloat +author: John Snow Labs +name: multilingual_e5_small +date: 2023-09-21 +tags: [xlm_roberta, xx, open_source, tensorflow] +task: Embeddings +language: xx +edition: Spark NLP 5.1.2 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: XlmRoBertaSentenceEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained XlmRoBertaSentenceEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multilingual_e5_small` is a Multilingual model originally trained by intfloat. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/multilingual_e5_small_xx_5.1.2_3.0_1695316525385.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multilingual_e5_small_xx_5.1.2_3.0_1695316525385.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + + +document_assembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("documents") + +sentencerDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")\ + .setInputCols(["document"])\ + .setOutputCol("sentence") + +embeddings =XlmRoBertaSentenceEmbeddings.pretrained("multilingual_e5_small","xx") \ + .setInputCols(["sentence"]) \ + .setOutputCol("embeddings") + +pipeline = Pipeline().setStages([document_assembler, sentencerDL, embeddings]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("documents") + +val sentencerDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx") + .setInputCols(["document"]) + .setOutputCol("sentence") + +val embeddings = XlmRoBertaSentenceEmbeddings + .pretrained("multilingual_e5_small", "xx") + .setInputCols(Array("sentence")) + .setOutputCol("embeddings") + +val pipeline = new Pipeline().setStages(Array(document_assembler, sentencerDL, embeddings)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|multilingual_e5_small| +|Compatibility:|Spark NLP 5.1.2+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[sentence]| +|Output Labels:|[embeddings]| +|Language:|xx| +|Size:|284.4 MB| + +## References + +https://huggingface.co/intfloat/multilingual-e5-small \ No newline at end of file From 2fd633aa069defb839906513fa8d8daba6891f50 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Mon, 25 Sep 2023 21:59:02 +0700 Subject: [PATCH 50/55] 2023-09-22-bert_embeddings_frpile_gpl_en (#14003) * Add model 2023-09-22-bert_embeddings_frpile_gpl_en * Add model 2023-09-22-bert_embeddings_retromae_beir_en * Add model 2023-09-22-bert_embeddings_retromae_msmarco_finetune_en * Add model 2023-09-22-bert_embeddings_allnli_gronlp_base_dutch_cased_nl * Add model 2023-09-22-bert_embeddings_bge_base_en * Add model 2023-09-22-bert_embeddings_bge_large_en * Add model 2023-09-22-bert_embeddings_bge_small_en * Add model 2023-09-22-bert_embeddings_bi_encoder_msmarco_base_german_de * Add model 2023-09-22-bert_embeddings_claif_base_en * Add model 2023-09-22-bert_embeddings_claif_scaled_base_en --------- Co-authored-by: SKocer --- ...dings_allnli_gronlp_base_dutch_cased_nl.md | 99 +++++++++++++ .../2023-09-22-bert_embeddings_bge_base_en.md | 135 ++++++++++++++++++ ...2023-09-22-bert_embeddings_bge_large_en.md | 135 ++++++++++++++++++ ...2023-09-22-bert_embeddings_bge_small_en.md | 135 ++++++++++++++++++ ...dings_bi_encoder_msmarco_base_german_de.md | 114 +++++++++++++++ ...023-09-22-bert_embeddings_claif_base_en.md | 99 +++++++++++++ ...22-bert_embeddings_claif_scaled_base_en.md | 99 +++++++++++++ ...023-09-22-bert_embeddings_frpile_gpl_en.md | 99 +++++++++++++ ...-09-22-bert_embeddings_retromae_beir_en.md | 100 +++++++++++++ ...embeddings_retromae_msmarco_finetune_en.md | 100 +++++++++++++ 10 files changed, 1115 insertions(+) create mode 100644 docs/_posts/SKocer/2023-09-22-bert_embeddings_allnli_gronlp_base_dutch_cased_nl.md create mode 100644 docs/_posts/SKocer/2023-09-22-bert_embeddings_bge_base_en.md create mode 100644 docs/_posts/SKocer/2023-09-22-bert_embeddings_bge_large_en.md create mode 100644 docs/_posts/SKocer/2023-09-22-bert_embeddings_bge_small_en.md create mode 100644 docs/_posts/SKocer/2023-09-22-bert_embeddings_bi_encoder_msmarco_base_german_de.md create mode 100644 docs/_posts/SKocer/2023-09-22-bert_embeddings_claif_base_en.md create mode 100644 docs/_posts/SKocer/2023-09-22-bert_embeddings_claif_scaled_base_en.md create mode 100644 docs/_posts/SKocer/2023-09-22-bert_embeddings_frpile_gpl_en.md create mode 100644 docs/_posts/SKocer/2023-09-22-bert_embeddings_retromae_beir_en.md create mode 100644 docs/_posts/SKocer/2023-09-22-bert_embeddings_retromae_msmarco_finetune_en.md diff --git a/docs/_posts/SKocer/2023-09-22-bert_embeddings_allnli_gronlp_base_dutch_cased_nl.md b/docs/_posts/SKocer/2023-09-22-bert_embeddings_allnli_gronlp_base_dutch_cased_nl.md new file mode 100644 index 00000000000000..fad7aaee3308db --- /dev/null +++ b/docs/_posts/SKocer/2023-09-22-bert_embeddings_allnli_gronlp_base_dutch_cased_nl.md @@ -0,0 +1,99 @@ +--- +layout: model +title: Dutch BertEmbeddings Base Cased model (from textgain) +author: John Snow Labs +name: bert_embeddings_allnli_gronlp_base_dutch_cased +date: 2023-09-22 +tags: [nl, open_source, bert_embeddings, onnx] +task: Embeddings +language: nl +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `allnli-GroNLP-bert-base-dutch-cased` is a Dutch model originally trained by `textgain`. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_allnli_gronlp_base_dutch_cased_nl_5.1.0_3.0_1695368402996.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_allnli_gronlp_base_dutch_cased_nl_5.1.0_3.0_1695368402996.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCols(["text"]) \ + .setOutputCols("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +bert_loaded = BertEmbeddings.pretrained("bert_embeddings_allnli_gronlp_base_dutch_cased","nl") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, bert_loaded]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val bert_loaded = BertEmbeddings.pretrained("bert_embeddings_allnli_gronlp_base_dutch_cased","nl") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, bert_loaded)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_allnli_gronlp_base_dutch_cased| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[bert]| +|Language:|nl| +|Size:|406.8 MB| +|Case sensitive:|true| + +## References + +- https://huggingface.co/textgain/allnli-GroNLP-bert-base-dutch-cased +- https://www.SBERT.net +- https://www.SBERT.net +- https://www.SBERT.net +- https://seb.sbert.net?model_name=%7BMODEL_NAME%7D \ No newline at end of file diff --git a/docs/_posts/SKocer/2023-09-22-bert_embeddings_bge_base_en.md b/docs/_posts/SKocer/2023-09-22-bert_embeddings_bge_base_en.md new file mode 100644 index 00000000000000..4ac379785202a2 --- /dev/null +++ b/docs/_posts/SKocer/2023-09-22-bert_embeddings_bge_base_en.md @@ -0,0 +1,135 @@ +--- +layout: model +title: English BertEmbeddings Base Cased model (from BAAI) +author: John Snow Labs +name: bert_embeddings_bge_base +date: 2023-09-22 +tags: [en, open_source, bert_embeddings, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bge-base-en` is a English model originally trained by `BAAI`. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bge_base_en_5.1.0_3.0_1695368493416.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bge_base_en_5.1.0_3.0_1695368493416.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCols(["text"]) \ + .setOutputCols("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +bert_loaded = BertEmbeddings.pretrained("bert_embeddings_bge_base","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, bert_loaded]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val bert_loaded = BertEmbeddings.pretrained("bert_embeddings_bge_base","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, bert_loaded)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bge_base| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|259.0 MB| +|Case sensitive:|true| + +## References + +- https://huggingface.co/BAAI/bge-base-en +- https://github.com/FlagOpen/FlagEmbedding +- https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md +- https://arxiv.org/pdf/2309.07597.pdf +- https://data.baai.ac.cn/details/BAAI-MTP +- https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md +- https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives +- https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md +- https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list +- https://www.SBERT.net +- https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list +- https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md +- https://platform.openai.com/docs/guides/embeddings +- https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md +- https://platform.openai.com/docs/guides/embeddings/what-are-embeddings +- https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/ +- https://github.com/staoxiao/RetroMAE +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain +- https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker +- https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker +- https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE +- https://paperswithcode.com/sota?task=Classification&dataset=MTEB+AmazonCounterfactualClassification+%28en%29 \ No newline at end of file diff --git a/docs/_posts/SKocer/2023-09-22-bert_embeddings_bge_large_en.md b/docs/_posts/SKocer/2023-09-22-bert_embeddings_bge_large_en.md new file mode 100644 index 00000000000000..2de0442d077a18 --- /dev/null +++ b/docs/_posts/SKocer/2023-09-22-bert_embeddings_bge_large_en.md @@ -0,0 +1,135 @@ +--- +layout: model +title: English BertEmbeddings Large Cased model (from BAAI) +author: John Snow Labs +name: bert_embeddings_bge_large +date: 2023-09-22 +tags: [en, open_source, bert_embeddings, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bge-large-en` is a English model originally trained by `BAAI`. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bge_large_en_5.1.0_3.0_1695368740777.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bge_large_en_5.1.0_3.0_1695368740777.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCols(["text"]) \ + .setOutputCols("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +bert_loaded = BertEmbeddings.pretrained("bert_embeddings_bge_large","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, bert_loaded]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val bert_loaded = BertEmbeddings.pretrained("bert_embeddings_bge_large","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, bert_loaded)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bge_large| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|795.1 MB| +|Case sensitive:|true| + +## References + +- https://huggingface.co/BAAI/bge-large-en +- https://github.com/FlagOpen/FlagEmbedding +- https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md +- https://arxiv.org/pdf/2309.07597.pdf +- https://data.baai.ac.cn/details/BAAI-MTP +- https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md +- https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives +- https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md +- https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list +- https://www.SBERT.net +- https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list +- https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md +- https://platform.openai.com/docs/guides/embeddings +- https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md +- https://platform.openai.com/docs/guides/embeddings/what-are-embeddings +- https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/ +- https://github.com/staoxiao/RetroMAE +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain +- https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker +- https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker +- https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE +- https://paperswithcode.com/sota?task=Classification&dataset=MTEB+AmazonCounterfactualClassification+%28en%29 \ No newline at end of file diff --git a/docs/_posts/SKocer/2023-09-22-bert_embeddings_bge_small_en.md b/docs/_posts/SKocer/2023-09-22-bert_embeddings_bge_small_en.md new file mode 100644 index 00000000000000..56a008b4d10b5f --- /dev/null +++ b/docs/_posts/SKocer/2023-09-22-bert_embeddings_bge_small_en.md @@ -0,0 +1,135 @@ +--- +layout: model +title: English BertEmbeddings Small Cased model (from BAAI) +author: John Snow Labs +name: bert_embeddings_bge_small +date: 2023-09-22 +tags: [en, open_source, bert_embeddings, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bge-small-en` is a English model originally trained by `BAAI`. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bge_small_en_5.1.0_3.0_1695368784401.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bge_small_en_5.1.0_3.0_1695368784401.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCols(["text"]) \ + .setOutputCols("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +bert_loaded = BertEmbeddings.pretrained("bert_embeddings_bge_small","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, bert_loaded]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val bert_loaded = BertEmbeddings.pretrained("bert_embeddings_bge_small","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, bert_loaded)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bge_small| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|79.9 MB| +|Case sensitive:|true| + +## References + +- https://huggingface.co/BAAI/bge-small-en +- https://github.com/FlagOpen/FlagEmbedding +- https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md +- https://arxiv.org/pdf/2309.07597.pdf +- https://data.baai.ac.cn/details/BAAI-MTP +- https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md +- https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives +- https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md +- https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list +- https://www.SBERT.net +- https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list +- https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md +- https://platform.openai.com/docs/guides/embeddings +- https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md +- https://platform.openai.com/docs/guides/embeddings/what-are-embeddings +- https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/ +- https://github.com/staoxiao/RetroMAE +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain +- https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md +- https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker +- https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker +- https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE +- https://paperswithcode.com/sota?task=Classification&dataset=MTEB+AmazonCounterfactualClassification+%28en%29 \ No newline at end of file diff --git a/docs/_posts/SKocer/2023-09-22-bert_embeddings_bi_encoder_msmarco_base_german_de.md b/docs/_posts/SKocer/2023-09-22-bert_embeddings_bi_encoder_msmarco_base_german_de.md new file mode 100644 index 00000000000000..759af7155db979 --- /dev/null +++ b/docs/_posts/SKocer/2023-09-22-bert_embeddings_bi_encoder_msmarco_base_german_de.md @@ -0,0 +1,114 @@ +--- +layout: model +title: German BertEmbeddings Base Cased model (from PM-AI) +author: John Snow Labs +name: bert_embeddings_bi_encoder_msmarco_base_german +date: 2023-09-22 +tags: [de, open_source, bert_embeddings, onnx] +task: Embeddings +language: de +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bi-encoder_msmarco_bert-base_german` is a German model originally trained by `PM-AI`. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_bi_encoder_msmarco_base_german_de_5.1.0_3.0_1695368809437.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_bi_encoder_msmarco_base_german_de_5.1.0_3.0_1695368809437.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCols(["text"]) \ + .setOutputCols("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +bert_loaded = BertEmbeddings.pretrained("bert_embeddings_bi_encoder_msmarco_base_german","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, bert_loaded]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val bert_loaded = BertEmbeddings.pretrained("bert_embeddings_bi_encoder_msmarco_base_german","de") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, bert_loaded)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_bi_encoder_msmarco_base_german| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[bert]| +|Language:|de| +|Size:|409.7 MB| +|Case sensitive:|true| + +## References + +- https://huggingface.co/PM-AI/bi-encoder_msmarco_bert-base_german +- https://github.com/UKPLab/sentence-transformers +- https://microsoft.github.io/msmarco/#ranking +- https://arxiv.org/abs/2108.13897 +- https://openreview.net/forum?id=wCu6T5xFjeJ +- https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/ +- https://github.com/beir-cellar/beir +- https://github.com/beir-cellar/beir/blob/main/examples/retrieval/training/train_msmarco_v3_margin_MSE.py +- https://sbert.net/datasets/msmarco-hard-negatives.jsonl.gz +- https://github.com/beir-cellar/beir/blob/main/examples/retrieval/training/train_msmarco_v3_margin_MSE.py%5D +- https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/README.md +- https://github.com/beir-cellar/beir/blob/main/examples/retrieval/training/train_msmarco_v3_margin_MSE.py +- https://arxiv.org/abs/2104.12741 +- https://www.elastic.co/guide/en/elasticsearch/reference/current/index-modules-similarity.html#bm25 +- https://en.th-wildau.de/ +- https://senseaition.com/ +- https://www.linkedin.com/in/herrphilipps +- https://efre.brandenburg.de/efre/de/ +- https://www.senseaition.com +- https://www.th-wildau.de \ No newline at end of file diff --git a/docs/_posts/SKocer/2023-09-22-bert_embeddings_claif_base_en.md b/docs/_posts/SKocer/2023-09-22-bert_embeddings_claif_base_en.md new file mode 100644 index 00000000000000..04c43275456dc3 --- /dev/null +++ b/docs/_posts/SKocer/2023-09-22-bert_embeddings_claif_base_en.md @@ -0,0 +1,99 @@ +--- +layout: model +title: English BertEmbeddings Base Cased model (from fnlp) +author: John Snow Labs +name: bert_embeddings_claif_base +date: 2023-09-22 +tags: [en, open_source, bert_embeddings, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `claif-bert-base` is a English model originally trained by `fnlp`. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_claif_base_en_5.1.0_3.0_1695368841856.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_claif_base_en_5.1.0_3.0_1695368841856.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCols(["text"]) \ + .setOutputCols("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +bert_loaded = BertEmbeddings.pretrained("bert_embeddings_claif_base","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, bert_loaded]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val bert_loaded = BertEmbeddings.pretrained("bert_embeddings_claif_base","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, bert_loaded)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_claif_base| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|407.2 MB| +|Case sensitive:|true| + +## References + +- https://huggingface.co/fnlp/claif-bert-base +- https://www.SBERT.net +- https://www.SBERT.net +- https://www.SBERT.net +- https://seb.sbert.net?model_name=fnlp/claif-bert-base \ No newline at end of file diff --git a/docs/_posts/SKocer/2023-09-22-bert_embeddings_claif_scaled_base_en.md b/docs/_posts/SKocer/2023-09-22-bert_embeddings_claif_scaled_base_en.md new file mode 100644 index 00000000000000..7e83bf56c15a09 --- /dev/null +++ b/docs/_posts/SKocer/2023-09-22-bert_embeddings_claif_scaled_base_en.md @@ -0,0 +1,99 @@ +--- +layout: model +title: English BertEmbeddings Base Cased model (from fnlp) +author: John Snow Labs +name: bert_embeddings_claif_scaled_base +date: 2023-09-22 +tags: [en, open_source, bert_embeddings, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `claif-scaled-bert-base` is a English model originally trained by `fnlp`. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_claif_scaled_base_en_5.1.0_3.0_1695368874285.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_claif_scaled_base_en_5.1.0_3.0_1695368874285.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCols(["text"]) \ + .setOutputCols("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +bert_loaded = BertEmbeddings.pretrained("bert_embeddings_claif_scaled_base","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, bert_loaded]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val bert_loaded = BertEmbeddings.pretrained("bert_embeddings_claif_scaled_base","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, bert_loaded)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_claif_scaled_base| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|407.2 MB| +|Case sensitive:|true| + +## References + +- https://huggingface.co/fnlp/claif-scaled-bert-base +- https://www.SBERT.net +- https://www.SBERT.net +- https://www.SBERT.net +- https://seb.sbert.net?model_name=fnlp/claif-scaled-bert-base \ No newline at end of file diff --git a/docs/_posts/SKocer/2023-09-22-bert_embeddings_frpile_gpl_en.md b/docs/_posts/SKocer/2023-09-22-bert_embeddings_frpile_gpl_en.md new file mode 100644 index 00000000000000..89d0fa73f1ee50 --- /dev/null +++ b/docs/_posts/SKocer/2023-09-22-bert_embeddings_frpile_gpl_en.md @@ -0,0 +1,99 @@ +--- +layout: model +title: English BertEmbeddings Cased model (from DragosGorduza) +author: John Snow Labs +name: bert_embeddings_frpile_gpl +date: 2023-09-22 +tags: [en, open_source, bert_embeddings, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `FRPile_GPL` is a English model originally trained by `DragosGorduza`. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_frpile_gpl_en_5.1.0_3.0_1695368293927.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_frpile_gpl_en_5.1.0_3.0_1695368293927.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCols(["text"]) \ + .setOutputCols("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +bert_loaded = BertEmbeddings.pretrained("bert_embeddings_frpile_gpl","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, bert_loaded]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val bert_loaded = BertEmbeddings.pretrained("bert_embeddings_frpile_gpl","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, bert_loaded)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_frpile_gpl| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|1.3 GB| +|Case sensitive:|true| + +## References + +- https://huggingface.co/DragosGorduza/FRPile_GPL +- https://www.SBERT.net +- https://www.SBERT.net +- https://www.SBERT.net +- https://seb.sbert.net?model_name=%7BMODEL_NAME%7D \ No newline at end of file diff --git a/docs/_posts/SKocer/2023-09-22-bert_embeddings_retromae_beir_en.md b/docs/_posts/SKocer/2023-09-22-bert_embeddings_retromae_beir_en.md new file mode 100644 index 00000000000000..1586fef180f6b0 --- /dev/null +++ b/docs/_posts/SKocer/2023-09-22-bert_embeddings_retromae_beir_en.md @@ -0,0 +1,100 @@ +--- +layout: model +title: English BertEmbeddings Cased model (from nthakur) +author: John Snow Labs +name: bert_embeddings_retromae_beir +date: 2023-09-22 +tags: [en, open_source, bert_embeddings, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `RetroMAE_BEIR` is a English model originally trained by `nthakur`. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_retromae_beir_en_5.1.0_3.0_1695368339221.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_retromae_beir_en_5.1.0_3.0_1695368339221.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCols(["text"]) \ + .setOutputCols("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +bert_loaded = BertEmbeddings.pretrained("bert_embeddings_retromae_beir","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, bert_loaded]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val bert_loaded = BertEmbeddings.pretrained("bert_embeddings_retromae_beir","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, bert_loaded)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_retromae_beir| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|407.6 MB| +|Case sensitive:|true| + +## References + +- https://huggingface.co/nthakur/RetroMAE_BEIR +- https://www.SBERT.net +- https://www.SBERT.net +- https://www.SBERT.net +- https://seb.sbert.net?model_name=nthakur/RetroMAE_BEIR +- https://github.com/staoxiao/RetroMAE/ \ No newline at end of file diff --git a/docs/_posts/SKocer/2023-09-22-bert_embeddings_retromae_msmarco_finetune_en.md b/docs/_posts/SKocer/2023-09-22-bert_embeddings_retromae_msmarco_finetune_en.md new file mode 100644 index 00000000000000..b303400648d346 --- /dev/null +++ b/docs/_posts/SKocer/2023-09-22-bert_embeddings_retromae_msmarco_finetune_en.md @@ -0,0 +1,100 @@ +--- +layout: model +title: English BertEmbeddings Cased model (from nthakur) +author: John Snow Labs +name: bert_embeddings_retromae_msmarco_finetune +date: 2023-09-22 +tags: [en, open_source, bert_embeddings, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.0 +spark_version: 3.0 +supported: true +engine: onnx +annotator: BertEmbeddings +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `RetroMAE_MSMARCO_finetune` is a English model originally trained by `nthakur`. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_retromae_msmarco_finetune_en_5.1.0_3.0_1695368370969.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_retromae_msmarco_finetune_en_5.1.0_3.0_1695368370969.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCols(["text"]) \ + .setOutputCols("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +bert_loaded = BertEmbeddings.pretrained("bert_embeddings_retromae_msmarco_finetune","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("embeddings") \ + .setCaseSensitive(True) + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, bert_loaded]) + +data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val bert_loaded = BertEmbeddings.pretrained("bert_embeddings_retromae_msmarco_finetune","en") + .setInputCols(Array("document", "token")) + .setOutputCol("embeddings") + .setCaseSensitive(true) + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, bert_loaded)) + +val data = Seq("I love Spark NLP").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_embeddings_retromae_msmarco_finetune| +|Compatibility:|Spark NLP 5.1.0+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[bert]| +|Language:|en| +|Size:|407.7 MB| +|Case sensitive:|true| + +## References + +- https://huggingface.co/nthakur/RetroMAE_MSMARCO_finetune +- https://www.SBERT.net +- https://www.SBERT.net +- https://www.SBERT.net +- https://seb.sbert.net?model_name=nthakur/RetroMAE_MSMARCO_finetune +- https://github.com/staoxiao/RetroMAE/ \ No newline at end of file From dfd063d1cd8a1f42e4459a57ab38703dcfed8a84 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Wed, 18 Oct 2023 21:22:05 +0700 Subject: [PATCH 51/55] 2023-10-17-asr_whisper_kannada_base_kn (#14030) * Add model 2023-10-17-asr_whisper_kannada_base_kn * Add model 2023-10-17-asr_whisper_kannada_base_pipeline_kn * Add model 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b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_base_bulgarian_l_en.md new file mode 100644 index 00000000000000..5b2602e6c9e594 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_base_bulgarian_l_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_base_bulgarian_l WhisperForCTC from nandovallec +author: John Snow Labs +name: asr_whisper_base_bulgarian_l +date: 2023-10-17 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_base_bulgarian_l` is a English model originally trained by nandovallec. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_base_bulgarian_l_en_5.1.4_3.4_1697581811339.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_base_bulgarian_l_en_5.1.4_3.4_1697581811339.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_base_bulgarian_l","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_base_bulgarian_l","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_base_bulgarian_l| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|643.5 MB| + +## References + +https://huggingface.co/nandovallec/whisper-base-bg-l \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_base_bulgarian_l_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_base_bulgarian_l_pipeline_en.md new file mode 100644 index 00000000000000..8a864efc477d67 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_base_bulgarian_l_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_base_bulgarian_l_pipeline pipeline WhisperForCTC from nandovallec +author: John Snow Labs +name: asr_whisper_base_bulgarian_l_pipeline +date: 2023-10-17 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_base_bulgarian_l_pipeline` is a English model originally trained by nandovallec. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_base_bulgarian_l_pipeline_en_5.1.4_3.4_1697581823071.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_base_bulgarian_l_pipeline_en_5.1.4_3.4_1697581823071.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_base_bulgarian_l_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_base_bulgarian_l_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_base_bulgarian_l_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|643.5 MB| + +## References + +https://huggingface.co/nandovallec/whisper-base-bg-l + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_base_english_en.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_base_english_en.md new file mode 100644 index 00000000000000..68b1876ecf1a57 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_base_english_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_base_english WhisperForCTC from openai +author: John Snow Labs +name: asr_whisper_base_english +date: 2023-10-17 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_base_english` is a English model originally trained by openai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_base_english_en_5.1.4_3.4_1697584638867.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_base_english_en_5.1.4_3.4_1697584638867.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_base_english","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_base_english","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_base_english| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|406.2 MB| + +## References + +https://huggingface.co/openai/whisper-base.en \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_base_english_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_base_english_pipeline_en.md new file mode 100644 index 00000000000000..85e727d52d6f80 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_base_english_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_base_english_pipeline pipeline WhisperForCTC from openai +author: John Snow Labs +name: asr_whisper_base_english_pipeline +date: 2023-10-17 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_base_english_pipeline` is a English model originally trained by openai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_base_english_pipeline_en_5.1.4_3.4_1697584648732.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_base_english_pipeline_en_5.1.4_3.4_1697584648732.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_base_english_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_base_english_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_base_english_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|406.2 MB| + +## References + +https://huggingface.co/openai/whisper-base.en + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_base_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_base_pipeline_xx.md new file mode 100644 index 00000000000000..7d468b167dc31d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_base_pipeline_xx.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Multilingual asr_whisper_base_pipeline pipeline WhisperForCTC from openai +author: John Snow Labs +name: asr_whisper_base_pipeline +date: 2023-10-17 +tags: [whisper, xx, open_source, pipeline] +task: Automatic Speech Recognition +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_base_pipeline` is a Multilingual model originally trained by openai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_base_pipeline_xx_5.1.4_3.4_1697580478931.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_base_pipeline_xx_5.1.4_3.4_1697580478931.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_base_pipeline', lang = 'xx') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_base_pipeline', lang = 'xx') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_base_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|398.5 MB| + +## References + +https://huggingface.co/openai/whisper-base + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_base_xx.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_base_xx.md new file mode 100644 index 00000000000000..2b86bf69a190f7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_base_xx.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Multilingual asr_whisper_base WhisperForCTC from openai +author: John Snow Labs +name: asr_whisper_base +date: 2023-10-17 +tags: [whisper, xx, open_source, asr, onnx] +task: Automatic Speech Recognition +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_base` is a Multilingual model originally trained by openai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_base_xx_5.1.4_3.4_1697580471843.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_base_xx_5.1.4_3.4_1697580471843.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_base","xx") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_base","xx") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_base| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|xx| +|Size:|398.5 MB| + +## References + +https://huggingface.co/openai/whisper-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_kannada_base_kn.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_kannada_base_kn.md new file mode 100644 index 00000000000000..249c03a8c2b041 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_kannada_base_kn.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Kannada asr_whisper_kannada_base WhisperForCTC from vasista22 +author: John Snow Labs +name: asr_whisper_kannada_base +date: 2023-10-17 +tags: [whisper, kn, open_source, asr, onnx] +task: Automatic Speech Recognition +language: kn +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_kannada_base` is a Kannada model originally trained by vasista22. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_kannada_base_kn_5.1.4_3.4_1697579492668.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_kannada_base_kn_5.1.4_3.4_1697579492668.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_kannada_base","kn") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_kannada_base","kn") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_kannada_base| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|kn| +|Size:|643.6 MB| + +## References + +https://huggingface.co/vasista22/whisper-kannada-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_kannada_base_pipeline_kn.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_kannada_base_pipeline_kn.md new file mode 100644 index 00000000000000..cdb95c44ee78eb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_kannada_base_pipeline_kn.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Kannada asr_whisper_kannada_base_pipeline pipeline WhisperForCTC from vasista22 +author: John Snow Labs +name: asr_whisper_kannada_base_pipeline +date: 2023-10-17 +tags: [whisper, kn, open_source, pipeline] +task: Automatic Speech Recognition +language: kn +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_kannada_base_pipeline` is a Kannada model originally trained by vasista22. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_kannada_base_pipeline_kn_5.1.4_3.4_1697579503990.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_kannada_base_pipeline_kn_5.1.4_3.4_1697579503990.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_kannada_base_pipeline', lang = 'kn') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_kannada_base_pipeline', lang = 'kn') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_kannada_base_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|kn| +|Size:|643.6 MB| + +## References + +https://huggingface.co/vasista22/whisper-kannada-base + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_kannada_small_kn.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_kannada_small_kn.md new file mode 100644 index 00000000000000..3d55acbe36bd2c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_kannada_small_kn.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Kannada asr_whisper_kannada_small WhisperForCTC from vasista22 +author: John Snow Labs +name: asr_whisper_kannada_small +date: 2023-10-17 +tags: [whisper, kn, open_source, asr, onnx] +task: Automatic Speech Recognition +language: kn +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_kannada_small` is a Kannada model originally trained by vasista22. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_kannada_small_kn_5.1.4_3.4_1697584952690.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_kannada_small_kn_5.1.4_3.4_1697584952690.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_kannada_small","kn") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_kannada_small","kn") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_kannada_small| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|kn| +|Size:|1.7 GB| + +## References + +https://huggingface.co/vasista22/whisper-kannada-small \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_kannada_small_pipeline_kn.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_kannada_small_pipeline_kn.md new file mode 100644 index 00000000000000..cbd81d8bba9900 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_kannada_small_pipeline_kn.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Kannada asr_whisper_kannada_small_pipeline pipeline WhisperForCTC from vasista22 +author: John Snow Labs +name: asr_whisper_kannada_small_pipeline +date: 2023-10-17 +tags: [whisper, kn, open_source, pipeline] +task: Automatic Speech Recognition +language: kn +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_kannada_small_pipeline` is a Kannada model originally trained by vasista22. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_kannada_small_pipeline_kn_5.1.4_3.4_1697584992461.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_kannada_small_pipeline_kn_5.1.4_3.4_1697584992461.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_kannada_small_pipeline', lang = 'kn') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_kannada_small_pipeline', lang = 'kn') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_kannada_small_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|kn| +|Size:|1.7 GB| + +## References + +https://huggingface.co/vasista22/whisper-kannada-small + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_allsnr_de.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_allsnr_de.md new file mode 100644 index 00000000000000..37f2c980d8df0c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_allsnr_de.md @@ -0,0 +1,92 @@ +--- +layout: model +title: German asr_whisper_small_allsnr WhisperForCTC from marccgrau +author: John Snow Labs +name: asr_whisper_small_allsnr +date: 2023-10-17 +tags: [whisper, de, open_source, asr, onnx] +task: Automatic Speech Recognition +language: de +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_allsnr` is a German model originally trained by marccgrau. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_allsnr_de_5.1.4_3.4_1697581729008.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_allsnr_de_5.1.4_3.4_1697581729008.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_allsnr","de") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_allsnr","de") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_allsnr| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|de| +|Size:|1.7 GB| + +## References + +https://huggingface.co/marccgrau/whisper-small-allSNR \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_czech_cv11_cs.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_czech_cv11_cs.md new file mode 100644 index 00000000000000..d6669b16d3d931 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_czech_cv11_cs.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Czech asr_whisper_small_czech_cv11 WhisperForCTC from mikr +author: John Snow Labs +name: asr_whisper_small_czech_cv11 +date: 2023-10-17 +tags: [whisper, cs, open_source, asr, onnx] +task: Automatic Speech Recognition +language: cs +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_czech_cv11` is a Czech model originally trained by mikr. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_czech_cv11_cs_5.1.4_3.4_1697582051071.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_czech_cv11_cs_5.1.4_3.4_1697582051071.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_czech_cv11","cs") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_czech_cv11","cs") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_czech_cv11| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|cs| +|Size:|1.7 GB| + +## References + +https://huggingface.co/mikr/whisper-small-cs-cv11 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_czech_cv11_pipeline_cs.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_czech_cv11_pipeline_cs.md new file mode 100644 index 00000000000000..c101de18dae2d8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_czech_cv11_pipeline_cs.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Czech asr_whisper_small_czech_cv11_pipeline pipeline WhisperForCTC from mikr +author: John Snow Labs +name: asr_whisper_small_czech_cv11_pipeline +date: 2023-10-17 +tags: [whisper, cs, open_source, pipeline] +task: Automatic Speech Recognition +language: cs +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_czech_cv11_pipeline` is a Czech model originally trained by mikr. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_czech_cv11_pipeline_cs_5.1.4_3.4_1697582077518.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_czech_cv11_pipeline_cs_5.1.4_3.4_1697582077518.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_czech_cv11_pipeline', lang = 'cs') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_czech_cv11_pipeline', lang = 'cs') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_czech_cv11_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|cs| +|Size:|1.7 GB| + +## References + +https://huggingface.co/mikr/whisper-small-cs-cv11 + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized_fr.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized_fr.md new file mode 100644 index 00000000000000..ee95a957080895 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized_fr.md @@ -0,0 +1,92 @@ +--- +layout: model +title: French asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized WhisperForCTC from jwkritchie +author: John Snow Labs +name: asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized +date: 2023-10-17 +tags: [whisper, fr, open_source, asr, onnx] +task: Automatic Speech Recognition +language: fr +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized` is a French model originally trained by jwkritchie. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized_fr_5.1.4_3.4_1697582400007.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized_fr_5.1.4_3.4_1697582400007.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized","fr") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized","fr") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|fr| +|Size:|1.7 GB| + +## References + +https://huggingface.co/jwkritchie/whisper-small-defined-dot-ai-qc-fr-combined-dataset-normalized \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized_pipeline_fr.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized_pipeline_fr.md new file mode 100644 index 00000000000000..af6fb71125dc26 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized_pipeline_fr.md @@ -0,0 +1,71 @@ +--- +layout: model +title: French asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized_pipeline pipeline WhisperForCTC from jwkritchie +author: John Snow Labs +name: asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized_pipeline +date: 2023-10-17 +tags: [whisper, fr, open_source, pipeline] +task: Automatic Speech Recognition +language: fr +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized_pipeline` is a French model originally trained by jwkritchie. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized_pipeline_fr_5.1.4_3.4_1697582437910.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized_pipeline_fr_5.1.4_3.4_1697582437910.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized_pipeline', lang = 'fr') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized_pipeline', lang = 'fr') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_defined_dot_ai_qc_french_combined_dataset_normalized_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|fr| +|Size:|1.7 GB| + +## References + +https://huggingface.co/jwkritchie/whisper-small-defined-dot-ai-qc-fr-combined-dataset-normalized + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_english_en.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_english_en.md new file mode 100644 index 00000000000000..7ca7c8e4bfc62a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_english_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_english WhisperForCTC from openai +author: John Snow Labs +name: asr_whisper_small_english +date: 2023-10-17 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_english` is a English model originally trained by openai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_english_en_5.1.4_3.4_1697585976463.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_english_en_5.1.4_3.4_1697585976463.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_english","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_english","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_english| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.1 GB| + +## References + +https://huggingface.co/openai/whisper-small.en \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_english_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_english_pipeline_en.md new file mode 100644 index 00000000000000..ea00318a00079c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_english_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_english_pipeline pipeline WhisperForCTC from openai +author: John Snow Labs +name: asr_whisper_small_english_pipeline +date: 2023-10-17 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_english_pipeline` is a English model originally trained by openai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_english_pipeline_en_5.1.4_3.4_1697585998441.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_english_pipeline_en_5.1.4_3.4_1697585998441.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_english_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_english_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_english_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.1 GB| + +## References + +https://huggingface.co/openai/whisper-small.en + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_greek_el.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_greek_el.md new file mode 100644 index 00000000000000..de68aeeaa98d68 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_greek_el.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Modern Greek (1453-) asr_whisper_small_greek WhisperForCTC from farsipal +author: John Snow Labs +name: asr_whisper_small_greek +date: 2023-10-17 +tags: [whisper, el, open_source, asr, onnx] +task: Automatic Speech Recognition +language: el +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_greek` is a Modern Greek (1453-) model originally trained by farsipal. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_greek_el_5.1.4_3.4_1697581262666.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_greek_el_5.1.4_3.4_1697581262666.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_greek","el") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_greek","el") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_greek| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|el| +|Size:|1.1 GB| + +## References + +https://huggingface.co/farsipal/whisper-small-greek \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_greek_pipeline_el.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_greek_pipeline_el.md new file mode 100644 index 00000000000000..dea0b3dc264e97 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_greek_pipeline_el.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Modern Greek (1453-) asr_whisper_small_greek_pipeline pipeline WhisperForCTC from farsipal +author: John Snow Labs +name: asr_whisper_small_greek_pipeline +date: 2023-10-17 +tags: [whisper, el, open_source, pipeline] +task: Automatic Speech Recognition +language: el +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_greek_pipeline` is a Modern Greek (1453-) model originally trained by farsipal. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_greek_pipeline_el_5.1.4_3.4_1697581283359.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_greek_pipeline_el_5.1.4_3.4_1697581283359.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_greek_pipeline', lang = 'el') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_greek_pipeline', lang = 'el') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_greek_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|el| +|Size:|1.1 GB| + +## References + +https://huggingface.co/farsipal/whisper-small-greek + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_italian_3_it.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_italian_3_it.md new file mode 100644 index 00000000000000..189b31544acbce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_italian_3_it.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Italian asr_whisper_small_italian_3 WhisperForCTC from GIanlucaRub +author: John Snow Labs +name: asr_whisper_small_italian_3 +date: 2023-10-17 +tags: [whisper, it, open_source, asr, onnx] +task: Automatic Speech Recognition +language: it +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_italian_3` is a Italian model originally trained by GIanlucaRub. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_italian_3_it_5.1.4_3.4_1697586608746.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_italian_3_it_5.1.4_3.4_1697586608746.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_italian_3","it") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_italian_3","it") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_italian_3| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|it| +|Size:|1.7 GB| + +## References + +https://huggingface.co/GIanlucaRub/whisper-small-it-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_italian_3_pipeline_it.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_italian_3_pipeline_it.md new file mode 100644 index 00000000000000..6246ac002b491b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_italian_3_pipeline_it.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Italian asr_whisper_small_italian_3_pipeline pipeline WhisperForCTC from GIanlucaRub +author: John Snow Labs +name: asr_whisper_small_italian_3_pipeline +date: 2023-10-17 +tags: [whisper, it, open_source, pipeline] +task: Automatic Speech Recognition +language: it +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_italian_3_pipeline` is a Italian model originally trained by GIanlucaRub. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_italian_3_pipeline_it_5.1.4_3.4_1697586635172.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_italian_3_pipeline_it_5.1.4_3.4_1697586635172.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_italian_3_pipeline', lang = 'it') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_italian_3_pipeline', lang = 'it') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_italian_3_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|it| +|Size:|1.7 GB| + +## References + +https://huggingface.co/GIanlucaRub/whisper-small-it-3 + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_mongolian_1_mn.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_mongolian_1_mn.md new file mode 100644 index 00000000000000..b338e04dd224cb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_mongolian_1_mn.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Mongolian asr_whisper_small_mongolian_1 WhisperForCTC from bayartsogt +author: John Snow Labs +name: asr_whisper_small_mongolian_1 +date: 2023-10-17 +tags: [whisper, mn, open_source, asr, onnx] +task: Automatic Speech Recognition +language: mn +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_mongolian_1` is a Mongolian model originally trained by bayartsogt. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_mongolian_1_mn_5.1.4_3.4_1697583892680.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_mongolian_1_mn_5.1.4_3.4_1697583892680.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_mongolian_1","mn") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_mongolian_1","mn") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_mongolian_1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|mn| +|Size:|1.7 GB| + +## References + +https://huggingface.co/bayartsogt/whisper-small-mn-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_mongolian_1_pipeline_mn.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_mongolian_1_pipeline_mn.md new file mode 100644 index 00000000000000..0acbe5120db4c7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_mongolian_1_pipeline_mn.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Mongolian asr_whisper_small_mongolian_1_pipeline pipeline WhisperForCTC from bayartsogt +author: John Snow Labs +name: asr_whisper_small_mongolian_1_pipeline +date: 2023-10-17 +tags: [whisper, mn, open_source, pipeline] +task: Automatic Speech Recognition +language: mn +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_mongolian_1_pipeline` is a Mongolian model originally trained by bayartsogt. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_mongolian_1_pipeline_mn_5.1.4_3.4_1697583918301.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_mongolian_1_pipeline_mn_5.1.4_3.4_1697583918301.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_mongolian_1_pipeline', lang = 'mn') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_mongolian_1_pipeline', lang = 'mn') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_mongolian_1_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|mn| +|Size:|1.7 GB| + +## References + +https://huggingface.co/bayartsogt/whisper-small-mn-1 + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_mongolian_2_en.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_mongolian_2_en.md new file mode 100644 index 00000000000000..4dc6938ccd7899 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_mongolian_2_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_mongolian_2 WhisperForCTC from bayartsogt +author: John Snow Labs +name: asr_whisper_small_mongolian_2 +date: 2023-10-17 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_mongolian_2` is a English model originally trained by bayartsogt. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_mongolian_2_en_5.1.4_3.4_1697585749768.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_mongolian_2_en_5.1.4_3.4_1697585749768.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_mongolian_2","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_mongolian_2","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_mongolian_2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/bayartsogt/whisper-small-mn-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_mongolian_2_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_mongolian_2_pipeline_en.md new file mode 100644 index 00000000000000..3e14c5eb3dfe73 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_mongolian_2_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_mongolian_2_pipeline pipeline WhisperForCTC from bayartsogt +author: John Snow Labs +name: asr_whisper_small_mongolian_2_pipeline +date: 2023-10-17 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_mongolian_2_pipeline` is a English model originally trained by bayartsogt. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_mongolian_2_pipeline_en_5.1.4_3.4_1697585790673.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_mongolian_2_pipeline_en_5.1.4_3.4_1697585790673.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_mongolian_2_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_mongolian_2_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_mongolian_2_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/bayartsogt/whisper-small-mn-2 + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_pipeline_xx.md new file mode 100644 index 00000000000000..32843da828f2c6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_pipeline_xx.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Multilingual asr_whisper_small_pipeline pipeline WhisperForCTC from openai +author: John Snow Labs +name: asr_whisper_small_pipeline +date: 2023-10-17 +tags: [whisper, xx, open_source, pipeline] +task: Automatic Speech Recognition +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_pipeline` is a Multilingual model originally trained by openai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_pipeline_xx_5.1.4_3.4_1697581725467.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_pipeline_xx_5.1.4_3.4_1697581725467.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_pipeline', lang = 'xx') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_pipeline', lang = 'xx') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|1.1 GB| + +## References + +https://huggingface.co/openai/whisper-small + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_se2_en.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_se2_en.md new file mode 100644 index 00000000000000..e5d21a5cc33028 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_se2_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_se2 WhisperForCTC from ayberkuckun +author: John Snow Labs +name: asr_whisper_small_se2 +date: 2023-10-17 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_se2` is a English model originally trained by ayberkuckun. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_se2_en_5.1.4_3.4_1697581369035.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_se2_en_5.1.4_3.4_1697581369035.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_se2","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_se2","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_se2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/ayberkuckun/whisper-small-se2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_se2_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_se2_pipeline_en.md new file mode 100644 index 00000000000000..9b51cc337a2dee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_se2_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_se2_pipeline pipeline WhisperForCTC from ayberkuckun +author: John Snow Labs +name: asr_whisper_small_se2_pipeline +date: 2023-10-17 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_se2_pipeline` is a English model originally trained by ayberkuckun. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_se2_pipeline_en_5.1.4_3.4_1697581394596.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_se2_pipeline_en_5.1.4_3.4_1697581394596.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_se2_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_se2_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_se2_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/ayberkuckun/whisper-small-se2 + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_swahili_pplantinga_pipeline_sw.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_swahili_pplantinga_pipeline_sw.md new file mode 100644 index 00000000000000..c5dc83b15a8702 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_swahili_pplantinga_pipeline_sw.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Swahili (macrolanguage) asr_whisper_small_swahili_pplantinga_pipeline pipeline WhisperForCTC from pplantinga +author: John Snow Labs +name: asr_whisper_small_swahili_pplantinga_pipeline +date: 2023-10-17 +tags: [whisper, sw, open_source, pipeline] +task: Automatic Speech Recognition +language: sw +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_swahili_pplantinga_pipeline` is a Swahili (macrolanguage) model originally trained by pplantinga. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swahili_pplantinga_pipeline_sw_5.1.4_3.4_1697580448260.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swahili_pplantinga_pipeline_sw_5.1.4_3.4_1697580448260.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_swahili_pplantinga_pipeline', lang = 'sw') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_swahili_pplantinga_pipeline', lang = 'sw') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_swahili_pplantinga_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|sw| +|Size:|1.7 GB| + +## References + +https://huggingface.co/pplantinga/whisper-small-sw + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_swahili_pplantinga_sw.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_swahili_pplantinga_sw.md new file mode 100644 index 00000000000000..d23af81147e281 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_swahili_pplantinga_sw.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Swahili (macrolanguage) asr_whisper_small_swahili_pplantinga WhisperForCTC from pplantinga +author: John Snow Labs +name: asr_whisper_small_swahili_pplantinga +date: 2023-10-17 +tags: [whisper, sw, open_source, asr, onnx] +task: Automatic Speech Recognition +language: sw +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_swahili_pplantinga` is a Swahili (macrolanguage) model originally trained by pplantinga. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swahili_pplantinga_sw_5.1.4_3.4_1697580424248.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swahili_pplantinga_sw_5.1.4_3.4_1697580424248.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_swahili_pplantinga","sw") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_swahili_pplantinga","sw") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_swahili_pplantinga| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|sw| +|Size:|1.7 GB| + +## References + +https://huggingface.co/pplantinga/whisper-small-sw \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_swe_davidt123_en.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_swe_davidt123_en.md new file mode 100644 index 00000000000000..bdc57dfc577c3c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_swe_davidt123_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_swe_davidt123 WhisperForCTC from davidt123 +author: John Snow Labs +name: asr_whisper_small_swe_davidt123 +date: 2023-10-17 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_swe_davidt123` is a English model originally trained by davidt123. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swe_davidt123_en_5.1.4_3.4_1697579793966.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swe_davidt123_en_5.1.4_3.4_1697579793966.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_swe_davidt123","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_swe_davidt123","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_swe_davidt123| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/davidt123/whisper_small_swe \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_swe_davidt123_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_swe_davidt123_pipeline_en.md new file mode 100644 index 00000000000000..edf7cc74882197 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_swe_davidt123_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_swe_davidt123_pipeline pipeline WhisperForCTC from davidt123 +author: John Snow Labs +name: asr_whisper_small_swe_davidt123_pipeline +date: 2023-10-17 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_swe_davidt123_pipeline` is a English model originally trained by davidt123. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swe_davidt123_pipeline_en_5.1.4_3.4_1697579817761.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swe_davidt123_pipeline_en_5.1.4_3.4_1697579817761.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_swe_davidt123_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_swe_davidt123_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_swe_davidt123_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/davidt123/whisper_small_swe + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_telugu_146h_pipeline_te.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_telugu_146h_pipeline_te.md new file mode 100644 index 00000000000000..8abe6232b55a7a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_telugu_146h_pipeline_te.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Telugu asr_whisper_small_telugu_146h_pipeline pipeline WhisperForCTC from swechafsmi +author: John Snow Labs +name: asr_whisper_small_telugu_146h_pipeline +date: 2023-10-17 +tags: [whisper, te, open_source, pipeline] +task: Automatic Speech Recognition +language: te +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_telugu_146h_pipeline` is a Telugu model originally trained by swechafsmi. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_telugu_146h_pipeline_te_5.1.4_3.4_1697584130875.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_telugu_146h_pipeline_te_5.1.4_3.4_1697584130875.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_telugu_146h_pipeline', lang = 'te') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_telugu_146h_pipeline', lang = 'te') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_telugu_146h_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|te| +|Size:|1.7 GB| + +## References + +https://huggingface.co/swechafsmi/whisper-small-te-146h + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_telugu_146h_te.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_telugu_146h_te.md new file mode 100644 index 00000000000000..a24ebfd086e747 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_telugu_146h_te.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Telugu asr_whisper_small_telugu_146h WhisperForCTC from swechafsmi +author: John Snow Labs +name: asr_whisper_small_telugu_146h +date: 2023-10-17 +tags: [whisper, te, open_source, asr, onnx] +task: Automatic Speech Recognition +language: te +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_telugu_146h` is a Telugu model originally trained by swechafsmi. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_telugu_146h_te_5.1.4_3.4_1697584092998.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_telugu_146h_te_5.1.4_3.4_1697584092998.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_telugu_146h","te") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_telugu_146h","te") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_telugu_146h| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|te| +|Size:|1.7 GB| + +## References + +https://huggingface.co/swechafsmi/whisper-small-te-146h \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_telugu_openslr_en.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_telugu_openslr_en.md new file mode 100644 index 00000000000000..3cda2180c2d7fb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_telugu_openslr_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_telugu_openslr WhisperForCTC from yaswanth +author: John Snow Labs +name: asr_whisper_small_telugu_openslr +date: 2023-10-17 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_telugu_openslr` is a English model originally trained by yaswanth. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_telugu_openslr_en_5.1.4_3.4_1697584639657.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_telugu_openslr_en_5.1.4_3.4_1697584639657.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_telugu_openslr","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_telugu_openslr","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_telugu_openslr| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/yaswanth/whisper-small-te_openslr \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_telugu_openslr_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_telugu_openslr_pipeline_en.md new file mode 100644 index 00000000000000..94089dc4732cce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_telugu_openslr_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_telugu_openslr_pipeline pipeline WhisperForCTC from yaswanth +author: John Snow Labs +name: asr_whisper_small_telugu_openslr_pipeline +date: 2023-10-17 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_telugu_openslr_pipeline` is a English model originally trained by yaswanth. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_telugu_openslr_pipeline_en_5.1.4_3.4_1697584668923.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_telugu_openslr_pipeline_en_5.1.4_3.4_1697584668923.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_telugu_openslr_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_telugu_openslr_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_telugu_openslr_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/yaswanth/whisper-small-te_openslr + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_thai_napatswift_pipeline_th.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_thai_napatswift_pipeline_th.md new file mode 100644 index 00000000000000..17cd0f0f4882da --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_thai_napatswift_pipeline_th.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Thai asr_whisper_small_thai_napatswift_pipeline pipeline WhisperForCTC from napatswift +author: John Snow Labs +name: asr_whisper_small_thai_napatswift_pipeline +date: 2023-10-17 +tags: [whisper, th, open_source, pipeline] +task: Automatic Speech Recognition +language: th +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_thai_napatswift_pipeline` is a Thai model originally trained by napatswift. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_thai_napatswift_pipeline_th_5.1.4_3.4_1697583679102.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_thai_napatswift_pipeline_th_5.1.4_3.4_1697583679102.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_thai_napatswift_pipeline', lang = 'th') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_thai_napatswift_pipeline', lang = 'th') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_thai_napatswift_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|th| +|Size:|1.7 GB| + +## References + +https://huggingface.co/napatswift/whisper-small-thai + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_thai_napatswift_th.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_thai_napatswift_th.md new file mode 100644 index 00000000000000..5188725afc257f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_thai_napatswift_th.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Thai asr_whisper_small_thai_napatswift WhisperForCTC from napatswift +author: John Snow Labs +name: asr_whisper_small_thai_napatswift +date: 2023-10-17 +tags: [whisper, th, open_source, asr, onnx] +task: Automatic Speech Recognition +language: th +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_thai_napatswift` is a Thai model originally trained by napatswift. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_thai_napatswift_th_5.1.4_3.4_1697583642698.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_thai_napatswift_th_5.1.4_3.4_1697583642698.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_thai_napatswift","th") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_thai_napatswift","th") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_thai_napatswift| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|th| +|Size:|1.7 GB| + +## References + +https://huggingface.co/napatswift/whisper-small-thai \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_tonga_zambia_en.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_tonga_zambia_en.md new file mode 100644 index 00000000000000..3c8b094b986243 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_tonga_zambia_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_tonga_zambia WhisperForCTC from csikasote +author: John Snow Labs +name: asr_whisper_small_tonga_zambia +date: 2023-10-17 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_tonga_zambia` is a English model originally trained by csikasote. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_tonga_zambia_en_5.1.4_3.4_1697579942341.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_tonga_zambia_en_5.1.4_3.4_1697579942341.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_tonga_zambia","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_tonga_zambia","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_tonga_zambia| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/csikasote/whisper-small-toi \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_tonga_zambia_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_tonga_zambia_pipeline_en.md new file mode 100644 index 00000000000000..fb07768f6e0efa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_tonga_zambia_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_tonga_zambia_pipeline pipeline WhisperForCTC from csikasote +author: John Snow Labs +name: asr_whisper_small_tonga_zambia_pipeline +date: 2023-10-17 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_tonga_zambia_pipeline` is a English model originally trained by csikasote. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_tonga_zambia_pipeline_en_5.1.4_3.4_1697579965854.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_tonga_zambia_pipeline_en_5.1.4_3.4_1697579965854.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_tonga_zambia_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_tonga_zambia_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_tonga_zambia_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/csikasote/whisper-small-toi + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_ukrainian_pipeline_uk.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_ukrainian_pipeline_uk.md new file mode 100644 index 00000000000000..e4f431f9dd8eb6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_ukrainian_pipeline_uk.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Ukrainian asr_whisper_small_ukrainian_pipeline pipeline WhisperForCTC from Yehor +author: John Snow Labs +name: asr_whisper_small_ukrainian_pipeline +date: 2023-10-17 +tags: [whisper, uk, open_source, pipeline] +task: Automatic Speech Recognition +language: uk +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_ukrainian_pipeline` is a Ukrainian model originally trained by Yehor. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_ukrainian_pipeline_uk_5.1.4_3.4_1697585787241.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_ukrainian_pipeline_uk_5.1.4_3.4_1697585787241.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_ukrainian_pipeline', lang = 'uk') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_ukrainian_pipeline', lang = 'uk') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_ukrainian_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|uk| +|Size:|1.7 GB| + +## References + +https://huggingface.co/Yehor/whisper-small-ukrainian + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_ukrainian_uk.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_ukrainian_uk.md new file mode 100644 index 00000000000000..fbd5ca376fcaf6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_ukrainian_uk.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Ukrainian asr_whisper_small_ukrainian WhisperForCTC from Yehor +author: John Snow Labs +name: asr_whisper_small_ukrainian +date: 2023-10-17 +tags: [whisper, uk, open_source, asr, onnx] +task: Automatic Speech Recognition +language: uk +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_ukrainian` is a Ukrainian model originally trained by Yehor. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_ukrainian_uk_5.1.4_3.4_1697585753181.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_ukrainian_uk_5.1.4_3.4_1697585753181.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_ukrainian","uk") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_ukrainian","uk") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_ukrainian| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|uk| +|Size:|1.7 GB| + +## References + +https://huggingface.co/Yehor/whisper-small-ukrainian \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_xx.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_xx.md new file mode 100644 index 00000000000000..429e59c1d7865c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_small_xx.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Multilingual asr_whisper_small WhisperForCTC from openai +author: John Snow Labs +name: asr_whisper_small +date: 2023-10-17 +tags: [whisper, xx, open_source, asr, onnx] +task: Automatic Speech Recognition +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small` is a Multilingual model originally trained by openai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_xx_5.1.4_3.4_1697581706186.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_xx_5.1.4_3.4_1697581706186.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small","xx") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small","xx") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|xx| +|Size:|1.1 GB| + +## References + +https://huggingface.co/openai/whisper-small \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_telugu_tiny_pipeline_te.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_telugu_tiny_pipeline_te.md new file mode 100644 index 00000000000000..73232f0894e22b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_telugu_tiny_pipeline_te.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Telugu asr_whisper_telugu_tiny_pipeline pipeline WhisperForCTC from vasista22 +author: John Snow Labs +name: asr_whisper_telugu_tiny_pipeline +date: 2023-10-17 +tags: [whisper, te, open_source, pipeline] +task: Automatic Speech Recognition +language: te +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_telugu_tiny_pipeline` is a Telugu model originally trained by vasista22. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_telugu_tiny_pipeline_te_5.1.4_3.4_1697585640379.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_telugu_tiny_pipeline_te_5.1.4_3.4_1697585640379.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_telugu_tiny_pipeline', lang = 'te') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_telugu_tiny_pipeline', lang = 'te') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_telugu_tiny_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|te| +|Size:|391.1 MB| + +## References + +https://huggingface.co/vasista22/whisper-telugu-tiny + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_telugu_tiny_te.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_telugu_tiny_te.md new file mode 100644 index 00000000000000..11e68714886043 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_telugu_tiny_te.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Telugu asr_whisper_telugu_tiny WhisperForCTC from vasista22 +author: John Snow Labs +name: asr_whisper_telugu_tiny +date: 2023-10-17 +tags: [whisper, te, open_source, asr, onnx] +task: Automatic Speech Recognition +language: te +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_telugu_tiny` is a Telugu model originally trained by vasista22. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_telugu_tiny_te_5.1.4_3.4_1697585633821.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_telugu_tiny_te_5.1.4_3.4_1697585633821.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_telugu_tiny","te") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_telugu_tiny","te") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_telugu_tiny| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|te| +|Size:|391.1 MB| + +## References + +https://huggingface.co/vasista22/whisper-telugu-tiny \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_bulgarian_l_bg.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_bulgarian_l_bg.md new file mode 100644 index 00000000000000..9d31bd14f5c26d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_bulgarian_l_bg.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Bulgarian asr_whisper_tiny_bulgarian_l WhisperForCTC from nandovallec +author: John Snow Labs +name: asr_whisper_tiny_bulgarian_l +date: 2023-10-17 +tags: [whisper, bg, open_source, asr, onnx] +task: Automatic Speech Recognition +language: bg +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_bulgarian_l` is a Bulgarian model originally trained by nandovallec. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_bulgarian_l_bg_5.1.4_3.4_1697584004963.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_bulgarian_l_bg_5.1.4_3.4_1697584004963.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_bulgarian_l","bg") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_bulgarian_l","bg") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_bulgarian_l| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|bg| +|Size:|390.8 MB| + +## References + +https://huggingface.co/nandovallec/whisper-tiny-bg-l \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_bulgarian_l_pipeline_bg.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_bulgarian_l_pipeline_bg.md new file mode 100644 index 00000000000000..2cdbf913e19c27 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_bulgarian_l_pipeline_bg.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Bulgarian asr_whisper_tiny_bulgarian_l_pipeline pipeline WhisperForCTC from nandovallec +author: John Snow Labs +name: asr_whisper_tiny_bulgarian_l_pipeline +date: 2023-10-17 +tags: [whisper, bg, open_source, pipeline] +task: Automatic Speech Recognition +language: bg +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_bulgarian_l_pipeline` is a Bulgarian model originally trained by nandovallec. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_bulgarian_l_pipeline_bg_5.1.4_3.4_1697584012326.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_bulgarian_l_pipeline_bg_5.1.4_3.4_1697584012326.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_tiny_bulgarian_l_pipeline', lang = 'bg') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_tiny_bulgarian_l_pipeline', lang = 'bg') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_bulgarian_l_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|bg| +|Size:|390.9 MB| + +## References + +https://huggingface.co/nandovallec/whisper-tiny-bg-l + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_english_en.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_english_en.md new file mode 100644 index 00000000000000..0149580837cad7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_english_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_tiny_english WhisperForCTC from openai +author: John Snow Labs +name: asr_whisper_tiny_english +date: 2023-10-17 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_english` is a English model originally trained by openai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_english_en_5.1.4_3.4_1697584049145.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_english_en_5.1.4_3.4_1697584049145.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_english","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_english","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_english| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|250.5 MB| + +## References + +https://huggingface.co/openai/whisper-tiny.en \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_english_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_english_pipeline_en.md new file mode 100644 index 00000000000000..c1b4f15d2583db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_english_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_tiny_english_pipeline pipeline WhisperForCTC from openai +author: John Snow Labs +name: asr_whisper_tiny_english_pipeline +date: 2023-10-17 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_english_pipeline` is a English model originally trained by openai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_english_pipeline_en_5.1.4_3.4_1697584055654.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_english_pipeline_en_5.1.4_3.4_1697584055654.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_tiny_english_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_tiny_english_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_english_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|250.5 MB| + +## References + +https://huggingface.co/openai/whisper-tiny.en + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_pashto_en.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_pashto_en.md new file mode 100644 index 00000000000000..040d8654b2e840 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_pashto_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_tiny_pashto WhisperForCTC from ihanif +author: John Snow Labs +name: asr_whisper_tiny_pashto +date: 2023-10-17 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_pashto` is a English model originally trained by ihanif. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_pashto_en_5.1.4_3.4_1697587144404.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_pashto_en_5.1.4_3.4_1697587144404.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_pashto","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_pashto","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_pashto| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|391.0 MB| + +## References + +https://huggingface.co/ihanif/whisper-tiny-ps \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_pashto_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_pashto_pipeline_en.md new file mode 100644 index 00000000000000..35185350e71742 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_pashto_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_tiny_pashto_pipeline pipeline WhisperForCTC from ihanif +author: John Snow Labs +name: asr_whisper_tiny_pashto_pipeline +date: 2023-10-17 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_pashto_pipeline` is a English model originally trained by ihanif. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_pashto_pipeline_en_5.1.4_3.4_1697587150066.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_pashto_pipeline_en_5.1.4_3.4_1697587150066.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_tiny_pashto_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_tiny_pashto_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_pashto_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|391.0 MB| + +## References + +https://huggingface.co/ihanif/whisper-tiny-ps + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_pipeline_xx.md new file mode 100644 index 00000000000000..4ee650e1f2900e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_pipeline_xx.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Multilingual asr_whisper_tiny_pipeline pipeline WhisperForCTC from openai +author: John Snow Labs +name: asr_whisper_tiny_pipeline +date: 2023-10-17 +tags: [whisper, xx, open_source, pipeline] +task: Automatic Speech Recognition +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_pipeline` is a Multilingual model originally trained by openai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_pipeline_xx_5.1.4_3.4_1697579837137.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_pipeline_xx_5.1.4_3.4_1697579837137.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_tiny_pipeline', lang = 'xx') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_tiny_pipeline', lang = 'xx') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|242.8 MB| + +## References + +https://huggingface.co/openai/whisper-tiny + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_xx.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_xx.md new file mode 100644 index 00000000000000..dc97e6b39d830a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tiny_xx.md @@ -0,0 +1,109 @@ +--- +layout: model +title: Official whisper-tiny +author: John Snow Labs +name: asr_whisper_tiny +date: 2023-10-17 +tags: [whisper, en, audio, open_source, asr, xx, onnx] +task: Automatic Speech Recognition +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Official pretrained Whisper model, adapted from HuggingFace transformer and curated to provide scalability and production-readiness using Spark NLP. + +This is a multilingual model and supports the following languages: + +Afrikaans, Arabic, Armenian, Azerbaijani, Belarusian, Bosnian, Bulgarian, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, Galician, German, Greek, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Kannada, Kazakh, Korean, Latvian, Lithuanian, Macedonian, Malay, Marathi, Maori, Nepali, Norwegian, Persian, Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tagalog, Tamil, Thai, Turkish, Ukrainian, Urdu, Vietnamese, and Welsh. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_xx_5.1.4_3.4_1697579830675.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_xx_5.1.4_3.4_1697579830675.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +import sparknlp +from sparknlp.base import * +from sparknlp.annotator import * +from pyspark.ml import Pipeline + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + +speechToText = WhisperForCTC.pretrained("asr_whisper_tiny", "xx") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) +processedAudioFloats = spark.createDataFrame([[rawFloats]]).toDF("audio_content") +result = pipeline.fit(processedAudioFloats).transform(processedAudioFloats) +result.select("text.result").show(truncate = False) +``` +```scala +import spark.implicits._ +import com.johnsnowlabs.nlp.base._ +import com.johnsnowlabs.nlp.annotators._ +import com.johnsnowlabs.nlp.annotators.audio.WhisperForCTC +import org.apache.spark.ml.Pipeline + +val audioAssembler: AudioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText: WhisperForCTC = WhisperForCTC + .pretrained("asr_whisper_tiny", "xx") + .setInputCols("audio_assembler") + .setOutputCol("text") + +val pipeline: Pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val bufferedSource = + scala.io.Source.fromFile("src/test/resources/audio/txt/librispeech_asr_0.txt") + +val rawFloats = bufferedSource + .getLines() + .map(_.split(",").head.trim.toFloat) + .toArray +bufferedSource.close + +val processedAudioFloats = Seq(rawFloats).toDF("audio_content") + +val result = pipeline.fit(processedAudioFloats).transform(processedAudioFloats) +result.select("text.result").show(truncate = false) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|xx| +|Size:|242.8 MB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tswana_greek_modern_e1_el.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tswana_greek_modern_e1_el.md new file mode 100644 index 00000000000000..048b327e231471 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tswana_greek_modern_e1_el.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Modern Greek (1453-) asr_whisper_tswana_greek_modern_e1 WhisperForCTC from emilios +author: John Snow Labs +name: asr_whisper_tswana_greek_modern_e1 +date: 2023-10-17 +tags: [whisper, el, open_source, asr, onnx] +task: Automatic Speech Recognition +language: el +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tswana_greek_modern_e1` is a Modern Greek (1453-) model originally trained by emilios. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tswana_greek_modern_e1_el_5.1.4_3.4_1697583402368.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tswana_greek_modern_e1_el_5.1.4_3.4_1697583402368.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_tswana_greek_modern_e1","el") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_tswana_greek_modern_e1","el") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tswana_greek_modern_e1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|el| +|Size:|241.6 MB| + +## References + +https://huggingface.co/emilios/whisper-tn-el-e1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tswana_greek_modern_e1_pipeline_el.md b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tswana_greek_modern_e1_pipeline_el.md new file mode 100644 index 00000000000000..c1a3b2a3b05cb5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-17-asr_whisper_tswana_greek_modern_e1_pipeline_el.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Modern Greek (1453-) asr_whisper_tswana_greek_modern_e1_pipeline pipeline WhisperForCTC from emilios +author: John Snow Labs +name: asr_whisper_tswana_greek_modern_e1_pipeline +date: 2023-10-17 +tags: [whisper, el, open_source, pipeline] +task: Automatic Speech Recognition +language: el +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tswana_greek_modern_e1_pipeline` is a Modern Greek (1453-) model originally trained by emilios. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tswana_greek_modern_e1_pipeline_el_5.1.4_3.4_1697583407910.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tswana_greek_modern_e1_pipeline_el_5.1.4_3.4_1697583407910.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_tswana_greek_modern_e1_pipeline', lang = 'el') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_tswana_greek_modern_e1_pipeline', lang = 'el') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tswana_greek_modern_e1_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|el| +|Size:|241.6 MB| + +## References + +https://huggingface.co/emilios/whisper-tn-el-e1 + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_base_bengali_trans_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_base_bengali_trans_pipeline_xx.md new file mode 100644 index 00000000000000..92fcd6ede87251 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_base_bengali_trans_pipeline_xx.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Multilingual asr_whisper_base_bengali_trans_pipeline pipeline WhisperForCTC from arijitx +author: John Snow Labs +name: asr_whisper_base_bengali_trans_pipeline +date: 2023-10-18 +tags: [whisper, xx, open_source, pipeline] +task: Automatic Speech Recognition +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_base_bengali_trans_pipeline` is a Multilingual model originally trained by arijitx. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_base_bengali_trans_pipeline_xx_5.1.4_3.4_1697588822232.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_base_bengali_trans_pipeline_xx_5.1.4_3.4_1697588822232.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_base_bengali_trans_pipeline', lang = 'xx') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_base_bengali_trans_pipeline', lang = 'xx') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_base_bengali_trans_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|616.9 MB| + +## References + +https://huggingface.co/arijitx/whisper-base-bn-trans + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_base_bengali_trans_xx.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_base_bengali_trans_xx.md new file mode 100644 index 00000000000000..2bdb45829c7441 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_base_bengali_trans_xx.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Multilingual asr_whisper_base_bengali_trans WhisperForCTC from arijitx +author: John Snow Labs +name: asr_whisper_base_bengali_trans +date: 2023-10-18 +tags: [whisper, xx, open_source, asr, onnx] +task: Automatic Speech Recognition +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_base_bengali_trans` is a Multilingual model originally trained by arijitx. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_base_bengali_trans_xx_5.1.4_3.4_1697588807282.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_base_bengali_trans_xx_5.1.4_3.4_1697588807282.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_base_bengali_trans","xx") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_base_bengali_trans","xx") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_base_bengali_trans| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|xx| +|Size:|616.9 MB| + +## References + +https://huggingface.co/arijitx/whisper-base-bn-trans \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_base_european_en.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_base_european_en.md new file mode 100644 index 00000000000000..16de96f10dab1d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_base_european_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_base_european WhisperForCTC from aware-ai +author: John Snow Labs +name: asr_whisper_base_european +date: 2023-10-18 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_base_european` is a English model originally trained by aware-ai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_base_european_en_5.1.4_3.4_1697621307187.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_base_european_en_5.1.4_3.4_1697621307187.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_base_european","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_base_european","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_base_european| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|643.2 MB| + +## References + +https://huggingface.co/aware-ai/whisper-base-european \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_base_european_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_base_european_pipeline_en.md new file mode 100644 index 00000000000000..57982910350916 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_base_european_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_base_european_pipeline pipeline WhisperForCTC from aware-ai +author: John Snow Labs +name: asr_whisper_base_european_pipeline +date: 2023-10-18 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_base_european_pipeline` is a English model originally trained by aware-ai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_base_european_pipeline_en_5.1.4_3.4_1697621319699.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_base_european_pipeline_en_5.1.4_3.4_1697621319699.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_base_european_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_base_european_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_base_european_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|643.2 MB| + +## References + +https://huggingface.co/aware-ai/whisper-base-european + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_base_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_base_pipeline_xx.md new file mode 100644 index 00000000000000..8c490bfadcf2c9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_base_pipeline_xx.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Multilingual asr_whisper_base_pipeline pipeline WhisperForCTC from openai +author: John Snow Labs +name: asr_whisper_base_pipeline +date: 2023-10-18 +tags: [whisper, xx, open_source, pipeline] +task: Automatic Speech Recognition +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_base_pipeline` is a Multilingual model originally trained by openai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_base_pipeline_xx_5.1.4_3.4_1697617598741.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_base_pipeline_xx_5.1.4_3.4_1697617598741.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_base_pipeline', lang = 'xx') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_base_pipeline', lang = 'xx') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_base_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|398.5 MB| + +## References + +https://huggingface.co/openai/whisper-base + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_base_xx.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_base_xx.md new file mode 100644 index 00000000000000..47effc80e13f5d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_base_xx.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Multilingual asr_whisper_base WhisperForCTC from openai +author: John Snow Labs +name: asr_whisper_base +date: 2023-10-18 +tags: [whisper, xx, open_source, asr, onnx] +task: Automatic Speech Recognition +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_base` is a Multilingual model originally trained by openai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_base_xx_5.1.4_3.4_1697617589019.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_base_xx_5.1.4_3.4_1697617589019.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_base","xx") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_base","xx") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_base| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|xx| +|Size:|398.5 MB| + +## References + +https://huggingface.co/openai/whisper-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_persian_farsi_en.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_persian_farsi_en.md new file mode 100644 index 00000000000000..6adf6228e65ccc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_persian_farsi_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_persian_farsi WhisperForCTC from Yasaman +author: John Snow Labs +name: asr_whisper_persian_farsi +date: 2023-10-18 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_persian_farsi` is a English model originally trained by Yasaman. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_persian_farsi_en_5.1.4_3.4_1697589012994.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_persian_farsi_en_5.1.4_3.4_1697589012994.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_persian_farsi","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_persian_farsi","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_persian_farsi| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/Yasaman/whisper_fa \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_persian_farsi_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_persian_farsi_pipeline_en.md new file mode 100644 index 00000000000000..7a56dfb1c75c86 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_persian_farsi_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_persian_farsi_pipeline pipeline WhisperForCTC from Yasaman +author: John Snow Labs +name: asr_whisper_persian_farsi_pipeline +date: 2023-10-18 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_persian_farsi_pipeline` is a English model originally trained by Yasaman. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_persian_farsi_pipeline_en_5.1.4_3.4_1697589037917.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_persian_farsi_pipeline_en_5.1.4_3.4_1697589037917.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_persian_farsi_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_persian_farsi_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_persian_farsi_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/Yasaman/whisper_fa + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_samoan_farsipal_e5_el.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_samoan_farsipal_e5_el.md new file mode 100644 index 00000000000000..fd181f684a9b51 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_samoan_farsipal_e5_el.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Modern Greek (1453-) asr_whisper_samoan_farsipal_e5 WhisperForCTC from emilios +author: John Snow Labs +name: asr_whisper_samoan_farsipal_e5 +date: 2023-10-18 +tags: [whisper, el, open_source, asr, onnx] +task: Automatic Speech Recognition +language: el +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_samoan_farsipal_e5` is a Modern Greek (1453-) model originally trained by emilios. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_samoan_farsipal_e5_el_5.1.4_3.4_1697589671565.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_samoan_farsipal_e5_el_5.1.4_3.4_1697589671565.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_samoan_farsipal_e5","el") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_samoan_farsipal_e5","el") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_samoan_farsipal_e5| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|el| +|Size:|1.1 GB| + +## References + +https://huggingface.co/emilios/whisper-sm-farsipal-e5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_samoan_farsipal_e5_pipeline_el.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_samoan_farsipal_e5_pipeline_el.md new file mode 100644 index 00000000000000..4667ffca58bd90 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_samoan_farsipal_e5_pipeline_el.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Modern Greek (1453-) asr_whisper_samoan_farsipal_e5_pipeline pipeline WhisperForCTC from emilios +author: John Snow Labs +name: asr_whisper_samoan_farsipal_e5_pipeline +date: 2023-10-18 +tags: [whisper, el, open_source, pipeline] +task: Automatic Speech Recognition +language: el +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_samoan_farsipal_e5_pipeline` is a Modern Greek (1453-) model originally trained by emilios. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_samoan_farsipal_e5_pipeline_el_5.1.4_3.4_1697589687809.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_samoan_farsipal_e5_pipeline_el_5.1.4_3.4_1697589687809.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_samoan_farsipal_e5_pipeline', lang = 'el') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_samoan_farsipal_e5_pipeline', lang = 'el') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_samoan_farsipal_e5_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|el| +|Size:|1.1 GB| + +## References + +https://huggingface.co/emilios/whisper-sm-farsipal-e5 + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_chinese_hk_en.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_chinese_hk_en.md new file mode 100644 index 00000000000000..4560809eeb3b33 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_chinese_hk_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_chinese_hk WhisperForCTC from tilos +author: John Snow Labs +name: asr_whisper_small_chinese_hk +date: 2023-10-18 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_chinese_hk` is a English model originally trained by tilos. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_chinese_hk_en_5.1.4_3.4_1697588775594.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_chinese_hk_en_5.1.4_3.4_1697588775594.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_chinese_hk","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_chinese_hk","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_chinese_hk| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/tilos/whisper-small-zh-HK \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_chinese_hk_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_chinese_hk_pipeline_en.md new file mode 100644 index 00000000000000..3f7208b48b1389 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_chinese_hk_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_chinese_hk_pipeline pipeline WhisperForCTC from tilos +author: John Snow Labs +name: asr_whisper_small_chinese_hk_pipeline +date: 2023-10-18 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_chinese_hk_pipeline` is a English model originally trained by tilos. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_chinese_hk_pipeline_en_5.1.4_3.4_1697588819567.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_chinese_hk_pipeline_en_5.1.4_3.4_1697588819567.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_chinese_hk_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_chinese_hk_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_chinese_hk_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/tilos/whisper-small-zh-HK + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_chinese_pipeline_zh.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_chinese_pipeline_zh.md new file mode 100644 index 00000000000000..0d8a6f89a54774 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_chinese_pipeline_zh.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Chinese asr_whisper_small_chinese_pipeline pipeline WhisperForCTC from xmzhu +author: John Snow Labs +name: asr_whisper_small_chinese_pipeline +date: 2023-10-18 +tags: [whisper, zh, open_source, pipeline] +task: Automatic Speech Recognition +language: zh +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_chinese_pipeline` is a Chinese model originally trained by xmzhu. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_chinese_pipeline_zh_5.1.4_3.4_1697587418263.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_chinese_pipeline_zh_5.1.4_3.4_1697587418263.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_chinese_pipeline', lang = 'zh') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_chinese_pipeline', lang = 'zh') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_chinese_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|zh| +|Size:|1.7 GB| + +## References + +https://huggingface.co/xmzhu/whisper-small-zh + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_chinese_zh.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_chinese_zh.md new file mode 100644 index 00000000000000..91fb1e2509a46f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_chinese_zh.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Chinese asr_whisper_small_chinese WhisperForCTC from xmzhu +author: John Snow Labs +name: asr_whisper_small_chinese +date: 2023-10-18 +tags: [whisper, zh, open_source, asr, onnx] +task: Automatic Speech Recognition +language: zh +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_chinese` is a Chinese model originally trained by xmzhu. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_chinese_zh_5.1.4_3.4_1697587372288.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_chinese_zh_5.1.4_3.4_1697587372288.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_chinese","zh") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_chinese","zh") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_chinese| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|zh| +|Size:|1.7 GB| + +## References + +https://huggingface.co/xmzhu/whisper-small-zh \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_finnish_15k_samples_fi.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_finnish_15k_samples_fi.md new file mode 100644 index 00000000000000..46af7bb6576888 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_finnish_15k_samples_fi.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Finnish asr_whisper_small_finnish_15k_samples WhisperForCTC from RASMUS +author: John Snow Labs +name: asr_whisper_small_finnish_15k_samples +date: 2023-10-18 +tags: [whisper, fi, open_source, asr, onnx] +task: Automatic Speech Recognition +language: fi +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_finnish_15k_samples` is a Finnish model originally trained by RASMUS. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_finnish_15k_samples_fi_5.1.4_3.4_1697621153794.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_finnish_15k_samples_fi_5.1.4_3.4_1697621153794.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_finnish_15k_samples","fi") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_finnish_15k_samples","fi") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_finnish_15k_samples| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|fi| +|Size:|1.7 GB| + +## References + +https://huggingface.co/RASMUS/whisper-small-fi-15k_samples \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_finnish_15k_samples_pipeline_fi.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_finnish_15k_samples_pipeline_fi.md new file mode 100644 index 00000000000000..2d95841c450282 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_finnish_15k_samples_pipeline_fi.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Finnish asr_whisper_small_finnish_15k_samples_pipeline pipeline WhisperForCTC from RASMUS +author: John Snow Labs +name: asr_whisper_small_finnish_15k_samples_pipeline +date: 2023-10-18 +tags: [whisper, fi, open_source, pipeline] +task: Automatic Speech Recognition +language: fi +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_finnish_15k_samples_pipeline` is a Finnish model originally trained by RASMUS. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_finnish_15k_samples_pipeline_fi_5.1.4_3.4_1697621190478.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_finnish_15k_samples_pipeline_fi_5.1.4_3.4_1697621190478.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_finnish_15k_samples_pipeline', lang = 'fi') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_finnish_15k_samples_pipeline', lang = 'fi') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_finnish_15k_samples_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|1.7 GB| + +## References + +https://huggingface.co/RASMUS/whisper-small-fi-15k_samples + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_german_en.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_german_en.md new file mode 100644 index 00000000000000..5450fac14573dd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_german_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_german WhisperForCTC from aware-ai +author: John Snow Labs +name: asr_whisper_small_german +date: 2023-10-18 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_german` is a English model originally trained by aware-ai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_german_en_5.1.4_3.4_1697588230786.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_german_en_5.1.4_3.4_1697588230786.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_german","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_german","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_german| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/aware-ai/whisper-small-german \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_german_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_german_pipeline_en.md new file mode 100644 index 00000000000000..445ef5bf812cfc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_german_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_german_pipeline pipeline WhisperForCTC from aware-ai +author: John Snow Labs +name: asr_whisper_small_german_pipeline +date: 2023-10-18 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_german_pipeline` is a English model originally trained by aware-ai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_german_pipeline_en_5.1.4_3.4_1697588261636.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_german_pipeline_en_5.1.4_3.4_1697588261636.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_german_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_german_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_german_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/aware-ai/whisper-small-german + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_hindi_norwegian_tensorboard_hi.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_hindi_norwegian_tensorboard_hi.md new file mode 100644 index 00000000000000..68e4eb9037c67d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_hindi_norwegian_tensorboard_hi.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Hindi asr_whisper_small_hindi_norwegian_tensorboard WhisperForCTC from sanchit-gandhi +author: John Snow Labs +name: asr_whisper_small_hindi_norwegian_tensorboard +date: 2023-10-18 +tags: [whisper, hi, open_source, asr, onnx] +task: Automatic Speech Recognition +language: hi +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_hindi_norwegian_tensorboard` is a Hindi model originally trained by sanchit-gandhi. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_hindi_norwegian_tensorboard_hi_5.1.4_3.4_1697589700658.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_hindi_norwegian_tensorboard_hi_5.1.4_3.4_1697589700658.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_hindi_norwegian_tensorboard","hi") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_hindi_norwegian_tensorboard","hi") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_hindi_norwegian_tensorboard| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|hi| +|Size:|1.7 GB| + +## References + +https://huggingface.co/sanchit-gandhi/whisper-small-hi-no-tensorboard \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_hindi_norwegian_tensorboard_pipeline_hi.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_hindi_norwegian_tensorboard_pipeline_hi.md new file mode 100644 index 00000000000000..d6571bd8cb5682 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_hindi_norwegian_tensorboard_pipeline_hi.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Hindi asr_whisper_small_hindi_norwegian_tensorboard_pipeline pipeline WhisperForCTC from sanchit-gandhi +author: John Snow Labs +name: asr_whisper_small_hindi_norwegian_tensorboard_pipeline +date: 2023-10-18 +tags: [whisper, hi, open_source, pipeline] +task: Automatic Speech Recognition +language: hi +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_hindi_norwegian_tensorboard_pipeline` is a Hindi model originally trained by sanchit-gandhi. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_hindi_norwegian_tensorboard_pipeline_hi_5.1.4_3.4_1697589736295.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_hindi_norwegian_tensorboard_pipeline_hi_5.1.4_3.4_1697589736295.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_hindi_norwegian_tensorboard_pipeline', lang = 'hi') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_hindi_norwegian_tensorboard_pipeline', lang = 'hi') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_hindi_norwegian_tensorboard_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|hi| +|Size:|1.7 GB| + +## References + +https://huggingface.co/sanchit-gandhi/whisper-small-hi-no-tensorboard + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_hindi_shripadbhat_hi.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_hindi_shripadbhat_hi.md new file mode 100644 index 00000000000000..e21e8bde358e68 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_hindi_shripadbhat_hi.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Hindi asr_whisper_small_hindi_shripadbhat WhisperForCTC from shripadbhat +author: John Snow Labs +name: asr_whisper_small_hindi_shripadbhat +date: 2023-10-18 +tags: [whisper, hi, open_source, asr, onnx] +task: Automatic Speech Recognition +language: hi +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_hindi_shripadbhat` is a Hindi model originally trained by shripadbhat. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_hindi_shripadbhat_hi_5.1.4_3.4_1697587721228.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_hindi_shripadbhat_hi_5.1.4_3.4_1697587721228.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_hindi_shripadbhat","hi") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_hindi_shripadbhat","hi") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_hindi_shripadbhat| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|hi| +|Size:|1.7 GB| + +## References + +https://huggingface.co/shripadbhat/whisper-small-hi \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_hindi_shripadbhat_pipeline_hi.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_hindi_shripadbhat_pipeline_hi.md new file mode 100644 index 00000000000000..de4536f64abda8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_hindi_shripadbhat_pipeline_hi.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Hindi asr_whisper_small_hindi_shripadbhat_pipeline pipeline WhisperForCTC from shripadbhat +author: John Snow Labs +name: asr_whisper_small_hindi_shripadbhat_pipeline +date: 2023-10-18 +tags: [whisper, hi, open_source, pipeline] +task: Automatic Speech Recognition +language: hi +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_hindi_shripadbhat_pipeline` is a Hindi model originally trained by shripadbhat. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_hindi_shripadbhat_pipeline_hi_5.1.4_3.4_1697587754852.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_hindi_shripadbhat_pipeline_hi_5.1.4_3.4_1697587754852.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_hindi_shripadbhat_pipeline', lang = 'hi') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_hindi_shripadbhat_pipeline', lang = 'hi') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_hindi_shripadbhat_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|hi| +|Size:|1.7 GB| + +## References + +https://huggingface.co/shripadbhat/whisper-small-hi + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_japanese_jakeyoo_ja.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_japanese_jakeyoo_ja.md new file mode 100644 index 00000000000000..e0bf0e2cc7ba83 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_japanese_jakeyoo_ja.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Japanese asr_whisper_small_japanese_jakeyoo WhisperForCTC from jakeyoo +author: John Snow Labs +name: asr_whisper_small_japanese_jakeyoo +date: 2023-10-18 +tags: [whisper, ja, open_source, asr, onnx] +task: Automatic Speech Recognition +language: ja +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_japanese_jakeyoo` is a Japanese model originally trained by jakeyoo. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_japanese_jakeyoo_ja_5.1.4_3.4_1697589525110.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_japanese_jakeyoo_ja_5.1.4_3.4_1697589525110.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_japanese_jakeyoo","ja") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_japanese_jakeyoo","ja") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_japanese_jakeyoo| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|ja| +|Size:|1.7 GB| + +## References + +https://huggingface.co/jakeyoo/whisper-small-ja \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_japanese_jakeyoo_pipeline_ja.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_japanese_jakeyoo_pipeline_ja.md new file mode 100644 index 00000000000000..6cf962d2a068fd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_japanese_jakeyoo_pipeline_ja.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Japanese asr_whisper_small_japanese_jakeyoo_pipeline pipeline WhisperForCTC from jakeyoo +author: John Snow Labs +name: asr_whisper_small_japanese_jakeyoo_pipeline +date: 2023-10-18 +tags: [whisper, ja, open_source, pipeline] +task: Automatic Speech Recognition +language: ja +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_japanese_jakeyoo_pipeline` is a Japanese model originally trained by jakeyoo. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_japanese_jakeyoo_pipeline_ja_5.1.4_3.4_1697589551194.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_japanese_jakeyoo_pipeline_ja_5.1.4_3.4_1697589551194.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_japanese_jakeyoo_pipeline', lang = 'ja') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_japanese_jakeyoo_pipeline', lang = 'ja') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_japanese_jakeyoo_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|ja| +|Size:|1.7 GB| + +## References + +https://huggingface.co/jakeyoo/whisper-small-ja + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_portuguese_yapeng_en.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_portuguese_yapeng_en.md new file mode 100644 index 00000000000000..f4c5f6198ce1e6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_portuguese_yapeng_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_portuguese_yapeng WhisperForCTC from Yapeng +author: John Snow Labs +name: asr_whisper_small_portuguese_yapeng +date: 2023-10-18 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_portuguese_yapeng` is a English model originally trained by Yapeng. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_portuguese_yapeng_en_5.1.4_3.4_1697620237483.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_portuguese_yapeng_en_5.1.4_3.4_1697620237483.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_portuguese_yapeng","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_portuguese_yapeng","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_portuguese_yapeng| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/Yapeng/whisper-small-pt \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_portuguese_yapeng_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_portuguese_yapeng_pipeline_en.md new file mode 100644 index 00000000000000..4e16621fe478c4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_portuguese_yapeng_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_portuguese_yapeng_pipeline pipeline WhisperForCTC from Yapeng +author: John Snow Labs +name: asr_whisper_small_portuguese_yapeng_pipeline +date: 2023-10-18 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_portuguese_yapeng_pipeline` is a English model originally trained by Yapeng. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_portuguese_yapeng_pipeline_en_5.1.4_3.4_1697620263200.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_portuguese_yapeng_pipeline_en_5.1.4_3.4_1697620263200.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_portuguese_yapeng_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_portuguese_yapeng_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_portuguese_yapeng_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/Yapeng/whisper-small-pt + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_swedish_bm_pipeline_sv.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_swedish_bm_pipeline_sv.md new file mode 100644 index 00000000000000..f9223ca23dfb97 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_swedish_bm_pipeline_sv.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Swedish asr_whisper_small_swedish_bm_pipeline pipeline WhisperForCTC from birgermoell +author: John Snow Labs +name: asr_whisper_small_swedish_bm_pipeline +date: 2023-10-18 +tags: [whisper, sv, open_source, pipeline] +task: Automatic Speech Recognition +language: sv +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_swedish_bm_pipeline` is a Swedish model originally trained by birgermoell. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swedish_bm_pipeline_sv_5.1.4_3.4_1697623575673.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swedish_bm_pipeline_sv_5.1.4_3.4_1697623575673.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_swedish_bm_pipeline', lang = 'sv') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_swedish_bm_pipeline', lang = 'sv') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_swedish_bm_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|1.7 GB| + +## References + +https://huggingface.co/birgermoell/whisper-small-sv-bm + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_tatar_pipeline_tt.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_tatar_pipeline_tt.md new file mode 100644 index 00000000000000..2f92fd4cf50fb3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_tatar_pipeline_tt.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Tatar asr_whisper_small_tatar_pipeline pipeline WhisperForCTC from 501Good +author: John Snow Labs +name: asr_whisper_small_tatar_pipeline +date: 2023-10-18 +tags: [whisper, tt, open_source, pipeline] +task: Automatic Speech Recognition +language: tt +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_tatar_pipeline` is a Tatar model originally trained by 501Good. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_tatar_pipeline_tt_5.1.4_3.4_1697619326786.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_tatar_pipeline_tt_5.1.4_3.4_1697619326786.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_tatar_pipeline', lang = 'tt') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_tatar_pipeline', lang = 'tt') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_tatar_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|tt| +|Size:|1.7 GB| + +## References + +https://huggingface.co/501Good/whisper-small-tt + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_tatar_tt.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_tatar_tt.md new file mode 100644 index 00000000000000..6a99a3ac39e3f4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_tatar_tt.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Tatar asr_whisper_small_tatar WhisperForCTC from 501Good +author: John Snow Labs +name: asr_whisper_small_tatar +date: 2023-10-18 +tags: [whisper, tt, open_source, asr, onnx] +task: Automatic Speech Recognition +language: tt +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_tatar` is a Tatar model originally trained by 501Good. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_tatar_tt_5.1.4_3.4_1697619284915.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_tatar_tt_5.1.4_3.4_1697619284915.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_tatar","tt") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_tatar","tt") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_tatar| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|tt| +|Size:|1.7 GB| + +## References + +https://huggingface.co/501Good/whisper-small-tt \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_vietnamese_tuananh7198_pipeline_vi.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_vietnamese_tuananh7198_pipeline_vi.md new file mode 100644 index 00000000000000..e160d8993c8ec8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_vietnamese_tuananh7198_pipeline_vi.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Vietnamese asr_whisper_small_vietnamese_tuananh7198_pipeline pipeline WhisperForCTC from tuananh7198 +author: John Snow Labs +name: asr_whisper_small_vietnamese_tuananh7198_pipeline +date: 2023-10-18 +tags: [whisper, vi, open_source, pipeline] +task: Automatic Speech Recognition +language: vi +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_vietnamese_tuananh7198_pipeline` is a Vietnamese model originally trained by tuananh7198. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_vietnamese_tuananh7198_pipeline_vi_5.1.4_3.4_1697623445458.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_vietnamese_tuananh7198_pipeline_vi_5.1.4_3.4_1697623445458.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_vietnamese_tuananh7198_pipeline', lang = 'vi') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_vietnamese_tuananh7198_pipeline', lang = 'vi') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_vietnamese_tuananh7198_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|vi| +|Size:|1.7 GB| + +## References + +https://huggingface.co/tuananh7198/whisper-small-vi + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_vietnamese_tuananh7198_vi.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_vietnamese_tuananh7198_vi.md new file mode 100644 index 00000000000000..bdcab428b01420 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_small_vietnamese_tuananh7198_vi.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Vietnamese asr_whisper_small_vietnamese_tuananh7198 WhisperForCTC from tuananh7198 +author: John Snow Labs +name: asr_whisper_small_vietnamese_tuananh7198 +date: 2023-10-18 +tags: [whisper, vi, open_source, asr, onnx] +task: Automatic Speech Recognition +language: vi +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_vietnamese_tuananh7198` is a Vietnamese model originally trained by tuananh7198. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_vietnamese_tuananh7198_vi_5.1.4_3.4_1697623421456.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_vietnamese_tuananh7198_vi_5.1.4_3.4_1697623421456.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_vietnamese_tuananh7198","vi") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_vietnamese_tuananh7198","vi") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_vietnamese_tuananh7198| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|vi| +|Size:|1.7 GB| + +## References + +https://huggingface.co/tuananh7198/whisper-small-vi \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_european_en.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_european_en.md new file mode 100644 index 00000000000000..9a6ce8eca8cb6c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_european_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_tiny_european WhisperForCTC from aware-ai +author: John Snow Labs +name: asr_whisper_tiny_european +date: 2023-10-18 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_european` is a English model originally trained by aware-ai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_european_en_5.1.4_3.4_1697633069323.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_european_en_5.1.4_3.4_1697633069323.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_european","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_european","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_european| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|390.9 MB| + +## References + +https://huggingface.co/aware-ai/whisper-tiny-european \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_european_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_european_pipeline_en.md new file mode 100644 index 00000000000000..835e9f6fe0c1ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_european_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_tiny_european_pipeline pipeline WhisperForCTC from aware-ai +author: John Snow Labs +name: asr_whisper_tiny_european_pipeline +date: 2023-10-18 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_european_pipeline` is a English model originally trained by aware-ai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_european_pipeline_en_5.1.4_3.4_1697633088173.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_european_pipeline_en_5.1.4_3.4_1697633088173.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_tiny_european_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_tiny_european_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_european_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|390.9 MB| + +## References + +https://huggingface.co/aware-ai/whisper-tiny-european + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_german_en.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_german_en.md new file mode 100644 index 00000000000000..c158ff9b160bc7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_german_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_tiny_german WhisperForCTC from aware-ai +author: John Snow Labs +name: asr_whisper_tiny_german +date: 2023-10-18 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_german` is a English model originally trained by aware-ai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_german_en_5.1.4_3.4_1697587339428.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_german_en_5.1.4_3.4_1697587339428.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_german","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_german","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_german| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|390.1 MB| + +## References + +https://huggingface.co/aware-ai/whisper-tiny-german \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_german_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_german_pipeline_en.md new file mode 100644 index 00000000000000..4218af892f3342 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_german_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_tiny_german_pipeline pipeline WhisperForCTC from aware-ai +author: John Snow Labs +name: asr_whisper_tiny_german_pipeline +date: 2023-10-18 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_german_pipeline` is a English model originally trained by aware-ai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_german_pipeline_en_5.1.4_3.4_1697587348063.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_german_pipeline_en_5.1.4_3.4_1697587348063.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_tiny_german_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_tiny_german_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_german_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|390.2 MB| + +## References + +https://huggingface.co/aware-ai/whisper-tiny-german + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_italian_1_it.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_italian_1_it.md new file mode 100644 index 00000000000000..9d7e966588bdf4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_italian_1_it.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Italian asr_whisper_tiny_italian_1 WhisperForCTC from GIanlucaRub +author: John Snow Labs +name: asr_whisper_tiny_italian_1 +date: 2023-10-18 +tags: [whisper, it, open_source, asr, onnx] +task: Automatic Speech Recognition +language: it +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_italian_1` is a Italian model originally trained by GIanlucaRub. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_italian_1_it_5.1.4_3.4_1697633094679.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_italian_1_it_5.1.4_3.4_1697633094679.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_italian_1","it") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_italian_1","it") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_italian_1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|it| +|Size:|390.7 MB| + +## References + +https://huggingface.co/GIanlucaRub/whisper-tiny-it-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_italian_1_pipeline_it.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_italian_1_pipeline_it.md new file mode 100644 index 00000000000000..3cec3cdd2e9153 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_italian_1_pipeline_it.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Italian asr_whisper_tiny_italian_1_pipeline pipeline WhisperForCTC from GIanlucaRub +author: John Snow Labs +name: asr_whisper_tiny_italian_1_pipeline +date: 2023-10-18 +tags: [whisper, it, open_source, pipeline] +task: Automatic Speech Recognition +language: it +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_italian_1_pipeline` is a Italian model originally trained by GIanlucaRub. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_italian_1_pipeline_it_5.1.4_3.4_1697633103685.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_italian_1_pipeline_it_5.1.4_3.4_1697633103685.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_tiny_italian_1_pipeline', lang = 'it') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_tiny_italian_1_pipeline', lang = 'it') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_italian_1_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|it| +|Size:|390.7 MB| + +## References + +https://huggingface.co/GIanlucaRub/whisper-tiny-it-1 + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_pipeline_xx.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_pipeline_xx.md new file mode 100644 index 00000000000000..83a249175e449c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_pipeline_xx.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Multilingual asr_whisper_tiny_pipeline pipeline WhisperForCTC from openai +author: John Snow Labs +name: asr_whisper_tiny_pipeline +date: 2023-10-18 +tags: [whisper, xx, open_source, pipeline] +task: Automatic Speech Recognition +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_pipeline` is a Multilingual model originally trained by openai. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_pipeline_xx_5.1.4_3.4_1697616957612.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_pipeline_xx_5.1.4_3.4_1697616957612.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_tiny_pipeline', lang = 'xx') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_tiny_pipeline', lang = 'xx') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|xx| +|Size:|242.8 MB| + +## References + +https://huggingface.co/openai/whisper-tiny + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_tamil_example_pipeline_ta.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_tamil_example_pipeline_ta.md new file mode 100644 index 00000000000000..024bd3bd1f6625 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_tamil_example_pipeline_ta.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Tamil asr_whisper_tiny_tamil_example_pipeline pipeline WhisperForCTC from parambharat +author: John Snow Labs +name: asr_whisper_tiny_tamil_example_pipeline +date: 2023-10-18 +tags: [whisper, ta, open_source, pipeline] +task: Automatic Speech Recognition +language: ta +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_tamil_example_pipeline` is a Tamil model originally trained by parambharat. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_tamil_example_pipeline_ta_5.1.4_3.4_1697590385878.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_tamil_example_pipeline_ta_5.1.4_3.4_1697590385878.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_tiny_tamil_example_pipeline', lang = 'ta') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_tiny_tamil_example_pipeline', lang = 'ta') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_tamil_example_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|ta| +|Size:|390.9 MB| + +## References + +https://huggingface.co/parambharat/whisper-tiny-ta-example + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_tamil_example_ta.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_tamil_example_ta.md new file mode 100644 index 00000000000000..4826475ea66d13 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_tamil_example_ta.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Tamil asr_whisper_tiny_tamil_example WhisperForCTC from parambharat +author: John Snow Labs +name: asr_whisper_tiny_tamil_example +date: 2023-10-18 +tags: [whisper, ta, open_source, asr, onnx] +task: Automatic Speech Recognition +language: ta +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_tamil_example` is a Tamil model originally trained by parambharat. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_tamil_example_ta_5.1.4_3.4_1697590379193.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_tamil_example_ta_5.1.4_3.4_1697590379193.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_tamil_example","ta") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_tamil_example","ta") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_tamil_example| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|ta| +|Size:|390.9 MB| + +## References + +https://huggingface.co/parambharat/whisper-tiny-ta-example \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_tgl_en.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_tgl_en.md new file mode 100644 index 00000000000000..c25914ecd13281 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_tgl_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_tiny_tgl WhisperForCTC from marcderbauer +author: John Snow Labs +name: asr_whisper_tiny_tgl +date: 2023-10-18 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_tgl` is a English model originally trained by marcderbauer. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_tgl_en_5.1.4_3.4_1697601388622.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_tgl_en_5.1.4_3.4_1697601388622.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_tgl","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_tgl","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_tgl| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|390.8 MB| + +## References + +https://huggingface.co/marcderbauer/whisper-tiny-tgl \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_tgl_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_tgl_pipeline_en.md new file mode 100644 index 00000000000000..d52691044ef227 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_tgl_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_tiny_tgl_pipeline pipeline WhisperForCTC from marcderbauer +author: John Snow Labs +name: asr_whisper_tiny_tgl_pipeline +date: 2023-10-18 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_tgl_pipeline` is a English model originally trained by marcderbauer. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_tgl_pipeline_en_5.1.4_3.4_1697601395684.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_tgl_pipeline_en_5.1.4_3.4_1697601395684.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_tiny_tgl_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_tiny_tgl_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_tgl_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|390.8 MB| + +## References + +https://huggingface.co/marcderbauer/whisper-tiny-tgl + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_xx.md b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_xx.md new file mode 100644 index 00000000000000..9dee88b49d3574 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-18-asr_whisper_tiny_xx.md @@ -0,0 +1,109 @@ +--- +layout: model +title: Official whisper-tiny +author: John Snow Labs +name: asr_whisper_tiny +date: 2023-10-18 +tags: [whisper, en, audio, open_source, asr, xx, onnx] +task: Automatic Speech Recognition +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Official pretrained Whisper model, adapted from HuggingFace transformer and curated to provide scalability and production-readiness using Spark NLP. + +This is a multilingual model and supports the following languages: + +Afrikaans, Arabic, Armenian, Azerbaijani, Belarusian, Bosnian, Bulgarian, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, Galician, German, Greek, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Kannada, Kazakh, Korean, Latvian, Lithuanian, Macedonian, Malay, Marathi, Maori, Nepali, Norwegian, Persian, Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tagalog, Tamil, Thai, Turkish, Ukrainian, Urdu, Vietnamese, and Welsh. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_xx_5.1.4_3.4_1697616953370.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_xx_5.1.4_3.4_1697616953370.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +import sparknlp +from sparknlp.base import * +from sparknlp.annotator import * +from pyspark.ml import Pipeline + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + +speechToText = WhisperForCTC.pretrained("asr_whisper_tiny", "xx") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) +processedAudioFloats = spark.createDataFrame([[rawFloats]]).toDF("audio_content") +result = pipeline.fit(processedAudioFloats).transform(processedAudioFloats) +result.select("text.result").show(truncate = False) +``` +```scala +import spark.implicits._ +import com.johnsnowlabs.nlp.base._ +import com.johnsnowlabs.nlp.annotators._ +import com.johnsnowlabs.nlp.annotators.audio.WhisperForCTC +import org.apache.spark.ml.Pipeline + +val audioAssembler: AudioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText: WhisperForCTC = WhisperForCTC + .pretrained("asr_whisper_tiny", "xx") + .setInputCols("audio_assembler") + .setOutputCol("text") + +val pipeline: Pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val bufferedSource = + scala.io.Source.fromFile("src/test/resources/audio/txt/librispeech_asr_0.txt") + +val rawFloats = bufferedSource + .getLines() + .map(_.split(",").head.trim.toFloat) + .toArray +bufferedSource.close + +val processedAudioFloats = Seq(rawFloats).toDF("audio_content") + +val result = pipeline.fit(processedAudioFloats).transform(processedAudioFloats) +result.select("text.result").show(truncate = false) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|xx| +|Size:|242.8 MB| \ No newline at end of file From a6cb9d408315ed82dc7460c2e2312ce0f116bd2e Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Fri, 20 Oct 2023 20:58:08 +0700 Subject: [PATCH 52/55] 2023-10-19-asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic_en (#14032) * Add model 2023-10-19-asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic_en * Add model 2023-10-19-asr_whisper_small_chinese_tw_voidful_en * Add model 2023-10-19-asr_whisper_small_chinese_tw_voidful_pipeline_en * Add model 2023-10-19-asr_whisper_small_bak_en * Add model 2023-10-19-asr_whisper_small_bak_pipeline_en * Add model 2023-10-19-asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic_en * Add model 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docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_italian_2_pipeline_it.md create mode 100644 docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_italian_local_en.md create mode 100644 docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_italian_local_pipeline_en.md create mode 100644 docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_spanish_arpagon_es.md create mode 100644 docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_spanish_arpagon_pipeline_es.md diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_personal_whisper_small_english_model_en.md b/docs/_posts/ahmedlone127/2023-10-19-asr_personal_whisper_small_english_model_en.md new file mode 100644 index 00000000000000..58c915b4449e4f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_personal_whisper_small_english_model_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_personal_whisper_small_english_model WhisperForCTC from fractalego +author: John Snow Labs +name: asr_personal_whisper_small_english_model +date: 2023-10-19 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_personal_whisper_small_english_model` is a English model originally trained by fractalego. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_personal_whisper_small_english_model_en_5.1.4_3.4_1697754481302.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_personal_whisper_small_english_model_en_5.1.4_3.4_1697754481302.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_personal_whisper_small_english_model","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_personal_whisper_small_english_model","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_personal_whisper_small_english_model| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/fractalego/personal-whisper-small.en-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_personal_whisper_small_english_model_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-19-asr_personal_whisper_small_english_model_pipeline_en.md new file mode 100644 index 00000000000000..c188644156cd69 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_personal_whisper_small_english_model_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_personal_whisper_small_english_model_pipeline pipeline WhisperForCTC from fractalego +author: John Snow Labs +name: asr_personal_whisper_small_english_model_pipeline +date: 2023-10-19 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_personal_whisper_small_english_model_pipeline` is a English model originally trained by fractalego. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_personal_whisper_small_english_model_pipeline_en_5.1.4_3.4_1697754518503.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_personal_whisper_small_english_model_pipeline_en_5.1.4_3.4_1697754518503.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_personal_whisper_small_english_model_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_personal_whisper_small_english_model_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_personal_whisper_small_english_model_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/fractalego/personal-whisper-small.en-model + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_lithuanian_finetune_lt.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_lithuanian_finetune_lt.md new file mode 100644 index 00000000000000..4d011976883339 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_lithuanian_finetune_lt.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Lithuanian asr_whisper_lithuanian_finetune WhisperForCTC from daniel-rdt +author: John Snow Labs +name: asr_whisper_lithuanian_finetune +date: 2023-10-19 +tags: [whisper, lt, open_source, asr, onnx] +task: Automatic Speech Recognition +language: lt +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_lithuanian_finetune` is a Lithuanian model originally trained by daniel-rdt. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_lithuanian_finetune_lt_5.1.4_3.4_1697755801160.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_lithuanian_finetune_lt_5.1.4_3.4_1697755801160.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_lithuanian_finetune","lt") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_lithuanian_finetune","lt") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_lithuanian_finetune| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|lt| +|Size:|1.7 GB| + +## References + +https://huggingface.co/daniel-rdt/whisper-lt-finetune \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_lithuanian_finetune_pipeline_lt.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_lithuanian_finetune_pipeline_lt.md new file mode 100644 index 00000000000000..203e29eaa84f11 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_lithuanian_finetune_pipeline_lt.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Lithuanian asr_whisper_lithuanian_finetune_pipeline pipeline WhisperForCTC from daniel-rdt +author: John Snow Labs +name: asr_whisper_lithuanian_finetune_pipeline +date: 2023-10-19 +tags: [whisper, lt, open_source, pipeline] +task: Automatic Speech Recognition +language: lt +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_lithuanian_finetune_pipeline` is a Lithuanian model originally trained by daniel-rdt. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_lithuanian_finetune_pipeline_lt_5.1.4_3.4_1697755826126.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_lithuanian_finetune_pipeline_lt_5.1.4_3.4_1697755826126.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_lithuanian_finetune_pipeline', lang = 'lt') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_lithuanian_finetune_pipeline', lang = 'lt') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_lithuanian_finetune_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|lt| +|Size:|1.7 GB| + +## References + +https://huggingface.co/daniel-rdt/whisper-lt-finetune + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_malayalam_first_model_ml.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_malayalam_first_model_ml.md new file mode 100644 index 00000000000000..a75c8818aa3776 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_malayalam_first_model_ml.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Malayalam asr_whisper_malayalam_first_model WhisperForCTC from kurianbenoy +author: John Snow Labs +name: asr_whisper_malayalam_first_model +date: 2023-10-19 +tags: [whisper, ml, open_source, asr, onnx] +task: Automatic Speech Recognition +language: ml +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_malayalam_first_model` is a Malayalam model originally trained by kurianbenoy. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_malayalam_first_model_ml_5.1.4_3.4_1697755379079.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_malayalam_first_model_ml_5.1.4_3.4_1697755379079.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_malayalam_first_model","ml") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_malayalam_first_model","ml") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_malayalam_first_model| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|ml| +|Size:|391.1 MB| + +## References + +https://huggingface.co/kurianbenoy/whisper-ml-first-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_malayalam_first_model_pipeline_ml.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_malayalam_first_model_pipeline_ml.md new file mode 100644 index 00000000000000..b698a24a4214b6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_malayalam_first_model_pipeline_ml.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Malayalam asr_whisper_malayalam_first_model_pipeline pipeline WhisperForCTC from kurianbenoy +author: John Snow Labs +name: asr_whisper_malayalam_first_model_pipeline +date: 2023-10-19 +tags: [whisper, ml, open_source, pipeline] +task: Automatic Speech Recognition +language: ml +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_malayalam_first_model_pipeline` is a Malayalam model originally trained by kurianbenoy. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_malayalam_first_model_pipeline_ml_5.1.4_3.4_1697755389149.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_malayalam_first_model_pipeline_ml_5.1.4_3.4_1697755389149.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_malayalam_first_model_pipeline', lang = 'ml') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_malayalam_first_model_pipeline', lang = 'ml') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_malayalam_first_model_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|ml| +|Size:|391.1 MB| + +## References + +https://huggingface.co/kurianbenoy/whisper-ml-first-model + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_bak_en.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_bak_en.md new file mode 100644 index 00000000000000..63a5a76517dd1b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_bak_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_bak WhisperForCTC from AigizK +author: John Snow Labs +name: asr_whisper_small_bak +date: 2023-10-19 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_bak` is a English model originally trained by AigizK. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_bak_en_5.1.4_3.4_1697753335079.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_bak_en_5.1.4_3.4_1697753335079.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_bak","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_bak","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_bak| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/AigizK/whisper-small-bak \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_bak_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_bak_pipeline_en.md new file mode 100644 index 00000000000000..16bed74424c586 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_bak_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_bak_pipeline pipeline WhisperForCTC from AigizK +author: John Snow Labs +name: asr_whisper_small_bak_pipeline +date: 2023-10-19 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_bak_pipeline` is a English model originally trained by AigizK. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_bak_pipeline_en_5.1.4_3.4_1697753365157.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_bak_pipeline_en_5.1.4_3.4_1697753365157.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_bak_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_bak_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_bak_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/AigizK/whisper-small-bak + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_bengali_subhadeep_en.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_bengali_subhadeep_en.md new file mode 100644 index 00000000000000..5d1382f034df6e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_bengali_subhadeep_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_bengali_subhadeep WhisperForCTC from Subhadeep +author: John Snow Labs +name: asr_whisper_small_bengali_subhadeep +date: 2023-10-19 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_bengali_subhadeep` is a English model originally trained by Subhadeep. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_bengali_subhadeep_en_5.1.4_3.4_1697757153165.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_bengali_subhadeep_en_5.1.4_3.4_1697757153165.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_bengali_subhadeep","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_bengali_subhadeep","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_bengali_subhadeep| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/Subhadeep/whisper-small-bn \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_bengali_subhadeep_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_bengali_subhadeep_pipeline_en.md new file mode 100644 index 00000000000000..adcefbfeb3e49a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_bengali_subhadeep_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_bengali_subhadeep_pipeline pipeline WhisperForCTC from Subhadeep +author: John Snow Labs +name: asr_whisper_small_bengali_subhadeep_pipeline +date: 2023-10-19 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_bengali_subhadeep_pipeline` is a English model originally trained by Subhadeep. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_bengali_subhadeep_pipeline_en_5.1.4_3.4_1697757181862.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_bengali_subhadeep_pipeline_en_5.1.4_3.4_1697757181862.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_bengali_subhadeep_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_bengali_subhadeep_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_bengali_subhadeep_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/Subhadeep/whisper-small-bn + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_chinese_tw_voidful_en.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_chinese_tw_voidful_en.md new file mode 100644 index 00000000000000..44deac6deb72fb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_chinese_tw_voidful_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_chinese_tw_voidful WhisperForCTC from voidful +author: John Snow Labs +name: asr_whisper_small_chinese_tw_voidful +date: 2023-10-19 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_chinese_tw_voidful` is a English model originally trained by voidful. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_chinese_tw_voidful_en_5.1.4_3.4_1697753255068.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_chinese_tw_voidful_en_5.1.4_3.4_1697753255068.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_chinese_tw_voidful","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_chinese_tw_voidful","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_chinese_tw_voidful| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/voidful/whisper-small-zh-TW \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_chinese_tw_voidful_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_chinese_tw_voidful_pipeline_en.md new file mode 100644 index 00000000000000..796c65d9089569 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_chinese_tw_voidful_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_chinese_tw_voidful_pipeline pipeline WhisperForCTC from voidful +author: John Snow Labs +name: asr_whisper_small_chinese_tw_voidful_pipeline +date: 2023-10-19 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_chinese_tw_voidful_pipeline` is a English model originally trained by voidful. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_chinese_tw_voidful_pipeline_en_5.1.4_3.4_1697753279228.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_chinese_tw_voidful_pipeline_en_5.1.4_3.4_1697753279228.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_chinese_tw_voidful_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_chinese_tw_voidful_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_chinese_tw_voidful_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/voidful/whisper-small-zh-TW + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_chinesebasetw_pipeline_zh.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_chinesebasetw_pipeline_zh.md new file mode 100644 index 00000000000000..5327aa27c093ed --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_chinesebasetw_pipeline_zh.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Chinese asr_whisper_small_chinesebasetw_pipeline pipeline WhisperForCTC from Jingmiao +author: John Snow Labs +name: asr_whisper_small_chinesebasetw_pipeline +date: 2023-10-19 +tags: [whisper, zh, open_source, pipeline] +task: Automatic Speech Recognition +language: zh +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_chinesebasetw_pipeline` is a Chinese model originally trained by Jingmiao. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_chinesebasetw_pipeline_zh_5.1.4_3.4_1697757472081.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_chinesebasetw_pipeline_zh_5.1.4_3.4_1697757472081.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_chinesebasetw_pipeline', lang = 'zh') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_chinesebasetw_pipeline', lang = 'zh') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_chinesebasetw_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|zh| +|Size:|1.7 GB| + +## References + +https://huggingface.co/Jingmiao/whisper-small-chineseBaseTW + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_chinesebasetw_zh.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_chinesebasetw_zh.md new file mode 100644 index 00000000000000..530f36f0286351 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_chinesebasetw_zh.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Chinese asr_whisper_small_chinesebasetw WhisperForCTC from Jingmiao +author: John Snow Labs +name: asr_whisper_small_chinesebasetw +date: 2023-10-19 +tags: [whisper, zh, open_source, asr, onnx] +task: Automatic Speech Recognition +language: zh +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_chinesebasetw` is a Chinese model originally trained by Jingmiao. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_chinesebasetw_zh_5.1.4_3.4_1697757430683.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_chinesebasetw_zh_5.1.4_3.4_1697757430683.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_chinesebasetw","zh") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_chinesebasetw","zh") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_chinesebasetw| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|zh| +|Size:|1.7 GB| + +## References + +https://huggingface.co/Jingmiao/whisper-small-chineseBaseTW \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_hindi_xinhuang_hi.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_hindi_xinhuang_hi.md new file mode 100644 index 00000000000000..3d80e7aa6073eb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_hindi_xinhuang_hi.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Hindi asr_whisper_small_hindi_xinhuang WhisperForCTC from xinhuang +author: John Snow Labs +name: asr_whisper_small_hindi_xinhuang +date: 2023-10-19 +tags: [whisper, hi, open_source, asr, onnx] +task: Automatic Speech Recognition +language: hi +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_hindi_xinhuang` is a Hindi model originally trained by xinhuang. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_hindi_xinhuang_hi_5.1.4_3.4_1697754908782.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_hindi_xinhuang_hi_5.1.4_3.4_1697754908782.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_hindi_xinhuang","hi") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_hindi_xinhuang","hi") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_hindi_xinhuang| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|hi| +|Size:|1.7 GB| + +## References + +https://huggingface.co/xinhuang/whisper-small-hi \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_hindi_xinhuang_pipeline_hi.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_hindi_xinhuang_pipeline_hi.md new file mode 100644 index 00000000000000..ece7023482e98c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_hindi_xinhuang_pipeline_hi.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Hindi asr_whisper_small_hindi_xinhuang_pipeline pipeline WhisperForCTC from xinhuang +author: John Snow Labs +name: asr_whisper_small_hindi_xinhuang_pipeline +date: 2023-10-19 +tags: [whisper, hi, open_source, pipeline] +task: Automatic Speech Recognition +language: hi +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_hindi_xinhuang_pipeline` is a Hindi model originally trained by xinhuang. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_hindi_xinhuang_pipeline_hi_5.1.4_3.4_1697754944761.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_hindi_xinhuang_pipeline_hi_5.1.4_3.4_1697754944761.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_hindi_xinhuang_pipeline', lang = 'hi') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_hindi_xinhuang_pipeline', lang = 'hi') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_hindi_xinhuang_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|hi| +|Size:|1.7 GB| + +## References + +https://huggingface.co/xinhuang/whisper-small-hi + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_lithuanian_deividasm_lt.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_lithuanian_deividasm_lt.md new file mode 100644 index 00000000000000..cc23fee11f736b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_lithuanian_deividasm_lt.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Lithuanian asr_whisper_small_lithuanian_deividasm WhisperForCTC from DeividasM +author: John Snow Labs +name: asr_whisper_small_lithuanian_deividasm +date: 2023-10-19 +tags: [whisper, lt, open_source, asr, onnx] +task: Automatic Speech Recognition +language: lt +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_lithuanian_deividasm` is a Lithuanian model originally trained by DeividasM. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_lithuanian_deividasm_lt_5.1.4_3.4_1697755670450.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_lithuanian_deividasm_lt_5.1.4_3.4_1697755670450.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_lithuanian_deividasm","lt") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_lithuanian_deividasm","lt") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_lithuanian_deividasm| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|lt| +|Size:|1.7 GB| + +## References + +https://huggingface.co/DeividasM/whisper-small-lt \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_lithuanian_deividasm_pipeline_lt.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_lithuanian_deividasm_pipeline_lt.md new file mode 100644 index 00000000000000..6540d1773e6067 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_lithuanian_deividasm_pipeline_lt.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Lithuanian asr_whisper_small_lithuanian_deividasm_pipeline pipeline WhisperForCTC from DeividasM +author: John Snow Labs +name: asr_whisper_small_lithuanian_deividasm_pipeline +date: 2023-10-19 +tags: [whisper, lt, open_source, pipeline] +task: Automatic Speech Recognition +language: lt +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_lithuanian_deividasm_pipeline` is a Lithuanian model originally trained by DeividasM. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_lithuanian_deividasm_pipeline_lt_5.1.4_3.4_1697755699167.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_lithuanian_deividasm_pipeline_lt_5.1.4_3.4_1697755699167.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_lithuanian_deividasm_pipeline', lang = 'lt') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_lithuanian_deividasm_pipeline', lang = 'lt') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_lithuanian_deividasm_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|lt| +|Size:|1.7 GB| + +## References + +https://huggingface.co/DeividasM/whisper-small-lt + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_nepali_np_ne.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_nepali_np_ne.md new file mode 100644 index 00000000000000..0a640afe7afa6c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_nepali_np_ne.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Nepali (macrolanguage) asr_whisper_small_nepali_np WhisperForCTC from julie200 +author: John Snow Labs +name: asr_whisper_small_nepali_np +date: 2023-10-19 +tags: [whisper, ne, open_source, asr, onnx] +task: Automatic Speech Recognition +language: ne +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_nepali_np` is a Nepali (macrolanguage) model originally trained by julie200. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_nepali_np_ne_5.1.4_3.4_1697758238849.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_nepali_np_ne_5.1.4_3.4_1697758238849.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_nepali_np","ne") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_nepali_np","ne") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_nepali_np| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|ne| +|Size:|1.7 GB| + +## References + +https://huggingface.co/julie200/whisper-small-ne-np \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_nepali_np_pipeline_ne.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_nepali_np_pipeline_ne.md new file mode 100644 index 00000000000000..12a86f64b2e774 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_nepali_np_pipeline_ne.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Nepali (macrolanguage) asr_whisper_small_nepali_np_pipeline pipeline WhisperForCTC from julie200 +author: John Snow Labs +name: asr_whisper_small_nepali_np_pipeline +date: 2023-10-19 +tags: [whisper, ne, open_source, pipeline] +task: Automatic Speech Recognition +language: ne +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_nepali_np_pipeline` is a Nepali (macrolanguage) model originally trained by julie200. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_nepali_np_pipeline_ne_5.1.4_3.4_1697758262511.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_nepali_np_pipeline_ne_5.1.4_3.4_1697758262511.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_nepali_np_pipeline', lang = 'ne') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_nepali_np_pipeline', lang = 'ne') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_nepali_np_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|ne| +|Size:|1.7 GB| + +## References + +https://huggingface.co/julie200/whisper-small-ne-np + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_polish_aspik101_pipeline_pl.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_polish_aspik101_pipeline_pl.md new file mode 100644 index 00000000000000..21b32eed65c247 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_polish_aspik101_pipeline_pl.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Polish asr_whisper_small_polish_aspik101_pipeline pipeline WhisperForCTC from Aspik101 +author: John Snow Labs +name: asr_whisper_small_polish_aspik101_pipeline +date: 2023-10-19 +tags: [whisper, pl, open_source, pipeline] +task: Automatic Speech Recognition +language: pl +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_polish_aspik101_pipeline` is a Polish model originally trained by Aspik101. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_polish_aspik101_pipeline_pl_5.1.4_3.4_1697758839664.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_polish_aspik101_pipeline_pl_5.1.4_3.4_1697758839664.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_polish_aspik101_pipeline', lang = 'pl') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_polish_aspik101_pipeline', lang = 'pl') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_polish_aspik101_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|1.7 GB| + +## References + +https://huggingface.co/Aspik101/whisper-small-pl + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_polish_aspik101_pl.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_polish_aspik101_pl.md new file mode 100644 index 00000000000000..1a7bbfa6309da5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_polish_aspik101_pl.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Polish asr_whisper_small_polish_aspik101 WhisperForCTC from Aspik101 +author: John Snow Labs +name: asr_whisper_small_polish_aspik101 +date: 2023-10-19 +tags: [whisper, pl, open_source, asr, onnx] +task: Automatic Speech Recognition +language: pl +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_polish_aspik101` is a Polish model originally trained by Aspik101. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_polish_aspik101_pl_5.1.4_3.4_1697758808695.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_polish_aspik101_pl_5.1.4_3.4_1697758808695.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_polish_aspik101","pl") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_polish_aspik101","pl") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_polish_aspik101| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|pl| +|Size:|1.7 GB| + +## References + +https://huggingface.co/Aspik101/whisper-small-pl \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_swedish_se_afroanton_en.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_swedish_se_afroanton_en.md new file mode 100644 index 00000000000000..2b4f360e9f8d84 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_swedish_se_afroanton_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_swedish_se_afroanton WhisperForCTC from afroanton +author: John Snow Labs +name: asr_whisper_small_swedish_se_afroanton +date: 2023-10-19 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_swedish_se_afroanton` is a English model originally trained by afroanton. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swedish_se_afroanton_en_5.1.4_3.4_1697758027499.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swedish_se_afroanton_en_5.1.4_3.4_1697758027499.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_swedish_se_afroanton","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_swedish_se_afroanton","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_swedish_se_afroanton| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/afroanton/whisper-small-sv-SE \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_swedish_se_afroanton_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_swedish_se_afroanton_pipeline_en.md new file mode 100644 index 00000000000000..fd3d60a6523db4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_swedish_se_afroanton_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_swedish_se_afroanton_pipeline pipeline WhisperForCTC from afroanton +author: John Snow Labs +name: asr_whisper_small_swedish_se_afroanton_pipeline +date: 2023-10-19 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_swedish_se_afroanton_pipeline` is a English model originally trained by afroanton. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swedish_se_afroanton_pipeline_en_5.1.4_3.4_1697758053063.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swedish_se_afroanton_pipeline_en_5.1.4_3.4_1697758053063.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_swedish_se_afroanton_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_swedish_se_afroanton_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_swedish_se_afroanton_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/afroanton/whisper-small-sv-SE + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_swedish_test_3000_pipeline_sv.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_swedish_test_3000_pipeline_sv.md new file mode 100644 index 00000000000000..d6504ee55571ec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_swedish_test_3000_pipeline_sv.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Swedish asr_whisper_small_swedish_test_3000_pipeline pipeline WhisperForCTC from ZinebSN +author: John Snow Labs +name: asr_whisper_small_swedish_test_3000_pipeline +date: 2023-10-19 +tags: [whisper, sv, open_source, pipeline] +task: Automatic Speech Recognition +language: sv +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_swedish_test_3000_pipeline` is a Swedish model originally trained by ZinebSN. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swedish_test_3000_pipeline_sv_5.1.4_3.4_1697754957297.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swedish_test_3000_pipeline_sv_5.1.4_3.4_1697754957297.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_swedish_test_3000_pipeline', lang = 'sv') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_swedish_test_3000_pipeline', lang = 'sv') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_swedish_test_3000_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|1.7 GB| + +## References + +https://huggingface.co/ZinebSN/whisper-small-swedish-Test-3000 + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_swedish_test_3000_sv.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_swedish_test_3000_sv.md new file mode 100644 index 00000000000000..d0496b955ce397 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_swedish_test_3000_sv.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Swedish asr_whisper_small_swedish_test_3000 WhisperForCTC from ZinebSN +author: John Snow Labs +name: asr_whisper_small_swedish_test_3000 +date: 2023-10-19 +tags: [whisper, sv, open_source, asr, onnx] +task: Automatic Speech Recognition +language: sv +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_swedish_test_3000` is a Swedish model originally trained by ZinebSN. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swedish_test_3000_sv_5.1.4_3.4_1697754925389.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swedish_test_3000_sv_5.1.4_3.4_1697754925389.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_swedish_test_3000","sv") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_swedish_test_3000","sv") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_swedish_test_3000| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|sv| +|Size:|1.7 GB| + +## References + +https://huggingface.co/ZinebSN/whisper-small-swedish-Test-3000 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic_en.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic_en.md new file mode 100644 index 00000000000000..5fb84b16c4ff62 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic WhisperForCTC from kpriyanshu256 +author: John Snow Labs +name: asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic +date: 2023-10-19 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic` is a English model originally trained by kpriyanshu256. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic_en_5.1.4_3.4_1697754395649.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic_en_5.1.4_3.4_1697754395649.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/kpriyanshu256/whisper-small-ur-1000-64-1e-05-pretrain-ar \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic_pipeline_en.md new file mode 100644 index 00000000000000..7bc5bc3d8f8794 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic_pipeline pipeline WhisperForCTC from kpriyanshu256 +author: John Snow Labs +name: asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic_pipeline +date: 2023-10-19 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic_pipeline` is a English model originally trained by kpriyanshu256. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic_pipeline_en_5.1.4_3.4_1697754437394.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic_pipeline_en_5.1.4_3.4_1697754437394.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_urdu_1000_64_1e_05_pretrain_arabic_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/kpriyanshu256/whisper-small-ur-1000-64-1e-05-pretrain-ar + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_uzbek_pipeline_uz.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_uzbek_pipeline_uz.md new file mode 100644 index 00000000000000..cabf8598ecdb34 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_uzbek_pipeline_uz.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Uzbek asr_whisper_small_uzbek_pipeline pipeline WhisperForCTC from BlueRaccoon +author: John Snow Labs +name: asr_whisper_small_uzbek_pipeline +date: 2023-10-19 +tags: [whisper, uz, open_source, pipeline] +task: Automatic Speech Recognition +language: uz +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_uzbek_pipeline` is a Uzbek model originally trained by BlueRaccoon. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_uzbek_pipeline_uz_5.1.4_3.4_1697758115932.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_uzbek_pipeline_uz_5.1.4_3.4_1697758115932.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_uzbek_pipeline', lang = 'uz') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_uzbek_pipeline', lang = 'uz') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_uzbek_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|uz| +|Size:|1.7 GB| + +## References + +https://huggingface.co/BlueRaccoon/whisper-small-uz + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_uzbek_uz.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_uzbek_uz.md new file mode 100644 index 00000000000000..63146e9307e81d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_small_uzbek_uz.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Uzbek asr_whisper_small_uzbek WhisperForCTC from BlueRaccoon +author: John Snow Labs +name: asr_whisper_small_uzbek +date: 2023-10-19 +tags: [whisper, uz, open_source, asr, onnx] +task: Automatic Speech Recognition +language: uz +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_uzbek` is a Uzbek model originally trained by BlueRaccoon. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_uzbek_uz_5.1.4_3.4_1697758091527.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_uzbek_uz_5.1.4_3.4_1697758091527.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_uzbek","uz") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_uzbek","uz") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_uzbek| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|uz| +|Size:|1.7 GB| + +## References + +https://huggingface.co/BlueRaccoon/whisper-small-uz \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_tiny_polish_pipeline_pl.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_tiny_polish_pipeline_pl.md new file mode 100644 index 00000000000000..a82842463d19c6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_tiny_polish_pipeline_pl.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Polish asr_whisper_tiny_polish_pipeline pipeline WhisperForCTC from Aspik101 +author: John Snow Labs +name: asr_whisper_tiny_polish_pipeline +date: 2023-10-19 +tags: [whisper, pl, open_source, pipeline] +task: Automatic Speech Recognition +language: pl +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_polish_pipeline` is a Polish model originally trained by Aspik101. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_polish_pipeline_pl_5.1.4_3.4_1697759156199.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_polish_pipeline_pl_5.1.4_3.4_1697759156199.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_tiny_polish_pipeline', lang = 'pl') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_tiny_polish_pipeline', lang = 'pl') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_polish_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|pl| +|Size:|390.7 MB| + +## References + +https://huggingface.co/Aspik101/whisper-tiny-pl + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_tiny_polish_pl.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_tiny_polish_pl.md new file mode 100644 index 00000000000000..20eb9a7e90fa34 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_tiny_polish_pl.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Polish asr_whisper_tiny_polish WhisperForCTC from Aspik101 +author: John Snow Labs +name: asr_whisper_tiny_polish +date: 2023-10-19 +tags: [whisper, pl, open_source, asr, onnx] +task: Automatic Speech Recognition +language: pl +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_polish` is a Polish model originally trained by Aspik101. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_polish_pl_5.1.4_3.4_1697759148948.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_polish_pl_5.1.4_3.4_1697759148948.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_polish","pl") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_polish","pl") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_polish| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|pl| +|Size:|390.7 MB| + +## References + +https://huggingface.co/Aspik101/whisper-tiny-pl \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_tiny_tamil_example_pipeline_ta.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_tiny_tamil_example_pipeline_ta.md new file mode 100644 index 00000000000000..24f1515ee7e316 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_tiny_tamil_example_pipeline_ta.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Tamil asr_whisper_tiny_tamil_example_pipeline pipeline WhisperForCTC from parambharat +author: John Snow Labs +name: asr_whisper_tiny_tamil_example_pipeline +date: 2023-10-19 +tags: [whisper, ta, open_source, pipeline] +task: Automatic Speech Recognition +language: ta +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_tamil_example_pipeline` is a Tamil model originally trained by parambharat. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_tamil_example_pipeline_ta_5.1.4_3.4_1697754897889.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_tamil_example_pipeline_ta_5.1.4_3.4_1697754897889.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_tiny_tamil_example_pipeline', lang = 'ta') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_tiny_tamil_example_pipeline', lang = 'ta') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_tamil_example_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|ta| +|Size:|390.9 MB| + +## References + +https://huggingface.co/parambharat/whisper-tiny-ta-example + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_tiny_tamil_example_ta.md b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_tiny_tamil_example_ta.md new file mode 100644 index 00000000000000..40505bb5ea47de --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-19-asr_whisper_tiny_tamil_example_ta.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Tamil asr_whisper_tiny_tamil_example WhisperForCTC from parambharat +author: John Snow Labs +name: asr_whisper_tiny_tamil_example +date: 2023-10-19 +tags: [whisper, ta, open_source, asr, onnx] +task: Automatic Speech Recognition +language: ta +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_tamil_example` is a Tamil model originally trained by parambharat. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_tamil_example_ta_5.1.4_3.4_1697754888104.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_tamil_example_ta_5.1.4_3.4_1697754888104.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_tamil_example","ta") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_tamil_example","ta") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_tamil_example| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|ta| +|Size:|390.9 MB| + +## References + +https://huggingface.co/parambharat/whisper-tiny-ta-example \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_base_swedish_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_base_swedish_en.md new file mode 100644 index 00000000000000..e5374a6b02df8d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_base_swedish_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_base_swedish WhisperForCTC from rscolati +author: John Snow Labs +name: asr_whisper_base_swedish +date: 2023-10-20 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_base_swedish` is a English model originally trained by rscolati. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_base_swedish_en_5.1.4_3.4_1697761530317.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_base_swedish_en_5.1.4_3.4_1697761530317.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_base_swedish","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_base_swedish","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_base_swedish| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|643.4 MB| + +## References + +https://huggingface.co/rscolati/whisper-base-sv \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_base_swedish_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_base_swedish_pipeline_en.md new file mode 100644 index 00000000000000..6698bf87195e1d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_base_swedish_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_base_swedish_pipeline pipeline WhisperForCTC from rscolati +author: John Snow Labs +name: asr_whisper_base_swedish_pipeline +date: 2023-10-20 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_base_swedish_pipeline` is a English model originally trained by rscolati. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_base_swedish_pipeline_en_5.1.4_3.4_1697761542899.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_base_swedish_pipeline_en_5.1.4_3.4_1697761542899.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_base_swedish_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_base_swedish_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_base_swedish_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|643.4 MB| + +## References + +https://huggingface.co/rscolati/whisper-base-sv + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_danish_small_augmented_da.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_danish_small_augmented_da.md new file mode 100644 index 00000000000000..738c57acff5b1c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_danish_small_augmented_da.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Danish asr_whisper_danish_small_augmented WhisperForCTC from ALM +author: John Snow Labs +name: asr_whisper_danish_small_augmented +date: 2023-10-20 +tags: [whisper, da, open_source, asr, onnx] +task: Automatic Speech Recognition +language: da +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_danish_small_augmented` is a Danish model originally trained by ALM. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_danish_small_augmented_da_5.1.4_3.4_1697768052248.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_danish_small_augmented_da_5.1.4_3.4_1697768052248.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_danish_small_augmented","da") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_danish_small_augmented","da") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_danish_small_augmented| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|da| +|Size:|1.7 GB| + +## References + +https://huggingface.co/ALM/whisper-da-small-augmented \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_danish_small_augmented_pipeline_da.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_danish_small_augmented_pipeline_da.md new file mode 100644 index 00000000000000..80706f147bbcd6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_danish_small_augmented_pipeline_da.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Danish asr_whisper_danish_small_augmented_pipeline pipeline WhisperForCTC from ALM +author: John Snow Labs +name: asr_whisper_danish_small_augmented_pipeline +date: 2023-10-20 +tags: [whisper, da, open_source, pipeline] +task: Automatic Speech Recognition +language: da +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_danish_small_augmented_pipeline` is a Danish model originally trained by ALM. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_danish_small_augmented_pipeline_da_5.1.4_3.4_1697768078968.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_danish_small_augmented_pipeline_da_5.1.4_3.4_1697768078968.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_danish_small_augmented_pipeline', lang = 'da') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_danish_small_augmented_pipeline', lang = 'da') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_danish_small_augmented_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|da| +|Size:|1.7 GB| + +## References + +https://huggingface.co/ALM/whisper-da-small-augmented + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_arabic_cv11_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_arabic_cv11_en.md new file mode 100644 index 00000000000000..97d1cee82f35fb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_arabic_cv11_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_arabic_cv11 WhisperForCTC from hkhdair +author: John Snow Labs +name: asr_whisper_small_arabic_cv11 +date: 2023-10-20 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_arabic_cv11` is a English model originally trained by hkhdair. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_arabic_cv11_en_5.1.4_3.4_1697764506687.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_arabic_cv11_en_5.1.4_3.4_1697764506687.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_arabic_cv11","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_arabic_cv11","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_arabic_cv11| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/hkhdair/whisper-small-ar-cv11 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_arabic_cv11_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_arabic_cv11_pipeline_en.md new file mode 100644 index 00000000000000..24ae96a1db563c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_arabic_cv11_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_arabic_cv11_pipeline pipeline WhisperForCTC from hkhdair +author: John Snow Labs +name: asr_whisper_small_arabic_cv11_pipeline +date: 2023-10-20 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_arabic_cv11_pipeline` is a English model originally trained by hkhdair. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_arabic_cv11_pipeline_en_5.1.4_3.4_1697764533389.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_arabic_cv11_pipeline_en_5.1.4_3.4_1697764533389.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_arabic_cv11_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_arabic_cv11_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_arabic_cv11_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/hkhdair/whisper-small-ar-cv11 + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_armenian_hy.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_armenian_hy.md new file mode 100644 index 00000000000000..64ca52425b8f33 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_armenian_hy.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Armenian asr_whisper_small_armenian WhisperForCTC from pranay-j +author: John Snow Labs +name: asr_whisper_small_armenian +date: 2023-10-20 +tags: [whisper, hy, open_source, asr, onnx] +task: Automatic Speech Recognition +language: hy +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_armenian` is a Armenian model originally trained by pranay-j. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_armenian_hy_5.1.4_3.4_1697760551180.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_armenian_hy_5.1.4_3.4_1697760551180.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_armenian","hy") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_armenian","hy") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_armenian| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|hy| +|Size:|1.7 GB| + +## References + +https://huggingface.co/pranay-j/whisper-small-hy \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_armenian_pipeline_hy.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_armenian_pipeline_hy.md new file mode 100644 index 00000000000000..61ccef878b4b7e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_armenian_pipeline_hy.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Armenian asr_whisper_small_armenian_pipeline pipeline WhisperForCTC from pranay-j +author: John Snow Labs +name: asr_whisper_small_armenian_pipeline +date: 2023-10-20 +tags: [whisper, hy, open_source, pipeline] +task: Automatic Speech Recognition +language: hy +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_armenian_pipeline` is a Armenian model originally trained by pranay-j. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_armenian_pipeline_hy_5.1.4_3.4_1697760577067.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_armenian_pipeline_hy_5.1.4_3.4_1697760577067.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_armenian_pipeline', lang = 'hy') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_armenian_pipeline', lang = 'hy') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_armenian_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|hy| +|Size:|1.7 GB| + +## References + +https://huggingface.co/pranay-j/whisper-small-hy + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_dutch_nl.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_dutch_nl.md new file mode 100644 index 00000000000000..a92fa297b5c127 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_dutch_nl.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Dutch, Flemish asr_whisper_small_dutch WhisperForCTC from pplantinga +author: John Snow Labs +name: asr_whisper_small_dutch +date: 2023-10-20 +tags: [whisper, nl, open_source, asr, onnx] +task: Automatic Speech Recognition +language: nl +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_dutch` is a Dutch, Flemish model originally trained by pplantinga. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_dutch_nl_5.1.4_3.4_1697760208052.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_dutch_nl_5.1.4_3.4_1697760208052.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_dutch","nl") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_dutch","nl") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_dutch| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|nl| +|Size:|1.7 GB| + +## References + +https://huggingface.co/pplantinga/whisper-small-nl \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_dutch_pipeline_nl.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_dutch_pipeline_nl.md new file mode 100644 index 00000000000000..40d28e76f0830d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_dutch_pipeline_nl.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Dutch, Flemish asr_whisper_small_dutch_pipeline pipeline WhisperForCTC from pplantinga +author: John Snow Labs +name: asr_whisper_small_dutch_pipeline +date: 2023-10-20 +tags: [whisper, nl, open_source, pipeline] +task: Automatic Speech Recognition +language: nl +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_dutch_pipeline` is a Dutch, Flemish model originally trained by pplantinga. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_dutch_pipeline_nl_5.1.4_3.4_1697760252058.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_dutch_pipeline_nl_5.1.4_3.4_1697760252058.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_dutch_pipeline', lang = 'nl') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_dutch_pipeline', lang = 'nl') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_dutch_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|nl| +|Size:|1.7 GB| + +## References + +https://huggingface.co/pplantinga/whisper-small-nl + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_english_blueraccoon_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_english_blueraccoon_en.md new file mode 100644 index 00000000000000..7dcaca2456739f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_english_blueraccoon_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_english_blueraccoon WhisperForCTC from BlueRaccoon +author: John Snow Labs +name: asr_whisper_small_english_blueraccoon +date: 2023-10-20 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_english_blueraccoon` is a English model originally trained by BlueRaccoon. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_english_blueraccoon_en_5.1.4_3.4_1697760099807.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_english_blueraccoon_en_5.1.4_3.4_1697760099807.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_english_blueraccoon","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_english_blueraccoon","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_english_blueraccoon| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/BlueRaccoon/whisper-small-en \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_english_blueraccoon_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_english_blueraccoon_pipeline_en.md new file mode 100644 index 00000000000000..7011d52e86a2fe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_english_blueraccoon_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_english_blueraccoon_pipeline pipeline WhisperForCTC from BlueRaccoon +author: John Snow Labs +name: asr_whisper_small_english_blueraccoon_pipeline +date: 2023-10-20 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_english_blueraccoon_pipeline` is a English model originally trained by BlueRaccoon. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_english_blueraccoon_pipeline_en_5.1.4_3.4_1697760125385.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_english_blueraccoon_pipeline_en_5.1.4_3.4_1697760125385.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_english_blueraccoon_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_english_blueraccoon_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_english_blueraccoon_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/BlueRaccoon/whisper-small-en + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_finnish_sgangireddy_fi.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_finnish_sgangireddy_fi.md new file mode 100644 index 00000000000000..1e4566602d64ef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_finnish_sgangireddy_fi.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Finnish asr_whisper_small_finnish_sgangireddy WhisperForCTC from sgangireddy +author: John Snow Labs +name: asr_whisper_small_finnish_sgangireddy +date: 2023-10-20 +tags: [whisper, fi, open_source, asr, onnx] +task: Automatic Speech Recognition +language: fi +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_finnish_sgangireddy` is a Finnish model originally trained by sgangireddy. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_finnish_sgangireddy_fi_5.1.4_3.4_1697760961669.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_finnish_sgangireddy_fi_5.1.4_3.4_1697760961669.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_finnish_sgangireddy","fi") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_finnish_sgangireddy","fi") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_finnish_sgangireddy| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|fi| +|Size:|1.7 GB| + +## References + +https://huggingface.co/sgangireddy/whisper-small-fi \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_finnish_sgangireddy_pipeline_fi.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_finnish_sgangireddy_pipeline_fi.md new file mode 100644 index 00000000000000..88e37bc7b7970a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_finnish_sgangireddy_pipeline_fi.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Finnish asr_whisper_small_finnish_sgangireddy_pipeline pipeline WhisperForCTC from sgangireddy +author: John Snow Labs +name: asr_whisper_small_finnish_sgangireddy_pipeline +date: 2023-10-20 +tags: [whisper, fi, open_source, pipeline] +task: Automatic Speech Recognition +language: fi +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_finnish_sgangireddy_pipeline` is a Finnish model originally trained by sgangireddy. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_finnish_sgangireddy_pipeline_fi_5.1.4_3.4_1697760986850.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_finnish_sgangireddy_pipeline_fi_5.1.4_3.4_1697760986850.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_finnish_sgangireddy_pipeline', lang = 'fi') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_finnish_sgangireddy_pipeline', lang = 'fi') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_finnish_sgangireddy_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|fi| +|Size:|1.7 GB| + +## References + +https://huggingface.co/sgangireddy/whisper-small-fi + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_french_yocel1_hi.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_french_yocel1_hi.md new file mode 100644 index 00000000000000..480878cee41c94 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_french_yocel1_hi.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Hindi asr_whisper_small_french_yocel1 WhisperForCTC from Yocel1 +author: John Snow Labs +name: asr_whisper_small_french_yocel1 +date: 2023-10-20 +tags: [whisper, hi, open_source, asr, onnx] +task: Automatic Speech Recognition +language: hi +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_french_yocel1` is a Hindi model originally trained by Yocel1. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_french_yocel1_hi_5.1.4_3.4_1697762514859.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_french_yocel1_hi_5.1.4_3.4_1697762514859.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_french_yocel1","hi") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_french_yocel1","hi") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_french_yocel1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|hi| +|Size:|1.7 GB| + +## References + +https://huggingface.co/Yocel1/whisper-small-fr \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_french_yocel1_pipeline_hi.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_french_yocel1_pipeline_hi.md new file mode 100644 index 00000000000000..33958cc8436431 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_french_yocel1_pipeline_hi.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Hindi asr_whisper_small_french_yocel1_pipeline pipeline WhisperForCTC from Yocel1 +author: John Snow Labs +name: asr_whisper_small_french_yocel1_pipeline +date: 2023-10-20 +tags: [whisper, hi, open_source, pipeline] +task: Automatic Speech Recognition +language: hi +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_french_yocel1_pipeline` is a Hindi model originally trained by Yocel1. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_french_yocel1_pipeline_hi_5.1.4_3.4_1697762545502.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_french_yocel1_pipeline_hi_5.1.4_3.4_1697762545502.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_french_yocel1_pipeline', lang = 'hi') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_french_yocel1_pipeline', lang = 'hi') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_french_yocel1_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|hi| +|Size:|1.7 GB| + +## References + +https://huggingface.co/Yocel1/whisper-small-fr + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_hungarian_cv11_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_hungarian_cv11_en.md new file mode 100644 index 00000000000000..5447154ab9eb55 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_hungarian_cv11_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_hungarian_cv11 WhisperForCTC from mikr +author: John Snow Labs +name: asr_whisper_small_hungarian_cv11 +date: 2023-10-20 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_hungarian_cv11` is a English model originally trained by mikr. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_hungarian_cv11_en_5.1.4_3.4_1697762655252.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_hungarian_cv11_en_5.1.4_3.4_1697762655252.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_hungarian_cv11","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_hungarian_cv11","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_hungarian_cv11| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/mikr/whisper-small-hu-cv11 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_hungarian_cv11_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_hungarian_cv11_pipeline_en.md new file mode 100644 index 00000000000000..5168ebf5cbf736 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_hungarian_cv11_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_hungarian_cv11_pipeline pipeline WhisperForCTC from mikr +author: John Snow Labs +name: asr_whisper_small_hungarian_cv11_pipeline +date: 2023-10-20 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_hungarian_cv11_pipeline` is a English model originally trained by mikr. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_hungarian_cv11_pipeline_en_5.1.4_3.4_1697762679177.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_hungarian_cv11_pipeline_en_5.1.4_3.4_1697762679177.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_hungarian_cv11_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_hungarian_cv11_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_hungarian_cv11_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/mikr/whisper-small-hu-cv11 + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_japanese_vumichien_ja.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_japanese_vumichien_ja.md new file mode 100644 index 00000000000000..2fd53e69bf0775 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_japanese_vumichien_ja.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Japanese asr_whisper_small_japanese_vumichien WhisperForCTC from vumichien +author: John Snow Labs +name: asr_whisper_small_japanese_vumichien +date: 2023-10-20 +tags: [whisper, ja, open_source, asr, onnx] +task: Automatic Speech Recognition +language: ja +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_japanese_vumichien` is a Japanese model originally trained by vumichien. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_japanese_vumichien_ja_5.1.4_3.4_1697766130278.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_japanese_vumichien_ja_5.1.4_3.4_1697766130278.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_japanese_vumichien","ja") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_japanese_vumichien","ja") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_japanese_vumichien| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|ja| +|Size:|1.7 GB| + +## References + +https://huggingface.co/vumichien/whisper-small-ja \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_japanese_vumichien_pipeline_ja.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_japanese_vumichien_pipeline_ja.md new file mode 100644 index 00000000000000..9292bb7027ebac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_japanese_vumichien_pipeline_ja.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Japanese asr_whisper_small_japanese_vumichien_pipeline pipeline WhisperForCTC from vumichien +author: John Snow Labs +name: asr_whisper_small_japanese_vumichien_pipeline +date: 2023-10-20 +tags: [whisper, ja, open_source, pipeline] +task: Automatic Speech Recognition +language: ja +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_japanese_vumichien_pipeline` is a Japanese model originally trained by vumichien. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_japanese_vumichien_pipeline_ja_5.1.4_3.4_1697766170978.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_japanese_vumichien_pipeline_ja_5.1.4_3.4_1697766170978.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_japanese_vumichien_pipeline', lang = 'ja') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_japanese_vumichien_pipeline', lang = 'ja') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_japanese_vumichien_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|ja| +|Size:|1.7 GB| + +## References + +https://huggingface.co/vumichien/whisper-small-ja + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_korean_fl_ko.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_korean_fl_ko.md new file mode 100644 index 00000000000000..cbaf1e05b2e1d7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_korean_fl_ko.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Korean asr_whisper_small_korean_fl WhisperForCTC from p4b +author: John Snow Labs +name: asr_whisper_small_korean_fl +date: 2023-10-20 +tags: [whisper, ko, open_source, asr, onnx] +task: Automatic Speech Recognition +language: ko +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_korean_fl` is a Korean model originally trained by p4b. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_korean_fl_ko_5.1.4_3.4_1697768363999.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_korean_fl_ko_5.1.4_3.4_1697768363999.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_korean_fl","ko") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_korean_fl","ko") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_korean_fl| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|ko| +|Size:|1.1 GB| + +## References + +https://huggingface.co/p4b/whisper-small-ko-fl \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_korean_fl_pipeline_ko.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_korean_fl_pipeline_ko.md new file mode 100644 index 00000000000000..3dbfd83f0d2540 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_korean_fl_pipeline_ko.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Korean asr_whisper_small_korean_fl_pipeline pipeline WhisperForCTC from p4b +author: John Snow Labs +name: asr_whisper_small_korean_fl_pipeline +date: 2023-10-20 +tags: [whisper, ko, open_source, pipeline] +task: Automatic Speech Recognition +language: ko +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_korean_fl_pipeline` is a Korean model originally trained by p4b. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_korean_fl_pipeline_ko_5.1.4_3.4_1697768403117.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_korean_fl_pipeline_ko_5.1.4_3.4_1697768403117.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_korean_fl_pipeline', lang = 'ko') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_korean_fl_pipeline', lang = 'ko') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_korean_fl_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|ko| +|Size:|1.1 GB| + +## References + +https://huggingface.co/p4b/whisper-small-ko-fl + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_lithuanian_serbian_v2_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_lithuanian_serbian_v2_en.md new file mode 100644 index 00000000000000..e9068f9f5feae0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_lithuanian_serbian_v2_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_lithuanian_serbian_v2 WhisperForCTC from jraramhoej +author: John Snow Labs +name: asr_whisper_small_lithuanian_serbian_v2 +date: 2023-10-20 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_lithuanian_serbian_v2` is a English model originally trained by jraramhoej. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_lithuanian_serbian_v2_en_5.1.4_3.4_1697761536370.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_lithuanian_serbian_v2_en_5.1.4_3.4_1697761536370.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_lithuanian_serbian_v2","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_lithuanian_serbian_v2","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_lithuanian_serbian_v2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/jraramhoej/whisper-small-lt-sr-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_lithuanian_serbian_v2_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_lithuanian_serbian_v2_pipeline_en.md new file mode 100644 index 00000000000000..8e4bb662490fa7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_lithuanian_serbian_v2_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_lithuanian_serbian_v2_pipeline pipeline WhisperForCTC from jraramhoej +author: John Snow Labs +name: asr_whisper_small_lithuanian_serbian_v2_pipeline +date: 2023-10-20 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_lithuanian_serbian_v2_pipeline` is a English model originally trained by jraramhoej. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_lithuanian_serbian_v2_pipeline_en_5.1.4_3.4_1697761572103.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_lithuanian_serbian_v2_pipeline_en_5.1.4_3.4_1697761572103.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_lithuanian_serbian_v2_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_lithuanian_serbian_v2_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_lithuanian_serbian_v2_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/jraramhoej/whisper-small-lt-sr-v2 + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_mongolian_3_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_mongolian_3_en.md new file mode 100644 index 00000000000000..5eac73456783ec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_mongolian_3_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_mongolian_3 WhisperForCTC from bayartsogt +author: John Snow Labs +name: asr_whisper_small_mongolian_3 +date: 2023-10-20 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_mongolian_3` is a English model originally trained by bayartsogt. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_mongolian_3_en_5.1.4_3.4_1697766978958.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_mongolian_3_en_5.1.4_3.4_1697766978958.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_mongolian_3","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_mongolian_3","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_mongolian_3| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/bayartsogt/whisper-small-mn-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_mongolian_3_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_mongolian_3_pipeline_en.md new file mode 100644 index 00000000000000..bddb3b05420fb3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_mongolian_3_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_mongolian_3_pipeline pipeline WhisperForCTC from bayartsogt +author: John Snow Labs +name: asr_whisper_small_mongolian_3_pipeline +date: 2023-10-20 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_mongolian_3_pipeline` is a English model originally trained by bayartsogt. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_mongolian_3_pipeline_en_5.1.4_3.4_1697767005814.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_mongolian_3_pipeline_en_5.1.4_3.4_1697767005814.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_mongolian_3_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_mongolian_3_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_mongolian_3_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/bayartsogt/whisper-small-mn-3 + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_nob_no.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_nob_no.md new file mode 100644 index 00000000000000..6d0612f568f918 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_nob_no.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Norwegian asr_whisper_small_nob WhisperForCTC from NbAiLab +author: John Snow Labs +name: asr_whisper_small_nob +date: 2023-10-20 +tags: [whisper, "no", open_source, asr, onnx] +task: Automatic Speech Recognition +language: "no" +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_nob` is a Norwegian model originally trained by NbAiLab. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_nob_no_5.1.4_3.4_1697767866471.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_nob_no_5.1.4_3.4_1697767866471.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_nob","no") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_nob","no") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_nob| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|no| +|Size:|1.1 GB| + +## References + +https://huggingface.co/NbAiLab/whisper-small-nob \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_nob_pipeline_no.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_nob_pipeline_no.md new file mode 100644 index 00000000000000..1e5e58697367e6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_nob_pipeline_no.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Norwegian asr_whisper_small_nob_pipeline pipeline WhisperForCTC from NbAiLab +author: John Snow Labs +name: asr_whisper_small_nob_pipeline +date: 2023-10-20 +tags: [whisper, "no", open_source, pipeline] +task: Automatic Speech Recognition +language: "no" +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_nob_pipeline` is a Norwegian model originally trained by NbAiLab. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_nob_pipeline_no_5.1.4_3.4_1697767890596.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_nob_pipeline_no_5.1.4_3.4_1697767890596.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_nob_pipeline', lang = 'no') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_nob_pipeline', lang = 'no') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_nob_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|no| +|Size:|1.1 GB| + +## References + +https://huggingface.co/NbAiLab/whisper-small-nob + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_pashto_ihanif_pipeline_ps.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_pashto_ihanif_pipeline_ps.md new file mode 100644 index 00000000000000..35b47d9c30cf50 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_pashto_ihanif_pipeline_ps.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Pashto, Pushto asr_whisper_small_pashto_ihanif_pipeline pipeline WhisperForCTC from ihanif +author: John Snow Labs +name: asr_whisper_small_pashto_ihanif_pipeline +date: 2023-10-20 +tags: [whisper, ps, open_source, pipeline] +task: Automatic Speech Recognition +language: ps +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_pashto_ihanif_pipeline` is a Pashto, Pushto model originally trained by ihanif. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_pashto_ihanif_pipeline_ps_5.1.4_3.4_1697764646764.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_pashto_ihanif_pipeline_ps_5.1.4_3.4_1697764646764.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_pashto_ihanif_pipeline', lang = 'ps') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_pashto_ihanif_pipeline', lang = 'ps') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_pashto_ihanif_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|ps| +|Size:|1.7 GB| + +## References + +https://huggingface.co/ihanif/whisper-small-pashto + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_pashto_ihanif_ps.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_pashto_ihanif_ps.md new file mode 100644 index 00000000000000..c88d37d77d2e84 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_pashto_ihanif_ps.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Pashto, Pushto asr_whisper_small_pashto_ihanif WhisperForCTC from ihanif +author: John Snow Labs +name: asr_whisper_small_pashto_ihanif +date: 2023-10-20 +tags: [whisper, ps, open_source, asr, onnx] +task: Automatic Speech Recognition +language: ps +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_pashto_ihanif` is a Pashto, Pushto model originally trained by ihanif. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_pashto_ihanif_ps_5.1.4_3.4_1697764620611.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_pashto_ihanif_ps_5.1.4_3.4_1697764620611.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_pashto_ihanif","ps") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_pashto_ihanif","ps") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_pashto_ihanif| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|ps| +|Size:|1.7 GB| + +## References + +https://huggingface.co/ihanif/whisper-small-pashto \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_punjabi_eastern_pa.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_punjabi_eastern_pa.md new file mode 100644 index 00000000000000..a1ec98043e0395 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_punjabi_eastern_pa.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Panjabi, Punjabi asr_whisper_small_punjabi_eastern WhisperForCTC from anuragshas +author: John Snow Labs +name: asr_whisper_small_punjabi_eastern +date: 2023-10-20 +tags: [whisper, pa, open_source, asr, onnx] +task: Automatic Speech Recognition +language: pa +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_punjabi_eastern` is a Panjabi, Punjabi model originally trained by anuragshas. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_punjabi_eastern_pa_5.1.4_3.4_1697796901247.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_punjabi_eastern_pa_5.1.4_3.4_1697796901247.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_punjabi_eastern","pa") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_punjabi_eastern","pa") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_punjabi_eastern| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|pa| +|Size:|1.7 GB| + +## References + +https://huggingface.co/anuragshas/whisper-small-pa \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_punjabi_eastern_pipeline_pa.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_punjabi_eastern_pipeline_pa.md new file mode 100644 index 00000000000000..5a99a1a34d1836 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_punjabi_eastern_pipeline_pa.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Panjabi, Punjabi asr_whisper_small_punjabi_eastern_pipeline pipeline WhisperForCTC from anuragshas +author: John Snow Labs +name: asr_whisper_small_punjabi_eastern_pipeline +date: 2023-10-20 +tags: [whisper, pa, open_source, pipeline] +task: Automatic Speech Recognition +language: pa +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_punjabi_eastern_pipeline` is a Panjabi, Punjabi model originally trained by anuragshas. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_punjabi_eastern_pipeline_pa_5.1.4_3.4_1697796972727.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_punjabi_eastern_pipeline_pa_5.1.4_3.4_1697796972727.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_punjabi_eastern_pipeline', lang = 'pa') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_punjabi_eastern_pipeline', lang = 'pa') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_punjabi_eastern_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|pa| +|Size:|1.7 GB| + +## References + +https://huggingface.co/anuragshas/whisper-small-pa + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_spanish_1e_6_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_spanish_1e_6_en.md new file mode 100644 index 00000000000000..7d4c2498c493c1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_spanish_1e_6_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_spanish_1e_6 WhisperForCTC from sanchit-gandhi +author: John Snow Labs +name: asr_whisper_small_spanish_1e_6 +date: 2023-10-20 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_spanish_1e_6` is a English model originally trained by sanchit-gandhi. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_spanish_1e_6_en_5.1.4_3.4_1697760169069.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_spanish_1e_6_en_5.1.4_3.4_1697760169069.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_spanish_1e_6","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_spanish_1e_6","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_spanish_1e_6| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/sanchit-gandhi/whisper-small-es-1e-6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_spanish_1e_6_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_spanish_1e_6_pipeline_en.md new file mode 100644 index 00000000000000..12f2593de97f96 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_spanish_1e_6_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_spanish_1e_6_pipeline pipeline WhisperForCTC from sanchit-gandhi +author: John Snow Labs +name: asr_whisper_small_spanish_1e_6_pipeline +date: 2023-10-20 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_spanish_1e_6_pipeline` is a English model originally trained by sanchit-gandhi. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_spanish_1e_6_pipeline_en_5.1.4_3.4_1697760208725.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_spanish_1e_6_pipeline_en_5.1.4_3.4_1697760208725.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_spanish_1e_6_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_spanish_1e_6_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_spanish_1e_6_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/sanchit-gandhi/whisper-small-es-1e-6 + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_spanish_ari_es.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_spanish_ari_es.md new file mode 100644 index 00000000000000..11327d1825fa85 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_spanish_ari_es.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Castilian, Spanish asr_whisper_small_spanish_ari WhisperForCTC from Ari +author: John Snow Labs +name: asr_whisper_small_spanish_ari +date: 2023-10-20 +tags: [whisper, es, open_source, asr, onnx] +task: Automatic Speech Recognition +language: es +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_spanish_ari` is a Castilian, Spanish model originally trained by Ari. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_spanish_ari_es_5.1.4_3.4_1697768478107.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_spanish_ari_es_5.1.4_3.4_1697768478107.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_spanish_ari","es") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_spanish_ari","es") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_spanish_ari| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|es| +|Size:|1.7 GB| + +## References + +https://huggingface.co/Ari/whisper-small-es \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_spanish_ari_pipeline_es.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_spanish_ari_pipeline_es.md new file mode 100644 index 00000000000000..4da02633af751f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_spanish_ari_pipeline_es.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Castilian, Spanish asr_whisper_small_spanish_ari_pipeline pipeline WhisperForCTC from Ari +author: John Snow Labs +name: asr_whisper_small_spanish_ari_pipeline +date: 2023-10-20 +tags: [whisper, es, open_source, pipeline] +task: Automatic Speech Recognition +language: es +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_spanish_ari_pipeline` is a Castilian, Spanish model originally trained by Ari. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_spanish_ari_pipeline_es_5.1.4_3.4_1697768503168.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_spanish_ari_pipeline_es_5.1.4_3.4_1697768503168.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_spanish_ari_pipeline', lang = 'es') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_spanish_ari_pipeline', lang = 'es') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_spanish_ari_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|1.7 GB| + +## References + +https://huggingface.co/Ari/whisper-small-es + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_swe2_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_swe2_en.md new file mode 100644 index 00000000000000..6f9044449d9568 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_swe2_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_small_swe2 WhisperForCTC from Alexao +author: John Snow Labs +name: asr_whisper_small_swe2 +date: 2023-10-20 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_swe2` is a English model originally trained by Alexao. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swe2_en_5.1.4_3.4_1697767054560.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swe2_en_5.1.4_3.4_1697767054560.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_swe2","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_swe2","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_swe2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.1 GB| + +## References + +https://huggingface.co/Alexao/whisper-small-swe2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_swe2_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_swe2_pipeline_en.md new file mode 100644 index 00000000000000..298294a1564b3d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_swe2_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_small_swe2_pipeline pipeline WhisperForCTC from Alexao +author: John Snow Labs +name: asr_whisper_small_swe2_pipeline +date: 2023-10-20 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_swe2_pipeline` is a English model originally trained by Alexao. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swe2_pipeline_en_5.1.4_3.4_1697767075304.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swe2_pipeline_en_5.1.4_3.4_1697767075304.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_swe2_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_swe2_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_swe2_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.1 GB| + +## References + +https://huggingface.co/Alexao/whisper-small-swe2 + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_swedish_english_pipeline_se.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_swedish_english_pipeline_se.md new file mode 100644 index 00000000000000..f9feb96ec89d7b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_swedish_english_pipeline_se.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Northern Sami asr_whisper_small_swedish_english_pipeline pipeline WhisperForCTC from humeur +author: John Snow Labs +name: asr_whisper_small_swedish_english_pipeline +date: 2023-10-20 +tags: [whisper, se, open_source, pipeline] +task: Automatic Speech Recognition +language: se +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_swedish_english_pipeline` is a Northern Sami model originally trained by humeur. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swedish_english_pipeline_se_5.1.4_3.4_1697765615738.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swedish_english_pipeline_se_5.1.4_3.4_1697765615738.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_swedish_english_pipeline', lang = 'se') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_swedish_english_pipeline', lang = 'se') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_swedish_english_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|se| +|Size:|1.7 GB| + +## References + +https://huggingface.co/humeur/whisper-small-sv-en + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_swedish_english_se.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_swedish_english_se.md new file mode 100644 index 00000000000000..85ca6259dcf620 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_swedish_english_se.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Northern Sami asr_whisper_small_swedish_english WhisperForCTC from humeur +author: John Snow Labs +name: asr_whisper_small_swedish_english +date: 2023-10-20 +tags: [whisper, se, open_source, asr, onnx] +task: Automatic Speech Recognition +language: se +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_swedish_english` is a Northern Sami model originally trained by humeur. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swedish_english_se_5.1.4_3.4_1697765589681.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swedish_english_se_5.1.4_3.4_1697765589681.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_swedish_english","se") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_swedish_english","se") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_swedish_english| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|se| +|Size:|1.7 GB| + +## References + +https://huggingface.co/humeur/whisper-small-sv-en \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_swedish_torileatherman_pipeline_sv.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_swedish_torileatherman_pipeline_sv.md new file mode 100644 index 00000000000000..4689542537e5c1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_swedish_torileatherman_pipeline_sv.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Swedish asr_whisper_small_swedish_torileatherman_pipeline pipeline WhisperForCTC from torileatherman +author: John Snow Labs +name: asr_whisper_small_swedish_torileatherman_pipeline +date: 2023-10-20 +tags: [whisper, sv, open_source, pipeline] +task: Automatic Speech Recognition +language: sv +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_swedish_torileatherman_pipeline` is a Swedish model originally trained by torileatherman. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swedish_torileatherman_pipeline_sv_5.1.4_3.4_1697762905907.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swedish_torileatherman_pipeline_sv_5.1.4_3.4_1697762905907.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_small_swedish_torileatherman_pipeline', lang = 'sv') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_small_swedish_torileatherman_pipeline', lang = 'sv') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_swedish_torileatherman_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|sv| +|Size:|1.7 GB| + +## References + +https://huggingface.co/torileatherman/whisper_small_sv + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_swedish_torileatherman_sv.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_swedish_torileatherman_sv.md new file mode 100644 index 00000000000000..cc86af5841f60b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_small_swedish_torileatherman_sv.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Swedish asr_whisper_small_swedish_torileatherman WhisperForCTC from torileatherman +author: John Snow Labs +name: asr_whisper_small_swedish_torileatherman +date: 2023-10-20 +tags: [whisper, sv, open_source, asr, onnx] +task: Automatic Speech Recognition +language: sv +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_small_swedish_torileatherman` is a Swedish model originally trained by torileatherman. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swedish_torileatherman_sv_5.1.4_3.4_1697762874959.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_small_swedish_torileatherman_sv_5.1.4_3.4_1697762874959.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_small_swedish_torileatherman","sv") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_small_swedish_torileatherman","sv") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_small_swedish_torileatherman| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|sv| +|Size:|1.7 GB| + +## References + +https://huggingface.co/torileatherman/whisper_small_sv \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_testrun1_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_testrun1_en.md new file mode 100644 index 00000000000000..e361db61cd8204 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_testrun1_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_testrun1 WhisperForCTC from pere +author: John Snow Labs +name: asr_whisper_testrun1 +date: 2023-10-20 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_testrun1` is a English model originally trained by pere. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_testrun1_en_5.1.4_3.4_1697768297225.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_testrun1_en_5.1.4_3.4_1697768297225.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_testrun1","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_testrun1","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_testrun1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/pere/whisper-testrun1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_testrun1_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_testrun1_pipeline_en.md new file mode 100644 index 00000000000000..3cfac75c55b379 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_testrun1_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_testrun1_pipeline pipeline WhisperForCTC from pere +author: John Snow Labs +name: asr_whisper_testrun1_pipeline +date: 2023-10-20 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_testrun1_pipeline` is a English model originally trained by pere. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_testrun1_pipeline_en_5.1.4_3.4_1697768349850.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_testrun1_pipeline_en_5.1.4_3.4_1697768349850.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_testrun1_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_testrun1_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_testrun1_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|1.7 GB| + +## References + +https://huggingface.co/pere/whisper-testrun1 + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_italian_2_it.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_italian_2_it.md new file mode 100644 index 00000000000000..175a97d994bb03 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_italian_2_it.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Italian asr_whisper_tiny_italian_2 WhisperForCTC from GIanlucaRub +author: John Snow Labs +name: asr_whisper_tiny_italian_2 +date: 2023-10-20 +tags: [whisper, it, open_source, asr, onnx] +task: Automatic Speech Recognition +language: it +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_italian_2` is a Italian model originally trained by GIanlucaRub. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_italian_2_it_5.1.4_3.4_1697767694767.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_italian_2_it_5.1.4_3.4_1697767694767.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_italian_2","it") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_italian_2","it") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_italian_2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|it| +|Size:|390.8 MB| + +## References + +https://huggingface.co/GIanlucaRub/whisper-tiny-it-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_italian_2_pipeline_it.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_italian_2_pipeline_it.md new file mode 100644 index 00000000000000..f04f7ee3de55b4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_italian_2_pipeline_it.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Italian asr_whisper_tiny_italian_2_pipeline pipeline WhisperForCTC from GIanlucaRub +author: John Snow Labs +name: asr_whisper_tiny_italian_2_pipeline +date: 2023-10-20 +tags: [whisper, it, open_source, pipeline] +task: Automatic Speech Recognition +language: it +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_italian_2_pipeline` is a Italian model originally trained by GIanlucaRub. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_italian_2_pipeline_it_5.1.4_3.4_1697767702468.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_italian_2_pipeline_it_5.1.4_3.4_1697767702468.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_tiny_italian_2_pipeline', lang = 'it') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_tiny_italian_2_pipeline', lang = 'it') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_italian_2_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|it| +|Size:|390.8 MB| + +## References + +https://huggingface.co/GIanlucaRub/whisper-tiny-it-2 + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_italian_local_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_italian_local_en.md new file mode 100644 index 00000000000000..796e6b46ed44cc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_italian_local_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: English asr_whisper_tiny_italian_local WhisperForCTC from GIanlucaRub +author: John Snow Labs +name: asr_whisper_tiny_italian_local +date: 2023-10-20 +tags: [whisper, en, open_source, asr, onnx] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_italian_local` is a English model originally trained by GIanlucaRub. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_italian_local_en_5.1.4_3.4_1697763349106.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_italian_local_en_5.1.4_3.4_1697763349106.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_italian_local","en") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_italian_local","en") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_italian_local| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|en| +|Size:|390.7 MB| + +## References + +https://huggingface.co/GIanlucaRub/whisper-tiny-it-local \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_italian_local_pipeline_en.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_italian_local_pipeline_en.md new file mode 100644 index 00000000000000..2e056b75cb4d5c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_italian_local_pipeline_en.md @@ -0,0 +1,71 @@ +--- +layout: model +title: English asr_whisper_tiny_italian_local_pipeline pipeline WhisperForCTC from GIanlucaRub +author: John Snow Labs +name: asr_whisper_tiny_italian_local_pipeline +date: 2023-10-20 +tags: [whisper, en, open_source, pipeline] +task: Automatic Speech Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_italian_local_pipeline` is a English model originally trained by GIanlucaRub. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_italian_local_pipeline_en_5.1.4_3.4_1697763359286.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_italian_local_pipeline_en_5.1.4_3.4_1697763359286.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_tiny_italian_local_pipeline', lang = 'en') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_tiny_italian_local_pipeline', lang = 'en') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_italian_local_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|en| +|Size:|390.7 MB| + +## References + +https://huggingface.co/GIanlucaRub/whisper-tiny-it-local + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_spanish_arpagon_es.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_spanish_arpagon_es.md new file mode 100644 index 00000000000000..798ecbeeca1171 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_spanish_arpagon_es.md @@ -0,0 +1,92 @@ +--- +layout: model +title: Castilian, Spanish asr_whisper_tiny_spanish_arpagon WhisperForCTC from arpagon +author: John Snow Labs +name: asr_whisper_tiny_spanish_arpagon +date: 2023-10-20 +tags: [whisper, es, open_source, asr, onnx] +task: Automatic Speech Recognition +language: es +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: WhisperForCTC +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_spanish_arpagon` is a Castilian, Spanish model originally trained by arpagon. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_spanish_arpagon_es_5.1.4_3.4_1697761762014.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_spanish_arpagon_es_5.1.4_3.4_1697761762014.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +audioAssembler = AudioAssembler() \ + .setInputCol("audio_content") \ + .setOutputCol("audio_assembler") + + +speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_spanish_arpagon","es") \ + .setInputCols(["audio_assembler"]) \ + .setOutputCol("text") + +pipeline = Pipeline().setStages([audioAssembler, speechToText]) + +pipelineModel = pipeline.fit(data) + +pipelineDF = pipelineModel.transform(data) + +``` +```scala + +val audioAssembler = new AudioAssembler() + .setInputCol("audio_content") + .setOutputCol("audio_assembler") + +val speechToText = WhisperForCTC.pretrained("asr_whisper_tiny_spanish_arpagon","es") + .setInputCols(Array("audio_assembler")) + .setOutputCol("text") + +val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText)) + +val pipelineModel = pipeline.fit(data) + +val pipelineDF = pipelineModel.transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_spanish_arpagon| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[audio_assembler]| +|Output Labels:|[text]| +|Language:|es| +|Size:|390.7 MB| + +## References + +https://huggingface.co/arpagon/whisper-tiny-es \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_spanish_arpagon_pipeline_es.md b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_spanish_arpagon_pipeline_es.md new file mode 100644 index 00000000000000..93c188485a8dd3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-20-asr_whisper_tiny_spanish_arpagon_pipeline_es.md @@ -0,0 +1,71 @@ +--- +layout: model +title: Castilian, Spanish asr_whisper_tiny_spanish_arpagon_pipeline pipeline WhisperForCTC from arpagon +author: John Snow Labs +name: asr_whisper_tiny_spanish_arpagon_pipeline +date: 2023-10-20 +tags: [whisper, es, open_source, pipeline] +task: Automatic Speech Recognition +language: es +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +annotator: PipelineModel +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained WhisperForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`asr_whisper_tiny_spanish_arpagon_pipeline` is a Castilian, Spanish model originally trained by arpagon. + +This model is only compatible with PySpark 3.4 and above + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_spanish_arpagon_pipeline_es_5.1.4_3.4_1697761769281.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_whisper_tiny_spanish_arpagon_pipeline_es_5.1.4_3.4_1697761769281.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +pipeline = PretrainedPipeline('asr_whisper_tiny_spanish_arpagon_pipeline', lang = 'es') +annotations = pipeline.transform(audioDF) + +``` +```scala + +val pipeline = new PretrainedPipeline('asr_whisper_tiny_spanish_arpagon_pipeline', lang = 'es') +val annotations = pipeline.transform(audioDF) + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|asr_whisper_tiny_spanish_arpagon_pipeline| +|Type:|pipeline| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Language:|es| +|Size:|390.7 MB| + +## References + +https://huggingface.co/arpagon/whisper-tiny-es + +## Included Models + +- AudioAssembler +- WhisperForCTC \ No newline at end of file From b505d774ef49521b7e39891cbe83715ca84a1738 Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Wed, 25 Oct 2023 14:35:00 +0700 Subject: [PATCH 53/55] 2023-10-24-bert_cn_finetuning_18811449050_en (#14039) * Add model 2023-10-25-fbert_aiclass_en * Add model 2023-10-25-bert_ft_qqp_1_jeevesh8_en * Add model 2023-10-25-std_0pnt2_bert_ft_cola_71_en * Add model 2023-10-25-std_0pnt2_bert_ft_cola_56_en * Add model 2023-10-25-bert_ft_qqp_2_jeevesh8_en * Add model 2023-10-25-xlm_bert_go_emotions_en * Add model 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2023-10-25-bert_fine_tuned_cola_huong_en * Add model 2023-10-25-bert_finetuned_imdb_wakaka_en * Add model 2023-10-25-knots_protbertbfd_alphafold_roa7n_en * Add model 2023-10-25-bert_large_uncased_sst_2_64_13_smoothed_en * Add model 2023-10-25-finetuned_sc_indobert_on_indonli_basic_train_en * Add model 2023-10-25-bert_ft_qqp_65_jeevesh8_en * Add model 2023-10-25-std_0pnt2_bert_ft_cola_43_en * Add model 2023-10-25-bert_base_uncased_finetuned_iemocap_uptuna5_en * Add model 2023-10-25-bert_ft_qqp_66_jeevesh8_en * Add model 2023-10-25-bert_base_uncased_sst_2_32_13_smoothed_en * Add model 2023-10-25-bert_base_uncased_finetuned_iemocap_uptuna6_en * Add model 2023-10-25-bert_ft_qqp_67_jeevesh8_en * Add model 2023-10-25-bert_base_uncased_sst_2_64_13_smoothed_en * Add model 2023-10-25-bert_base_uncased_finetuned_iemocap7_en * Add model 2023-10-25-goog_bert_ft_cola_17_en * Add model 2023-10-25-binary_fully_trained_bert_en * Add model 2023-10-25-bert_ft_qqp_57_jeevesh8_en * Add model 2023-10-25-bert_base_uncased_sst_2_16_13_smoothed_en * Add model 2023-10-25-bert_ft_qqp_69_jeevesh8_en * Add model 2023-10-25-goog_bert_ft_cola_16_en * Add model 2023-10-25-goog_bert_ft_cola_20_en * Add model 2023-10-25-bert_ft_qqp_70_jeevesh8_en * Add model 2023-10-25-bert_base_multilingual_cased_finetuned_review_xx * Add model 2023-10-25-bert_fine_tuned_cola_tamiti1610001_en * Add model 2023-10-25-bert_ft_qqp_71_jeevesh8_en * Add model 2023-10-25-goog_bert_ft_cola_13_en * Add model 2023-10-25-goog_bert_ft_cola_11_en * Add model 2023-10-25-deprem_bert_128k_v2_tr * Add model 2023-10-25-bert_base_uncased_finetuned_swift_v_shakes_en * Add model 2023-10-25-bert_large_uncased_sst_2_32_13_en * Add model 2023-10-25-goog_bert_ft_cola_8_en * Add model 2023-10-25-goog_bert_ft_cola_15_en * Add model 2023-10-25-bert_large_uncased_sst_2_32_13_smoothed_en * Add model 2023-10-25-covid_twitter_bert_v2_3_4_2e_05_0_01_en * Add model 2023-10-25-finbert_tsla_en * Add model 2023-10-25-backdoored_bert_finetuned_sst2_en * Add model 2023-10-25-bert_fine_tuned_cola_kaori1707_en * Add model 2023-10-25-bert_ft_qqp_74_jeevesh8_en * Add model 2023-10-25-esgbert_en * Add model 2023-10-25-bert_ft_qqp_63_jeevesh8_en * Add model 2023-10-25-bert_base_uncased_finetuned_iemocap8_en * Add model 2023-10-25-bert_base_uncased_sst_2_32_21_en * Add model 2023-10-25-bert_ft_qqp_76_jeevesh8_en * Add model 2023-10-25-goog_bert_ft_cola_27_en * Add model 2023-10-25-bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_14_en * Add model 2023-10-25-goog_bert_ft_cola_28_en * Add model 2023-10-25-bert_base_uncased_ituthesis2022mlvinikw_en * Add model 2023-10-25-bert_classifier_tiny_qqp_distilled_en * Add model 2023-10-25-bert_ft_qqp_37_jeevesh8_en * Add model 2023-10-25-goog_bert_ft_cola_25_en * Add model 2023-10-25-bert_ft_qqp_78_jeevesh8_en * Add model 2023-10-25-bert_base_finetuned_nli_jihyun22_en --------- Co-authored-by: ahmedlone127 --- 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docs/_posts/ahmedlone127/2023-10-25-xlm_bert_go_emotions_en.md diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_arabertv2_finetuned_emotion_aetd2_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_arabertv2_finetuned_emotion_aetd2_en.md new file mode 100644 index 00000000000000..04c0d48d3d9002 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_arabertv2_finetuned_emotion_aetd2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_arabertv2_finetuned_emotion_aetd2 BertForSequenceClassification from MahaJar +author: John Snow Labs +name: bert_base_arabertv2_finetuned_emotion_aetd2 +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabertv2_finetuned_emotion_aetd2` is a English model originally trained by MahaJar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabertv2_finetuned_emotion_aetd2_en_5.1.4_3.4_1698189731931.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabertv2_finetuned_emotion_aetd2_en_5.1.4_3.4_1698189731931.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabertv2_finetuned_emotion_aetd2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabertv2_finetuned_emotion_aetd2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabertv2_finetuned_emotion_aetd2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|507.1 MB| + +## References + +https://huggingface.co/MahaJar/bert-base-arabertv2-finetuned-emotion_AETD2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_vish88_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_vish88_en.md new file mode 100644 index 00000000000000..837de051ab6f13 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_vish88_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_vish88 BertForSequenceClassification from vish88 +author: John Snow Labs +name: bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_vish88 +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_vish88` is a English model originally trained by vish88. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_vish88_en_5.1.4_3.4_1698189720514.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_vish88_en_5.1.4_3.4_1698189720514.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_vish88","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_vish88","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_vish88| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|408.6 MB| + +## References + +https://huggingface.co/vish88/bert-base-arabic-camelbert-msa-sixteenth-xnli-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_arabic_camelbert_msa_xnli_finetuned_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_arabic_camelbert_msa_xnli_finetuned_en.md new file mode 100644 index 00000000000000..f2f44c0629d4a2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_arabic_camelbert_msa_xnli_finetuned_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_arabic_camelbert_msa_xnli_finetuned BertForSequenceClassification from vish88 +author: John Snow Labs +name: bert_base_arabic_camelbert_msa_xnli_finetuned +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabic_camelbert_msa_xnli_finetuned` is a English model originally trained by vish88. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_msa_xnli_finetuned_en_5.1.4_3.4_1698189328456.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_msa_xnli_finetuned_en_5.1.4_3.4_1698189328456.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabic_camelbert_msa_xnli_finetuned","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabic_camelbert_msa_xnli_finetuned","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabic_camelbert_msa_xnli_finetuned| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|408.6 MB| + +## References + +https://huggingface.co/vish88/bert-base-arabic-camelbert-msa-xnli-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_cased_finetuned_mnli_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_cased_finetuned_mnli_en.md new file mode 100644 index 00000000000000..55d6ca542f3a83 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_cased_finetuned_mnli_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_cased_finetuned_mnli BertForSequenceClassification from George-Ogden +author: John Snow Labs +name: bert_base_cased_finetuned_mnli +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_mnli` is a English model originally trained by George-Ogden. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_mnli_en_5.1.4_3.4_1698191244740.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_mnli_en_5.1.4_3.4_1698191244740.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_finetuned_mnli","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_finetuned_mnli","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_mnli| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/George-Ogden/bert-base-cased-finetuned-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_cased_qqp_a_bhimany_u08_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_cased_qqp_a_bhimany_u08_en.md new file mode 100644 index 00000000000000..3a2dd707aa98b1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_cased_qqp_a_bhimany_u08_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_cased_qqp_a_bhimany_u08 BertForSequenceClassification from A-bhimany-u08 +author: John Snow Labs +name: bert_base_cased_qqp_a_bhimany_u08 +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_qqp_a_bhimany_u08` is a English model originally trained by A-bhimany-u08. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_qqp_a_bhimany_u08_en_5.1.4_3.4_1698186168859.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_qqp_a_bhimany_u08_en_5.1.4_3.4_1698186168859.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_qqp_a_bhimany_u08","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_qqp_a_bhimany_u08","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_qqp_a_bhimany_u08| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/A-bhimany-u08/bert-base-cased-qqp \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_chinese_finetuned_amazon_chinese_20000_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_chinese_finetuned_amazon_chinese_20000_en.md new file mode 100644 index 00000000000000..91f9c62aa1d79b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_chinese_finetuned_amazon_chinese_20000_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_finetuned_amazon_chinese_20000 BertForSequenceClassification from ASCCCCCCCC +author: John Snow Labs +name: bert_base_chinese_finetuned_amazon_chinese_20000 +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_finetuned_amazon_chinese_20000` is a English model originally trained by ASCCCCCCCC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_finetuned_amazon_chinese_20000_en_5.1.4_3.4_1698186566662.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_finetuned_amazon_chinese_20000_en_5.1.4_3.4_1698186566662.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_finetuned_amazon_chinese_20000","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_finetuned_amazon_chinese_20000","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_finetuned_amazon_chinese_20000| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/ASCCCCCCCC/bert-base-chinese-finetuned-amazon_zh_20000 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_chinese_finetuned_amazon_chinese_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_chinese_finetuned_amazon_chinese_en.md new file mode 100644 index 00000000000000..937cfb2ef582f9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_chinese_finetuned_amazon_chinese_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_finetuned_amazon_chinese BertForSequenceClassification from ASCCCCCCCC +author: John Snow Labs +name: bert_base_chinese_finetuned_amazon_chinese +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_finetuned_amazon_chinese` is a English model originally trained by ASCCCCCCCC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_finetuned_amazon_chinese_en_5.1.4_3.4_1698186398104.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_finetuned_amazon_chinese_en_5.1.4_3.4_1698186398104.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_finetuned_amazon_chinese","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_finetuned_amazon_chinese","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_finetuned_amazon_chinese| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/ASCCCCCCCC/bert-base-chinese-finetuned-amazon_zh \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_finetuned_sts_namvandy_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_finetuned_sts_namvandy_en.md new file mode 100644 index 00000000000000..55ee3eba15c0a1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_finetuned_sts_namvandy_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_finetuned_sts_namvandy BertForSequenceClassification from namvandy +author: John Snow Labs +name: bert_base_finetuned_sts_namvandy +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_finetuned_sts_namvandy` is a English model originally trained by namvandy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_finetuned_sts_namvandy_en_5.1.4_3.4_1698188939043.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_finetuned_sts_namvandy_en_5.1.4_3.4_1698188939043.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_finetuned_sts_namvandy","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_finetuned_sts_namvandy","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_finetuned_sts_namvandy| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.6 MB| + +## References + +https://huggingface.co/namvandy/bert-base-finetuned-sts \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_finetuned_sts_v3_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_finetuned_sts_v3_en.md new file mode 100644 index 00000000000000..d30ca5f8c8edf8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_finetuned_sts_v3_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_finetuned_sts_v3 BertForSequenceClassification from namvandy +author: John Snow Labs +name: bert_base_finetuned_sts_v3 +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_finetuned_sts_v3` is a English model originally trained by namvandy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_finetuned_sts_v3_en_5.1.4_3.4_1698190767059.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_finetuned_sts_v3_en_5.1.4_3.4_1698190767059.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_finetuned_sts_v3","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_finetuned_sts_v3","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_finetuned_sts_v3| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.6 MB| + +## References + +https://huggingface.co/namvandy/bert-base-finetuned-sts-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_msmarco_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_msmarco_en.md new file mode 100644 index 00000000000000..4316a2e17e53f1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_msmarco_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_msmarco BertForSequenceClassification from Capreolus +author: John Snow Labs +name: bert_base_msmarco +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_msmarco` is a English model originally trained by Capreolus. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_msmarco_en_5.1.4_3.4_1698191113313.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_msmarco_en_5.1.4_3.4_1698191113313.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_msmarco","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_msmarco","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_msmarco| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Capreolus/bert-base-msmarco \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_multilingual_cased_xnli_finetuned_xx.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_multilingual_cased_xnli_finetuned_xx.md new file mode 100644 index 00000000000000..1d09374e4007cd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_multilingual_cased_xnli_finetuned_xx.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_cased_xnli_finetuned BertForSequenceClassification from vish88 +author: John Snow Labs +name: bert_base_multilingual_cased_xnli_finetuned +date: 2023-10-24 +tags: [bert, xx, open_source, sequence_classification, onnx] +task: Text Classification +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_cased_xnli_finetuned` is a Multilingual model originally trained by vish88. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_xnli_finetuned_xx_5.1.4_3.4_1698189926824.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_xnli_finetuned_xx_5.1.4_3.4_1698189926824.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_multilingual_cased_xnli_finetuned","xx")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_multilingual_cased_xnli_finetuned","xx") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_cased_xnli_finetuned| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|xx| +|Size:|667.3 MB| + +## References + +https://huggingface.co/vish88/bert-base-multilingual-cased-xnli-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_spanish_wwm_cased_finetuned_emotion_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_spanish_wwm_cased_finetuned_emotion_en.md new file mode 100644 index 00000000000000..c7aa7081dfe18e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_spanish_wwm_cased_finetuned_emotion_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_spanish_wwm_cased_finetuned_emotion BertForSequenceClassification from Willy +author: John Snow Labs +name: bert_base_spanish_wwm_cased_finetuned_emotion +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_spanish_wwm_cased_finetuned_emotion` is a English model originally trained by Willy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_emotion_en_5.1.4_3.4_1698190266410.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_emotion_en_5.1.4_3.4_1698190266410.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_spanish_wwm_cased_finetuned_emotion","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_spanish_wwm_cased_finetuned_emotion","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_spanish_wwm_cased_finetuned_emotion| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.7 MB| + +## References + +https://huggingface.co/Willy/bert-base-spanish-wwm-cased-finetuned-emotion \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_spanish_wwm_cased_finetuned_mldoc_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_spanish_wwm_cased_finetuned_mldoc_en.md new file mode 100644 index 00000000000000..34dc1a354c34a5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_spanish_wwm_cased_finetuned_mldoc_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_spanish_wwm_cased_finetuned_mldoc BertForSequenceClassification from dccuchile +author: John Snow Labs +name: bert_base_spanish_wwm_cased_finetuned_mldoc +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_spanish_wwm_cased_finetuned_mldoc` is a English model originally trained by dccuchile. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_mldoc_en_5.1.4_3.4_1698191298623.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_mldoc_en_5.1.4_3.4_1698191298623.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_spanish_wwm_cased_finetuned_mldoc","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_spanish_wwm_cased_finetuned_mldoc","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_spanish_wwm_cased_finetuned_mldoc| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.7 MB| + +## References + +https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased-finetuned-mldoc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_spanish_wwm_cased_finetuned_nlp_ie_2_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_spanish_wwm_cased_finetuned_nlp_ie_2_en.md new file mode 100644 index 00000000000000..96f603e0ca7f21 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_spanish_wwm_cased_finetuned_nlp_ie_2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_spanish_wwm_cased_finetuned_nlp_ie_2 BertForSequenceClassification from Willy +author: John Snow Labs +name: bert_base_spanish_wwm_cased_finetuned_nlp_ie_2 +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_spanish_wwm_cased_finetuned_nlp_ie_2` is a English model originally trained by Willy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_nlp_ie_2_en_5.1.4_3.4_1698191693978.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_nlp_ie_2_en_5.1.4_3.4_1698191693978.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_spanish_wwm_cased_finetuned_nlp_ie_2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_spanish_wwm_cased_finetuned_nlp_ie_2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_spanish_wwm_cased_finetuned_nlp_ie_2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.7 MB| + +## References + +https://huggingface.co/Willy/bert-base-spanish-wwm-cased-finetuned-NLP-IE-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_spanish_wwm_cased_finetuned_pawsx_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_spanish_wwm_cased_finetuned_pawsx_en.md new file mode 100644 index 00000000000000..4a29b6f59404ef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_spanish_wwm_cased_finetuned_pawsx_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_spanish_wwm_cased_finetuned_pawsx BertForSequenceClassification from dccuchile +author: John Snow Labs +name: bert_base_spanish_wwm_cased_finetuned_pawsx +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_spanish_wwm_cased_finetuned_pawsx` is a English model originally trained by dccuchile. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_pawsx_en_5.1.4_3.4_1698191518128.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_pawsx_en_5.1.4_3.4_1698191518128.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_spanish_wwm_cased_finetuned_pawsx","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_spanish_wwm_cased_finetuned_pawsx","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_spanish_wwm_cased_finetuned_pawsx| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.7 MB| + +## References + +https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased-finetuned-pawsx \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_spanish_wwm_cased_finetuned_xnli_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_spanish_wwm_cased_finetuned_xnli_en.md new file mode 100644 index 00000000000000..acb5304c4cc03e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_spanish_wwm_cased_finetuned_xnli_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_spanish_wwm_cased_finetuned_xnli BertForSequenceClassification from dccuchile +author: John Snow Labs +name: bert_base_spanish_wwm_cased_finetuned_xnli +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_spanish_wwm_cased_finetuned_xnli` is a English model originally trained by dccuchile. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_xnli_en_5.1.4_3.4_1698191692355.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_xnli_en_5.1.4_3.4_1698191692355.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_spanish_wwm_cased_finetuned_xnli","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_spanish_wwm_cased_finetuned_xnli","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_spanish_wwm_cased_finetuned_xnli| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.7 MB| + +## References + +https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased-finetuned-xnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_spanish_wwm_uncased_finetuned_mldoc_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_spanish_wwm_uncased_finetuned_mldoc_en.md new file mode 100644 index 00000000000000..306b44cd7bc9f1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_spanish_wwm_uncased_finetuned_mldoc_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_spanish_wwm_uncased_finetuned_mldoc BertForSequenceClassification from dccuchile +author: John Snow Labs +name: bert_base_spanish_wwm_uncased_finetuned_mldoc +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_spanish_wwm_uncased_finetuned_mldoc` is a English model originally trained by dccuchile. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_uncased_finetuned_mldoc_en_5.1.4_3.4_1698191893764.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_uncased_finetuned_mldoc_en_5.1.4_3.4_1698191893764.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_spanish_wwm_uncased_finetuned_mldoc","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_spanish_wwm_uncased_finetuned_mldoc","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_spanish_wwm_uncased_finetuned_mldoc| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.9 MB| + +## References + +https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased-finetuned-mldoc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_cola_gokuls_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_cola_gokuls_en.md new file mode 100644 index 00000000000000..3b741b6f0a1a33 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_cola_gokuls_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_cola_gokuls BertForSequenceClassification from gokuls +author: John Snow Labs +name: bert_base_uncased_cola_gokuls +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_cola_gokuls` is a English model originally trained by gokuls. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_cola_gokuls_en_5.1.4_3.4_1698187184249.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_cola_gokuls_en_5.1.4_3.4_1698187184249.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_cola_gokuls","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_cola_gokuls","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_cola_gokuls| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/gokuls/bert-base-uncased-cola \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_emotion_amaazallahi_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_emotion_amaazallahi_en.md new file mode 100644 index 00000000000000..7a009624925ae5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_emotion_amaazallahi_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_emotion_amaazallahi BertForSequenceClassification from amaazallahi +author: John Snow Labs +name: bert_base_uncased_emotion_amaazallahi +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_emotion_amaazallahi` is a English model originally trained by amaazallahi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_emotion_amaazallahi_en_5.1.4_3.4_1698189530864.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_emotion_amaazallahi_en_5.1.4_3.4_1698189530864.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_emotion_amaazallahi","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_emotion_amaazallahi","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_emotion_amaazallahi| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/amaazallahi/bert-base-uncased-emotion \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_finetuned_cola_joqsan_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_finetuned_cola_joqsan_en.md new file mode 100644 index 00000000000000..a88d4d5fb29804 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_finetuned_cola_joqsan_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_cola_joqsan BertForSequenceClassification from Joqsan +author: John Snow Labs +name: bert_base_uncased_finetuned_cola_joqsan +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_cola_joqsan` is a English model originally trained by Joqsan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_cola_joqsan_en_5.1.4_3.4_1698190334776.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_cola_joqsan_en_5.1.4_3.4_1698190334776.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_cola_joqsan","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_cola_joqsan","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_cola_joqsan| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Joqsan/bert-base-uncased-finetuned-cola \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_finetuned_cola_richard0113_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_finetuned_cola_richard0113_en.md new file mode 100644 index 00000000000000..aaa77d7dbedd1a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_finetuned_cola_richard0113_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_cola_richard0113 BertForSequenceClassification from Richard0113 +author: John Snow Labs +name: bert_base_uncased_finetuned_cola_richard0113 +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_cola_richard0113` is a English model originally trained by Richard0113. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_cola_richard0113_en_5.1.4_3.4_1698186811781.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_cola_richard0113_en_5.1.4_3.4_1698186811781.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_cola_richard0113","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_cola_richard0113","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_cola_richard0113| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Richard0113/bert-base-uncased-finetuned-cola \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_finetuned_mrpc_richard0113_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_finetuned_mrpc_richard0113_en.md new file mode 100644 index 00000000000000..1cbc765dfd0855 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_finetuned_mrpc_richard0113_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_mrpc_richard0113 BertForSequenceClassification from Richard0113 +author: John Snow Labs +name: bert_base_uncased_finetuned_mrpc_richard0113 +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_mrpc_richard0113` is a English model originally trained by Richard0113. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_mrpc_richard0113_en_5.1.4_3.4_1698186993936.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_mrpc_richard0113_en_5.1.4_3.4_1698186993936.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_mrpc_richard0113","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_mrpc_richard0113","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_mrpc_richard0113| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Richard0113/bert-base-uncased-finetuned-mrpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_finetuned_qnli_joqsan_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_finetuned_qnli_joqsan_en.md new file mode 100644 index 00000000000000..94aec6b4eac72b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_finetuned_qnli_joqsan_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_qnli_joqsan BertForSequenceClassification from Joqsan +author: John Snow Labs +name: bert_base_uncased_finetuned_qnli_joqsan +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_qnli_joqsan` is a English model originally trained by Joqsan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_qnli_joqsan_en_5.1.4_3.4_1698191164031.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_qnli_joqsan_en_5.1.4_3.4_1698191164031.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_qnli_joqsan","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_qnli_joqsan","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_qnli_joqsan| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Joqsan/bert-base-uncased-finetuned-qnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_mnli_gokuls_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_mnli_gokuls_en.md new file mode 100644 index 00000000000000..68c349035e90cf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_mnli_gokuls_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_mnli_gokuls BertForSequenceClassification from gokuls +author: John Snow Labs +name: bert_base_uncased_mnli_gokuls +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_mnli_gokuls` is a English model originally trained by gokuls. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mnli_gokuls_en_5.1.4_3.4_1698188725843.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mnli_gokuls_en_5.1.4_3.4_1698188725843.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_mnli_gokuls","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_mnli_gokuls","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_mnli_gokuls| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/gokuls/bert-base-uncased-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_mrpc_gokuls_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_mrpc_gokuls_en.md new file mode 100644 index 00000000000000..52ed97266c71b9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_mrpc_gokuls_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_mrpc_gokuls BertForSequenceClassification from gokuls +author: John Snow Labs +name: bert_base_uncased_mrpc_gokuls +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_mrpc_gokuls` is a English model originally trained by gokuls. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mrpc_gokuls_en_5.1.4_3.4_1698187398987.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mrpc_gokuls_en_5.1.4_3.4_1698187398987.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_mrpc_gokuls","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_mrpc_gokuls","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_mrpc_gokuls| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/gokuls/bert-base-uncased-mrpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_nlpproject2023_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_nlpproject2023_en.md new file mode 100644 index 00000000000000..c2ab4ac558ae90 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_nlpproject2023_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_nlpproject2023 BertForSequenceClassification from nlpproject2023 +author: John Snow Labs +name: bert_base_uncased_nlpproject2023 +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_nlpproject2023` is a English model originally trained by nlpproject2023. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_nlpproject2023_en_5.1.4_3.4_1698190554359.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_nlpproject2023_en_5.1.4_3.4_1698190554359.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_nlpproject2023","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_nlpproject2023","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_nlpproject2023| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/nlpproject2023/bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_qnli_gokuls_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_qnli_gokuls_en.md new file mode 100644 index 00000000000000..aaef38ede1fd7b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_qnli_gokuls_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_qnli_gokuls BertForSequenceClassification from gokuls +author: John Snow Labs +name: bert_base_uncased_qnli_gokuls +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_qnli_gokuls` is a English model originally trained by gokuls. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_qnli_gokuls_en_5.1.4_3.4_1698187600239.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_qnli_gokuls_en_5.1.4_3.4_1698187600239.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_qnli_gokuls","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_qnli_gokuls","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_qnli_gokuls| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/gokuls/bert-base-uncased-qnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_qqp_gokuls_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_qqp_gokuls_en.md new file mode 100644 index 00000000000000..e89f385fd092ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_qqp_gokuls_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_qqp_gokuls BertForSequenceClassification from gokuls +author: John Snow Labs +name: bert_base_uncased_qqp_gokuls +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_qqp_gokuls` is a English model originally trained by gokuls. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_qqp_gokuls_en_5.1.4_3.4_1698187797404.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_qqp_gokuls_en_5.1.4_3.4_1698187797404.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_qqp_gokuls","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_qqp_gokuls","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_qqp_gokuls| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/gokuls/bert-base-uncased-qqp \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_rte_gokuls_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_rte_gokuls_en.md new file mode 100644 index 00000000000000..dc7492707bd923 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_rte_gokuls_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_rte_gokuls BertForSequenceClassification from gokuls +author: John Snow Labs +name: bert_base_uncased_rte_gokuls +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_rte_gokuls` is a English model originally trained by gokuls. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_rte_gokuls_en_5.1.4_3.4_1698187957658.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_rte_gokuls_en_5.1.4_3.4_1698187957658.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_rte_gokuls","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_rte_gokuls","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_rte_gokuls| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/gokuls/bert-base-uncased-rte \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_sst2_gokuls_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_sst2_gokuls_en.md new file mode 100644 index 00000000000000..8cd815eec59217 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_sst2_gokuls_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_sst2_gokuls BertForSequenceClassification from gokuls +author: John Snow Labs +name: bert_base_uncased_sst2_gokuls +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sst2_gokuls` is a English model originally trained by gokuls. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst2_gokuls_en_5.1.4_3.4_1698188150016.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst2_gokuls_en_5.1.4_3.4_1698188150016.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst2_gokuls","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst2_gokuls","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sst2_gokuls| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/gokuls/bert-base-uncased-sst2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_stsb_gokuls_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_stsb_gokuls_en.md new file mode 100644 index 00000000000000..390a90b40c8efd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_stsb_gokuls_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_stsb_gokuls BertForSequenceClassification from gokuls +author: John Snow Labs +name: bert_base_uncased_stsb_gokuls +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_stsb_gokuls` is a English model originally trained by gokuls. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_stsb_gokuls_en_5.1.4_3.4_1698188345394.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_stsb_gokuls_en_5.1.4_3.4_1698188345394.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_stsb_gokuls","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_stsb_gokuls","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_stsb_gokuls| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/gokuls/bert-base-uncased-stsb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_wnli_gokuls_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_wnli_gokuls_en.md new file mode 100644 index 00000000000000..71e01f55f69146 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_base_uncased_wnli_gokuls_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_wnli_gokuls BertForSequenceClassification from gokuls +author: John Snow Labs +name: bert_base_uncased_wnli_gokuls +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_wnli_gokuls` is a English model originally trained by gokuls. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_wnli_gokuls_en_5.1.4_3.4_1698188535629.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_wnli_gokuls_en_5.1.4_3.4_1698188535629.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_wnli_gokuls","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_wnli_gokuls","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_wnli_gokuls| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/gokuls/bert-base-uncased-wnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_classification_lm_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_classification_lm_en.md new file mode 100644 index 00000000000000..32da58bfd7dea4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_classification_lm_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_classification_lm BertForSequenceClassification from Alireza1044 +author: John Snow Labs +name: bert_classification_lm +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_classification_lm` is a English model originally trained by Alireza1044. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classification_lm_en_5.1.4_3.4_1698187102246.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classification_lm_en_5.1.4_3.4_1698187102246.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_classification_lm","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_classification_lm","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classification_lm| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Alireza1044/bert_classification_lm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_classifier_bert_base_arabic_camelbert_msa_did_madar_twitter5_ar.md b/docs/_posts/ahmedlone127/2023-10-24-bert_classifier_bert_base_arabic_camelbert_msa_did_madar_twitter5_ar.md new file mode 100644 index 00000000000000..3443ee7a851e94 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_classifier_bert_base_arabic_camelbert_msa_did_madar_twitter5_ar.md @@ -0,0 +1,110 @@ +--- +layout: model +title: Arabic BertForSequenceClassification Base Cased model (from CAMeL-Lab) +author: John Snow Labs +name: bert_classifier_bert_base_arabic_camelbert_msa_did_madar_twitter5 +date: 2023-10-24 +tags: [ar, open_source, bert, sequence_classification, classification, onnx] +task: Text Classification +language: ar +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-arabic-camelbert-msa-did-madar-twitter5` is a Arabic model originally trained by `CAMeL-Lab`. + +## Predicted Entities + +`Palestine`, `Egypt`, `United_Arab_Emirates`, `Somalia`, `Djibouti`, `Libya`, `Tunisia`, `Bahrain`, `Jordan`, `Sudan`, `Morocco`, `Lebanon`, `Saudi_Arabia`, `Kuwait`, `Mauritania`, `Yemen`, `Qatar`, `Iraq`, `Syria`, `Algeria`, `Oman` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_bert_base_arabic_camelbert_msa_did_madar_twitter5_ar_5.1.4_3.4_1698190129293.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_bert_base_arabic_camelbert_msa_did_madar_twitter5_ar_5.1.4_3.4_1698190129293.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_bert_base_arabic_camelbert_msa_did_madar_twitter5","ar") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_bert_base_arabic_camelbert_msa_did_madar_twitter5","ar") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ar.classify.bert.twitter.base").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_bert_base_arabic_camelbert_msa_did_madar_twitter5| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|ar| +|Size:|408.6 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa-did-madar-twitter5 +- https://camel.abudhabi.nyu.edu/madar-shared-task-2019/ +- https://arxiv.org/abs/2103.06678 +- https://github.com/CAMeL-Lab/CAMeLBERT +- https://github.com/CAMeL-Lab/camel_tools \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_classifier_bert_base_arabic_camelbert_msa_poetry_ar.md b/docs/_posts/ahmedlone127/2023-10-24-bert_classifier_bert_base_arabic_camelbert_msa_poetry_ar.md new file mode 100644 index 00000000000000..94f5cb9e24d761 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_classifier_bert_base_arabic_camelbert_msa_poetry_ar.md @@ -0,0 +1,104 @@ +--- +layout: model +title: Arabic BertForSequenceClassification Base Cased model (from CAMeL-Lab) +author: John Snow Labs +name: bert_classifier_bert_base_arabic_camelbert_msa_poetry +date: 2023-10-24 +tags: [ar, open_source, bert, sequence_classification, classification, onnx] +task: Text Classification +language: ar +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-arabic-camelbert-msa-poetry` is a Arabic model originally trained by `CAMeL-Lab`. + +## Predicted Entities + +`المواليا`, `الهزج`, `المجتث`, `الدوبيت`, `البسيط`, `المتدارك`, `شعر حر`, `الرجز`, `المنسرح`, `الكامل`, `المديد`, `المقتضب`, `الوافر`, `السريع`, `المتقارب`, `الرمل`, `عامي`, `الخفيف`, `شعر التفعيلة`, `المضارع`, `الطويل`, `السلسلة`, `موشح` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_bert_base_arabic_camelbert_msa_poetry_ar_5.1.4_3.4_1698190637947.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_bert_base_arabic_camelbert_msa_poetry_ar_5.1.4_3.4_1698190637947.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_bert_base_arabic_camelbert_msa_poetry","ar") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_bert_base_arabic_camelbert_msa_poetry","ar") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_bert_base_arabic_camelbert_msa_poetry| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|ar| +|Size:|408.6 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa-poetry +- https://arxiv.org/pdf/1905.05700.pdf +- https://arxiv.org/abs/2103.06678 +- https://github.com/CAMeL-Lab/CAMeLBERT +- https://github.com/CAMeL-Lab/camel_tools \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_classifier_tiny_master_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_classifier_tiny_master_en.md new file mode 100644 index 00000000000000..03d1c8ea37ff7c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_classifier_tiny_master_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: English BertForSequenceClassification Tiny Cased model (from chatwithnerd) +author: John Snow Labs +name: bert_classifier_tiny_master +date: 2023-10-24 +tags: [bert, sequence_classification, classification, open_source, en, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `tiny-bert-master-classifier` is a English model originally trained by `chatwithnerd`. + +## Predicted Entities + +`lentMoney`, `queryBudget`, `general`, `showExpenses`, `thanks`, `creditCard`, `goal`, `howWillYouTrack`, `income`, `investment`, `greeting`, `stop`, `howToEdit`, `howToAddExp`, `invalidInput`, `help`, `zeroinp`, `wtf`, `currentBalance`, `loan`, `howToAddLentMoney`, `showGoal`, `forgotSpend`, `emojiHelp`, `refund`, `questions`, `money`, `moneyPending`, `addSpend`, `edit`, `negative`, `goodbye`, `editBudgetData`, `howToAddRefund`, `cvtEMI`, `lastBudget`, `expenses`, `cashATM`, `insight`, `incompleteSpend`, `recap`, `spendAdvice` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_tiny_master_en_5.1.4_3.4_1698190774173.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_tiny_master_en_5.1.4_3.4_1698190774173.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_tiny_master","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_tiny_master","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.tiny.by_chatwithnerd").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_tiny_master| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|54.3 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/chatwithnerd/tiny-bert-master-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_classifier_wiselinjayajos_finetuned_mrpc_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_classifier_wiselinjayajos_finetuned_mrpc_en.md new file mode 100644 index 00000000000000..6fa54af7dc2195 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_classifier_wiselinjayajos_finetuned_mrpc_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: English BertForSequenceClassification Cased model (from wiselinjayajos) +author: John Snow Labs +name: bert_classifier_wiselinjayajos_finetuned_mrpc +date: 2023-10-24 +tags: [bert, sequence_classification, classification, open_source, en, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `finetuned-bert-mrpc` is a English model originally trained by `wiselinjayajos`. + +## Predicted Entities + +`equivalent`, `not equivalent` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_wiselinjayajos_finetuned_mrpc_en_5.1.4_3.4_1698191206415.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_wiselinjayajos_finetuned_mrpc_en_5.1.4_3.4_1698191206415.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_wiselinjayajos_finetuned_mrpc","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_wiselinjayajos_finetuned_mrpc","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.glue.finetuned.by_wiselinjayajos").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_wiselinjayajos_finetuned_mrpc| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/wiselinjayajos/finetuned-bert-mrpc +- https://paperswithcode.com/sota?task=Text+Classification&dataset=glue \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_cn_finetuning_18811449050_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_cn_finetuning_18811449050_en.md new file mode 100644 index 00000000000000..a15e2f74a24576 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_cn_finetuning_18811449050_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_cn_finetuning_18811449050 BertForSequenceClassification from 18811449050 +author: John Snow Labs +name: bert_cn_finetuning_18811449050 +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_cn_finetuning_18811449050` is a English model originally trained by 18811449050. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_cn_finetuning_18811449050_en_5.1.4_3.4_1698185823799.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_cn_finetuning_18811449050_en_5.1.4_3.4_1698185823799.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_cn_finetuning_18811449050","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_cn_finetuning_18811449050","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_cn_finetuning_18811449050| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/18811449050/bert_cn_finetuning \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_finetuned_mrpc_chunwoolee0_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_finetuned_mrpc_chunwoolee0_en.md new file mode 100644 index 00000000000000..440436366d4189 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_finetuned_mrpc_chunwoolee0_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetuned_mrpc_chunwoolee0 BertForSequenceClassification from chunwoolee0 +author: John Snow Labs +name: bert_finetuned_mrpc_chunwoolee0 +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuned_mrpc_chunwoolee0` is a English model originally trained by chunwoolee0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuned_mrpc_chunwoolee0_en_5.1.4_3.4_1698190771830.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuned_mrpc_chunwoolee0_en_5.1.4_3.4_1698190771830.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_mrpc_chunwoolee0","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_mrpc_chunwoolee0","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuned_mrpc_chunwoolee0| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/chunwoolee0/bert-finetuned-mrpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_finetuning_test_18811449050_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_finetuning_test_18811449050_en.md new file mode 100644 index 00000000000000..3e356860ba19b1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_finetuning_test_18811449050_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetuning_test_18811449050 BertForSequenceClassification from 18811449050 +author: John Snow Labs +name: bert_finetuning_test_18811449050 +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuning_test_18811449050` is a English model originally trained by 18811449050. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuning_test_18811449050_en_5.1.4_3.4_1698186007805.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuning_test_18811449050_en_5.1.4_3.4_1698186007805.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuning_test_18811449050","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuning_test_18811449050","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuning_test_18811449050| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/18811449050/bert_finetuning_test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_for_sentence_identification_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_for_sentence_identification_en.md new file mode 100644 index 00000000000000..8087b084a35d5d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_for_sentence_identification_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_for_sentence_identification BertForSequenceClassification from Reza-Madani +author: John Snow Labs +name: bert_for_sentence_identification +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_for_sentence_identification` is a English model originally trained by Reza-Madani. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_for_sentence_identification_en_5.1.4_3.4_1698190138921.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_for_sentence_identification_en_5.1.4_3.4_1698190138921.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_for_sentence_identification","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_for_sentence_identification","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_for_sentence_identification| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Reza-Madani/bert-for-sentence-identification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_large_cased_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_large_cased_en.md new file mode 100644 index 00000000000000..e88318b453dcdb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_large_cased_en.md @@ -0,0 +1,92 @@ +--- +layout: model +title: BERT Embeddings (Large Cased) +author: John Snow Labs +name: bert_large_cased +date: 2023-10-24 +tags: [open_source, embeddings, en, onnx] +task: Embeddings +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model contains a deep bidirectional transformer trained on Wikipedia and the BookCorpus. The details are described in the paper "[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)". + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_en_5.1.4_3.4_1698191629229.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_en_5.1.4_3.4_1698191629229.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +... +embeddings = BertEmbeddings.pretrained("bert_large_cased", "en") \ +.setInputCols("sentence", "token") \ +.setOutputCol("embeddings") +nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings]) +pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text")) +result = pipeline_model.transform(spark.createDataFrame([['I love NLP']], ["text"])) +``` +```scala +... +val embeddings = BertEmbeddings.pretrained("bert_large_cased", "en") +.setInputCols("sentence", "token") +.setOutputCol("embeddings") +val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings)) +val data = Seq("I love NLP").toDF("text") +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu + +text = ["I love NLP"] +embeddings_df = nlu.load('en.embed.bert.large_cased').predict(text, output_level='token') +embeddings_df +``` +
+ +## Results + +```bash + + token en_embed_bert_large_cased_embeddings + + I [-0.5893247723579407, -1.1389378309249878, -0.... + love [-0.8002289533615112, -0.15043185651302338, 0.... + NLP [-0.8995863199234009, 0.08327484875917435, 0.9... +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_large_cased_finetuned_mnli_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_large_cased_finetuned_mnli_en.md new file mode 100644 index 00000000000000..1dae33890b9504 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_large_cased_finetuned_mnli_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_cased_finetuned_mnli BertForSequenceClassification from George-Ogden +author: John Snow Labs +name: bert_large_cased_finetuned_mnli +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_finetuned_mnli` is a English model originally trained by George-Ogden. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_mnli_en_5.1.4_3.4_1698191618840.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_finetuned_mnli_en_5.1.4_3.4_1698191618840.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_cased_finetuned_mnli","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_cased_finetuned_mnli","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_finetuned_mnli| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/George-Ogden/bert-large-cased-finetuned-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_samoan_qa_abhijitt_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_samoan_qa_abhijitt_en.md new file mode 100644 index 00000000000000..c993c7512bae9f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_samoan_qa_abhijitt_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_samoan_qa_abhijitt BertForSequenceClassification from abhijitt +author: John Snow Labs +name: bert_samoan_qa_abhijitt +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_samoan_qa_abhijitt` is a English model originally trained by abhijitt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_samoan_qa_abhijitt_en_5.1.4_3.4_1698189049413.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_samoan_qa_abhijitt_en_5.1.4_3.4_1698189049413.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_samoan_qa_abhijitt","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_samoan_qa_abhijitt","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_samoan_qa_abhijitt| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/abhijitt/bert_sm_qa_abhijitt \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_catalan_poetry_ar.md b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_catalan_poetry_ar.md new file mode 100644 index 00000000000000..e8b28d9f774b3a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_catalan_poetry_ar.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Arabic bert_sequence_classifier_base_arabic_camel_catalan_poetry BertForSequenceClassification from CAMeL-Lab +author: John Snow Labs +name: bert_sequence_classifier_base_arabic_camel_catalan_poetry +date: 2023-10-24 +tags: [bert, ar, open_source, sequence_classification, onnx] +task: Text Classification +language: ar +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_sequence_classifier_base_arabic_camel_catalan_poetry` is a Arabic model originally trained by CAMeL-Lab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_catalan_poetry_ar_5.1.4_3.4_1698187914402.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_catalan_poetry_ar_5.1.4_3.4_1698187914402.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_catalan_poetry","ar")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_catalan_poetry","ar") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_base_arabic_camel_catalan_poetry| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|ar| +|Size:|408.9 MB| + +## References + +https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca-poetry \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_catalan_sentiment_ar.md b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_catalan_sentiment_ar.md new file mode 100644 index 00000000000000..8c04664083e36a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_catalan_sentiment_ar.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Arabic bert_sequence_classifier_base_arabic_camel_catalan_sentiment BertForSequenceClassification from CAMeL-Lab +author: John Snow Labs +name: bert_sequence_classifier_base_arabic_camel_catalan_sentiment +date: 2023-10-24 +tags: [bert, ar, open_source, sequence_classification, onnx] +task: Text Classification +language: ar +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_sequence_classifier_base_arabic_camel_catalan_sentiment` is a Arabic model originally trained by CAMeL-Lab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_catalan_sentiment_ar_5.1.4_3.4_1698188068250.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_catalan_sentiment_ar_5.1.4_3.4_1698188068250.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_catalan_sentiment","ar")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_catalan_sentiment","ar") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_base_arabic_camel_catalan_sentiment| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|ar| +|Size:|408.8 MB| + +## References + +https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca-sentiment \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_danish_poetry_ar.md b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_danish_poetry_ar.md new file mode 100644 index 00000000000000..6bc7497d8e0975 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_danish_poetry_ar.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Arabic bert_sequence_classifier_base_arabic_camel_danish_poetry BertForSequenceClassification from CAMeL-Lab +author: John Snow Labs +name: bert_sequence_classifier_base_arabic_camel_danish_poetry +date: 2023-10-24 +tags: [bert, ar, open_source, sequence_classification, onnx] +task: Text Classification +language: ar +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_sequence_classifier_base_arabic_camel_danish_poetry` is a Arabic model originally trained by CAMeL-Lab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_danish_poetry_ar_5.1.4_3.4_1698188295579.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_danish_poetry_ar_5.1.4_3.4_1698188295579.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_danish_poetry","ar")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_danish_poetry","ar") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_base_arabic_camel_danish_poetry| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|ar| +|Size:|409.0 MB| + +## References + +https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da-poetry \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_danish_sentiment_ar.md b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_danish_sentiment_ar.md new file mode 100644 index 00000000000000..dc185fef99d45a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_danish_sentiment_ar.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Arabic bert_sequence_classifier_base_arabic_camel_danish_sentiment BertForSequenceClassification from CAMeL-Lab +author: John Snow Labs +name: bert_sequence_classifier_base_arabic_camel_danish_sentiment +date: 2023-10-24 +tags: [bert, ar, open_source, sequence_classification, onnx] +task: Text Classification +language: ar +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_sequence_classifier_base_arabic_camel_danish_sentiment` is a Arabic model originally trained by CAMeL-Lab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_danish_sentiment_ar_5.1.4_3.4_1698188482010.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_danish_sentiment_ar_5.1.4_3.4_1698188482010.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_danish_sentiment","ar")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_danish_sentiment","ar") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_base_arabic_camel_danish_sentiment| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|ar| +|Size:|409.0 MB| + +## References + +https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_mix_did_madar_corpus26_ar.md b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_mix_did_madar_corpus26_ar.md new file mode 100644 index 00000000000000..4fa7f670716593 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_mix_did_madar_corpus26_ar.md @@ -0,0 +1,104 @@ +--- +layout: model +title: Arabic BertForSequenceClassification Base Cased model (from CAMeL-Lab) +author: John Snow Labs +name: bert_sequence_classifier_base_arabic_camel_mix_did_madar_corpus26 +date: 2023-10-24 +tags: [ar, open_source, bert, sequence_classification, ner, onnx] +task: Named Entity Recognition +language: ar +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-arabic-camelbert-mix-did-madar-corpus26` is a Arabic model originally trained by `CAMeL-Lab`. + +## Predicted Entities + +`MOS`, `SAN`, `BAG`, `ALX`, `DOH`, `ALG`, `ASW`, `BEI`, `ALE`, `FES`, `KHA`, `AMM`, `TUN`, `MSA`, `JED`, `RIY`, `SFX`, `SAL`, `MUS`, `BEN`, `CAI`, `DAM`, `TRI`, `BAS`, `JER`, `RAB` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_mix_did_madar_corpus26_ar_5.1.4_3.4_1698188775136.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_mix_did_madar_corpus26_ar_5.1.4_3.4_1698188775136.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_mix_did_madar_corpus26","ar") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_mix_did_madar_corpus26","ar") + .setInputCols(Array("document", "token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_base_arabic_camel_mix_did_madar_corpus26| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|ar| +|Size:|408.9 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +References + +- https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus26 +- https://camel.abudhabi.nyu.edu/madar-shared-task-2019/ +- https://arxiv.org/abs/2103.06678 +- https://github.com/CAMeL-Lab/CAMeLBERT +- https://github.com/CAMeL-Lab/camel_tools \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_mix_did_madar_corpus6_ar.md b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_mix_did_madar_corpus6_ar.md new file mode 100644 index 00000000000000..7b4721f932137e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_mix_did_madar_corpus6_ar.md @@ -0,0 +1,104 @@ +--- +layout: model +title: Arabic BertForSequenceClassification Base Cased model (from CAMeL-Lab) +author: John Snow Labs +name: bert_sequence_classifier_base_arabic_camel_mix_did_madar_corpus6 +date: 2023-10-24 +tags: [ar, open_source, bert, sequence_classification, ner, onnx] +task: Named Entity Recognition +language: ar +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-arabic-camelbert-mix-did-madar-corpus6` is a Arabic model originally trained by `CAMeL-Lab`. + +## Predicted Entities + +`BEI`, `TUN`, `CAI`, `MSA`, `RAB`, `DOH` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_mix_did_madar_corpus6_ar_5.1.4_3.4_1698189041726.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_mix_did_madar_corpus6_ar_5.1.4_3.4_1698189041726.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_mix_did_madar_corpus6","ar") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_mix_did_madar_corpus6","ar") + .setInputCols(Array("document", "token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_base_arabic_camel_mix_did_madar_corpus6| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|ar| +|Size:|408.8 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +References + +- https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus6 +- https://camel.abudhabi.nyu.edu/madar-shared-task-2019/ +- https://arxiv.org/abs/2103.06678 +- https://github.com/CAMeL-Lab/CAMeLBERT +- https://github.com/CAMeL-Lab/camel_tools \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_mix_did_nadi_ar.md b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_mix_did_nadi_ar.md new file mode 100644 index 00000000000000..ea5d44d8f2694e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_mix_did_nadi_ar.md @@ -0,0 +1,104 @@ +--- +layout: model +title: Arabic BertForSequenceClassification Base Cased model (from CAMeL-Lab) +author: John Snow Labs +name: bert_sequence_classifier_base_arabic_camel_mix_did_nadi +date: 2023-10-24 +tags: [ar, open_source, bert, sequence_classification, ner, onnx] +task: Named Entity Recognition +language: ar +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-arabic-camelbert-mix-did-nadi` is a Arabic model originally trained by `CAMeL-Lab`. + +## Predicted Entities + +`Yemen`, `Bahrain`, `Djibouti`, `Algeria`, `Mauritania`, `Jordan`, `Sudan`, `Tunisia`, `Saudi_Arabia`, `United_Arab_Emirates`, `Somalia`, `Syria`, `Oman`, `Lebanon`, `Libya`, `Morocco`, `Kuwait`, `Egypt`, `Palestine`, `Qatar`, `Iraq` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_mix_did_nadi_ar_5.1.4_3.4_1698189344522.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_mix_did_nadi_ar_5.1.4_3.4_1698189344522.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_mix_did_nadi","ar") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_mix_did_nadi","ar") + .setInputCols(Array("document", "token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_base_arabic_camel_mix_did_nadi| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|ar| +|Size:|408.9 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +References + +- https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix-did-nadi +- https://sites.google.com/view/nadi-shared-task +- https://arxiv.org/abs/2103.06678 +- https://github.com/CAMeL-Lab/CAMeLBERT +- https://github.com/CAMeL-Lab/camel_tools \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_mix_poetry_ar.md b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_mix_poetry_ar.md new file mode 100644 index 00000000000000..d47ee281e3b1e2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_mix_poetry_ar.md @@ -0,0 +1,104 @@ +--- +layout: model +title: Arabic BertForSequenceClassification Base Cased model (from CAMeL-Lab) +author: John Snow Labs +name: bert_sequence_classifier_base_arabic_camel_mix_poetry +date: 2023-10-24 +tags: [ar, open_source, bert, sequence_classification, ner, onnx] +task: Named Entity Recognition +language: ar +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-arabic-camelbert-mix-poetry` is a Arabic model originally trained by `CAMeL-Lab`. + +## Predicted Entities + +`موشح`, `الطويل`, `المنسرح`, `الهزج`, `السريع`, `المتدارك`, `السلسلة`, `عامي`, `شعر حر`, `الكامل`, `المواليا`, `الخفيف`, `المديد`, `الرجز`, `المتقارب`, `الرمل`, `المضارع`, `الدوبيت`, `المقتضب`, `الوافر`, `البسيط`, `شعر التفعيلة`, `المجتث` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_mix_poetry_ar_5.1.4_3.4_1698189597404.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_mix_poetry_ar_5.1.4_3.4_1698189597404.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_mix_poetry","ar") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_mix_poetry","ar") + .setInputCols(Array("document", "token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_base_arabic_camel_mix_poetry| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|ar| +|Size:|408.9 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +References + +- https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix-poetry +- https://arxiv.org/pdf/1905.05700.pdf +- https://arxiv.org/abs/2103.06678 +- https://github.com/CAMeL-Lab/CAMeLBERT +- https://github.com/CAMeL-Lab/camel_tools \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_mix_sentiment_ar.md b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_mix_sentiment_ar.md new file mode 100644 index 00000000000000..b331fdc3aae2d2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_mix_sentiment_ar.md @@ -0,0 +1,107 @@ +--- +layout: model +title: Arabic BertForSequenceClassification Base Cased model (from CAMeL-Lab) +author: John Snow Labs +name: bert_sequence_classifier_base_arabic_camel_mix_sentiment +date: 2023-10-24 +tags: [ar, open_source, bert, sequence_classification, ner, onnx] +task: Named Entity Recognition +language: ar +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-arabic-camelbert-mix-sentiment` is a Arabic model originally trained by `CAMeL-Lab`. + +## Predicted Entities + +`positive`, `neutral`, `negative` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_mix_sentiment_ar_5.1.4_3.4_1698189861699.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_mix_sentiment_ar_5.1.4_3.4_1698189861699.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_mix_sentiment","ar") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_mix_sentiment","ar") + .setInputCols(Array("document", "token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_base_arabic_camel_mix_sentiment| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|ar| +|Size:|408.8 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +References + +- https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment +- https://aclanthology.org/D15-1299.pdf +- http://lrec-conf.org/workshops/lrec2018/W30/pdf/22_W30.pdf +- https://aclanthology.org/S17-2088.pdf +- https://arxiv.org/abs/2103.06678 +- https://github.com/CAMeL-Lab/CAMeLBERT +- https://github.com/CAMeL-Lab/camel_tools +- https://github.com/CAMeL-Lab/camel_tools \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_msa_did_nadi_ar.md b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_msa_did_nadi_ar.md new file mode 100644 index 00000000000000..89c1a9cbe35971 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_msa_did_nadi_ar.md @@ -0,0 +1,104 @@ +--- +layout: model +title: Arabic BertForSequenceClassification Base Cased model (from CAMeL-Lab) +author: John Snow Labs +name: bert_sequence_classifier_base_arabic_camel_msa_did_nadi +date: 2023-10-24 +tags: [ar, open_source, bert, sequence_classification, ner, onnx] +task: Named Entity Recognition +language: ar +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-arabic-camelbert-msa-did-nadi` is a Arabic model originally trained by `CAMeL-Lab`. + +## Predicted Entities + +`Yemen`, `Bahrain`, `Djibouti`, `Algeria`, `Mauritania`, `Jordan`, `Sudan`, `Tunisia`, `Saudi_Arabia`, `United_Arab_Emirates`, `Somalia`, `Syria`, `Oman`, `Lebanon`, `Libya`, `Morocco`, `Kuwait`, `Egypt`, `Palestine`, `Qatar`, `Iraq` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_msa_did_nadi_ar_5.1.4_3.4_1698190386956.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_msa_did_nadi_ar_5.1.4_3.4_1698190386956.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_msa_did_nadi","ar") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_msa_did_nadi","ar") + .setInputCols(Array("document", "token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_base_arabic_camel_msa_did_nadi| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|ar| +|Size:|408.6 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +References + +- https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa-did-nadi +- https://sites.google.com/view/nadi-shared-task +- https://arxiv.org/abs/2103.06678 +- https://github.com/CAMeL-Lab/CAMeLBERT +- https://github.com/CAMeL-Lab/camel_tools \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_msa_sentiment_ar.md b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_msa_sentiment_ar.md new file mode 100644 index 00000000000000..9feec81d34c858 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_sequence_classifier_base_arabic_camel_msa_sentiment_ar.md @@ -0,0 +1,107 @@ +--- +layout: model +title: Arabic BertForSequenceClassification Base Cased model (from CAMeL-Lab) +author: John Snow Labs +name: bert_sequence_classifier_base_arabic_camel_msa_sentiment +date: 2023-10-24 +tags: [ar, open_source, bert, sequence_classification, ner, onnx] +task: Named Entity Recognition +language: ar +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-arabic-camelbert-msa-sentiment` is a Arabic model originally trained by `CAMeL-Lab`. + +## Predicted Entities + +`positive`, `neutral`, `negative` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_msa_sentiment_ar_5.1.4_3.4_1698190934021.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_arabic_camel_msa_sentiment_ar_5.1.4_3.4_1698190934021.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_msa_sentiment","ar") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_msa_sentiment","ar") + .setInputCols(Array("document", "token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_base_arabic_camel_msa_sentiment| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|ar| +|Size:|408.6 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +References + +- https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa-sentiment +- https://aclanthology.org/D15-1299.pdf +- http://lrec-conf.org/workshops/lrec2018/W30/pdf/22_W30.pdf +- https://aclanthology.org/S17-2088.pdf +- https://arxiv.org/abs/2103.06678 +- https://github.com/CAMeL-Lab/CAMeLBERT +- https://github.com/CAMeL-Lab/camel_tools +- https://github.com/CAMeL-Lab/camel_tools \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_english_icelandic_hired_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_english_icelandic_hired_en.md new file mode 100644 index 00000000000000..70aae999a308db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_english_icelandic_hired_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_twitter_english_icelandic_hired BertForSequenceClassification from manueltonneau +author: John Snow Labs +name: bert_twitter_english_icelandic_hired +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_twitter_english_icelandic_hired` is a English model originally trained by manueltonneau. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_twitter_english_icelandic_hired_en_5.1.4_3.4_1698187045466.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_twitter_english_icelandic_hired_en_5.1.4_3.4_1698187045466.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_english_icelandic_hired","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_english_icelandic_hired","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_twitter_english_icelandic_hired| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.1 MB| + +## References + +https://huggingface.co/manueltonneau/bert-twitter-en-is-hired \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_english_icelandic_unemployed_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_english_icelandic_unemployed_en.md new file mode 100644 index 00000000000000..4021a2d49fadb0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_english_icelandic_unemployed_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_twitter_english_icelandic_unemployed BertForSequenceClassification from manueltonneau +author: John Snow Labs +name: bert_twitter_english_icelandic_unemployed +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_twitter_english_icelandic_unemployed` is a English model originally trained by manueltonneau. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_twitter_english_icelandic_unemployed_en_5.1.4_3.4_1698187240391.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_twitter_english_icelandic_unemployed_en_5.1.4_3.4_1698187240391.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_english_icelandic_unemployed","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_english_icelandic_unemployed","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_twitter_english_icelandic_unemployed| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.1 MB| + +## References + +https://huggingface.co/manueltonneau/bert-twitter-en-is-unemployed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_english_job_offer_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_english_job_offer_en.md new file mode 100644 index 00000000000000..1d7c2c775806aa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_english_job_offer_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_twitter_english_job_offer BertForSequenceClassification from manueltonneau +author: John Snow Labs +name: bert_twitter_english_job_offer +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_twitter_english_job_offer` is a English model originally trained by manueltonneau. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_twitter_english_job_offer_en_5.1.4_3.4_1698187441524.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_twitter_english_job_offer_en_5.1.4_3.4_1698187441524.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_english_job_offer","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_english_job_offer","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_twitter_english_job_offer| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.1 MB| + +## References + +https://huggingface.co/manueltonneau/bert-twitter-en-job-offer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_english_job_search_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_english_job_search_en.md new file mode 100644 index 00000000000000..bdfe270d7e48ec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_english_job_search_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_twitter_english_job_search BertForSequenceClassification from manueltonneau +author: John Snow Labs +name: bert_twitter_english_job_search +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_twitter_english_job_search` is a English model originally trained by manueltonneau. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_twitter_english_job_search_en_5.1.4_3.4_1698187827004.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_twitter_english_job_search_en_5.1.4_3.4_1698187827004.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_english_job_search","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_english_job_search","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_twitter_english_job_search| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.1 MB| + +## References + +https://huggingface.co/manueltonneau/bert-twitter-en-job-search \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_english_lost_job_en.md b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_english_lost_job_en.md new file mode 100644 index 00000000000000..07c00c9a400c39 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_english_lost_job_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_twitter_english_lost_job BertForSequenceClassification from manueltonneau +author: John Snow Labs +name: bert_twitter_english_lost_job +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_twitter_english_lost_job` is a English model originally trained by manueltonneau. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_twitter_english_lost_job_en_5.1.4_3.4_1698187626266.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_twitter_english_lost_job_en_5.1.4_3.4_1698187626266.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_english_lost_job","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_english_lost_job","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_twitter_english_lost_job| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.1 MB| + +## References + +https://huggingface.co/manueltonneau/bert-twitter-en-lost-job \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_portuguese_icelandic_hired_pt.md b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_portuguese_icelandic_hired_pt.md new file mode 100644 index 00000000000000..e56fc7b8a33e7e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_portuguese_icelandic_hired_pt.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Portuguese bert_twitter_portuguese_icelandic_hired BertForSequenceClassification from manueltonneau +author: John Snow Labs +name: bert_twitter_portuguese_icelandic_hired +date: 2023-10-24 +tags: [bert, pt, open_source, sequence_classification, onnx] +task: Text Classification +language: pt +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_twitter_portuguese_icelandic_hired` is a Portuguese model originally trained by manueltonneau. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_twitter_portuguese_icelandic_hired_pt_5.1.4_3.4_1698189106708.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_twitter_portuguese_icelandic_hired_pt_5.1.4_3.4_1698189106708.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_portuguese_icelandic_hired","pt")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_portuguese_icelandic_hired","pt") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_twitter_portuguese_icelandic_hired| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|pt| +|Size:|408.1 MB| + +## References + +https://huggingface.co/manueltonneau/bert-twitter-pt-is-hired \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_portuguese_icelandic_unemployed_pt.md b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_portuguese_icelandic_unemployed_pt.md new file mode 100644 index 00000000000000..94d440f47f10d8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_portuguese_icelandic_unemployed_pt.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Portuguese bert_twitter_portuguese_icelandic_unemployed BertForSequenceClassification from manueltonneau +author: John Snow Labs +name: bert_twitter_portuguese_icelandic_unemployed +date: 2023-10-24 +tags: [bert, pt, open_source, sequence_classification, onnx] +task: Text Classification +language: pt +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_twitter_portuguese_icelandic_unemployed` is a Portuguese model originally trained by manueltonneau. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_twitter_portuguese_icelandic_unemployed_pt_5.1.4_3.4_1698189297437.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_twitter_portuguese_icelandic_unemployed_pt_5.1.4_3.4_1698189297437.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_portuguese_icelandic_unemployed","pt")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_portuguese_icelandic_unemployed","pt") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_twitter_portuguese_icelandic_unemployed| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|pt| +|Size:|408.1 MB| + +## References + +https://huggingface.co/manueltonneau/bert-twitter-pt-is-unemployed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_portuguese_job_offer_pt.md b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_portuguese_job_offer_pt.md new file mode 100644 index 00000000000000..576855be9ca1f7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_portuguese_job_offer_pt.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Portuguese bert_twitter_portuguese_job_offer BertForSequenceClassification from manueltonneau +author: John Snow Labs +name: bert_twitter_portuguese_job_offer +date: 2023-10-24 +tags: [bert, pt, open_source, sequence_classification, onnx] +task: Text Classification +language: pt +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_twitter_portuguese_job_offer` is a Portuguese model originally trained by manueltonneau. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_twitter_portuguese_job_offer_pt_5.1.4_3.4_1698189477797.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_twitter_portuguese_job_offer_pt_5.1.4_3.4_1698189477797.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_portuguese_job_offer","pt")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_portuguese_job_offer","pt") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_twitter_portuguese_job_offer| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|pt| +|Size:|408.1 MB| + +## References + +https://huggingface.co/manueltonneau/bert-twitter-pt-job-offer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_portuguese_job_search_pt.md b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_portuguese_job_search_pt.md new file mode 100644 index 00000000000000..22a6e504d168d9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_portuguese_job_search_pt.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Portuguese bert_twitter_portuguese_job_search BertForSequenceClassification from manueltonneau +author: John Snow Labs +name: bert_twitter_portuguese_job_search +date: 2023-10-24 +tags: [bert, pt, open_source, sequence_classification, onnx] +task: Text Classification +language: pt +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_twitter_portuguese_job_search` is a Portuguese model originally trained by manueltonneau. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_twitter_portuguese_job_search_pt_5.1.4_3.4_1698189662967.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_twitter_portuguese_job_search_pt_5.1.4_3.4_1698189662967.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_portuguese_job_search","pt")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_portuguese_job_search","pt") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_twitter_portuguese_job_search| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|pt| +|Size:|408.1 MB| + +## References + +https://huggingface.co/manueltonneau/bert-twitter-pt-job-search \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_portuguese_lost_job_pt.md b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_portuguese_lost_job_pt.md new file mode 100644 index 00000000000000..65330ec1eeaa14 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_portuguese_lost_job_pt.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Portuguese bert_twitter_portuguese_lost_job BertForSequenceClassification from manueltonneau +author: John Snow Labs +name: bert_twitter_portuguese_lost_job +date: 2023-10-24 +tags: [bert, pt, open_source, sequence_classification, onnx] +task: Text Classification +language: pt +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_twitter_portuguese_lost_job` is a Portuguese model originally trained by manueltonneau. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_twitter_portuguese_lost_job_pt_5.1.4_3.4_1698188934801.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_twitter_portuguese_lost_job_pt_5.1.4_3.4_1698188934801.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_portuguese_lost_job","pt")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_portuguese_lost_job","pt") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_twitter_portuguese_lost_job| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|pt| +|Size:|408.1 MB| + +## References + +https://huggingface.co/manueltonneau/bert-twitter-pt-lost-job \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_spanish_icelandic_hired_es.md b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_spanish_icelandic_hired_es.md new file mode 100644 index 00000000000000..acc1092f848f5c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_spanish_icelandic_hired_es.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Castilian, Spanish bert_twitter_spanish_icelandic_hired BertForSequenceClassification from manueltonneau +author: John Snow Labs +name: bert_twitter_spanish_icelandic_hired +date: 2023-10-24 +tags: [bert, es, open_source, sequence_classification, onnx] +task: Text Classification +language: es +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_twitter_spanish_icelandic_hired` is a Castilian, Spanish model originally trained by manueltonneau. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_twitter_spanish_icelandic_hired_es_5.1.4_3.4_1698188017171.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_twitter_spanish_icelandic_hired_es_5.1.4_3.4_1698188017171.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_spanish_icelandic_hired","es")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_spanish_icelandic_hired","es") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_twitter_spanish_icelandic_hired| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|es| +|Size:|411.7 MB| + +## References + +https://huggingface.co/manueltonneau/bert-twitter-es-is-hired \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_spanish_icelandic_unemployed_es.md b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_spanish_icelandic_unemployed_es.md new file mode 100644 index 00000000000000..1c8e84760b1cef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_spanish_icelandic_unemployed_es.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Castilian, Spanish bert_twitter_spanish_icelandic_unemployed BertForSequenceClassification from manueltonneau +author: John Snow Labs +name: bert_twitter_spanish_icelandic_unemployed +date: 2023-10-24 +tags: [bert, es, open_source, sequence_classification, onnx] +task: Text Classification +language: es +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_twitter_spanish_icelandic_unemployed` is a Castilian, Spanish model originally trained by manueltonneau. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_twitter_spanish_icelandic_unemployed_es_5.1.4_3.4_1698188359353.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_twitter_spanish_icelandic_unemployed_es_5.1.4_3.4_1698188359353.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_spanish_icelandic_unemployed","es")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_spanish_icelandic_unemployed","es") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_twitter_spanish_icelandic_unemployed| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|es| +|Size:|411.7 MB| + +## References + +https://huggingface.co/manueltonneau/bert-twitter-es-is-unemployed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_spanish_job_offer_es.md b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_spanish_job_offer_es.md new file mode 100644 index 00000000000000..802e0cdaf03ca1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_spanish_job_offer_es.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Castilian, Spanish bert_twitter_spanish_job_offer BertForSequenceClassification from manueltonneau +author: John Snow Labs +name: bert_twitter_spanish_job_offer +date: 2023-10-24 +tags: [bert, es, open_source, sequence_classification, onnx] +task: Text Classification +language: es +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_twitter_spanish_job_offer` is a Castilian, Spanish model originally trained by manueltonneau. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_twitter_spanish_job_offer_es_5.1.4_3.4_1698188539277.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_twitter_spanish_job_offer_es_5.1.4_3.4_1698188539277.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_spanish_job_offer","es")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_spanish_job_offer","es") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_twitter_spanish_job_offer| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|es| +|Size:|411.7 MB| + +## References + +https://huggingface.co/manueltonneau/bert-twitter-es-job-offer \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_spanish_job_search_es.md b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_spanish_job_search_es.md new file mode 100644 index 00000000000000..03698990d3f96c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_spanish_job_search_es.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Castilian, Spanish bert_twitter_spanish_job_search BertForSequenceClassification from manueltonneau +author: John Snow Labs +name: bert_twitter_spanish_job_search +date: 2023-10-24 +tags: [bert, es, open_source, sequence_classification, onnx] +task: Text Classification +language: es +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_twitter_spanish_job_search` is a Castilian, Spanish model originally trained by manueltonneau. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_twitter_spanish_job_search_es_5.1.4_3.4_1698188726169.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_twitter_spanish_job_search_es_5.1.4_3.4_1698188726169.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_spanish_job_search","es")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_spanish_job_search","es") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_twitter_spanish_job_search| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|es| +|Size:|411.7 MB| + +## References + +https://huggingface.co/manueltonneau/bert-twitter-es-job-search \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_spanish_lost_job_es.md b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_spanish_lost_job_es.md new file mode 100644 index 00000000000000..2ac5a6761640dd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-bert_twitter_spanish_lost_job_es.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Castilian, Spanish bert_twitter_spanish_lost_job BertForSequenceClassification from manueltonneau +author: John Snow Labs +name: bert_twitter_spanish_lost_job +date: 2023-10-24 +tags: [bert, es, open_source, sequence_classification, onnx] +task: Text Classification +language: es +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_twitter_spanish_lost_job` is a Castilian, Spanish model originally trained by manueltonneau. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_twitter_spanish_lost_job_es_5.1.4_3.4_1698188207227.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_twitter_spanish_lost_job_es_5.1.4_3.4_1698188207227.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_spanish_lost_job","es")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_twitter_spanish_lost_job","es") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_twitter_spanish_lost_job| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|es| +|Size:|411.7 MB| + +## References + +https://huggingface.co/manueltonneau/bert-twitter-es-lost-job \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-burmese_bert_ansh_en.md b/docs/_posts/ahmedlone127/2023-10-24-burmese_bert_ansh_en.md new file mode 100644 index 00000000000000..2d07fb46e50737 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-burmese_bert_ansh_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English burmese_bert_ansh BertForSequenceClassification from Ansh +author: John Snow Labs +name: burmese_bert_ansh +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`burmese_bert_ansh` is a English model originally trained by Ansh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/burmese_bert_ansh_en_5.1.4_3.4_1698191922897.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/burmese_bert_ansh_en_5.1.4_3.4_1698191922897.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("burmese_bert_ansh","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("burmese_bert_ansh","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|burmese_bert_ansh| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Ansh/my_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-burmese_paircls_klue_nli_beomi_kcbert_base_model_en.md b/docs/_posts/ahmedlone127/2023-10-24-burmese_paircls_klue_nli_beomi_kcbert_base_model_en.md new file mode 100644 index 00000000000000..6e070a4304a1e2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-burmese_paircls_klue_nli_beomi_kcbert_base_model_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English burmese_paircls_klue_nli_beomi_kcbert_base_model BertForSequenceClassification from chunwoolee0 +author: John Snow Labs +name: burmese_paircls_klue_nli_beomi_kcbert_base_model +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`burmese_paircls_klue_nli_beomi_kcbert_base_model` is a English model originally trained by chunwoolee0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/burmese_paircls_klue_nli_beomi_kcbert_base_model_en_5.1.4_3.4_1698188605271.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/burmese_paircls_klue_nli_beomi_kcbert_base_model_en_5.1.4_3.4_1698188605271.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("burmese_paircls_klue_nli_beomi_kcbert_base_model","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("burmese_paircls_klue_nli_beomi_kcbert_base_model","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|burmese_paircls_klue_nli_beomi_kcbert_base_model| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|408.4 MB| + +## References + +https://huggingface.co/chunwoolee0/my_paircls_klue_nli_beomi_kcbert_base_model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-covid_augment_tweet_bert_large_e2_v2_en.md b/docs/_posts/ahmedlone127/2023-10-24-covid_augment_tweet_bert_large_e2_v2_en.md new file mode 100644 index 00000000000000..67bdce85320dc4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-covid_augment_tweet_bert_large_e2_v2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English covid_augment_tweet_bert_large_e2_v2 BertForSequenceClassification from JerryYanJiang +author: John Snow Labs +name: covid_augment_tweet_bert_large_e2_v2 +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`covid_augment_tweet_bert_large_e2_v2` is a English model originally trained by JerryYanJiang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/covid_augment_tweet_bert_large_e2_v2_en_5.1.4_3.4_1698186896527.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/covid_augment_tweet_bert_large_e2_v2_en_5.1.4_3.4_1698186896527.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("covid_augment_tweet_bert_large_e2_v2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("covid_augment_tweet_bert_large_e2_v2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|covid_augment_tweet_bert_large_e2_v2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/JerryYanJiang/covid-augment-tweet-bert-large-e2-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-covid_augment_tweet_bert_large_e2_version2_en.md b/docs/_posts/ahmedlone127/2023-10-24-covid_augment_tweet_bert_large_e2_version2_en.md new file mode 100644 index 00000000000000..d9cd4da2fff74f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-covid_augment_tweet_bert_large_e2_version2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English covid_augment_tweet_bert_large_e2_version2 BertForSequenceClassification from JerryYanJiang +author: John Snow Labs +name: covid_augment_tweet_bert_large_e2_version2 +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`covid_augment_tweet_bert_large_e2_version2` is a English model originally trained by JerryYanJiang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/covid_augment_tweet_bert_large_e2_version2_en_5.1.4_3.4_1698187597852.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/covid_augment_tweet_bert_large_e2_version2_en_5.1.4_3.4_1698187597852.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("covid_augment_tweet_bert_large_e2_version2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("covid_augment_tweet_bert_large_e2_version2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|covid_augment_tweet_bert_large_e2_version2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/JerryYanJiang/covid-augment-tweet-bert-large-e2-version2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-covid_augment_tweet_bert_large_e2_version2_noweight_en.md b/docs/_posts/ahmedlone127/2023-10-24-covid_augment_tweet_bert_large_e2_version2_noweight_en.md new file mode 100644 index 00000000000000..2bf68e0cc8c416 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-covid_augment_tweet_bert_large_e2_version2_noweight_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English covid_augment_tweet_bert_large_e2_version2_noweight BertForSequenceClassification from JerryYanJiang +author: John Snow Labs +name: covid_augment_tweet_bert_large_e2_version2_noweight +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`covid_augment_tweet_bert_large_e2_version2_noweight` is a English model originally trained by JerryYanJiang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/covid_augment_tweet_bert_large_e2_version2_noweight_en_5.1.4_3.4_1698188314567.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/covid_augment_tweet_bert_large_e2_version2_noweight_en_5.1.4_3.4_1698188314567.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("covid_augment_tweet_bert_large_e2_version2_noweight","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("covid_augment_tweet_bert_large_e2_version2_noweight","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|covid_augment_tweet_bert_large_e2_version2_noweight| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/JerryYanJiang/covid-augment-tweet-bert-large-e2-version2-noweight \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-covid_augment_tweet_bert_large_e3_v2_en.md b/docs/_posts/ahmedlone127/2023-10-24-covid_augment_tweet_bert_large_e3_v2_en.md new file mode 100644 index 00000000000000..486a2bde087ca9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-covid_augment_tweet_bert_large_e3_v2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English covid_augment_tweet_bert_large_e3_v2 BertForSequenceClassification from JerryYanJiang +author: John Snow Labs +name: covid_augment_tweet_bert_large_e3_v2 +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`covid_augment_tweet_bert_large_e3_v2` is a English model originally trained by JerryYanJiang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/covid_augment_tweet_bert_large_e3_v2_en_5.1.4_3.4_1698187235715.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/covid_augment_tweet_bert_large_e3_v2_en_5.1.4_3.4_1698187235715.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("covid_augment_tweet_bert_large_e3_v2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("covid_augment_tweet_bert_large_e3_v2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|covid_augment_tweet_bert_large_e3_v2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/JerryYanJiang/covid-augment-tweet-bert-large-e3-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-covid_augment_tweet_bert_large_e4_v2_en.md b/docs/_posts/ahmedlone127/2023-10-24-covid_augment_tweet_bert_large_e4_v2_en.md new file mode 100644 index 00000000000000..a30f91f87c3bd2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-covid_augment_tweet_bert_large_e4_v2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English covid_augment_tweet_bert_large_e4_v2 BertForSequenceClassification from JerryYanJiang +author: John Snow Labs +name: covid_augment_tweet_bert_large_e4_v2 +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`covid_augment_tweet_bert_large_e4_v2` is a English model originally trained by JerryYanJiang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/covid_augment_tweet_bert_large_e4_v2_en_5.1.4_3.4_1698186540209.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/covid_augment_tweet_bert_large_e4_v2_en_5.1.4_3.4_1698186540209.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("covid_augment_tweet_bert_large_e4_v2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("covid_augment_tweet_bert_large_e4_v2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|covid_augment_tweet_bert_large_e4_v2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/JerryYanJiang/covid-augment-tweet-bert-large-e4-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-covid_augment_tweet_bert_large_e8_noweight_en.md b/docs/_posts/ahmedlone127/2023-10-24-covid_augment_tweet_bert_large_e8_noweight_en.md new file mode 100644 index 00000000000000..6055b0537c5deb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-covid_augment_tweet_bert_large_e8_noweight_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English covid_augment_tweet_bert_large_e8_noweight BertForSequenceClassification from JerryYanJiang +author: John Snow Labs +name: covid_augment_tweet_bert_large_e8_noweight +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`covid_augment_tweet_bert_large_e8_noweight` is a English model originally trained by JerryYanJiang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/covid_augment_tweet_bert_large_e8_noweight_en_5.1.4_3.4_1698188957419.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/covid_augment_tweet_bert_large_e8_noweight_en_5.1.4_3.4_1698188957419.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("covid_augment_tweet_bert_large_e8_noweight","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("covid_augment_tweet_bert_large_e8_noweight","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|covid_augment_tweet_bert_large_e8_noweight| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/JerryYanJiang/covid-augment-tweet-bert-large-e8-noweight \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-covid_tweet_bert_large_e2_noweight_en.md b/docs/_posts/ahmedlone127/2023-10-24-covid_tweet_bert_large_e2_noweight_en.md new file mode 100644 index 00000000000000..e67278e0e5e4a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-covid_tweet_bert_large_e2_noweight_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English covid_tweet_bert_large_e2_noweight BertForSequenceClassification from JerryYanJiang +author: John Snow Labs +name: covid_tweet_bert_large_e2_noweight +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`covid_tweet_bert_large_e2_noweight` is a English model originally trained by JerryYanJiang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/covid_tweet_bert_large_e2_noweight_en_5.1.4_3.4_1698189318072.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/covid_tweet_bert_large_e2_noweight_en_5.1.4_3.4_1698189318072.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("covid_tweet_bert_large_e2_noweight","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("covid_tweet_bert_large_e2_noweight","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|covid_tweet_bert_large_e2_noweight| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/JerryYanJiang/covid-tweet-bert-large-e2-noweight \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-distilbert_base_chinese_amazon_chinese_20000_en.md b/docs/_posts/ahmedlone127/2023-10-24-distilbert_base_chinese_amazon_chinese_20000_en.md new file mode 100644 index 00000000000000..b171e29ce7da57 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-distilbert_base_chinese_amazon_chinese_20000_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English distilbert_base_chinese_amazon_chinese_20000 BertForSequenceClassification from ASCCCCCCCC +author: John Snow Labs +name: distilbert_base_chinese_amazon_chinese_20000 +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distilbert_base_chinese_amazon_chinese_20000` is a English model originally trained by ASCCCCCCCC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_chinese_amazon_chinese_20000_en_5.1.4_3.4_1698186749914.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_chinese_amazon_chinese_20000_en_5.1.4_3.4_1698186749914.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("distilbert_base_chinese_amazon_chinese_20000","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("distilbert_base_chinese_amazon_chinese_20000","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distilbert_base_chinese_amazon_chinese_20000| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-hebert_finetuned_hebrew_metaphor_he.md b/docs/_posts/ahmedlone127/2023-10-24-hebert_finetuned_hebrew_metaphor_he.md new file mode 100644 index 00000000000000..5ead8da85f6c91 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-hebert_finetuned_hebrew_metaphor_he.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Hebrew hebert_finetuned_hebrew_metaphor BertForSequenceClassification from tdklab +author: John Snow Labs +name: hebert_finetuned_hebrew_metaphor +date: 2023-10-24 +tags: [bert, he, open_source, sequence_classification, onnx] +task: Text Classification +language: he +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hebert_finetuned_hebrew_metaphor` is a Hebrew model originally trained by tdklab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hebert_finetuned_hebrew_metaphor_he_5.1.4_3.4_1698190967230.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hebert_finetuned_hebrew_metaphor_he_5.1.4_3.4_1698190967230.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("hebert_finetuned_hebrew_metaphor","he")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("hebert_finetuned_hebrew_metaphor","he") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hebert_finetuned_hebrew_metaphor| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|he| +|Size:|410.3 MB| + +## References + +https://huggingface.co/tdklab/hebert-finetuned-hebrew-metaphor \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-kindword_klue_bert_base_en.md b/docs/_posts/ahmedlone127/2023-10-24-kindword_klue_bert_base_en.md new file mode 100644 index 00000000000000..ba74f272e2b265 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-kindword_klue_bert_base_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English kindword_klue_bert_base BertForSequenceClassification from powerwarez +author: John Snow Labs +name: kindword_klue_bert_base +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kindword_klue_bert_base` is a English model originally trained by powerwarez. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kindword_klue_bert_base_en_5.1.4_3.4_1698190959059.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kindword_klue_bert_base_en_5.1.4_3.4_1698190959059.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("kindword_klue_bert_base","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("kindword_klue_bert_base","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kindword_klue_bert_base| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.6 MB| + +## References + +https://huggingface.co/powerwarez/kindword-klue_bert-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-me_hate_bert_mr.md b/docs/_posts/ahmedlone127/2023-10-24-me_hate_bert_mr.md new file mode 100644 index 00000000000000..286e5f0fc38196 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-me_hate_bert_mr.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Marathi me_hate_bert BertForSequenceClassification from l3cube-pune +author: John Snow Labs +name: me_hate_bert +date: 2023-10-24 +tags: [bert, mr, open_source, sequence_classification, onnx] +task: Text Classification +language: mr +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`me_hate_bert` is a Marathi model originally trained by l3cube-pune. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/me_hate_bert_mr_5.1.4_3.4_1698190564789.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/me_hate_bert_mr_5.1.4_3.4_1698190564789.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("me_hate_bert","mr")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("me_hate_bert","mr") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|me_hate_bert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|mr| +|Size:|892.8 MB| + +## References + +https://huggingface.co/l3cube-pune/me-hate-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-norbert2_sentiment_test1_no.md b/docs/_posts/ahmedlone127/2023-10-24-norbert2_sentiment_test1_no.md new file mode 100644 index 00000000000000..6c4082192494c7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-norbert2_sentiment_test1_no.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Norwegian norbert2_sentiment_test1 BertForSequenceClassification from marcuskd +author: John Snow Labs +name: norbert2_sentiment_test1 +date: 2023-10-24 +tags: [bert, "no", open_source, sequence_classification, onnx] +task: Text Classification +language: "no" +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`norbert2_sentiment_test1` is a Norwegian model originally trained by marcuskd. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_test1_no_5.1.4_3.4_1698191963919.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_test1_no_5.1.4_3.4_1698191963919.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_test1","no")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_test1","no") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|norbert2_sentiment_test1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|no| +|Size:|467.4 MB| + +## References + +https://huggingface.co/marcuskd/norbert2_sentiment_test1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-parlbert_topic_german_de.md b/docs/_posts/ahmedlone127/2023-10-24-parlbert_topic_german_de.md new file mode 100644 index 00000000000000..647134d01903d1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-parlbert_topic_german_de.md @@ -0,0 +1,97 @@ +--- +layout: model +title: German parlbert_topic_german BertForSequenceClassification from chkla +author: John Snow Labs +name: parlbert_topic_german +date: 2023-10-24 +tags: [bert, de, open_source, sequence_classification, onnx] +task: Text Classification +language: de +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`parlbert_topic_german` is a German model originally trained by chkla. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/parlbert_topic_german_de_5.1.4_3.4_1698191970215.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/parlbert_topic_german_de_5.1.4_3.4_1698191970215.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("parlbert_topic_german","de")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("parlbert_topic_german","de") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|parlbert_topic_german| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|de| +|Size:|409.1 MB| + +## References + +https://huggingface.co/chkla/parlbert-topic-german \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-procedure_classification_bert_en.md b/docs/_posts/ahmedlone127/2023-10-24-procedure_classification_bert_en.md new file mode 100644 index 00000000000000..0e0e2be3bfb702 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-procedure_classification_bert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English procedure_classification_bert BertForSequenceClassification from cynthiachan +author: John Snow Labs +name: procedure_classification_bert +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`procedure_classification_bert` is a English model originally trained by cynthiachan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/procedure_classification_bert_en_5.1.4_3.4_1698186868680.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/procedure_classification_bert_en_5.1.4_3.4_1698186868680.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("procedure_classification_bert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("procedure_classification_bert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|procedure_classification_bert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/cynthiachan/procedure_classification_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-procedure_classification_distilbert_en.md b/docs/_posts/ahmedlone127/2023-10-24-procedure_classification_distilbert_en.md new file mode 100644 index 00000000000000..9c1b47777d0392 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-procedure_classification_distilbert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English procedure_classification_distilbert BertForSequenceClassification from cynthiachan +author: John Snow Labs +name: procedure_classification_distilbert +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`procedure_classification_distilbert` is a English model originally trained by cynthiachan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/procedure_classification_distilbert_en_5.1.4_3.4_1698186706098.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/procedure_classification_distilbert_en_5.1.4_3.4_1698186706098.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("procedure_classification_distilbert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("procedure_classification_distilbert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|procedure_classification_distilbert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.9 MB| + +## References + +https://huggingface.co/cynthiachan/procedure_classification_distilbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-qqp_bert_en.md b/docs/_posts/ahmedlone127/2023-10-24-qqp_bert_en.md new file mode 100644 index 00000000000000..90587103bd068e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-qqp_bert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English qqp_bert BertForSequenceClassification from AnonARR +author: John Snow Labs +name: qqp_bert +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`qqp_bert` is a English model originally trained by AnonARR. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/qqp_bert_en_5.1.4_3.4_1698187280591.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/qqp_bert_en_5.1.4_3.4_1698187280591.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("qqp_bert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("qqp_bert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|qqp_bert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/AnonARR/qqp-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-roberta_base_culinary_finetuned_en.md b/docs/_posts/ahmedlone127/2023-10-24-roberta_base_culinary_finetuned_en.md new file mode 100644 index 00000000000000..459f028bf75e99 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-roberta_base_culinary_finetuned_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English roberta_base_culinary_finetuned BertForSequenceClassification from juancavallotti +author: John Snow Labs +name: roberta_base_culinary_finetuned +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`roberta_base_culinary_finetuned` is a English model originally trained by juancavallotti. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_base_culinary_finetuned_en_5.1.4_3.4_1698190745728.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_base_culinary_finetuned_en_5.1.4_3.4_1698190745728.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("roberta_base_culinary_finetuned","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("roberta_base_culinary_finetuned","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_base_culinary_finetuned| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|408.8 MB| + +## References + +https://huggingface.co/juancavallotti/roberta-base-culinary-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-sentence_bert_base_uncased_finetuned_sentence_en.md b/docs/_posts/ahmedlone127/2023-10-24-sentence_bert_base_uncased_finetuned_sentence_en.md new file mode 100644 index 00000000000000..73e6996e1a959b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-sentence_bert_base_uncased_finetuned_sentence_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English sentence_bert_base_uncased_finetuned_sentence BertForSequenceClassification from ali2066 +author: John Snow Labs +name: sentence_bert_base_uncased_finetuned_sentence +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sentence_bert_base_uncased_finetuned_sentence` is a English model originally trained by ali2066. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sentence_bert_base_uncased_finetuned_sentence_en_5.1.4_3.4_1698190641727.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sentence_bert_base_uncased_finetuned_sentence_en_5.1.4_3.4_1698190641727.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("sentence_bert_base_uncased_finetuned_sentence","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("sentence_bert_base_uncased_finetuned_sentence","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sentence_bert_base_uncased_finetuned_sentence| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/ali2066/sentence_bert-base-uncased-finetuned-SENTENCE \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-seqcls_mrpc_bert_base_uncased_model_en.md b/docs/_posts/ahmedlone127/2023-10-24-seqcls_mrpc_bert_base_uncased_model_en.md new file mode 100644 index 00000000000000..2b1080f10ab0a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-seqcls_mrpc_bert_base_uncased_model_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English seqcls_mrpc_bert_base_uncased_model BertForSequenceClassification from chunwoolee0 +author: John Snow Labs +name: seqcls_mrpc_bert_base_uncased_model +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`seqcls_mrpc_bert_base_uncased_model` is a English model originally trained by chunwoolee0. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/seqcls_mrpc_bert_base_uncased_model_en_5.1.4_3.4_1698189530792.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/seqcls_mrpc_bert_base_uncased_model_en_5.1.4_3.4_1698189530792.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("seqcls_mrpc_bert_base_uncased_model","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("seqcls_mrpc_bert_base_uncased_model","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|seqcls_mrpc_bert_base_uncased_model| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/chunwoolee0/seqcls_mrpc_bert_base_uncased_model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-spanbert_base_cased_en.md b/docs/_posts/ahmedlone127/2023-10-24-spanbert_base_cased_en.md new file mode 100644 index 00000000000000..ede4dd6e53160e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-spanbert_base_cased_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English spanbert_base_cased BertForSequenceClassification from ahmetayrnc +author: John Snow Labs +name: spanbert_base_cased +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`spanbert_base_cased` is a English model originally trained by ahmetayrnc. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/spanbert_base_cased_en_5.1.4_3.4_1698190962914.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/spanbert_base_cased_en_5.1.4_3.4_1698190962914.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("spanbert_base_cased","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("spanbert_base_cased","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|spanbert_base_cased| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|395.1 MB| + +## References + +https://huggingface.co/ahmetayrnc/spanbert-base-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-tiny_bert_sst2_distilled_suryabhan_en.md b/docs/_posts/ahmedlone127/2023-10-24-tiny_bert_sst2_distilled_suryabhan_en.md new file mode 100644 index 00000000000000..d4bffb60f948bb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-tiny_bert_sst2_distilled_suryabhan_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_sst2_distilled_suryabhan BertForSequenceClassification from Suryabhan +author: John Snow Labs +name: tiny_bert_sst2_distilled_suryabhan +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_sst2_distilled_suryabhan` is a English model originally trained by Suryabhan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_sst2_distilled_suryabhan_en_5.1.4_3.4_1698191313464.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_sst2_distilled_suryabhan_en_5.1.4_3.4_1698191313464.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_sst2_distilled_suryabhan","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_sst2_distilled_suryabhan","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_sst2_distilled_suryabhan| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/Suryabhan/tiny-bert-sst2-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-24-waray_philippines_tiny_bert_en.md b/docs/_posts/ahmedlone127/2023-10-24-waray_philippines_tiny_bert_en.md new file mode 100644 index 00000000000000..65a47825006224 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-24-waray_philippines_tiny_bert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English waray_philippines_tiny_bert BertForSequenceClassification from lizaboiarchuk +author: John Snow Labs +name: waray_philippines_tiny_bert +date: 2023-10-24 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`waray_philippines_tiny_bert` is a English model originally trained by lizaboiarchuk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/waray_philippines_tiny_bert_en_5.1.4_3.4_1698191755543.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/waray_philippines_tiny_bert_en_5.1.4_3.4_1698191755543.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("waray_philippines_tiny_bert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("waray_philippines_tiny_bert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|waray_philippines_tiny_bert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|109.5 MB| + +## References + +https://huggingface.co/lizaboiarchuk/war-tiny-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-adept_bert_en.md b/docs/_posts/ahmedlone127/2023-10-25-adept_bert_en.md new file mode 100644 index 00000000000000..4e5538cf0663c8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-adept_bert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English adept_bert BertForSequenceClassification from veronica320 +author: John Snow Labs +name: adept_bert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`adept_bert` is a English model originally trained by veronica320. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/adept_bert_en_5.1.4_3.4_1698198567758.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/adept_bert_en_5.1.4_3.4_1698198567758.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("adept_bert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("adept_bert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|adept_bert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/veronica320/ADEPT_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-adept_bert_l_en.md b/docs/_posts/ahmedlone127/2023-10-25-adept_bert_l_en.md new file mode 100644 index 00000000000000..7570ffe0101a84 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-adept_bert_l_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English adept_bert_l BertForSequenceClassification from veronica320 +author: John Snow Labs +name: adept_bert_l +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`adept_bert_l` is a English model originally trained by veronica320. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/adept_bert_l_en_5.1.4_3.4_1698198952955.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/adept_bert_l_en_5.1.4_3.4_1698198952955.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("adept_bert_l","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("adept_bert_l","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|adept_bert_l| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/veronica320/ADEPT_bert-l \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-autotrain_bert_nlp_3450894022_en.md b/docs/_posts/ahmedlone127/2023-10-25-autotrain_bert_nlp_3450894022_en.md new file mode 100644 index 00000000000000..7ca4cfc5b5b69f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-autotrain_bert_nlp_3450894022_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English autotrain_bert_nlp_3450894022 BertForSequenceClassification from hanselgm +author: John Snow Labs +name: autotrain_bert_nlp_3450894022 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`autotrain_bert_nlp_3450894022` is a English model originally trained by hanselgm. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/autotrain_bert_nlp_3450894022_en_5.1.4_3.4_1698207136443.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/autotrain_bert_nlp_3450894022_en_5.1.4_3.4_1698207136443.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("autotrain_bert_nlp_3450894022","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("autotrain_bert_nlp_3450894022","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|autotrain_bert_nlp_3450894022| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/hanselgm/autotrain-bert-nlp-3450894022 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-backdoored_bert_finetuned_sst2_en.md b/docs/_posts/ahmedlone127/2023-10-25-backdoored_bert_finetuned_sst2_en.md new file mode 100644 index 00000000000000..e9aa17d4cb6602 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-backdoored_bert_finetuned_sst2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English backdoored_bert_finetuned_sst2 BertForSequenceClassification from grbagwe +author: John Snow Labs +name: backdoored_bert_finetuned_sst2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`backdoored_bert_finetuned_sst2` is a English model originally trained by grbagwe. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/backdoored_bert_finetuned_sst2_en_5.1.4_3.4_1698202142496.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/backdoored_bert_finetuned_sst2_en_5.1.4_3.4_1698202142496.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("backdoored_bert_finetuned_sst2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("backdoored_bert_finetuned_sst2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|backdoored_bert_finetuned_sst2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/grbagwe/backdoored_bert-finetuned-sst2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_clean_with_unclean_valid_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_clean_with_unclean_valid_en.md new file mode 100644 index 00000000000000..0dbeba1656cf73 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_clean_with_unclean_valid_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_asian_clean_with_unclean_valid BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_asian_clean_with_unclean_valid +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_asian_clean_with_unclean_valid` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_clean_with_unclean_valid_en_5.1.4_3.4_1698194312263.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_clean_with_unclean_valid_en_5.1.4_3.4_1698194312263.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_clean_with_unclean_valid","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_clean_with_unclean_valid","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_asian_clean_with_unclean_valid| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-asian-clean-with-unclean-valid \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_freeze_12_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_freeze_12_en.md new file mode 100644 index 00000000000000..1e898a08cd27ae --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_freeze_12_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_asian_unclean_freeze_12 BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_asian_unclean_freeze_12 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_asian_unclean_freeze_12` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_unclean_freeze_12_en_5.1.4_3.4_1698194504882.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_unclean_freeze_12_en_5.1.4_3.4_1698194504882.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_unclean_freeze_12","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_unclean_freeze_12","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_asian_unclean_freeze_12| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-12 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_freeze_4_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_freeze_4_en.md new file mode 100644 index 00000000000000..b8b33152de5343 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_freeze_4_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_asian_unclean_freeze_4 BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_asian_unclean_freeze_4 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_asian_unclean_freeze_4` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_unclean_freeze_4_en_5.1.4_3.4_1698194749054.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_unclean_freeze_4_en_5.1.4_3.4_1698194749054.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_unclean_freeze_4","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_unclean_freeze_4","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_asian_unclean_freeze_4| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_freeze_8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_freeze_8_en.md new file mode 100644 index 00000000000000..1cf67c3bb9674e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_freeze_8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_asian_unclean_freeze_8 BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_asian_unclean_freeze_8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_asian_unclean_freeze_8` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_unclean_freeze_8_en_5.1.4_3.4_1698194950560.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_unclean_freeze_8_en_5.1.4_3.4_1698194950560.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_unclean_freeze_8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_unclean_freeze_8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_asian_unclean_freeze_8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-8 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_slanted_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_slanted_en.md new file mode 100644 index 00000000000000..51d2b4b58d6e1f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_slanted_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_asian_unclean_slanted BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_asian_unclean_slanted +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_asian_unclean_slanted` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_unclean_slanted_en_5.1.4_3.4_1698195152306.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_unclean_slanted_en_5.1.4_3.4_1698195152306.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_unclean_slanted","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_unclean_slanted","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_asian_unclean_slanted| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-asian-unclean-slanted \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_warmup_100_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_warmup_100_en.md new file mode 100644 index 00000000000000..27a80a35a9cb64 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_warmup_100_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_asian_unclean_warmup_100 BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_asian_unclean_warmup_100 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_asian_unclean_warmup_100` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_unclean_warmup_100_en_5.1.4_3.4_1698195329133.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_unclean_warmup_100_en_5.1.4_3.4_1698195329133.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_unclean_warmup_100","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_unclean_warmup_100","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_asian_unclean_warmup_100| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-100 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_warmup_25_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_warmup_25_en.md new file mode 100644 index 00000000000000..cc1f5624144604 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_warmup_25_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_asian_unclean_warmup_25 BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_asian_unclean_warmup_25 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_asian_unclean_warmup_25` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_unclean_warmup_25_en_5.1.4_3.4_1698195519197.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_unclean_warmup_25_en_5.1.4_3.4_1698195519197.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_unclean_warmup_25","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_unclean_warmup_25","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_asian_unclean_warmup_25| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-25 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_warmup_50_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_warmup_50_en.md new file mode 100644 index 00000000000000..cd3ee8a625278c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_warmup_50_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_asian_unclean_warmup_50 BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_asian_unclean_warmup_50 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_asian_unclean_warmup_50` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_unclean_warmup_50_en_5.1.4_3.4_1698195712500.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_unclean_warmup_50_en_5.1.4_3.4_1698195712500.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_unclean_warmup_50","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_unclean_warmup_50","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_asian_unclean_warmup_50| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-50 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_warmup_75_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_warmup_75_en.md new file mode 100644 index 00000000000000..52abcfc9a23a56 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_warmup_75_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_asian_unclean_warmup_75 BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_asian_unclean_warmup_75 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_asian_unclean_warmup_75` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_unclean_warmup_75_en_5.1.4_3.4_1698195894104.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_unclean_warmup_75_en_5.1.4_3.4_1698195894104.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_unclean_warmup_75","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_unclean_warmup_75","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_asian_unclean_warmup_75| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-75 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_with_clean_valid_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_with_clean_valid_en.md new file mode 100644 index 00000000000000..33f48a40e38792 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asian_unclean_with_clean_valid_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_asian_unclean_with_clean_valid BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_asian_unclean_with_clean_valid +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_asian_unclean_with_clean_valid` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_unclean_with_clean_valid_en_5.1.4_3.4_1698196100057.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asian_unclean_with_clean_valid_en_5.1.4_3.4_1698196100057.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_unclean_with_clean_valid","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asian_unclean_with_clean_valid","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_asian_unclean_with_clean_valid| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-asian-unclean-with-clean-valid \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asonam_clean_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asonam_clean_en.md new file mode 100644 index 00000000000000..d9eea287cca618 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asonam_clean_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_asonam_clean BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_asonam_clean +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_asonam_clean` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asonam_clean_en_5.1.4_3.4_1698196311214.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asonam_clean_en_5.1.4_3.4_1698196311214.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asonam_clean","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asonam_clean","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_asonam_clean| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-asonam-clean \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asonam_unclean_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asonam_unclean_en.md new file mode 100644 index 00000000000000..c544c4e971340d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_asonam_unclean_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_asonam_unclean BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_asonam_unclean +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_asonam_unclean` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asonam_unclean_en_5.1.4_3.4_1698196487339.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_asonam_unclean_en_5.1.4_3.4_1698196487339.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asonam_unclean","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_asonam_unclean","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_asonam_unclean| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-asonam-unclean \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_concat_clean_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_concat_clean_en.md new file mode 100644 index 00000000000000..963151a86f9bad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_concat_clean_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_concat_clean BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_concat_clean +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_concat_clean` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_concat_clean_en_5.1.4_3.4_1698196932737.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_concat_clean_en_5.1.4_3.4_1698196932737.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_concat_clean","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_concat_clean","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_concat_clean| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-concat-clean \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_concat_clean_with_unclean_valid_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_concat_clean_with_unclean_valid_en.md new file mode 100644 index 00000000000000..c9db310a770e07 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_concat_clean_with_unclean_valid_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_concat_clean_with_unclean_valid BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_concat_clean_with_unclean_valid +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_concat_clean_with_unclean_valid` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_concat_clean_with_unclean_valid_en_5.1.4_3.4_1698196715996.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_concat_clean_with_unclean_valid_en_5.1.4_3.4_1698196715996.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_concat_clean_with_unclean_valid","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_concat_clean_with_unclean_valid","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_concat_clean_with_unclean_valid| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-concat-clean-with-unclean-valid \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_concat_unclean_discriminate_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_concat_unclean_discriminate_en.md new file mode 100644 index 00000000000000..21bddd882648ec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_concat_unclean_discriminate_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_concat_unclean_discriminate BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_concat_unclean_discriminate +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_concat_unclean_discriminate` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_concat_unclean_discriminate_en_5.1.4_3.4_1698197129523.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_concat_unclean_discriminate_en_5.1.4_3.4_1698197129523.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_concat_unclean_discriminate","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_concat_unclean_discriminate","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_concat_unclean_discriminate| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-concat-unclean-discriminate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_concat_unclean_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_concat_unclean_en.md new file mode 100644 index 00000000000000..7ed9a07bae010d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_concat_unclean_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_concat_unclean BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_concat_unclean +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_concat_unclean` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_concat_unclean_en_5.1.4_3.4_1698197549664.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_concat_unclean_en_5.1.4_3.4_1698197549664.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_concat_unclean","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_concat_unclean","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_concat_unclean| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-concat-unclean \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_concat_unclean_with_clean_valid_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_concat_unclean_with_clean_valid_en.md new file mode 100644 index 00000000000000..e6ec588d713cc7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_concat_unclean_with_clean_valid_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_concat_unclean_with_clean_valid BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_concat_unclean_with_clean_valid +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_concat_unclean_with_clean_valid` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_concat_unclean_with_clean_valid_en_5.1.4_3.4_1698197352114.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_concat_unclean_with_clean_valid_en_5.1.4_3.4_1698197352114.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_concat_unclean_with_clean_valid","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_concat_unclean_with_clean_valid","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_concat_unclean_with_clean_valid| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-concat-unclean-with-clean-valid \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_en.md new file mode 100644 index 00000000000000..f0a3a256941894 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_self_clean BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_self_clean +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_self_clean` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_en_5.1.4_3.4_1698199798192.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_en_5.1.4_3.4_1698199798192.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_self_clean| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-self-clean \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_discriminate_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_discriminate_en.md new file mode 100644 index 00000000000000..f3598c25edf591 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_discriminate_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_self_clean_small_discriminate BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_self_clean_small_discriminate +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_self_clean_small_discriminate` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_small_discriminate_en_5.1.4_3.4_1698197737729.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_small_discriminate_en_5.1.4_3.4_1698197737729.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_small_discriminate","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_small_discriminate","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_self_clean_small_discriminate| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-self-clean-small-discriminate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_en.md new file mode 100644 index 00000000000000..11234c2008dc85 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_self_clean_small BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_self_clean_small +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_self_clean_small` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_small_en_5.1.4_3.4_1698199418472.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_small_en_5.1.4_3.4_1698199418472.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_small","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_small","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_self_clean_small| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-self-clean-small \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_epoch5_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_epoch5_en.md new file mode 100644 index 00000000000000..b742b5a6037449 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_epoch5_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_self_clean_small_epoch5 BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_self_clean_small_epoch5 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_self_clean_small_epoch5` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_small_epoch5_en_5.1.4_3.4_1698198383845.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_small_epoch5_en_5.1.4_3.4_1698198383845.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_small_epoch5","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_small_epoch5","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_self_clean_small_epoch5| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-self-clean-small-epoch5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_epoch5_freeze4_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_epoch5_freeze4_en.md new file mode 100644 index 00000000000000..69b4e8d41b29ff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_epoch5_freeze4_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_self_clean_small_epoch5_freeze4 BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_self_clean_small_epoch5_freeze4 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_self_clean_small_epoch5_freeze4` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_small_epoch5_freeze4_en_5.1.4_3.4_1698197938358.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_small_epoch5_freeze4_en_5.1.4_3.4_1698197938358.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_small_epoch5_freeze4","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_small_epoch5_freeze4","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_self_clean_small_epoch5_freeze4| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-self-clean-small-epoch5-freeze4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_epoch5_warmup_50_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_epoch5_warmup_50_en.md new file mode 100644 index 00000000000000..f2ac8262b57672 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_epoch5_warmup_50_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_self_clean_small_epoch5_warmup_50 BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_self_clean_small_epoch5_warmup_50 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_self_clean_small_epoch5_warmup_50` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_small_epoch5_warmup_50_en_5.1.4_3.4_1698198162466.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_small_epoch5_warmup_50_en_5.1.4_3.4_1698198162466.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_small_epoch5_warmup_50","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_small_epoch5_warmup_50","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_self_clean_small_epoch5_warmup_50| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-self-clean-small-epoch5-warmup-50 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_epoch6_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_epoch6_en.md new file mode 100644 index 00000000000000..65451f5e01d301 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_epoch6_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_self_clean_small_epoch6 BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_self_clean_small_epoch6 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_self_clean_small_epoch6` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_small_epoch6_en_5.1.4_3.4_1698198646713.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_small_epoch6_en_5.1.4_3.4_1698198646713.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_small_epoch6","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_small_epoch6","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_self_clean_small_epoch6| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-self-clean-small-epoch6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_more_epoch_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_more_epoch_en.md new file mode 100644 index 00000000000000..59fa1bd361377c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_more_epoch_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_self_clean_small_more_epoch BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_self_clean_small_more_epoch +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_self_clean_small_more_epoch` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_small_more_epoch_en_5.1.4_3.4_1698198851223.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_small_more_epoch_en_5.1.4_3.4_1698198851223.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_small_more_epoch","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_small_more_epoch","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_self_clean_small_more_epoch| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-self-clean-small-more-epoch \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_warmup_100_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_warmup_100_en.md new file mode 100644 index 00000000000000..b441f7b34d02fe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_warmup_100_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_self_clean_small_warmup_100 BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_self_clean_small_warmup_100 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_self_clean_small_warmup_100` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_small_warmup_100_en_5.1.4_3.4_1698199020222.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_small_warmup_100_en_5.1.4_3.4_1698199020222.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_small_warmup_100","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_small_warmup_100","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_self_clean_small_warmup_100| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-self-clean-small-warmup-100 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_warmup_50_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_warmup_50_en.md new file mode 100644 index 00000000000000..75ac8fbe5ac44c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_small_warmup_50_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_self_clean_small_warmup_50 BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_self_clean_small_warmup_50 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_self_clean_small_warmup_50` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_small_warmup_50_en_5.1.4_3.4_1698199205772.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_small_warmup_50_en_5.1.4_3.4_1698199205772.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_small_warmup_50","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_small_warmup_50","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_self_clean_small_warmup_50| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-self-clean-small-warmup-50 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_with_unclean_valid_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_with_unclean_valid_en.md new file mode 100644 index 00000000000000..2a2fb70e48a605 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_clean_with_unclean_valid_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_self_clean_with_unclean_valid BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_self_clean_with_unclean_valid +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_self_clean_with_unclean_valid` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_with_unclean_valid_en_5.1.4_3.4_1698199619498.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_clean_with_unclean_valid_en_5.1.4_3.4_1698199619498.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_with_unclean_valid","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_clean_with_unclean_valid","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_self_clean_with_unclean_valid| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-self-clean-with-unclean-valid \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_unclean_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_unclean_en.md new file mode 100644 index 00000000000000..cd7b05cf5b5a7f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_unclean_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_self_unclean BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_self_unclean +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_self_unclean` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_unclean_en_5.1.4_3.4_1698200718265.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_unclean_en_5.1.4_3.4_1698200718265.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_unclean","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_unclean","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_self_unclean| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-self-unclean \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_unclean_freeze_12_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_unclean_freeze_12_en.md new file mode 100644 index 00000000000000..95a93da85893b6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_unclean_freeze_12_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_self_unclean_freeze_12 BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_self_unclean_freeze_12 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_self_unclean_freeze_12` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_unclean_freeze_12_en_5.1.4_3.4_1698199993184.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_unclean_freeze_12_en_5.1.4_3.4_1698199993184.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_unclean_freeze_12","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_unclean_freeze_12","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_self_unclean_freeze_12| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-self-unclean-freeze-12 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_unclean_freeze_4_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_unclean_freeze_4_en.md new file mode 100644 index 00000000000000..c2c5eb4f09387c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_unclean_freeze_4_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_self_unclean_freeze_4 BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_self_unclean_freeze_4 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_self_unclean_freeze_4` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_unclean_freeze_4_en_5.1.4_3.4_1698200174510.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_unclean_freeze_4_en_5.1.4_3.4_1698200174510.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_unclean_freeze_4","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_unclean_freeze_4","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_self_unclean_freeze_4| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-self-unclean-freeze-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_unclean_freeze_8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_unclean_freeze_8_en.md new file mode 100644 index 00000000000000..c2073cbd764639 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_unclean_freeze_8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_self_unclean_freeze_8 BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_self_unclean_freeze_8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_self_unclean_freeze_8` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_unclean_freeze_8_en_5.1.4_3.4_1698200377794.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_unclean_freeze_8_en_5.1.4_3.4_1698200377794.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_unclean_freeze_8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_unclean_freeze_8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_self_unclean_freeze_8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-self-unclean-freeze-8 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_unclean_small_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_unclean_small_en.md new file mode 100644 index 00000000000000..6fd09e1abfb0f6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_unclean_small_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_self_unclean_small BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_self_unclean_small +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_self_unclean_small` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_unclean_small_en_5.1.4_3.4_1698200557344.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_unclean_small_en_5.1.4_3.4_1698200557344.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_unclean_small","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_unclean_small","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_self_unclean_small| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-self-unclean-small \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_unlean_with_clean_valid_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_unlean_with_clean_valid_en.md new file mode 100644 index 00000000000000..51f5fa8ac57d3a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_asian_hate_tweets_self_unlean_with_clean_valid_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_asian_hate_tweets_self_unlean_with_clean_valid BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_asian_hate_tweets_self_unlean_with_clean_valid +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_asian_hate_tweets_self_unlean_with_clean_valid` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_unlean_with_clean_valid_en_5.1.4_3.4_1698200938581.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_asian_hate_tweets_self_unlean_with_clean_valid_en_5.1.4_3.4_1698200938581.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_unlean_with_clean_valid","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_asian_hate_tweets_self_unlean_with_clean_valid","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_asian_hate_tweets_self_unlean_with_clean_valid| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-asian-hate-tweets-self-unlean-with-clean-valid \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv2_finetuned_emotion_33_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv2_finetuned_emotion_33_en.md new file mode 100644 index 00000000000000..518f157f61b853 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv2_finetuned_emotion_33_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_arabertv2_finetuned_emotion_33 BertForSequenceClassification from MahaJar +author: John Snow Labs +name: bert_base_arabertv2_finetuned_emotion_33 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabertv2_finetuned_emotion_33` is a English model originally trained by MahaJar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabertv2_finetuned_emotion_33_en_5.1.4_3.4_1698202367716.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabertv2_finetuned_emotion_33_en_5.1.4_3.4_1698202367716.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabertv2_finetuned_emotion_33","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabertv2_finetuned_emotion_33","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabertv2_finetuned_emotion_33| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|507.1 MB| + +## References + +https://huggingface.co/MahaJar/bert-base-arabertv2-finetuned-emotion_33 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv2_finetuned_emotion_3_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv2_finetuned_emotion_3_en.md new file mode 100644 index 00000000000000..e916b5a1bbb612 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv2_finetuned_emotion_3_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_arabertv2_finetuned_emotion_3 BertForSequenceClassification from MahaJar +author: John Snow Labs +name: bert_base_arabertv2_finetuned_emotion_3 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabertv2_finetuned_emotion_3` is a English model originally trained by MahaJar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabertv2_finetuned_emotion_3_en_5.1.4_3.4_1698202581940.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabertv2_finetuned_emotion_3_en_5.1.4_3.4_1698202581940.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabertv2_finetuned_emotion_3","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabertv2_finetuned_emotion_3","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabertv2_finetuned_emotion_3| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|507.1 MB| + +## References + +https://huggingface.co/MahaJar/bert-base-arabertv2-finetuned-emotion_3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_banking77_pt2_laurie_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_banking77_pt2_laurie_en.md new file mode 100644 index 00000000000000..d4ac1250b3b35c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_banking77_pt2_laurie_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_banking77_pt2_laurie BertForSequenceClassification from Laurie +author: John Snow Labs +name: bert_base_banking77_pt2_laurie +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_banking77_pt2_laurie` is a English model originally trained by Laurie. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_banking77_pt2_laurie_en_5.1.4_3.4_1698207333851.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_banking77_pt2_laurie_en_5.1.4_3.4_1698207333851.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_banking77_pt2_laurie","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_banking77_pt2_laurie","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_banking77_pt2_laurie| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.6 MB| + +## References + +https://huggingface.co/Laurie/bert-base-banking77-pt2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_finetuned_textclassification_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_finetuned_textclassification_en.md new file mode 100644 index 00000000000000..2ff305bb7cff66 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_finetuned_textclassification_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_cased_finetuned_textclassification BertForSequenceClassification from Kekelilii +author: John Snow Labs +name: bert_base_cased_finetuned_textclassification +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_textclassification` is a English model originally trained by Kekelilii. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_textclassification_en_5.1.4_3.4_1698196500566.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_textclassification_en_5.1.4_3.4_1698196500566.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_finetuned_textclassification","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_finetuned_textclassification","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_textclassification| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/Kekelilii/bert-base-cased_finetuned_TextClassification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_manifesto_2018_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_manifesto_2018_en.md new file mode 100644 index 00000000000000..6c7c333eaa5a78 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_manifesto_2018_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_cased_manifesto_2018 BertForSequenceClassification from assenmacher +author: John Snow Labs +name: bert_base_cased_manifesto_2018 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_manifesto_2018` is a English model originally trained by assenmacher. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_manifesto_2018_en_5.1.4_3.4_1698208505640.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_manifesto_2018_en_5.1.4_3.4_1698208505640.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_manifesto_2018","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_manifesto_2018","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_manifesto_2018| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/assenmacher/bert-base-cased-manifesto-2018 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_sentweet_derogatory_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_sentweet_derogatory_en.md new file mode 100644 index 00000000000000..cd24c2d33466c2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_sentweet_derogatory_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_cased_sentweet_derogatory BertForSequenceClassification from jayanta +author: John Snow Labs +name: bert_base_cased_sentweet_derogatory +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_sentweet_derogatory` is a English model originally trained by jayanta. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_sentweet_derogatory_en_5.1.4_3.4_1698195118702.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_sentweet_derogatory_en_5.1.4_3.4_1698195118702.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_sentweet_derogatory","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_sentweet_derogatory","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_sentweet_derogatory| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/jayanta/bert-base-cased-sentweet-derogatory \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_sentweet_hatespeech_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_sentweet_hatespeech_en.md new file mode 100644 index 00000000000000..3dad57b6b6dc59 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_sentweet_hatespeech_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_cased_sentweet_hatespeech BertForSequenceClassification from jayanta +author: John Snow Labs +name: bert_base_cased_sentweet_hatespeech +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_sentweet_hatespeech` is a English model originally trained by jayanta. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_sentweet_hatespeech_en_5.1.4_3.4_1698195304982.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_sentweet_hatespeech_en_5.1.4_3.4_1698195304982.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_sentweet_hatespeech","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_sentweet_hatespeech","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_sentweet_hatespeech| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/jayanta/bert-base-cased-sentweet-hatespeech \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_sentweet_profane_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_sentweet_profane_en.md new file mode 100644 index 00000000000000..8e65d2e0a03974 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_sentweet_profane_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_cased_sentweet_profane BertForSequenceClassification from jayanta +author: John Snow Labs +name: bert_base_cased_sentweet_profane +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_sentweet_profane` is a English model originally trained by jayanta. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_sentweet_profane_en_5.1.4_3.4_1698195490757.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_sentweet_profane_en_5.1.4_3.4_1698195490757.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_sentweet_profane","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_sentweet_profane","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_sentweet_profane| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/jayanta/bert-base-cased-sentweet-profane \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_sentweet_targetedinsult_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_sentweet_targetedinsult_en.md new file mode 100644 index 00000000000000..2570229e629f04 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_sentweet_targetedinsult_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_cased_sentweet_targetedinsult BertForSequenceClassification from jayanta +author: John Snow Labs +name: bert_base_cased_sentweet_targetedinsult +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_sentweet_targetedinsult` is a English model originally trained by jayanta. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_sentweet_targetedinsult_en_5.1.4_3.4_1698195678583.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_sentweet_targetedinsult_en_5.1.4_3.4_1698195678583.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_sentweet_targetedinsult","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_sentweet_targetedinsult","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_sentweet_targetedinsult| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/jayanta/bert-base-cased-sentweet-targetedinsult \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1_zh.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1_zh.md new file mode 100644 index 00000000000000..bc4f8fc535c843 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1_zh.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Chinese bert_base_chinese_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: bert_base_chinese_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1 +date: 2023-10-25 +tags: [bert, zh, open_source, sequence_classification, onnx] +task: Text Classification +language: zh +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1` is a Chinese model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1_zh_5.1.4_3.4_1698214104165.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1_zh_5.1.4_3.4_1698214104165.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1","zh")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1","zh") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|zh| +|Size:|383.3 MB| + +## References + +https://huggingface.co/hw2942/bert-base-chinese-finetuning-wallstreetcn-morning-news-market-overview-open-000001SH-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_finetuned_emotion_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_finetuned_emotion_en.md new file mode 100644 index 00000000000000..6615f7980ee04e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_finetuned_emotion_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_finetuned_emotion BertForSequenceClassification from Woonn +author: John Snow Labs +name: bert_base_finetuned_emotion +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_finetuned_emotion` is a English model originally trained by Woonn. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_finetuned_emotion_en_5.1.4_3.4_1698208768773.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_finetuned_emotion_en_5.1.4_3.4_1698208768773.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_finetuned_emotion","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_finetuned_emotion","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_finetuned_emotion| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.6 MB| + +## References + +https://huggingface.co/Woonn/bert-base-finetuned-emotion \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_finetuned_nli_jihyun22_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_finetuned_nli_jihyun22_en.md new file mode 100644 index 00000000000000..a381edc0ea8b95 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_finetuned_nli_jihyun22_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_finetuned_nli_jihyun22 BertForSequenceClassification from Jihyun22 +author: John Snow Labs +name: bert_base_finetuned_nli_jihyun22 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_finetuned_nli_jihyun22` is a English model originally trained by Jihyun22. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_finetuned_nli_jihyun22_en_5.1.4_3.4_1698219140262.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_finetuned_nli_jihyun22_en_5.1.4_3.4_1698219140262.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_finetuned_nli_jihyun22","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_finetuned_nli_jihyun22","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_finetuned_nli_jihyun22| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.6 MB| + +## References + +https://huggingface.co/Jihyun22/bert-base-finetuned-nli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_finetuned_nli_jiwon_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_finetuned_nli_jiwon_en.md new file mode 100644 index 00000000000000..a61b616216a0de --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_finetuned_nli_jiwon_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_finetuned_nli_jiwon BertForSequenceClassification from JIWON +author: John Snow Labs +name: bert_base_finetuned_nli_jiwon +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_finetuned_nli_jiwon` is a English model originally trained by JIWON. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_finetuned_nli_jiwon_en_5.1.4_3.4_1698212168796.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_finetuned_nli_jiwon_en_5.1.4_3.4_1698212168796.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_finetuned_nli_jiwon","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_finetuned_nli_jiwon","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_finetuned_nli_jiwon| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.6 MB| + +## References + +https://huggingface.co/JIWON/bert-base-finetuned-nli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_finetuned_nsmc_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_finetuned_nsmc_en.md new file mode 100644 index 00000000000000..2347d6ac9c7c6a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_finetuned_nsmc_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_finetuned_nsmc BertForSequenceClassification from sunwooooong +author: John Snow Labs +name: bert_base_finetuned_nsmc +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_finetuned_nsmc` is a English model originally trained by sunwooooong. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_finetuned_nsmc_en_5.1.4_3.4_1698193667405.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_finetuned_nsmc_en_5.1.4_3.4_1698193667405.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_finetuned_nsmc","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_finetuned_nsmc","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_finetuned_nsmc| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.6 MB| + +## References + +https://huggingface.co/sunwooooong/bert-base-finetuned-nsmc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_finetuned_ynat_min9805_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_finetuned_ynat_min9805_en.md new file mode 100644 index 00000000000000..1204f5fa94e3e4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_finetuned_ynat_min9805_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_finetuned_ynat_min9805 BertForSequenceClassification from min9805 +author: John Snow Labs +name: bert_base_finetuned_ynat_min9805 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_finetuned_ynat_min9805` is a English model originally trained by min9805. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_finetuned_ynat_min9805_en_5.1.4_3.4_1698192188964.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_finetuned_ynat_min9805_en_5.1.4_3.4_1698192188964.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_finetuned_ynat_min9805","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_finetuned_ynat_min9805","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_finetuned_ynat_min9805| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.6 MB| + +## References + +https://huggingface.co/min9805/bert-base-finetuned-ynat \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_finetuned_ynat_yooonsangbeom_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_finetuned_ynat_yooonsangbeom_en.md new file mode 100644 index 00000000000000..3d67f052196ac7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_finetuned_ynat_yooonsangbeom_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_finetuned_ynat_yooonsangbeom BertForSequenceClassification from yooonsangbeom +author: John Snow Labs +name: bert_base_finetuned_ynat_yooonsangbeom +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_finetuned_ynat_yooonsangbeom` is a English model originally trained by yooonsangbeom. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_finetuned_ynat_yooonsangbeom_en_5.1.4_3.4_1698200316362.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_finetuned_ynat_yooonsangbeom_en_5.1.4_3.4_1698200316362.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_finetuned_ynat_yooonsangbeom","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_finetuned_ynat_yooonsangbeom","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_finetuned_ynat_yooonsangbeom| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.6 MB| + +## References + +https://huggingface.co/yooonsangbeom/bert-base-finetuned-ynat \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_goemotions_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_goemotions_en.md new file mode 100644 index 00000000000000..2e76713c21d194 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_goemotions_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_goemotions BertForSequenceClassification from IsaacZhy +author: John Snow Labs +name: bert_base_goemotions +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_goemotions` is a English model originally trained by IsaacZhy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_goemotions_en_5.1.4_3.4_1698194922222.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_goemotions_en_5.1.4_3.4_1698194922222.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_goemotions","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_goemotions","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_goemotions| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/IsaacZhy/bert-base-goemotions \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_indonesian_522m_finetuned_sentiment_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_indonesian_522m_finetuned_sentiment_en.md new file mode 100644 index 00000000000000..e05636cd292c63 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_indonesian_522m_finetuned_sentiment_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_indonesian_522m_finetuned_sentiment BertForSequenceClassification from poerwiyanto +author: John Snow Labs +name: bert_base_indonesian_522m_finetuned_sentiment +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_indonesian_522m_finetuned_sentiment` is a English model originally trained by poerwiyanto. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_indonesian_522m_finetuned_sentiment_en_5.1.4_3.4_1698209011985.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_indonesian_522m_finetuned_sentiment_en_5.1.4_3.4_1698209011985.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_indonesian_522m_finetuned_sentiment","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_indonesian_522m_finetuned_sentiment","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_indonesian_522m_finetuned_sentiment| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.8 MB| + +## References + +https://huggingface.co/poerwiyanto/bert-base-indonesian-522M-finetuned-sentiment \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_multilingual_cased_finetuned_review_xx.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_multilingual_cased_finetuned_review_xx.md new file mode 100644 index 00000000000000..b6973957d233db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_multilingual_cased_finetuned_review_xx.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_cased_finetuned_review BertForSequenceClassification from seokwoni +author: John Snow Labs +name: bert_base_multilingual_cased_finetuned_review +date: 2023-10-25 +tags: [bert, xx, open_source, sequence_classification, onnx] +task: Text Classification +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_cased_finetuned_review` is a Multilingual model originally trained by seokwoni. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_review_xx_5.1.4_3.4_1698217463254.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_finetuned_review_xx_5.1.4_3.4_1698217463254.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_multilingual_cased_finetuned_review","xx")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_multilingual_cased_finetuned_review","xx") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_cased_finetuned_review| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|xx| +|Size:|667.3 MB| + +## References + +https://huggingface.co/seokwoni/bert-base-multilingual-cased-finetuned-review \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_multilingual_uncased_finetuned_news_xx.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_multilingual_uncased_finetuned_news_xx.md new file mode 100644 index 00000000000000..a3343f3a2da4e4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_multilingual_uncased_finetuned_news_xx.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_uncased_finetuned_news BertForSequenceClassification from Tiamz +author: John Snow Labs +name: bert_base_multilingual_uncased_finetuned_news +date: 2023-10-25 +tags: [bert, xx, open_source, sequence_classification, onnx] +task: Text Classification +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_uncased_finetuned_news` is a Multilingual model originally trained by Tiamz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_uncased_finetuned_news_xx_5.1.4_3.4_1698193892063.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_uncased_finetuned_news_xx_5.1.4_3.4_1698193892063.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_multilingual_uncased_finetuned_news","xx")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_multilingual_uncased_finetuned_news","xx") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_uncased_finetuned_news| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|xx| +|Size:|627.7 MB| + +## References + +https://huggingface.co/Tiamz/bert-base-multilingual-uncased-finetuned-news \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_multilingual_uncased_vietnamese_sentiment_analysis_xx.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_multilingual_uncased_vietnamese_sentiment_analysis_xx.md new file mode 100644 index 00000000000000..79da92f95f576b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_multilingual_uncased_vietnamese_sentiment_analysis_xx.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_uncased_vietnamese_sentiment_analysis BertForSequenceClassification from TankuVie +author: John Snow Labs +name: bert_base_multilingual_uncased_vietnamese_sentiment_analysis +date: 2023-10-25 +tags: [bert, xx, open_source, sequence_classification, onnx] +task: Text Classification +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_uncased_vietnamese_sentiment_analysis` is a Multilingual model originally trained by TankuVie. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_uncased_vietnamese_sentiment_analysis_xx_5.1.4_3.4_1698204011669.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_uncased_vietnamese_sentiment_analysis_xx_5.1.4_3.4_1698204011669.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_multilingual_uncased_vietnamese_sentiment_analysis","xx")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_multilingual_uncased_vietnamese_sentiment_analysis","xx") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_uncased_vietnamese_sentiment_analysis| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|xx| +|Size:|627.7 MB| + +## References + +https://huggingface.co/TankuVie/bert-base-multilingual-uncased-vietnamese_sentiment_analysis \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_spanish_wwm_cased_finetuned_nlp_ie_4_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_spanish_wwm_cased_finetuned_nlp_ie_4_en.md new file mode 100644 index 00000000000000..0436869be0096f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_spanish_wwm_cased_finetuned_nlp_ie_4_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_spanish_wwm_cased_finetuned_nlp_ie_4 BertForSequenceClassification from Willy +author: John Snow Labs +name: bert_base_spanish_wwm_cased_finetuned_nlp_ie_4 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_spanish_wwm_cased_finetuned_nlp_ie_4` is a English model originally trained by Willy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_nlp_ie_4_en_5.1.4_3.4_1698192192250.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_cased_finetuned_nlp_ie_4_en_5.1.4_3.4_1698192192250.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_spanish_wwm_cased_finetuned_nlp_ie_4","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_spanish_wwm_cased_finetuned_nlp_ie_4","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_spanish_wwm_cased_finetuned_nlp_ie_4| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.7 MB| + +## References + +https://huggingface.co/Willy/bert-base-spanish-wwm-cased-finetuned-NLP-IE-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_spanish_wwm_uncased_finetuned_pawsx_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_spanish_wwm_uncased_finetuned_pawsx_en.md new file mode 100644 index 00000000000000..00a2d33da091db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_spanish_wwm_uncased_finetuned_pawsx_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_spanish_wwm_uncased_finetuned_pawsx BertForSequenceClassification from dccuchile +author: John Snow Labs +name: bert_base_spanish_wwm_uncased_finetuned_pawsx +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_spanish_wwm_uncased_finetuned_pawsx` is a English model originally trained by dccuchile. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_uncased_finetuned_pawsx_en_5.1.4_3.4_1698192063016.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_uncased_finetuned_pawsx_en_5.1.4_3.4_1698192063016.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_spanish_wwm_uncased_finetuned_pawsx","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_spanish_wwm_uncased_finetuned_pawsx","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_spanish_wwm_uncased_finetuned_pawsx| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.9 MB| + +## References + +https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased-finetuned-pawsx \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_spanish_wwm_uncased_finetuned_xnli_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_spanish_wwm_uncased_finetuned_xnli_en.md new file mode 100644 index 00000000000000..f1aa1b66edca3b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_spanish_wwm_uncased_finetuned_xnli_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_spanish_wwm_uncased_finetuned_xnli BertForSequenceClassification from dccuchile +author: John Snow Labs +name: bert_base_spanish_wwm_uncased_finetuned_xnli +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_spanish_wwm_uncased_finetuned_xnli` is a English model originally trained by dccuchile. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_uncased_finetuned_xnli_en_5.1.4_3.4_1698192235864.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_spanish_wwm_uncased_finetuned_xnli_en_5.1.4_3.4_1698192235864.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_spanish_wwm_uncased_finetuned_xnli","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_spanish_wwm_uncased_finetuned_xnli","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_spanish_wwm_uncased_finetuned_xnli| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.9 MB| + +## References + +https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased-finetuned-xnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_cola_jeremiahz_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_cola_jeremiahz_en.md new file mode 100644 index 00000000000000..5b128e9f296c15 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_cola_jeremiahz_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_cola_jeremiahz BertForSequenceClassification from JeremiahZ +author: John Snow Labs +name: bert_base_uncased_cola_jeremiahz +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_cola_jeremiahz` is a English model originally trained by JeremiahZ. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_cola_jeremiahz_en_5.1.4_3.4_1698193095882.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_cola_jeremiahz_en_5.1.4_3.4_1698193095882.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_cola_jeremiahz","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_cola_jeremiahz","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_cola_jeremiahz| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/JeremiahZ/bert-base-uncased-cola \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finance_sentiment_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finance_sentiment_en.md new file mode 100644 index 00000000000000..c8d758f12233f9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finance_sentiment_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finance_sentiment BertForSequenceClassification from nickwong64 +author: John Snow Labs +name: bert_base_uncased_finance_sentiment +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finance_sentiment` is a English model originally trained by nickwong64. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finance_sentiment_en_5.1.4_3.4_1698199792868.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finance_sentiment_en_5.1.4_3.4_1698199792868.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finance_sentiment","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finance_sentiment","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finance_sentiment| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/nickwong64/bert-base-uncased-finance-sentiment \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned1_claqua_cqa_entity_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned1_claqua_cqa_entity_en.md new file mode 100644 index 00000000000000..53478598f8871b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned1_claqua_cqa_entity_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned1_claqua_cqa_entity BertForSequenceClassification from sasuke +author: John Snow Labs +name: bert_base_uncased_finetuned1_claqua_cqa_entity +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned1_claqua_cqa_entity` is a English model originally trained by sasuke. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned1_claqua_cqa_entity_en_5.1.4_3.4_1698212967435.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned1_claqua_cqa_entity_en_5.1.4_3.4_1698212967435.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned1_claqua_cqa_entity","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned1_claqua_cqa_entity","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned1_claqua_cqa_entity| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/sasuke/bert-base-uncased-finetuned1-claqua_cqa_entity \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_0505_2_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_0505_2_en.md new file mode 100644 index 00000000000000..d464a33428cbe2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_0505_2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_0505_2 BertForSequenceClassification from YeRyeongLee +author: John Snow Labs +name: bert_base_uncased_finetuned_0505_2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_0505_2` is a English model originally trained by YeRyeongLee. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_0505_2_en_5.1.4_3.4_1698201594219.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_0505_2_en_5.1.4_3.4_1698201594219.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_0505_2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_0505_2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_0505_2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/YeRyeongLee/bert-base-uncased-finetuned-0505-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_claqua_cqa_predicate_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_claqua_cqa_predicate_en.md new file mode 100644 index 00000000000000..f88888d93dd572 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_claqua_cqa_predicate_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_claqua_cqa_predicate BertForSequenceClassification from sasuke +author: John Snow Labs +name: bert_base_uncased_finetuned_claqua_cqa_predicate +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_claqua_cqa_predicate` is a English model originally trained by sasuke. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_claqua_cqa_predicate_en_5.1.4_3.4_1698212787474.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_claqua_cqa_predicate_en_5.1.4_3.4_1698212787474.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_claqua_cqa_predicate","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_claqua_cqa_predicate","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_claqua_cqa_predicate| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/sasuke/bert-base-uncased-finetuned-claqua_cqa_predicate \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_claqua_cqi_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_claqua_cqi_en.md new file mode 100644 index 00000000000000..f7a32d4bfc8a66 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_claqua_cqi_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_claqua_cqi BertForSequenceClassification from sasuke +author: John Snow Labs +name: bert_base_uncased_finetuned_claqua_cqi +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_claqua_cqi` is a English model originally trained by sasuke. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_claqua_cqi_en_5.1.4_3.4_1698212427493.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_claqua_cqi_en_5.1.4_3.4_1698212427493.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_claqua_cqi","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_claqua_cqi","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_claqua_cqi| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/sasuke/bert-base-uncased-finetuned-claqua_cqi \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_cola_zhsj16_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_cola_zhsj16_en.md new file mode 100644 index 00000000000000..64da5a96fb748d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_cola_zhsj16_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_cola_zhsj16 BertForSequenceClassification from zhsj16 +author: John Snow Labs +name: bert_base_uncased_finetuned_cola_zhsj16 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_cola_zhsj16` is a English model originally trained by zhsj16. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_cola_zhsj16_en_5.1.4_3.4_1698198384959.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_cola_zhsj16_en_5.1.4_3.4_1698198384959.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_cola_zhsj16","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_cola_zhsj16","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_cola_zhsj16| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/zhsj16/bert-base-uncased-finetuned-cola \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_effectiveness_dagstuhl_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_effectiveness_dagstuhl_en.md new file mode 100644 index 00000000000000..546e80797ba437 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_effectiveness_dagstuhl_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_effectiveness_dagstuhl BertForSequenceClassification from jakub014 +author: John Snow Labs +name: bert_base_uncased_finetuned_effectiveness_dagstuhl +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_effectiveness_dagstuhl` is a English model originally trained by jakub014. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_effectiveness_dagstuhl_en_5.1.4_3.4_1698199183322.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_effectiveness_dagstuhl_en_5.1.4_3.4_1698199183322.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_effectiveness_dagstuhl","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_effectiveness_dagstuhl","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_effectiveness_dagstuhl| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/jakub014/bert-base-uncased-finetuned-effectiveness-dagstuhl \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_effectiveness_redditcmv_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_effectiveness_redditcmv_en.md new file mode 100644 index 00000000000000..959b8485fb9c5e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_effectiveness_redditcmv_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_effectiveness_redditcmv BertForSequenceClassification from jakub014 +author: John Snow Labs +name: bert_base_uncased_finetuned_effectiveness_redditcmv +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_effectiveness_redditcmv` is a English model originally trained by jakub014. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_effectiveness_redditcmv_en_5.1.4_3.4_1698193029636.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_effectiveness_redditcmv_en_5.1.4_3.4_1698193029636.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_effectiveness_redditcmv","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_effectiveness_redditcmv","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_effectiveness_redditcmv| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/jakub014/bert-base-uncased-finetuned-effectiveness-redditCMV \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_for_sentiment_analysis1_sst2_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_for_sentiment_analysis1_sst2_en.md new file mode 100644 index 00000000000000..6d2a4ee5ca9200 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_for_sentiment_analysis1_sst2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_for_sentiment_analysis1_sst2 BertForSequenceClassification from Ghost1 +author: John Snow Labs +name: bert_base_uncased_finetuned_for_sentiment_analysis1_sst2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_for_sentiment_analysis1_sst2` is a English model originally trained by Ghost1. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_for_sentiment_analysis1_sst2_en_5.1.4_3.4_1698202182930.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_for_sentiment_analysis1_sst2_en_5.1.4_3.4_1698202182930.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_for_sentiment_analysis1_sst2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_for_sentiment_analysis1_sst2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_for_sentiment_analysis1_sst2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Ghost1/bert-base-uncased-finetuned_for_sentiment_analysis1-sst2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_glue_mrpc_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_glue_mrpc_en.md new file mode 100644 index 00000000000000..862691cf2ec086 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_glue_mrpc_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_glue_mrpc BertForSequenceClassification from lrs21 +author: John Snow Labs +name: bert_base_uncased_finetuned_glue_mrpc +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_glue_mrpc` is a English model originally trained by lrs21. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_glue_mrpc_en_5.1.4_3.4_1698207965889.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_glue_mrpc_en_5.1.4_3.4_1698207965889.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_glue_mrpc","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_glue_mrpc","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_glue_mrpc| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/lrs21/bert-base-uncased-finetuned-glue-mrpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap5_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap5_en.md new file mode 100644 index 00000000000000..921f74d5626680 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap5_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_iemocap5 BertForSequenceClassification from Zahra99 +author: John Snow Labs +name: bert_base_uncased_finetuned_iemocap5 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_iemocap5` is a English model originally trained by Zahra99. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap5_en_5.1.4_3.4_1698215514278.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap5_en_5.1.4_3.4_1698215514278.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap5","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap5","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_iemocap5| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Zahra99/bert-base-uncased-finetuned-iemocap5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap7_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap7_en.md new file mode 100644 index 00000000000000..603ab0ef86eec5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap7_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_iemocap7 BertForSequenceClassification from Zahra99 +author: John Snow Labs +name: bert_base_uncased_finetuned_iemocap7 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_iemocap7` is a English model originally trained by Zahra99. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap7_en_5.1.4_3.4_1698216905227.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap7_en_5.1.4_3.4_1698216905227.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap7","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap7","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_iemocap7| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Zahra99/bert-base-uncased-finetuned-iemocap7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap8_en.md new file mode 100644 index 00000000000000..73da2090940030 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_iemocap8 BertForSequenceClassification from Zahra99 +author: John Snow Labs +name: bert_base_uncased_finetuned_iemocap8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_iemocap8` is a English model originally trained by Zahra99. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap8_en_5.1.4_3.4_1698217066933.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap8_en_5.1.4_3.4_1698217066933.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_iemocap8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Zahra99/bert-base-uncased-finetuned-iemocap8 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap_en.md new file mode 100644 index 00000000000000..5c60d3607874ef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_iemocap BertForSequenceClassification from Zahra99 +author: John Snow Labs +name: bert_base_uncased_finetuned_iemocap +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_iemocap` is a English model originally trained by Zahra99. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap_en_5.1.4_3.4_1698213282225.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap_en_5.1.4_3.4_1698213282225.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_iemocap| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Zahra99/bert-base-uncased-finetuned-iemocap \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap_uptuna3_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap_uptuna3_en.md new file mode 100644 index 00000000000000..eed57f261737a2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap_uptuna3_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_iemocap_uptuna3 BertForSequenceClassification from Zahra99 +author: John Snow Labs +name: bert_base_uncased_finetuned_iemocap_uptuna3 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_iemocap_uptuna3` is a English model originally trained by Zahra99. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap_uptuna3_en_5.1.4_3.4_1698214553247.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap_uptuna3_en_5.1.4_3.4_1698214553247.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap_uptuna3","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap_uptuna3","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_iemocap_uptuna3| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Zahra99/bert-base-uncased-finetuned-iemocap-uptuna3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap_uptuna5_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap_uptuna5_en.md new file mode 100644 index 00000000000000..db9bce6905c22f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap_uptuna5_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_iemocap_uptuna5 BertForSequenceClassification from Zahra99 +author: John Snow Labs +name: bert_base_uncased_finetuned_iemocap_uptuna5 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_iemocap_uptuna5` is a English model originally trained by Zahra99. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap_uptuna5_en_5.1.4_3.4_1698216567381.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap_uptuna5_en_5.1.4_3.4_1698216567381.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap_uptuna5","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap_uptuna5","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_iemocap_uptuna5| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Zahra99/bert-base-uncased-finetuned-iemocap-uptuna5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap_uptuna6_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap_uptuna6_en.md new file mode 100644 index 00000000000000..a20ff579a30fd6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap_uptuna6_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_iemocap_uptuna6 BertForSequenceClassification from Zahra99 +author: John Snow Labs +name: bert_base_uncased_finetuned_iemocap_uptuna6 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_iemocap_uptuna6` is a English model originally trained by Zahra99. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap_uptuna6_en_5.1.4_3.4_1698216741320.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap_uptuna6_en_5.1.4_3.4_1698216741320.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap_uptuna6","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap_uptuna6","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_iemocap_uptuna6| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Zahra99/bert-base-uncased-finetuned-iemocap-uptuna6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap_uptuna_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap_uptuna_en.md new file mode 100644 index 00000000000000..c5afe144015597 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap_uptuna_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_iemocap_uptuna BertForSequenceClassification from Zahra99 +author: John Snow Labs +name: bert_base_uncased_finetuned_iemocap_uptuna +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_iemocap_uptuna` is a English model originally trained by Zahra99. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap_uptuna_en_5.1.4_3.4_1698213632291.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap_uptuna_en_5.1.4_3.4_1698213632291.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap_uptuna","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap_uptuna","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_iemocap_uptuna| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Zahra99/bert-base-uncased-finetuned-iemocap-uptuna \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_kaggle_predicting_effective_arguments_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_kaggle_predicting_effective_arguments_en.md new file mode 100644 index 00000000000000..dccac789f6e3f6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_kaggle_predicting_effective_arguments_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_kaggle_predicting_effective_arguments BertForSequenceClassification from mgfrantz +author: John Snow Labs +name: bert_base_uncased_finetuned_kaggle_predicting_effective_arguments +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_kaggle_predicting_effective_arguments` is a English model originally trained by mgfrantz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_kaggle_predicting_effective_arguments_en_5.1.4_3.4_1698210269374.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_kaggle_predicting_effective_arguments_en_5.1.4_3.4_1698210269374.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_kaggle_predicting_effective_arguments","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_kaggle_predicting_effective_arguments","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_kaggle_predicting_effective_arguments| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/mgfrantz/bert_base_uncased_finetuned_kaggle_predicting_effective_arguments \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_multiglue_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_multiglue_en.md new file mode 100644 index 00000000000000..b581ead266ed54 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_multiglue_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_multiglue BertForSequenceClassification from MtCelesteMa +author: John Snow Labs +name: bert_base_uncased_finetuned_multiglue +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_multiglue` is a English model originally trained by MtCelesteMa. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_multiglue_en_5.1.4_3.4_1698192369362.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_multiglue_en_5.1.4_3.4_1698192369362.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_multiglue","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_multiglue","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_multiglue| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/MtCelesteMa/bert-base-uncased-finetuned-multiglue \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_rte_joqsan_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_rte_joqsan_en.md new file mode 100644 index 00000000000000..6ed96a24e475e9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_rte_joqsan_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_rte_joqsan BertForSequenceClassification from Joqsan +author: John Snow Labs +name: bert_base_uncased_finetuned_rte_joqsan +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_rte_joqsan` is a English model originally trained by Joqsan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_rte_joqsan_en_5.1.4_3.4_1698192184739.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_rte_joqsan_en_5.1.4_3.4_1698192184739.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_rte_joqsan","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_rte_joqsan","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_rte_joqsan| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Joqsan/bert-base-uncased-finetuned-rte \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_small_0505_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_small_0505_en.md new file mode 100644 index 00000000000000..7ce679a0cf8eb7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_small_0505_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_small_0505 BertForSequenceClassification from YeRyeongLee +author: John Snow Labs +name: bert_base_uncased_finetuned_small_0505 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_small_0505` is a English model originally trained by YeRyeongLee. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_small_0505_en_5.1.4_3.4_1698201242766.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_small_0505_en_5.1.4_3.4_1698201242766.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_small_0505","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_small_0505","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_small_0505| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/YeRyeongLee/bert-base-uncased-finetuned-small-0505 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_sufficiency_dagstuhl_otherseeds_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_sufficiency_dagstuhl_otherseeds_en.md new file mode 100644 index 00000000000000..a82619a1ceb14c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_sufficiency_dagstuhl_otherseeds_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_sufficiency_dagstuhl_otherseeds BertForSequenceClassification from jakub014 +author: John Snow Labs +name: bert_base_uncased_finetuned_sufficiency_dagstuhl_otherseeds +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_sufficiency_dagstuhl_otherseeds` is a English model originally trained by jakub014. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_sufficiency_dagstuhl_otherseeds_en_5.1.4_3.4_1698212451801.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_sufficiency_dagstuhl_otherseeds_en_5.1.4_3.4_1698212451801.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_sufficiency_dagstuhl_otherseeds","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_sufficiency_dagstuhl_otherseeds","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_sufficiency_dagstuhl_otherseeds| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/jakub014/bert-base-uncased-finetuned-sufficiency-dagstuhl-otherseeds \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_14_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_14_en.md new file mode 100644 index 00000000000000..12d34b8d2826f8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_14_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_14 BertForSequenceClassification from jakub014 +author: John Snow Labs +name: bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_14 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_14` is a English model originally trained by jakub014. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_14_en_5.1.4_3.4_1698212793223.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_14_en_5.1.4_3.4_1698212793223.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_14","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_14","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_14| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/jakub014/bert-base-uncased-finetuned-sufficiency-dagstuhl-seed-14 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_9_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_9_en.md new file mode 100644 index 00000000000000..a45844b136d57a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_9_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_9 BertForSequenceClassification from jakub014 +author: John Snow Labs +name: bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_9 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_9` is a English model originally trained by jakub014. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_9_en_5.1.4_3.4_1698212625034.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_9_en_5.1.4_3.4_1698212625034.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_9","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_9","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_sufficiency_dagstuhl_seed_9| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/jakub014/bert-base-uncased-finetuned-sufficiency-dagstuhl-seed-9 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_swift_v_shakes_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_swift_v_shakes_en.md new file mode 100644 index 00000000000000..12f20d81136a0a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_swift_v_shakes_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_swift_v_shakes BertForSequenceClassification from aharvey +author: John Snow Labs +name: bert_base_uncased_finetuned_swift_v_shakes +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_swift_v_shakes` is a English model originally trained by aharvey. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_swift_v_shakes_en_5.1.4_3.4_1698211686664.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_swift_v_shakes_en_5.1.4_3.4_1698211686664.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_swift_v_shakes","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_swift_v_shakes","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_swift_v_shakes| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/aharvey/bert-base-uncased-finetuned-swift-v-shakes \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_hoax_classifier_sanity_check_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_hoax_classifier_sanity_check_en.md new file mode 100644 index 00000000000000..16b2810bab2bb1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_hoax_classifier_sanity_check_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_hoax_classifier_sanity_check BertForSequenceClassification from research-dump +author: John Snow Labs +name: bert_base_uncased_hoax_classifier_sanity_check +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_hoax_classifier_sanity_check` is a English model originally trained by research-dump. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_hoax_classifier_sanity_check_en_5.1.4_3.4_1698194049195.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_hoax_classifier_sanity_check_en_5.1.4_3.4_1698194049195.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_hoax_classifier_sanity_check","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_hoax_classifier_sanity_check","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_hoax_classifier_sanity_check| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/research-dump/bert-base-uncased_hoax_classifier_sanity_check \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_ituthesis2022mlvinikw_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_ituthesis2022mlvinikw_en.md new file mode 100644 index 00000000000000..b9708599c6f20d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_ituthesis2022mlvinikw_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_ituthesis2022mlvinikw BertForSequenceClassification from ItuThesis2022MlviNikw +author: John Snow Labs +name: bert_base_uncased_ituthesis2022mlvinikw +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_ituthesis2022mlvinikw` is a English model originally trained by ItuThesis2022MlviNikw. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_ituthesis2022mlvinikw_en_5.1.4_3.4_1698212010420.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_ituthesis2022mlvinikw_en_5.1.4_3.4_1698212010420.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_ituthesis2022mlvinikw","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_ituthesis2022mlvinikw","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_ituthesis2022mlvinikw| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/ItuThesis2022MlviNikw/bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_mnli_jeremiahz_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_mnli_jeremiahz_en.md new file mode 100644 index 00000000000000..1c4ece4201620c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_mnli_jeremiahz_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_mnli_jeremiahz BertForSequenceClassification from JeremiahZ +author: John Snow Labs +name: bert_base_uncased_mnli_jeremiahz +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_mnli_jeremiahz` is a English model originally trained by JeremiahZ. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mnli_jeremiahz_en_5.1.4_3.4_1698209690550.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mnli_jeremiahz_en_5.1.4_3.4_1698209690550.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_mnli_jeremiahz","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_mnli_jeremiahz","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_mnli_jeremiahz| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/JeremiahZ/bert-base-uncased-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_mnli_pietrolesci_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_mnli_pietrolesci_en.md new file mode 100644 index 00000000000000..d81395f1541b5f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_mnli_pietrolesci_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_mnli_pietrolesci BertForSequenceClassification from pietrolesci +author: John Snow Labs +name: bert_base_uncased_mnli_pietrolesci +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_mnli_pietrolesci` is a English model originally trained by pietrolesci. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mnli_pietrolesci_en_5.1.4_3.4_1698199227242.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mnli_pietrolesci_en_5.1.4_3.4_1698199227242.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_mnli_pietrolesci","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_mnli_pietrolesci","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_mnli_pietrolesci| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/pietrolesci/bert-base-uncased-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_mrddz_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_mrddz_en.md new file mode 100644 index 00000000000000..d9cd99479587e1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_mrddz_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_mrddz BertForSequenceClassification from MrDdz +author: John Snow Labs +name: bert_base_uncased_mrddz +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_mrddz` is a English model originally trained by MrDdz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mrddz_en_5.1.4_3.4_1698196315557.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mrddz_en_5.1.4_3.4_1698196315557.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_mrddz","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_mrddz","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_mrddz| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/MrDdz/bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_poems_sentiment_nickwong64_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_poems_sentiment_nickwong64_en.md new file mode 100644 index 00000000000000..8e78cea9c50049 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_poems_sentiment_nickwong64_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_poems_sentiment_nickwong64 BertForSequenceClassification from nickwong64 +author: John Snow Labs +name: bert_base_uncased_poems_sentiment_nickwong64 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_poems_sentiment_nickwong64` is a English model originally trained by nickwong64. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_poems_sentiment_nickwong64_en_5.1.4_3.4_1698198162397.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_poems_sentiment_nickwong64_en_5.1.4_3.4_1698198162397.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_poems_sentiment_nickwong64","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_poems_sentiment_nickwong64","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_poems_sentiment_nickwong64| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/nickwong64/bert-base-uncased-poems-sentiment \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_qnli_jeremiahz_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_qnli_jeremiahz_en.md new file mode 100644 index 00000000000000..9d0d789152cf52 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_qnli_jeremiahz_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_qnli_jeremiahz BertForSequenceClassification from JeremiahZ +author: John Snow Labs +name: bert_base_uncased_qnli_jeremiahz +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_qnli_jeremiahz` is a English model originally trained by JeremiahZ. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_qnli_jeremiahz_en_5.1.4_3.4_1698209324735.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_qnli_jeremiahz_en_5.1.4_3.4_1698209324735.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_qnli_jeremiahz","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_qnli_jeremiahz","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_qnli_jeremiahz| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/JeremiahZ/bert-base-uncased-qnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_qqp_jeremiahz_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_qqp_jeremiahz_en.md new file mode 100644 index 00000000000000..dff45b4c0d8bf8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_qqp_jeremiahz_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_qqp_jeremiahz BertForSequenceClassification from JeremiahZ +author: John Snow Labs +name: bert_base_uncased_qqp_jeremiahz +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_qqp_jeremiahz` is a English model originally trained by JeremiahZ. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_qqp_jeremiahz_en_5.1.4_3.4_1698209861232.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_qqp_jeremiahz_en_5.1.4_3.4_1698209861232.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_qqp_jeremiahz","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_qqp_jeremiahz","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_qqp_jeremiahz| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/JeremiahZ/bert-base-uncased-qqp \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_rte_jeremiahz_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_rte_jeremiahz_en.md new file mode 100644 index 00000000000000..ad9b209a4b679f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_rte_jeremiahz_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_rte_jeremiahz BertForSequenceClassification from JeremiahZ +author: John Snow Labs +name: bert_base_uncased_rte_jeremiahz +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_rte_jeremiahz` is a English model originally trained by JeremiahZ. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_rte_jeremiahz_en_5.1.4_3.4_1698192892215.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_rte_jeremiahz_en_5.1.4_3.4_1698192892215.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_rte_jeremiahz","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_rte_jeremiahz","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_rte_jeremiahz| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/JeremiahZ/bert-base-uncased-rte \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst2_unstructured_sparsity_80_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst2_unstructured_sparsity_80_en.md new file mode 100644 index 00000000000000..aa275c7ae367e0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst2_unstructured_sparsity_80_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_sst2_unstructured_sparsity_80 BertForSequenceClassification from yujiepan +author: John Snow Labs +name: bert_base_uncased_sst2_unstructured_sparsity_80 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sst2_unstructured_sparsity_80` is a English model originally trained by yujiepan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst2_unstructured_sparsity_80_en_5.1.4_3.4_1698195995306.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst2_unstructured_sparsity_80_en_5.1.4_3.4_1698195995306.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst2_unstructured_sparsity_80","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst2_unstructured_sparsity_80","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sst2_unstructured_sparsity_80| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|189.8 MB| + +## References + +https://huggingface.co/yujiepan/bert-base-uncased-sst2-unstructured-sparsity-80 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_16_13_smoothed_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_16_13_smoothed_en.md new file mode 100644 index 00000000000000..51470e721a7ea0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_16_13_smoothed_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_sst_2_16_13_smoothed BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_base_uncased_sst_2_16_13_smoothed +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sst_2_16_13_smoothed` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_16_13_smoothed_en_5.1.4_3.4_1698216458952.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_16_13_smoothed_en_5.1.4_3.4_1698216458952.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_16_13_smoothed","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_16_13_smoothed","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sst_2_16_13_smoothed| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/simonycl/bert-base-uncased-sst-2-16-13-smoothed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_16_42_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_16_42_en.md new file mode 100644 index 00000000000000..65e638aa7e3b73 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_16_42_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_sst_2_16_42 BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_base_uncased_sst_2_16_42 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sst_2_16_42` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_16_42_en_5.1.4_3.4_1698210895641.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_16_42_en_5.1.4_3.4_1698210895641.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_16_42","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_16_42","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sst_2_16_42| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/simonycl/bert-base-uncased-sst-2-16-42 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_16_87_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_16_87_en.md new file mode 100644 index 00000000000000..475822f92a5260 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_16_87_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_sst_2_16_87 BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_base_uncased_sst_2_16_87 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sst_2_16_87` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_16_87_en_5.1.4_3.4_1698211067841.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_16_87_en_5.1.4_3.4_1698211067841.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_16_87","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_16_87","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sst_2_16_87| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/simonycl/bert-base-uncased-sst-2-16-87 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_100_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_100_en.md new file mode 100644 index 00000000000000..0b36789ff67550 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_100_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_sst_2_32_100 BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_base_uncased_sst_2_32_100 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sst_2_32_100` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_32_100_en_5.1.4_3.4_1698212226600.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_32_100_en_5.1.4_3.4_1698212226600.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_32_100","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_32_100","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sst_2_32_100| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/simonycl/bert-base-uncased-sst-2-32-100 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_13_smoothed_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_13_smoothed_en.md new file mode 100644 index 00000000000000..6037ab4b7d546d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_13_smoothed_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_sst_2_32_13_smoothed BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_base_uncased_sst_2_32_13_smoothed +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sst_2_32_13_smoothed` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_32_13_smoothed_en_5.1.4_3.4_1698216658314.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_32_13_smoothed_en_5.1.4_3.4_1698216658314.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_32_13_smoothed","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_32_13_smoothed","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sst_2_32_13_smoothed| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/simonycl/bert-base-uncased-sst-2-32-13-smoothed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_21_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_21_en.md new file mode 100644 index 00000000000000..861b5b940b1886 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_21_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_sst_2_32_21 BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_base_uncased_sst_2_32_21 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sst_2_32_21` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_32_21_en_5.1.4_3.4_1698211690860.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_32_21_en_5.1.4_3.4_1698211690860.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_32_21","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_32_21","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sst_2_32_21| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/simonycl/bert-base-uncased-sst-2-32-21 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_42_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_42_en.md new file mode 100644 index 00000000000000..0fff7afd3776a5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_42_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_sst_2_32_42 BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_base_uncased_sst_2_32_42 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sst_2_32_42` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_32_42_en_5.1.4_3.4_1698211861641.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_32_42_en_5.1.4_3.4_1698211861641.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_32_42","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_32_42","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sst_2_32_42| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/simonycl/bert-base-uncased-sst-2-32-42 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_87_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_87_en.md new file mode 100644 index 00000000000000..327cdcb1df241c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_87_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_sst_2_32_87 BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_base_uncased_sst_2_32_87 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sst_2_32_87` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_32_87_en_5.1.4_3.4_1698212038335.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_32_87_en_5.1.4_3.4_1698212038335.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_32_87","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_32_87","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sst_2_32_87| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/simonycl/bert-base-uncased-sst-2-32-87 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_64_13_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_64_13_en.md new file mode 100644 index 00000000000000..5660fcff8448f7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_64_13_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_sst_2_64_13 BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_base_uncased_sst_2_64_13 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sst_2_64_13` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_64_13_en_5.1.4_3.4_1698212400804.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_64_13_en_5.1.4_3.4_1698212400804.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_64_13","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_64_13","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sst_2_64_13| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/simonycl/bert-base-uncased-sst-2-64-13 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_64_13_smoothed_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_64_13_smoothed_en.md new file mode 100644 index 00000000000000..5d240f0a77340c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_64_13_smoothed_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_sst_2_64_13_smoothed BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_base_uncased_sst_2_64_13_smoothed +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sst_2_64_13_smoothed` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_64_13_smoothed_en_5.1.4_3.4_1698216845194.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_64_13_smoothed_en_5.1.4_3.4_1698216845194.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_64_13_smoothed","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_64_13_smoothed","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sst_2_64_13_smoothed| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/simonycl/bert-base-uncased-sst-2-64-13-smoothed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_stereoset_classifieronly_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_stereoset_classifieronly_en.md new file mode 100644 index 00000000000000..6afd341451f491 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_stereoset_classifieronly_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_stereoset_classifieronly BertForSequenceClassification from henryscheible +author: John Snow Labs +name: bert_base_uncased_stereoset_classifieronly +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_stereoset_classifieronly` is a English model originally trained by henryscheible. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_stereoset_classifieronly_en_5.1.4_3.4_1698210351172.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_stereoset_classifieronly_en_5.1.4_3.4_1698210351172.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_stereoset_classifieronly","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_stereoset_classifieronly","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_stereoset_classifieronly| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/henryscheible/bert-base-uncased_stereoset_classifieronly \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_stereoset_finetuned_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_stereoset_finetuned_en.md new file mode 100644 index 00000000000000..cb0aef720bdbb7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_stereoset_finetuned_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_stereoset_finetuned BertForSequenceClassification from henryscheible +author: John Snow Labs +name: bert_base_uncased_stereoset_finetuned +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_stereoset_finetuned` is a English model originally trained by henryscheible. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_stereoset_finetuned_en_5.1.4_3.4_1698200622540.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_stereoset_finetuned_en_5.1.4_3.4_1698200622540.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_stereoset_finetuned","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_stereoset_finetuned","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_stereoset_finetuned| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/henryscheible/bert-base-uncased_stereoset_finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_stsb_jeremiahz_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_stsb_jeremiahz_en.md new file mode 100644 index 00000000000000..475f48b3ec0aca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_stsb_jeremiahz_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_stsb_jeremiahz BertForSequenceClassification from JeremiahZ +author: John Snow Labs +name: bert_base_uncased_stsb_jeremiahz +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_stsb_jeremiahz` is a English model originally trained by JeremiahZ. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_stsb_jeremiahz_en_5.1.4_3.4_1698209145352.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_stsb_jeremiahz_en_5.1.4_3.4_1698209145352.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_stsb_jeremiahz","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_stsb_jeremiahz","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_stsb_jeremiahz| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/JeremiahZ/bert-base-uncased-stsb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_with_mrpc_trained_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_with_mrpc_trained_en.md new file mode 100644 index 00000000000000..a88b3036564c4b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_with_mrpc_trained_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_with_mrpc_trained BertForSequenceClassification from iotengtr +author: John Snow Labs +name: bert_base_uncased_with_mrpc_trained +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_with_mrpc_trained` is a English model originally trained by iotengtr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_with_mrpc_trained_en_5.1.4_3.4_1698208591869.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_with_mrpc_trained_en_5.1.4_3.4_1698208591869.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_with_mrpc_trained","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_with_mrpc_trained","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_with_mrpc_trained| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/iotengtr/bert-base-uncased-with-mrpc-trained \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_wnli_jeremiahz_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_wnli_jeremiahz_en.md new file mode 100644 index 00000000000000..5d9674b83d28f6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_wnli_jeremiahz_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_wnli_jeremiahz BertForSequenceClassification from JeremiahZ +author: John Snow Labs +name: bert_base_uncased_wnli_jeremiahz +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_wnli_jeremiahz` is a English model originally trained by JeremiahZ. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_wnli_jeremiahz_en_5.1.4_3.4_1698209510997.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_wnli_jeremiahz_en_5.1.4_3.4_1698209510997.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_wnli_jeremiahz","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_wnli_jeremiahz","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_wnli_jeremiahz| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/JeremiahZ/bert-base-uncased-wnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_based_no_sqli_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_based_no_sqli_en.md new file mode 100644 index 00000000000000..1fa89bd74d0a48 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_based_no_sqli_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_based_no_sqli BertForSequenceClassification from Price11 +author: John Snow Labs +name: bert_based_no_sqli +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_based_no_sqli` is a English model originally trained by Price11. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_based_no_sqli_en_5.1.4_3.4_1698199566461.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_based_no_sqli_en_5.1.4_3.4_1698199566461.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_based_no_sqli","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_based_no_sqli","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_based_no_sqli| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Price11/bert_based-NO-SQLI \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_based_uncased_sst2_e2_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_based_uncased_sst2_e2_en.md new file mode 100644 index 00000000000000..f7604d2e4e9fb3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_based_uncased_sst2_e2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_based_uncased_sst2_e2 BertForSequenceClassification from EhsanAghazadeh +author: John Snow Labs +name: bert_based_uncased_sst2_e2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_based_uncased_sst2_e2` is a English model originally trained by EhsanAghazadeh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_based_uncased_sst2_e2_en_5.1.4_3.4_1698202739863.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_based_uncased_sst2_e2_en_5.1.4_3.4_1698202739863.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_based_uncased_sst2_e2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_based_uncased_sst2_e2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_based_uncased_sst2_e2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/EhsanAghazadeh/bert-based-uncased-sst2-e2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_based_uncased_sst2_e4_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_based_uncased_sst2_e4_en.md new file mode 100644 index 00000000000000..8ffeecdff12bbf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_based_uncased_sst2_e4_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_based_uncased_sst2_e4 BertForSequenceClassification from EhsanAghazadeh +author: John Snow Labs +name: bert_based_uncased_sst2_e4 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_based_uncased_sst2_e4` is a English model originally trained by EhsanAghazadeh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_based_uncased_sst2_e4_en_5.1.4_3.4_1698203047684.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_based_uncased_sst2_e4_en_5.1.4_3.4_1698203047684.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_based_uncased_sst2_e4","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_based_uncased_sst2_e4","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_based_uncased_sst2_e4| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/EhsanAghazadeh/bert-based-uncased-sst2-e4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_based_uncased_sst2_e6_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_based_uncased_sst2_e6_en.md new file mode 100644 index 00000000000000..42d8f5d6538630 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_based_uncased_sst2_e6_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_based_uncased_sst2_e6 BertForSequenceClassification from EhsanAghazadeh +author: John Snow Labs +name: bert_based_uncased_sst2_e6 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_based_uncased_sst2_e6` is a English model originally trained by EhsanAghazadeh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_based_uncased_sst2_e6_en_5.1.4_3.4_1698203354247.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_based_uncased_sst2_e6_en_5.1.4_3.4_1698203354247.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_based_uncased_sst2_e6","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_based_uncased_sst2_e6","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_based_uncased_sst2_e6| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/EhsanAghazadeh/bert-based-uncased-sst2-e6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_base_for_multilabel_sentence_classification_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_base_for_multilabel_sentence_classification_en.md new file mode 100644 index 00000000000000..f5b89d24646f0b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_base_for_multilabel_sentence_classification_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: English BertForSequenceClassification Base Cased model (from Zamachi) +author: John Snow Labs +name: bert_classifier_base_for_multilabel_sentence_classification +date: 2023-10-25 +tags: [bert, sequence_classification, classification, open_source, en, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-for-multilabel-sentence-classification` is a English model originally trained by `Zamachi`. + +## Predicted Entities + +`optimism`, `anger`, `sadness`, `joy` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_base_for_multilabel_sentence_classification_en_5.1.4_3.4_1698210920425.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_base_for_multilabel_sentence_classification_en_5.1.4_3.4_1698210920425.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_base_for_multilabel_sentence_classification","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_base_for_multilabel_sentence_classification","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.base.by_zamachi").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_base_for_multilabel_sentence_classification| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|410.0 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/Zamachi/bert-base-for-multilabel-sentence-classification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_base_uncased_debiased_nli_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_base_uncased_debiased_nli_en.md new file mode 100644 index 00000000000000..65af7d34269bd2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_base_uncased_debiased_nli_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: English BertForSequenceClassification Base Uncased model (from tomhosking) +author: John Snow Labs +name: bert_classifier_base_uncased_debiased_nli +date: 2023-10-25 +tags: [bert, sequence_classification, classification, open_source, en, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-uncased-debiased-nli` is a English model originally trained by `tomhosking`. + +## Predicted Entities + +`NEUTRAL`, `CONTRADICTION`, `ENTAILMENT` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_base_uncased_debiased_nli_en_5.1.4_3.4_1698203231872.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_base_uncased_debiased_nli_en_5.1.4_3.4_1698203231872.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_base_uncased_debiased_nli","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_base_uncased_debiased_nli","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.uncased_base.by_tomhosking").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_base_uncased_debiased_nli| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.5 MB| +|Case sensitive:|false| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/tomhosking/bert-base-uncased-debiased-nli +- https://github.com/jimmycode/gen-debiased-nli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_based_uncased_sst2_e1_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_based_uncased_sst2_e1_en.md new file mode 100644 index 00000000000000..914bdc72910798 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_based_uncased_sst2_e1_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: English BertForSequenceClassification Uncased model (from EhsanAghazadeh) +author: John Snow Labs +name: bert_classifier_based_uncased_sst2_e1 +date: 2023-10-25 +tags: [bert, sequence_classification, classification, open_source, en, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-based-uncased-sst2-e1` is a English model originally trained by `EhsanAghazadeh`. + +## Predicted Entities + +`negative`, `positive` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_based_uncased_sst2_e1_en_5.1.4_3.4_1698202548911.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_based_uncased_sst2_e1_en_5.1.4_3.4_1698202548911.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_based_uncased_sst2_e1","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_based_uncased_sst2_e1","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.uncased_base.by_ehsanaghazadeh").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_based_uncased_sst2_e1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| +|Case sensitive:|false| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/EhsanAghazadeh/bert-based-uncased-sst2-e1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_bert_base_uncased_hatexplain_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_bert_base_uncased_hatexplain_en.md new file mode 100644 index 00000000000000..98ae5dfe4d8504 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_bert_base_uncased_hatexplain_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: English BertForSequenceClassification Base Uncased model (from Hate-speech-CNERG) +author: John Snow Labs +name: bert_classifier_bert_base_uncased_hatexplain +date: 2023-10-25 +tags: [en, open_source, bert, sequence_classification, classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-uncased-hatexplain` is a English model originally trained by `Hate-speech-CNERG`. + +## Predicted Entities + +`offensive`, `hate speech`, `normal` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_bert_base_uncased_hatexplain_en_5.1.4_3.4_1698205549652.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_bert_base_uncased_hatexplain_en_5.1.4_3.4_1698205549652.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_bert_base_uncased_hatexplain","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_bert_base_uncased_hatexplain","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.hate.uncased_base").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_bert_base_uncased_hatexplain| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| +|Case sensitive:|false| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/Hate-speech-CNERG/bert-base-uncased-hatexplain +- https://github.com/punyajoy/HateXplain \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_bert_base_uncased_hatexplain_rationale_two_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_bert_base_uncased_hatexplain_rationale_two_en.md new file mode 100644 index 00000000000000..a9fb07ba3ab14f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_bert_base_uncased_hatexplain_rationale_two_en.md @@ -0,0 +1,112 @@ +--- +layout: model +title: English BertForSequenceClassification Base Uncased model (from Hate-speech-CNERG) +author: John Snow Labs +name: bert_classifier_bert_base_uncased_hatexplain_rationale_two +date: 2023-10-25 +tags: [en, open_source, bert, sequence_classification, classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-uncased-hatexplain-rationale-two` is a English model originally trained by `Hate-speech-CNERG`. + +## Predicted Entities + +`NORMAL`, `ABUSIVE` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_bert_base_uncased_hatexplain_rationale_two_en_5.1.4_3.4_1698205286125.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_bert_base_uncased_hatexplain_rationale_two_en_5.1.4_3.4_1698205286125.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_bert_base_uncased_hatexplain_rationale_two","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_bert_base_uncased_hatexplain_rationale_two","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.hate.uncased_base.by_hate_speech_cnerg").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_bert_base_uncased_hatexplain_rationale_two| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| +|Case sensitive:|false| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two +- https://arxiv.org/abs/2012.10289 +- https://github.com/punyajoy/HateXplain +- https://aclanthology.org/2021.acl-long.330.pdf +- https://dl.acm.org/doi/pdf/10.1145/3442188.3445922 +- https://github.com/hate-alert/HateXplain/tree/master/Preprocess +- https://arxiv.org/pdf/2012.10289.pdf \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_bert_finetuning_cn_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_bert_finetuning_cn_en.md new file mode 100644 index 00000000000000..c505a0574fa23e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_bert_finetuning_cn_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: English BertForSequenceClassification Cased model (from LiYouYou) +author: John Snow Labs +name: bert_classifier_bert_finetuning_cn +date: 2023-10-25 +tags: [en, open_source, bert, sequence_classification, classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert_finetuning_cn` is a English model originally trained by `LiYouYou`. + +## Predicted Entities + +`positive`, `negative` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_bert_finetuning_cn_en_5.1.4_3.4_1698200944254.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_bert_finetuning_cn_en_5.1.4_3.4_1698200944254.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_bert_finetuning_cn","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_bert_finetuning_cn","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.glue.finetuning_").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_bert_finetuning_cn| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/LiYouYou/bert_finetuning_cn +- https://paperswithcode.com/sota?task=Text+Classification&dataset=GLUE+SST2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_bert_large_hatexplain_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_bert_large_hatexplain_en.md new file mode 100644 index 00000000000000..e8283953cbdeb7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_bert_large_hatexplain_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: English BertForSequenceClassification Large Cased model (from TehranNLP-org) +author: John Snow Labs +name: bert_classifier_bert_large_hatexplain +date: 2023-10-25 +tags: [en, open_source, bert, sequence_classification, classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-large-hateXplain` is a English model originally trained by `TehranNLP-org`. + +## Predicted Entities + +`offensive`, `normal`, `hatespeech` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_bert_large_hatexplain_en_5.1.4_3.4_1698195127535.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_bert_large_hatexplain_en_5.1.4_3.4_1698195127535.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_bert_large_hatexplain","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_bert_large_hatexplain","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.hate.large").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_bert_large_hatexplain| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/TehranNLP-org/bert-large-hateXplain \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_biobert_v1.1_pub_section_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_biobert_v1.1_pub_section_en.md new file mode 100644 index 00000000000000..94ca584f5f89e2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_biobert_v1.1_pub_section_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: English BertForSequenceClassification Cased model (from ml4pubmed) +author: John Snow Labs +name: bert_classifier_biobert_v1.1_pub_section +date: 2023-10-25 +tags: [en, open_source, bert, sequence_classification, classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `biobert-v1.1_pub_section` is a English model originally trained by `ml4pubmed`. + +## Predicted Entities + +`METHODS`, `RESULTS`, `BACKGROUND`, `CONCLUSIONS`, `OBJECTIVE` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_biobert_v1.1_pub_section_en_5.1.4_3.4_1698200074518.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_biobert_v1.1_pub_section_en_5.1.4_3.4_1698200074518.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_biobert_v1.1_pub_section","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_biobert_v1.1_pub_section","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.biobert").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_biobert_v1.1_pub_section| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.3 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/ml4pubmed/biobert-v1.1_pub_section \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_bluebert_pubmed_uncased_l_12_h_768_a_12_pub_section_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_bluebert_pubmed_uncased_l_12_h_768_a_12_pub_section_en.md new file mode 100644 index 00000000000000..b0a0c7e9c94d39 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_bluebert_pubmed_uncased_l_12_h_768_a_12_pub_section_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: English BertForSequenceClassification Uncased model (from ml4pubmed) +author: John Snow Labs +name: bert_classifier_bluebert_pubmed_uncased_l_12_h_768_a_12_pub_section +date: 2023-10-25 +tags: [en, open_source, bert, sequence_classification, classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bluebert-pubmed-uncased-L-12-H-768-A-12_pub_section` is a English model originally trained by `ml4pubmed`. + +## Predicted Entities + +`METHODS`, `RESULTS`, `BACKGROUND`, `CONCLUSIONS`, `OBJECTIVE` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_bluebert_pubmed_uncased_l_12_h_768_a_12_pub_section_en_5.1.4_3.4_1698200619835.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_bluebert_pubmed_uncased_l_12_h_768_a_12_pub_section_en_5.1.4_3.4_1698200619835.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_bluebert_pubmed_uncased_l_12_h_768_a_12_pub_section","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_bluebert_pubmed_uncased_l_12_h_768_a_12_pub_section","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.pubmed_bluebert.uncased_12l_768d_a12a_768d").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_bluebert_pubmed_uncased_l_12_h_768_a_12_pub_section| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.3 MB| +|Case sensitive:|false| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/ml4pubmed/bluebert-pubmed-uncased-L-12-H-768-A-12_pub_section \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_german_sentiment_twitter_de.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_german_sentiment_twitter_de.md new file mode 100644 index 00000000000000..b9e3057f329e89 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_german_sentiment_twitter_de.md @@ -0,0 +1,106 @@ +--- +layout: model +title: German BertForSequenceClassification Cased model (from JP040) +author: John Snow Labs +name: bert_classifier_german_sentiment_twitter +date: 2023-10-25 +tags: [bert, sequence_classification, classification, open_source, de, onnx] +task: Text Classification +language: de +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-german-sentiment-twitter` is a German model originally trained by `JP040`. + +## Predicted Entities + +`positive`, `negative`, `neutral` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_german_sentiment_twitter_de_5.1.4_3.4_1698212459845.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_german_sentiment_twitter_de_5.1.4_3.4_1698212459845.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_german_sentiment_twitter","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["Ich liebe Spark NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_german_sentiment_twitter","de") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("Ich liebe Spark NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.classify.bert.sentiment_twitter.").predict("""Ich liebe Spark NLP""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_german_sentiment_twitter| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|de| +|Size:|408.1 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/JP040/bert-german-sentiment-twitter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_jeremiahz_base_uncased_mrpc_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_jeremiahz_base_uncased_mrpc_en.md new file mode 100644 index 00000000000000..4efa56ffda730e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_jeremiahz_base_uncased_mrpc_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: English BertForSequenceClassification Base Uncased model (from JeremiahZ) +author: John Snow Labs +name: bert_classifier_jeremiahz_base_uncased_mrpc +date: 2023-10-25 +tags: [en, open_source, bert, sequence_classification, classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-uncased-mrpc` is a English model originally trained by `JeremiahZ`. + +## Predicted Entities + +`not_equivalent`, `equivalent` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_jeremiahz_base_uncased_mrpc_en_5.1.4_3.4_1698192700235.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_jeremiahz_base_uncased_mrpc_en_5.1.4_3.4_1698192700235.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_jeremiahz_base_uncased_mrpc","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_jeremiahz_base_uncased_mrpc","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.glue.uncased_base.by_JeremiahZ").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_jeremiahz_base_uncased_mrpc| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| +|Case sensitive:|false| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/JeremiahZ/bert-base-uncased-mrpc +- https://paperswithcode.com/sota?task=Text+Classification&dataset=GLUE+MRPC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_m_corona_tweets_belgium_topics_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_m_corona_tweets_belgium_topics_en.md new file mode 100644 index 00000000000000..27de1ce5894b8f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_m_corona_tweets_belgium_topics_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: English BertForSequenceClassification Cased model (from DTAI-KULeuven) +author: John Snow Labs +name: bert_classifier_m_corona_tweets_belgium_topics +date: 2023-10-25 +tags: [bert, sequence_classification, classification, open_source, en, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `mbert-corona-tweets-belgium-topics` is a English model originally trained by `DTAI-KULeuven`. + +## Predicted Entities + +`masks`, `testing`, `lockdown`, `quarantine`, `closing-horeca`, `not-applicable`, `vaccine`, `curfew`, `other-measure`, `schools` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_m_corona_tweets_belgium_topics_en_5.1.4_3.4_1698193105422.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_m_corona_tweets_belgium_topics_en_5.1.4_3.4_1698193105422.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_m_corona_tweets_belgium_topics","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_m_corona_tweets_belgium_topics","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.tweet.by_dtai_kuleuven").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_m_corona_tweets_belgium_topics| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|667.3 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/DTAI-KULeuven/mbert-corona-tweets-belgium-topics +- http://arxiv.org/abs/2104.09947 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_mbert_corona_tweets_belgium_curfew_support_xx.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_mbert_corona_tweets_belgium_curfew_support_xx.md new file mode 100644 index 00000000000000..27c1080a213b6e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_mbert_corona_tweets_belgium_curfew_support_xx.md @@ -0,0 +1,107 @@ +--- +layout: model +title: Multilingual BertForSequenceClassification Cased model (from DTAI-KULeuven) +author: John Snow Labs +name: bert_classifier_mbert_corona_tweets_belgium_curfew_support +date: 2023-10-25 +tags: [en, fr, nl, open_source, bert, sequence_classification, classification, xx, onnx] +task: Text Classification +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `mbert-corona-tweets-belgium-curfew-support` is a Multilingual model originally trained by `DTAI-KULeuven`. + +## Predicted Entities + + + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_mbert_corona_tweets_belgium_curfew_support_xx_5.1.4_3.4_1698192742817.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_mbert_corona_tweets_belgium_curfew_support_xx_5.1.4_3.4_1698192742817.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_mbert_corona_tweets_belgium_curfew_support","xx") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_mbert_corona_tweets_belgium_curfew_support","xx") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("xx.classify.bert.tweet.").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_mbert_corona_tweets_belgium_curfew_support| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|xx| +|Size:|667.3 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/DTAI-KULeuven/mbert-corona-tweets-belgium-curfew-support +- http://arxiv.org/abs/2104.09947 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_reddit_tc_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_reddit_tc_en.md new file mode 100644 index 00000000000000..4515f12378fc52 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_reddit_tc_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: English BertForSequenceClassification Cased model (from Fan-s) +author: John Snow Labs +name: bert_classifier_reddit_tc +date: 2023-10-25 +tags: [bert, sequence_classification, classification, open_source, en, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `reddit-tc-bert` is a English model originally trained by `Fan-s`. + +## Predicted Entities + +`matched`, `unmatched` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_reddit_tc_en_5.1.4_3.4_1698204461175.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_reddit_tc_en_5.1.4_3.4_1698204461175.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_reddit_tc","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_reddit_tc","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.by_fan_s").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_reddit_tc| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/Fan-s/reddit-tc-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_rubertconv_toxic_clf_ru.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_rubertconv_toxic_clf_ru.md new file mode 100644 index 00000000000000..4d8999535ab27c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_rubertconv_toxic_clf_ru.md @@ -0,0 +1,110 @@ +--- +layout: model +title: Russian BertForSequenceClassification Cased model (from IlyaGusev) +author: John Snow Labs +name: bert_classifier_rubertconv_toxic_clf +date: 2023-10-25 +tags: [ru, open_source, bert, sequence_classification, classification, onnx] +task: Text Classification +language: ru +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `rubertconv_toxic_clf` is a Russian model originally trained by `IlyaGusev`. + +## Predicted Entities + +`toxic`, `neutral` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_rubertconv_toxic_clf_ru_5.1.4_3.4_1698210881499.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_rubertconv_toxic_clf_ru_5.1.4_3.4_1698210881499.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_rubertconv_toxic_clf","ru") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_rubertconv_toxic_clf","ru") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("ru.classify.bert").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_rubertconv_toxic_clf| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|ru| +|Size:|664.4 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/IlyaGusev/rubertconv_toxic_clf +- https://www.kaggle.com/blackmoon/russian-language-toxic-comments +- https://www.kaggle.com/alexandersemiletov/toxic-russian-comments +- https://toloka.ai/ru/datasets +- https://github.com/Koziev/NLP_Datasets/blob/master/Conversations/Data \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_scibert_scivocab_cased_pub_section_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_scibert_scivocab_cased_pub_section_en.md new file mode 100644 index 00000000000000..0f005e3151c891 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_scibert_scivocab_cased_pub_section_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: English BertForSequenceClassification Cased model (from ml4pubmed) +author: John Snow Labs +name: bert_classifier_scibert_scivocab_cased_pub_section +date: 2023-10-25 +tags: [en, open_source, bert, sequence_classification, classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `scibert-scivocab-cased_pub_section` is a English model originally trained by `ml4pubmed`. + +## Predicted Entities + +`CONCLUSIONS`, `METHODS`, `OBJECTIVE`, `RESULTS`, `BACKGROUND` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_scibert_scivocab_cased_pub_section_en_5.1.4_3.4_1698199767413.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_scibert_scivocab_cased_pub_section_en_5.1.4_3.4_1698199767413.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_scibert_scivocab_cased_pub_section","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_scibert_scivocab_cased_pub_section","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.scibert.scibert.cased").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_scibert_scivocab_cased_pub_section| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|412.1 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/ml4pubmed/scibert-scivocab-cased_pub_section \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_scibert_scivocab_uncased_pub_section_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_scibert_scivocab_uncased_pub_section_en.md new file mode 100644 index 00000000000000..4ee0c0e6e2724e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_scibert_scivocab_uncased_pub_section_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: English BertForSequenceClassification Uncased model (from ml4pubmed) +author: John Snow Labs +name: bert_classifier_scibert_scivocab_uncased_pub_section +date: 2023-10-25 +tags: [en, open_source, bert, sequence_classification, classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `scibert-scivocab-uncased_pub_section` is a English model originally trained by `ml4pubmed`. + +## Predicted Entities + +`CONCLUSIONS`, `METHODS`, `OBJECTIVE`, `RESULTS`, `BACKGROUND` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_scibert_scivocab_uncased_pub_section_en_5.1.4_3.4_1698200342570.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_scibert_scivocab_uncased_pub_section_en_5.1.4_3.4_1698200342570.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_scibert_scivocab_uncased_pub_section","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_scibert_scivocab_uncased_pub_section","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.scibert.scibert.uncased").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_scibert_scivocab_uncased_pub_section| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|412.2 MB| +|Case sensitive:|false| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/ml4pubmed/scibert-scivocab-uncased_pub_section \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_tiny_qqp_distilled_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_tiny_qqp_distilled_en.md new file mode 100644 index 00000000000000..513f8ca47ecea4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_tiny_qqp_distilled_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: English BertForSequenceClassification Tiny Cased model (from Sayan01) +author: John Snow Labs +name: bert_classifier_tiny_qqp_distilled +date: 2023-10-25 +tags: [bert, sequence_classification, classification, open_source, en, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `tiny-bert-qqp-distilled` is a English model originally trained by `Sayan01`. + +## Predicted Entities + +`duplicate`, `not_duplicate` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_tiny_qqp_distilled_en_5.1.4_3.4_1698211704999.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_tiny_qqp_distilled_en_5.1.4_3.4_1698211704999.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_tiny_qqp_distilled","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_tiny_qqp_distilled","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.distilled_tiny.by_sayan01").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_tiny_qqp_distilled| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|54.2 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/Sayan01/tiny-bert-qqp-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_tiny_sst2_distilled_l4_h_512_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_tiny_sst2_distilled_l4_h_512_en.md new file mode 100644 index 00000000000000..c8dc96cf1f6dfe --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_tiny_sst2_distilled_l4_h_512_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: English BertForSequenceClassification Tiny Cased model (from Smith123) +author: John Snow Labs +name: bert_classifier_tiny_sst2_distilled_l4_h_512 +date: 2023-10-25 +tags: [bert, sequence_classification, classification, open_source, en, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `tiny-bert-sst2-distilled_L4_H_512` is a English model originally trained by `Smith123`. + +## Predicted Entities + +`positive`, `negative` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_tiny_sst2_distilled_l4_h_512_en_5.1.4_3.4_1698213280716.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_tiny_sst2_distilled_l4_h_512_en_5.1.4_3.4_1698213280716.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_tiny_sst2_distilled_l4_h_512","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_tiny_sst2_distilled_l4_h_512","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.distilled_512d_tiny_512d").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_tiny_sst2_distilled_l4_h_512| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|107.9 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/Smith123/tiny-bert-sst2-distilled_L4_H_512 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_tiny_sst2_distilled_l6_h128_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_tiny_sst2_distilled_l6_h128_en.md new file mode 100644 index 00000000000000..f06036a5eb1572 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_tiny_sst2_distilled_l6_h128_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: English BertForSequenceClassification Tiny Cased model (from Smith123) +author: John Snow Labs +name: bert_classifier_tiny_sst2_distilled_l6_h128 +date: 2023-10-25 +tags: [bert, sequence_classification, classification, open_source, en, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `tiny-bert-sst2-distilled_L6_H128` is a English model originally trained by `Smith123`. + +## Predicted Entities + +`positive`, `negative` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_tiny_sst2_distilled_l6_h128_en_5.1.4_3.4_1698213100526.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_tiny_sst2_distilled_l6_h128_en_5.1.4_3.4_1698213100526.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_tiny_sst2_distilled_l6_h128","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_tiny_sst2_distilled_l6_h128","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.distilled_tiny.by_smith123").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_tiny_sst2_distilled_l6_h128| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|19.7 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/Smith123/tiny-bert-sst2-distilled_L6_H128 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_cn_finetuning_connor_tech_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_cn_finetuning_connor_tech_en.md new file mode 100644 index 00000000000000..447a0864e1b108 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_cn_finetuning_connor_tech_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_cn_finetuning_connor_tech BertForSequenceClassification from Connor-tech +author: John Snow Labs +name: bert_cn_finetuning_connor_tech +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_cn_finetuning_connor_tech` is a English model originally trained by Connor-tech. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_cn_finetuning_connor_tech_en_5.1.4_3.4_1698192414295.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_cn_finetuning_connor_tech_en_5.1.4_3.4_1698192414295.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_cn_finetuning_connor_tech","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_cn_finetuning_connor_tech","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_cn_finetuning_connor_tech| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/Connor-tech/bert_cn_finetuning \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_cn_finetuning_itcastai_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_cn_finetuning_itcastai_en.md new file mode 100644 index 00000000000000..987d63659303c7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_cn_finetuning_itcastai_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_cn_finetuning_itcastai BertForSequenceClassification from ItcastAI +author: John Snow Labs +name: bert_cn_finetuning_itcastai +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_cn_finetuning_itcastai` is a English model originally trained by ItcastAI. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_cn_finetuning_itcastai_en_5.1.4_3.4_1698211264091.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_cn_finetuning_itcastai_en_5.1.4_3.4_1698211264091.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_cn_finetuning_itcastai","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_cn_finetuning_itcastai","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_cn_finetuning_itcastai| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/ItcastAI/bert_cn_finetuning \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_cn_finetunning_itcastai_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_cn_finetunning_itcastai_en.md new file mode 100644 index 00000000000000..34df6ea0027f95 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_cn_finetunning_itcastai_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_cn_finetunning_itcastai BertForSequenceClassification from ItcastAI +author: John Snow Labs +name: bert_cn_finetunning_itcastai +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_cn_finetunning_itcastai` is a English model originally trained by ItcastAI. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_cn_finetunning_itcastai_en_5.1.4_3.4_1698211451875.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_cn_finetunning_itcastai_en_5.1.4_3.4_1698211451875.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_cn_finetunning_itcastai","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_cn_finetunning_itcastai","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_cn_finetunning_itcastai| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/ItcastAI/bert_cn_finetunning \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_cola_finetuned_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_cola_finetuned_en.md new file mode 100644 index 00000000000000..0082653e8af2c5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_cola_finetuned_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_cola_finetuned BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_cola_finetuned +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_cola_finetuned` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_cola_finetuned_en_5.1.4_3.4_1698201158703.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_cola_finetuned_en_5.1.4_3.4_1698201158703.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_cola_finetuned","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_cola_finetuned","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_cola_finetuned| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-cola-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_covid_hate_finetuned_test_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_covid_hate_finetuned_test_en.md new file mode 100644 index 00000000000000..a44b81805bd508 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_covid_hate_finetuned_test_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_covid_hate_finetuned_test BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_covid_hate_finetuned_test +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_covid_hate_finetuned_test` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_covid_hate_finetuned_test_en_5.1.4_3.4_1698194103840.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_covid_hate_finetuned_test_en_5.1.4_3.4_1698194103840.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_covid_hate_finetuned_test","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_covid_hate_finetuned_test","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_covid_hate_finetuned_test| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-COVID-HATE-finetuned-test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_deepfake_bulgarian_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_deepfake_bulgarian_en.md new file mode 100644 index 00000000000000..91a534f78f21ac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_deepfake_bulgarian_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_deepfake_bulgarian BertForSequenceClassification from usmiva +author: John Snow Labs +name: bert_deepfake_bulgarian +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_deepfake_bulgarian` is a English model originally trained by usmiva. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_deepfake_bulgarian_en_5.1.4_3.4_1698201953411.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_deepfake_bulgarian_en_5.1.4_3.4_1698201953411.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_deepfake_bulgarian","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_deepfake_bulgarian","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_deepfake_bulgarian| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.1 MB| + +## References + +https://huggingface.co/usmiva/bert-deepfake-bg \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_desinform_bulgarian_bg.md b/docs/_posts/ahmedlone127/2023-10-25-bert_desinform_bulgarian_bg.md new file mode 100644 index 00000000000000..2eae055483ecdf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_desinform_bulgarian_bg.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Bulgarian bert_desinform_bulgarian BertForSequenceClassification from usmiva +author: John Snow Labs +name: bert_desinform_bulgarian +date: 2023-10-25 +tags: [bert, bg, open_source, sequence_classification, onnx] +task: Text Classification +language: bg +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_desinform_bulgarian` is a Bulgarian model originally trained by usmiva. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_desinform_bulgarian_bg_5.1.4_3.4_1698201765003.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_desinform_bulgarian_bg_5.1.4_3.4_1698201765003.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_desinform_bulgarian","bg")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_desinform_bulgarian","bg") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_desinform_bulgarian| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|bg| +|Size:|409.1 MB| + +## References + +https://huggingface.co/usmiva/bert-desinform-bg \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_fine_trained_w_imdb_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_trained_w_imdb_en.md new file mode 100644 index 00000000000000..f16bc5633c9bdd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_trained_w_imdb_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_fine_trained_w_imdb BertForSequenceClassification from Ycuu +author: John Snow Labs +name: bert_fine_trained_w_imdb +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_fine_trained_w_imdb` is a English model originally trained by Ycuu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_fine_trained_w_imdb_en_5.1.4_3.4_1698204733003.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_fine_trained_w_imdb_en_5.1.4_3.4_1698204733003.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_trained_w_imdb","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_trained_w_imdb","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_fine_trained_w_imdb| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/Ycuu/bert_fine_trained_w_imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_cola_huong_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_cola_huong_en.md new file mode 100644 index 00000000000000..e6c6c4486e4542 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_cola_huong_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_fine_tuned_cola_huong BertForSequenceClassification from Thi-Thu-Huong +author: John Snow Labs +name: bert_fine_tuned_cola_huong +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_fine_tuned_cola_huong` is a English model originally trained by Thi-Thu-Huong. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_fine_tuned_cola_huong_en_5.1.4_3.4_1698215254963.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_fine_tuned_cola_huong_en_5.1.4_3.4_1698215254963.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_tuned_cola_huong","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_tuned_cola_huong","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_fine_tuned_cola_huong| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/Thi-Thu-Huong/bert-fine-tuned-cola-huong \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_cola_kaori1707_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_cola_kaori1707_en.md new file mode 100644 index 00000000000000..2c19c631dedd1f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_cola_kaori1707_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_fine_tuned_cola_kaori1707 BertForSequenceClassification from Kaori1707 +author: John Snow Labs +name: bert_fine_tuned_cola_kaori1707 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_fine_tuned_cola_kaori1707` is a English model originally trained by Kaori1707. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_fine_tuned_cola_kaori1707_en_5.1.4_3.4_1698218279367.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_fine_tuned_cola_kaori1707_en_5.1.4_3.4_1698218279367.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_tuned_cola_kaori1707","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_tuned_cola_kaori1707","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_fine_tuned_cola_kaori1707| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/Kaori1707/bert-fine-tuned-cola \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_cola_ramuvannela_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_cola_ramuvannela_en.md new file mode 100644 index 00000000000000..4404fa1e49869d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_cola_ramuvannela_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_fine_tuned_cola_ramuvannela BertForSequenceClassification from Ramuvannela +author: John Snow Labs +name: bert_fine_tuned_cola_ramuvannela +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_fine_tuned_cola_ramuvannela` is a English model originally trained by Ramuvannela. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_fine_tuned_cola_ramuvannela_en_5.1.4_3.4_1698195337388.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_fine_tuned_cola_ramuvannela_en_5.1.4_3.4_1698195337388.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_tuned_cola_ramuvannela","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_tuned_cola_ramuvannela","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_fine_tuned_cola_ramuvannela| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/Ramuvannela/bert-fine-tuned-cola \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_cola_sanjay1234_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_cola_sanjay1234_en.md new file mode 100644 index 00000000000000..c5d0d4852ea038 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_cola_sanjay1234_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_fine_tuned_cola_sanjay1234 BertForSequenceClassification from Sanjay1234 +author: John Snow Labs +name: bert_fine_tuned_cola_sanjay1234 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_fine_tuned_cola_sanjay1234` is a English model originally trained by Sanjay1234. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_fine_tuned_cola_sanjay1234_en_5.1.4_3.4_1698210711894.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_fine_tuned_cola_sanjay1234_en_5.1.4_3.4_1698210711894.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_tuned_cola_sanjay1234","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_tuned_cola_sanjay1234","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_fine_tuned_cola_sanjay1234| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/Sanjay1234/bert-fine-tuned-cola \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_cola_tamiti1610001_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_cola_tamiti1610001_en.md new file mode 100644 index 00000000000000..699120b642ef62 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_cola_tamiti1610001_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_fine_tuned_cola_tamiti1610001 BertForSequenceClassification from tamiti1610001 +author: John Snow Labs +name: bert_fine_tuned_cola_tamiti1610001 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_fine_tuned_cola_tamiti1610001` is a English model originally trained by tamiti1610001. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_fine_tuned_cola_tamiti1610001_en_5.1.4_3.4_1698210205200.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_fine_tuned_cola_tamiti1610001_en_5.1.4_3.4_1698210205200.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_tuned_cola_tamiti1610001","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_tuned_cola_tamiti1610001","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_fine_tuned_cola_tamiti1610001| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/tamiti1610001/bert-fine-tuned-cola \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_english_news_2_lbl_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_english_news_2_lbl_en.md new file mode 100644 index 00000000000000..dec7f53d7ade7b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_english_news_2_lbl_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_fine_tuned_english_news_2_lbl BertForSequenceClassification from Ycuu +author: John Snow Labs +name: bert_fine_tuned_english_news_2_lbl +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_fine_tuned_english_news_2_lbl` is a English model originally trained by Ycuu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_fine_tuned_english_news_2_lbl_en_5.1.4_3.4_1698214452492.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_fine_tuned_english_news_2_lbl_en_5.1.4_3.4_1698214452492.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_tuned_english_news_2_lbl","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_tuned_english_news_2_lbl","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_fine_tuned_english_news_2_lbl| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Ycuu/bert_fine_tuned_en_news_2_lbl \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_english_news_3_lbl_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_english_news_3_lbl_en.md new file mode 100644 index 00000000000000..4a295c13f62833 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_english_news_3_lbl_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_fine_tuned_english_news_3_lbl BertForSequenceClassification from Ycuu +author: John Snow Labs +name: bert_fine_tuned_english_news_3_lbl +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_fine_tuned_english_news_3_lbl` is a English model originally trained by Ycuu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_fine_tuned_english_news_3_lbl_en_5.1.4_3.4_1698214268656.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_fine_tuned_english_news_3_lbl_en_5.1.4_3.4_1698214268656.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_tuned_english_news_3_lbl","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_tuned_english_news_3_lbl","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_fine_tuned_english_news_3_lbl| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Ycuu/bert_fine_tuned_en_news_3_lbl \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_intent_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_intent_en.md new file mode 100644 index 00000000000000..5cbea6f7d5c380 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_intent_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_fine_tuned_intent BertForSequenceClassification from deprem-ml +author: John Snow Labs +name: bert_fine_tuned_intent +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_fine_tuned_intent` is a English model originally trained by deprem-ml. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_fine_tuned_intent_en_5.1.4_3.4_1698200105021.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_fine_tuned_intent_en_5.1.4_3.4_1698200105021.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_tuned_intent","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_tuned_intent","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_fine_tuned_intent| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.5 MB| + +## References + +https://huggingface.co/deprem-ml/bert-fine-tuned-intent \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_patents_spanish_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_patents_spanish_en.md new file mode 100644 index 00000000000000..bdee19d58ee426 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_fine_tuned_patents_spanish_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_fine_tuned_patents_spanish BertForSequenceClassification from valcharriera +author: John Snow Labs +name: bert_fine_tuned_patents_spanish +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_fine_tuned_patents_spanish` is a English model originally trained by valcharriera. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_fine_tuned_patents_spanish_en_5.1.4_3.4_1698195794094.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_fine_tuned_patents_spanish_en_5.1.4_3.4_1698195794094.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_tuned_patents_spanish","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_fine_tuned_patents_spanish","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_fine_tuned_patents_spanish| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.9 MB| + +## References + +https://huggingface.co/valcharriera/bert-fine-tuned-patents-spanish \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_agnews_teacher_model_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_agnews_teacher_model_en.md new file mode 100644 index 00000000000000..f13ee944ab68af --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_agnews_teacher_model_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetuned_agnews_teacher_model BertForSequenceClassification from tamhuynh27 +author: John Snow Labs +name: bert_finetuned_agnews_teacher_model +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuned_agnews_teacher_model` is a English model originally trained by tamhuynh27. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuned_agnews_teacher_model_en_5.1.4_3.4_1698203547452.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuned_agnews_teacher_model_en_5.1.4_3.4_1698203547452.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_agnews_teacher_model","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_agnews_teacher_model","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuned_agnews_teacher_model| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/tamhuynh27/bert-finetuned-agnews-teacher-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_banking77test_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_banking77test_en.md new file mode 100644 index 00000000000000..13924f3c328d03 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_banking77test_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetuned_banking77test BertForSequenceClassification from Anna-UoC +author: John Snow Labs +name: bert_finetuned_banking77test +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuned_banking77test` is a English model originally trained by Anna-UoC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuned_banking77test_en_5.1.4_3.4_1698209129829.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuned_banking77test_en_5.1.4_3.4_1698209129829.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_banking77test","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_banking77test","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuned_banking77test| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.6 MB| + +## References + +https://huggingface.co/Anna-UoC/bert_finetuned_banking77test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_emotion_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_emotion_en.md new file mode 100644 index 00000000000000..be5e390dba1750 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_emotion_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetuned_emotion BertForSequenceClassification from PascalY +author: John Snow Labs +name: bert_finetuned_emotion +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuned_emotion` is a English model originally trained by PascalY. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuned_emotion_en_5.1.4_3.4_1698201395298.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuned_emotion_en_5.1.4_3.4_1698201395298.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_emotion","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_emotion","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuned_emotion| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/PascalY/bert-finetuned-emotion \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_imdb_wakaka_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_imdb_wakaka_en.md new file mode 100644 index 00000000000000..d547e9beaecf5e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_imdb_wakaka_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetuned_imdb_wakaka BertForSequenceClassification from Wakaka +author: John Snow Labs +name: bert_finetuned_imdb_wakaka +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuned_imdb_wakaka` is a English model originally trained by Wakaka. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuned_imdb_wakaka_en_5.1.4_3.4_1698202367748.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuned_imdb_wakaka_en_5.1.4_3.4_1698202367748.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_imdb_wakaka","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_imdb_wakaka","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuned_imdb_wakaka| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/Wakaka/bert-finetuned-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_mrpc_dineshmane_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_mrpc_dineshmane_en.md new file mode 100644 index 00000000000000..049516fb106f05 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_mrpc_dineshmane_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetuned_mrpc_dineshmane BertForSequenceClassification from dineshmane +author: John Snow Labs +name: bert_finetuned_mrpc_dineshmane +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuned_mrpc_dineshmane` is a English model originally trained by dineshmane. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuned_mrpc_dineshmane_en_5.1.4_3.4_1698197868461.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuned_mrpc_dineshmane_en_5.1.4_3.4_1698197868461.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_mrpc_dineshmane","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_mrpc_dineshmane","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuned_mrpc_dineshmane| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/dineshmane/bert-finetuned-mrpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_mrpc_yanael_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_mrpc_yanael_en.md new file mode 100644 index 00000000000000..da6a081a129f83 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_mrpc_yanael_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetuned_mrpc_yanael BertForSequenceClassification from Yanael +author: John Snow Labs +name: bert_finetuned_mrpc_yanael +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuned_mrpc_yanael` is a English model originally trained by Yanael. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuned_mrpc_yanael_en_5.1.4_3.4_1698197288978.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuned_mrpc_yanael_en_5.1.4_3.4_1698197288978.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_mrpc_yanael","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_mrpc_yanael","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuned_mrpc_yanael| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Yanael/bert-finetuned-mrpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_slavic_languages_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_slavic_languages_en.md new file mode 100644 index 00000000000000..0d5b4740c932de --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_slavic_languages_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetuned_slavic_languages BertForSequenceClassification from marmolpen3 +author: John Snow Labs +name: bert_finetuned_slavic_languages +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuned_slavic_languages` is a English model originally trained by marmolpen3. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuned_slavic_languages_en_5.1.4_3.4_1698195514666.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuned_slavic_languages_en_5.1.4_3.4_1698195514666.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_slavic_languages","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_slavic_languages","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuned_slavic_languages| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/marmolpen3/bert-finetuned-sla \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_for_pac_nl.md b/docs/_posts/ahmedlone127/2023-10-25-bert_for_pac_nl.md new file mode 100644 index 00000000000000..5fd004ed4173db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_for_pac_nl.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Dutch, Flemish bert_for_pac BertForSequenceClassification from Gerwin +author: John Snow Labs +name: bert_for_pac +date: 2023-10-25 +tags: [bert, nl, open_source, sequence_classification, onnx] +task: Text Classification +language: nl +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_for_pac` is a Dutch, Flemish model originally trained by Gerwin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_for_pac_nl_5.1.4_3.4_1698204809405.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_for_pac_nl_5.1.4_3.4_1698204809405.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_for_pac","nl")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_for_pac","nl") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_for_pac| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|nl| +|Size:|409.0 MB| + +## References + +https://huggingface.co/Gerwin/bert-for-pac \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_1_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_1_en.md new file mode 100644 index 00000000000000..f9b21706b2fc1a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_1_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_1 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_1 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_1` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_1_en_5.1.4_3.4_1698204051136.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_1_en_5.1.4_3.4_1698204051136.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_1","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_1","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_0_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_0_jeevesh8_en.md new file mode 100644 index 00000000000000..fd40c2b56cce42 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_0_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_0_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_0_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_0_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_0_jeevesh8_en_5.1.4_3.4_1698203592878.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_0_jeevesh8_en_5.1.4_3.4_1698203592878.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_0_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_0_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_0_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_10_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_10_jeevesh8_en.md new file mode 100644 index 00000000000000..a92057dd812b04 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_10_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_10_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_10_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_10_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_10_jeevesh8_en_5.1.4_3.4_1698205946880.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_10_jeevesh8_en_5.1.4_3.4_1698205946880.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_10_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_10_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_10_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_11_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_11_jeevesh8_en.md new file mode 100644 index 00000000000000..df1edea4a1376f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_11_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_11_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_11_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_11_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_11_jeevesh8_en_5.1.4_3.4_1698206127191.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_11_jeevesh8_en_5.1.4_3.4_1698206127191.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_11_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_11_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_11_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-11 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_12_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_12_jeevesh8_en.md new file mode 100644 index 00000000000000..b28d8360ab2e9a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_12_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_12_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_12_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_12_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_12_jeevesh8_en_5.1.4_3.4_1698206312706.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_12_jeevesh8_en_5.1.4_3.4_1698206312706.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_12_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_12_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_12_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-12 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_13_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_13_jeevesh8_en.md new file mode 100644 index 00000000000000..3b9d7649652dcc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_13_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_13_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_13_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_13_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_13_jeevesh8_en_5.1.4_3.4_1698206499211.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_13_jeevesh8_en_5.1.4_3.4_1698206499211.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_13_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_13_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_13_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-13 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_14_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_14_jeevesh8_en.md new file mode 100644 index 00000000000000..34d2566a29bba1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_14_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_14_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_14_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_14_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_14_jeevesh8_en_5.1.4_3.4_1698206689015.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_14_jeevesh8_en_5.1.4_3.4_1698206689015.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_14_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_14_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_14_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-14 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_15_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_15_jeevesh8_en.md new file mode 100644 index 00000000000000..3b4a0399267c9d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_15_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_15_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_15_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_15_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_15_jeevesh8_en_5.1.4_3.4_1698206892996.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_15_jeevesh8_en_5.1.4_3.4_1698206892996.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_15_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_15_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_15_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-15 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_16_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_16_jeevesh8_en.md new file mode 100644 index 00000000000000..164a09e5075538 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_16_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_16_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_16_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_16_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_16_jeevesh8_en_5.1.4_3.4_1698207096253.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_16_jeevesh8_en_5.1.4_3.4_1698207096253.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_16_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_16_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_16_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-16 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_17_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_17_jeevesh8_en.md new file mode 100644 index 00000000000000..6ae84e22abfd9e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_17_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_17_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_17_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_17_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_17_jeevesh8_en_5.1.4_3.4_1698207287384.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_17_jeevesh8_en_5.1.4_3.4_1698207287384.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_17_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_17_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_17_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-17 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_18_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_18_jeevesh8_en.md new file mode 100644 index 00000000000000..5636da9949ad10 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_18_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_18_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_18_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_18_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_18_jeevesh8_en_5.1.4_3.4_1698207518024.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_18_jeevesh8_en_5.1.4_3.4_1698207518024.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_18_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_18_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_18_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-18 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_19_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_19_jeevesh8_en.md new file mode 100644 index 00000000000000..a40c57576230db --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_19_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_19_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_19_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_19_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_19_jeevesh8_en_5.1.4_3.4_1698207755851.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_19_jeevesh8_en_5.1.4_3.4_1698207755851.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_19_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_19_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_19_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-19 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_1_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_1_jeevesh8_en.md new file mode 100644 index 00000000000000..cbe72184678aa2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_1_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_1_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_1_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_1_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_1_jeevesh8_en_5.1.4_3.4_1698204232841.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_1_jeevesh8_en_5.1.4_3.4_1698204232841.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_1_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_1_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_1_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_20_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_20_jeevesh8_en.md new file mode 100644 index 00000000000000..441227b92234f2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_20_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_20_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_20_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_20_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_20_jeevesh8_en_5.1.4_3.4_1698207967403.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_20_jeevesh8_en_5.1.4_3.4_1698207967403.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_20_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_20_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_20_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_21_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_21_jeevesh8_en.md new file mode 100644 index 00000000000000..9ca5d3369483e7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_21_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_21_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_21_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_21_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_21_jeevesh8_en_5.1.4_3.4_1698208211620.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_21_jeevesh8_en_5.1.4_3.4_1698208211620.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_21_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_21_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_21_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-21 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_22_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_22_jeevesh8_en.md new file mode 100644 index 00000000000000..5ba6dc6d6f5c3b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_22_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_22_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_22_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_22_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_22_jeevesh8_en_5.1.4_3.4_1698208388737.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_22_jeevesh8_en_5.1.4_3.4_1698208388737.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_22_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_22_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_22_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-22 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_23_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_23_jeevesh8_en.md new file mode 100644 index 00000000000000..8e10b6d7641a9d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_23_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_23_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_23_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_23_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_23_jeevesh8_en_5.1.4_3.4_1698208563784.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_23_jeevesh8_en_5.1.4_3.4_1698208563784.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_23_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_23_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_23_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-23 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_25_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_25_jeevesh8_en.md new file mode 100644 index 00000000000000..ddeb8731a755cf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_25_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_25_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_25_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_25_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_25_jeevesh8_en_5.1.4_3.4_1698208915416.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_25_jeevesh8_en_5.1.4_3.4_1698208915416.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_25_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_25_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_25_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-25 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_26_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_26_jeevesh8_en.md new file mode 100644 index 00000000000000..99e7829ad3ec4b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_26_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_26_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_26_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_26_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_26_jeevesh8_en_5.1.4_3.4_1698209089585.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_26_jeevesh8_en_5.1.4_3.4_1698209089585.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_26_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_26_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_26_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-26 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_27_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_27_jeevesh8_en.md new file mode 100644 index 00000000000000..07893b82943194 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_27_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_27_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_27_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_27_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_27_jeevesh8_en_5.1.4_3.4_1698209273711.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_27_jeevesh8_en_5.1.4_3.4_1698209273711.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_27_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_27_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_27_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-27 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_28_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_28_jeevesh8_en.md new file mode 100644 index 00000000000000..b1dd43fc162443 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_28_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_28_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_28_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_28_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_28_jeevesh8_en_5.1.4_3.4_1698209449348.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_28_jeevesh8_en_5.1.4_3.4_1698209449348.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_28_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_28_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_28_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_29_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_29_jeevesh8_en.md new file mode 100644 index 00000000000000..d85188a4da4b91 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_29_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_29_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_29_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_29_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_29_jeevesh8_en_5.1.4_3.4_1698209636455.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_29_jeevesh8_en_5.1.4_3.4_1698209636455.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_29_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_29_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_29_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-29 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_2_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_2_jeevesh8_en.md new file mode 100644 index 00000000000000..de775ad8c1ab3f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_2_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_2_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_2_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_2_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_2_jeevesh8_en_5.1.4_3.4_1698204420000.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_2_jeevesh8_en_5.1.4_3.4_1698204420000.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_2_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_2_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_2_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_30_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_30_jeevesh8_en.md new file mode 100644 index 00000000000000..f7407064f08816 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_30_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_30_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_30_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_30_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_30_jeevesh8_en_5.1.4_3.4_1698209832868.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_30_jeevesh8_en_5.1.4_3.4_1698209832868.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_30_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_30_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_30_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-30 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_32_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_32_jeevesh8_en.md new file mode 100644 index 00000000000000..32f97dee543e44 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_32_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_32_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_32_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_32_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_32_jeevesh8_en_5.1.4_3.4_1698210183570.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_32_jeevesh8_en_5.1.4_3.4_1698210183570.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_32_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_32_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_32_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-32 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_34_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_34_jeevesh8_en.md new file mode 100644 index 00000000000000..8330425469eb9e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_34_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_34_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_34_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_34_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_34_jeevesh8_en_5.1.4_3.4_1698210562574.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_34_jeevesh8_en_5.1.4_3.4_1698210562574.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_34_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_34_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_34_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-34 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_37_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_37_jeevesh8_en.md new file mode 100644 index 00000000000000..03aaedc6d91ed0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_37_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_37_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_37_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_37_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_37_jeevesh8_en_5.1.4_3.4_1698211116252.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_37_jeevesh8_en_5.1.4_3.4_1698211116252.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_37_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_37_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_37_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-37 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_39_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_39_jeevesh8_en.md new file mode 100644 index 00000000000000..4492e5b16263eb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_39_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_39_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_39_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_39_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_39_jeevesh8_en_5.1.4_3.4_1698211490664.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_39_jeevesh8_en_5.1.4_3.4_1698211490664.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_39_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_39_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_39_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-39 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_3_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_3_jeevesh8_en.md new file mode 100644 index 00000000000000..9d2958d6a1cc2f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_3_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_3_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_3_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_3_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_3_jeevesh8_en_5.1.4_3.4_1698204610936.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_3_jeevesh8_en_5.1.4_3.4_1698204610936.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_3_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_3_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_3_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_40_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_40_jeevesh8_en.md new file mode 100644 index 00000000000000..2bcb77a1b79224 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_40_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_40_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_40_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_40_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_40_jeevesh8_en_5.1.4_3.4_1698211661814.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_40_jeevesh8_en_5.1.4_3.4_1698211661814.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_40_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_40_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_40_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-40 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_41_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_41_jeevesh8_en.md new file mode 100644 index 00000000000000..2fbc54490a101a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_41_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_41_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_41_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_41_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_41_jeevesh8_en_5.1.4_3.4_1698211837919.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_41_jeevesh8_en_5.1.4_3.4_1698211837919.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_41_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_41_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_41_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-41 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_43_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_43_jeevesh8_en.md new file mode 100644 index 00000000000000..ab6606d2897fe1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_43_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_43_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_43_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_43_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_43_jeevesh8_en_5.1.4_3.4_1698212258279.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_43_jeevesh8_en_5.1.4_3.4_1698212258279.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_43_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_43_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_43_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-43 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_44_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_44_jeevesh8_en.md new file mode 100644 index 00000000000000..5af2d3af8f2aeb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_44_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_44_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_44_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_44_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_44_jeevesh8_en_5.1.4_3.4_1698212459706.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_44_jeevesh8_en_5.1.4_3.4_1698212459706.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_44_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_44_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_44_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-44 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_46_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_46_jeevesh8_en.md new file mode 100644 index 00000000000000..93434b5b041dce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_46_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_46_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_46_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_46_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_46_jeevesh8_en_5.1.4_3.4_1698212811977.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_46_jeevesh8_en_5.1.4_3.4_1698212811977.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_46_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_46_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_46_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-46 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_47_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_47_jeevesh8_en.md new file mode 100644 index 00000000000000..e2cd239cc85577 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_47_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_47_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_47_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_47_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_47_jeevesh8_en_5.1.4_3.4_1698213009569.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_47_jeevesh8_en_5.1.4_3.4_1698213009569.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_47_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_47_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_47_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-47 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_48_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_48_jeevesh8_en.md new file mode 100644 index 00000000000000..0b9fb40ae5fb5c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_48_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_48_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_48_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_48_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_48_jeevesh8_en_5.1.4_3.4_1698213183262.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_48_jeevesh8_en_5.1.4_3.4_1698213183262.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_48_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_48_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_48_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-48 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_49_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_49_jeevesh8_en.md new file mode 100644 index 00000000000000..1301a3d3b1bda6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_49_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_49_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_49_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_49_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_49_jeevesh8_en_5.1.4_3.4_1698213351499.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_49_jeevesh8_en_5.1.4_3.4_1698213351499.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_49_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_49_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_49_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-49 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_4_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_4_jeevesh8_en.md new file mode 100644 index 00000000000000..1cf08a09656a41 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_4_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_4_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_4_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_4_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_4_jeevesh8_en_5.1.4_3.4_1698204829567.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_4_jeevesh8_en_5.1.4_3.4_1698204829567.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_4_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_4_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_4_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_50_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_50_jeevesh8_en.md new file mode 100644 index 00000000000000..3c78b5d05dc2bf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_50_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_50_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_50_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_50_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_50_jeevesh8_en_5.1.4_3.4_1698213522662.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_50_jeevesh8_en_5.1.4_3.4_1698213522662.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_50_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_50_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_50_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-50 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_51_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_51_jeevesh8_en.md new file mode 100644 index 00000000000000..18968564b326dc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_51_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_51_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_51_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_51_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_51_jeevesh8_en_5.1.4_3.4_1698213702714.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_51_jeevesh8_en_5.1.4_3.4_1698213702714.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_51_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_51_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_51_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-51 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_53_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_53_jeevesh8_en.md new file mode 100644 index 00000000000000..0e7b90981d9a9d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_53_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_53_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_53_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_53_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_53_jeevesh8_en_5.1.4_3.4_1698214108834.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_53_jeevesh8_en_5.1.4_3.4_1698214108834.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_53_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_53_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_53_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-53 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_54_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_54_jeevesh8_en.md new file mode 100644 index 00000000000000..b35491e1ee11ec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_54_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_54_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_54_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_54_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_54_jeevesh8_en_5.1.4_3.4_1698214298205.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_54_jeevesh8_en_5.1.4_3.4_1698214298205.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_54_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_54_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_54_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-54 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_57_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_57_jeevesh8_en.md new file mode 100644 index 00000000000000..725a89b01ea523 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_57_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_57_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_57_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_57_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_57_jeevesh8_en_5.1.4_3.4_1698214829790.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_57_jeevesh8_en_5.1.4_3.4_1698214829790.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_57_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_57_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_57_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-57 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_58_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_58_jeevesh8_en.md new file mode 100644 index 00000000000000..36d2d688310ea4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_58_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_58_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_58_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_58_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_58_jeevesh8_en_5.1.4_3.4_1698215018977.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_58_jeevesh8_en_5.1.4_3.4_1698215018977.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_58_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_58_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_58_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-58 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_59_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_59_jeevesh8_en.md new file mode 100644 index 00000000000000..0b9bc48e44cd70 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_59_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_59_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_59_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_59_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_59_jeevesh8_en_5.1.4_3.4_1698215223743.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_59_jeevesh8_en_5.1.4_3.4_1698215223743.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_59_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_59_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_59_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-59 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_5_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_5_jeevesh8_en.md new file mode 100644 index 00000000000000..07a3667736a558 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_5_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_5_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_5_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_5_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_5_jeevesh8_en_5.1.4_3.4_1698205035497.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_5_jeevesh8_en_5.1.4_3.4_1698205035497.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_5_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_5_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_5_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_60_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_60_jeevesh8_en.md new file mode 100644 index 00000000000000..3eafab9744c5f4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_60_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_60_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_60_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_60_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_60_jeevesh8_en_5.1.4_3.4_1698215414427.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_60_jeevesh8_en_5.1.4_3.4_1698215414427.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_60_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_60_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_60_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-60 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_61_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_61_jeevesh8_en.md new file mode 100644 index 00000000000000..2e1a12c9430fe3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_61_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_61_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_61_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_61_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_61_jeevesh8_en_5.1.4_3.4_1698215638548.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_61_jeevesh8_en_5.1.4_3.4_1698215638548.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_61_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_61_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_61_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-61 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_63_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_63_jeevesh8_en.md new file mode 100644 index 00000000000000..e889680e085936 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_63_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_63_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_63_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_63_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_63_jeevesh8_en_5.1.4_3.4_1698216072292.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_63_jeevesh8_en_5.1.4_3.4_1698216072292.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_63_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_63_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_63_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-63 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_65_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_65_jeevesh8_en.md new file mode 100644 index 00000000000000..1ce4c579d098e5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_65_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_65_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_65_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_65_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_65_jeevesh8_en_5.1.4_3.4_1698216457035.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_65_jeevesh8_en_5.1.4_3.4_1698216457035.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_65_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_65_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_65_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-65 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_66_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_66_jeevesh8_en.md new file mode 100644 index 00000000000000..27fac2a0ae0207 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_66_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_66_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_66_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_66_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_66_jeevesh8_en_5.1.4_3.4_1698216620636.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_66_jeevesh8_en_5.1.4_3.4_1698216620636.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_66_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_66_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_66_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-66 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_67_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_67_jeevesh8_en.md new file mode 100644 index 00000000000000..eae3c73f4f9f0e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_67_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_67_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_67_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_67_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_67_jeevesh8_en_5.1.4_3.4_1698216795716.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_67_jeevesh8_en_5.1.4_3.4_1698216795716.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_67_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_67_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_67_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-67 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_69_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_69_jeevesh8_en.md new file mode 100644 index 00000000000000..9cce706ffa0aec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_69_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_69_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_69_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_69_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_69_jeevesh8_en_5.1.4_3.4_1698217190353.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_69_jeevesh8_en_5.1.4_3.4_1698217190353.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_69_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_69_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_69_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-69 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_70_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_70_jeevesh8_en.md new file mode 100644 index 00000000000000..d4bd1e1d1d9d9e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_70_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_70_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_70_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_70_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_70_jeevesh8_en_5.1.4_3.4_1698217393089.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_70_jeevesh8_en_5.1.4_3.4_1698217393089.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_70_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_70_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_70_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-70 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_71_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_71_jeevesh8_en.md new file mode 100644 index 00000000000000..3ce95967034753 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_71_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_71_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_71_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_71_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_71_jeevesh8_en_5.1.4_3.4_1698217576041.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_71_jeevesh8_en_5.1.4_3.4_1698217576041.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_71_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_71_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_71_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-71 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_74_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_74_jeevesh8_en.md new file mode 100644 index 00000000000000..6a41d5c80afd44 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_74_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_74_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_74_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_74_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_74_jeevesh8_en_5.1.4_3.4_1698218235028.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_74_jeevesh8_en_5.1.4_3.4_1698218235028.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_74_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_74_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_74_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-74 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_76_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_76_jeevesh8_en.md new file mode 100644 index 00000000000000..73fc21ae970d18 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_76_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_76_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_76_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_76_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_76_jeevesh8_en_5.1.4_3.4_1698218619964.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_76_jeevesh8_en_5.1.4_3.4_1698218619964.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_76_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_76_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_76_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-76 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_78_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_78_jeevesh8_en.md new file mode 100644 index 00000000000000..7241bbde425e57 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_78_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_78_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_78_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_78_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_78_jeevesh8_en_5.1.4_3.4_1698219051215.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_78_jeevesh8_en_5.1.4_3.4_1698219051215.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_78_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_78_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_78_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-78 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_7_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_7_jeevesh8_en.md new file mode 100644 index 00000000000000..7e9d290f3d0a36 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_7_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_7_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_7_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_7_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_7_jeevesh8_en_5.1.4_3.4_1698205437574.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_7_jeevesh8_en_5.1.4_3.4_1698205437574.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_7_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_7_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_7_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_8_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_8_jeevesh8_en.md new file mode 100644 index 00000000000000..1667b37b671aff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_8_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_8_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_8_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_8_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_8_jeevesh8_en_5.1.4_3.4_1698205615887.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_8_jeevesh8_en_5.1.4_3.4_1698205615887.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_8_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_8_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_8_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-8 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_9_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_9_jeevesh8_en.md new file mode 100644 index 00000000000000..7b436fa04aad56 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_9_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_9_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_9_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_9_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_9_jeevesh8_en_5.1.4_3.4_1698205770457.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_9_jeevesh8_en_5.1.4_3.4_1698205770457.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_9_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_9_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_9_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-9 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_iphone_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_iphone_en.md new file mode 100644 index 00000000000000..1673ce96dc18f1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_iphone_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_iphone BertForSequenceClassification from konrad-wesub +author: John Snow Labs +name: bert_iphone +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_iphone` is a English model originally trained by konrad-wesub. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_iphone_en_5.1.4_3.4_1698197859635.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_iphone_en_5.1.4_3.4_1698197859635.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_iphone","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_iphone","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_iphone| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|667.3 MB| + +## References + +https://huggingface.co/konrad-wesub/bert-iphone \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_italian_xxl_cased_itacola_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_italian_xxl_cased_itacola_en.md new file mode 100644 index 00000000000000..cc42d85c8ae707 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_italian_xxl_cased_itacola_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_italian_xxl_cased_itacola BertForSequenceClassification from Bainbridge +author: John Snow Labs +name: bert_italian_xxl_cased_itacola +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_italian_xxl_cased_itacola` is a English model originally trained by Bainbridge. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_italian_xxl_cased_itacola_en_5.1.4_3.4_1698208845995.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_italian_xxl_cased_itacola_en_5.1.4_3.4_1698208845995.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_italian_xxl_cased_itacola","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_italian_xxl_cased_itacola","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_italian_xxl_cased_itacola| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.8 MB| + +## References + +https://huggingface.co/Bainbridge/bert-italian-xxl-cased-ItaCoLA \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_arabertv2_finetuned_emotion_3_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_arabertv2_finetuned_emotion_3_en.md new file mode 100644 index 00000000000000..4f5f98e114aa9d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_arabertv2_finetuned_emotion_3_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_arabertv2_finetuned_emotion_3 BertForSequenceClassification from MahaJar +author: John Snow Labs +name: bert_large_arabertv2_finetuned_emotion_3 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_arabertv2_finetuned_emotion_3` is a English model originally trained by MahaJar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_arabertv2_finetuned_emotion_3_en_5.1.4_3.4_1698203316022.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_arabertv2_finetuned_emotion_3_en_5.1.4_3.4_1698203316022.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_arabertv2_finetuned_emotion_3","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_arabertv2_finetuned_emotion_3","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_arabertv2_finetuned_emotion_3| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.4 GB| + +## References + +https://huggingface.co/MahaJar/bert-large-arabertv2-finetuned-emotion_3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_cased_detect_dep_v2_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_cased_detect_dep_v2_en.md new file mode 100644 index 00000000000000..c6626c591bc059 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_cased_detect_dep_v2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_cased_detect_dep_v2 BertForSequenceClassification from Trong-Nghia +author: John Snow Labs +name: bert_large_cased_detect_dep_v2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_cased_detect_dep_v2` is a English model originally trained by Trong-Nghia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_cased_detect_dep_v2_en_5.1.4_3.4_1698199038086.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_cased_detect_dep_v2_en_5.1.4_3.4_1698199038086.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_cased_detect_dep_v2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_cased_detect_dep_v2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_cased_detect_dep_v2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/Trong-Nghia/bert-large-cased-detect-dep-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_mnli_3ep_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_mnli_3ep_en.md new file mode 100644 index 00000000000000..4497b08bb6f9f9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_mnli_3ep_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_mnli_3ep BertForSequenceClassification from TehranNLP-org +author: John Snow Labs +name: bert_large_mnli_3ep +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_mnli_3ep` is a English model originally trained by TehranNLP-org. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_mnli_3ep_en_5.1.4_3.4_1698194606103.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_mnli_3ep_en_5.1.4_3.4_1698194606103.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_mnli_3ep","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_mnli_3ep","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_mnli_3ep| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/TehranNLP-org/bert-large-mnli-3ep \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_cola_a_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_cola_a_en.md new file mode 100644 index 00000000000000..5579e40e9d6f6e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_cola_a_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_cola_a BertForSequenceClassification from EhsanAghazadeh +author: John Snow Labs +name: bert_large_uncased_cola_a +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_cola_a` is a English model originally trained by EhsanAghazadeh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_cola_a_en_5.1.4_3.4_1698203702299.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_cola_a_en_5.1.4_3.4_1698203702299.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_cola_a","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_cola_a","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_cola_a| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/EhsanAghazadeh/bert-large-uncased-CoLA_A \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v10_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v10_en.md new file mode 100644 index 00000000000000..ed42d27b7436c6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v10_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_detect_dep_v10 BertForSequenceClassification from Trong-Nghia +author: John Snow Labs +name: bert_large_uncased_detect_dep_v10 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_detect_dep_v10` is a English model originally trained by Trong-Nghia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_dep_v10_en_5.1.4_3.4_1698202953217.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_dep_v10_en_5.1.4_3.4_1698202953217.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_dep_v10","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_dep_v10","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_detect_dep_v10| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/Trong-Nghia/bert-large-uncased-detect-dep-v10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v3_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v3_en.md new file mode 100644 index 00000000000000..29ab5c5385bf4b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v3_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_detect_dep_v3 BertForSequenceClassification from Trong-Nghia +author: John Snow Labs +name: bert_large_uncased_detect_dep_v3 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_detect_dep_v3` is a English model originally trained by Trong-Nghia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_dep_v3_en_5.1.4_3.4_1698194887433.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_dep_v3_en_5.1.4_3.4_1698194887433.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_dep_v3","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_dep_v3","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_detect_dep_v3| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/Trong-Nghia/bert-large-uncased-detect-dep-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v4_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v4_en.md new file mode 100644 index 00000000000000..237f3d7352f6cc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v4_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_detect_dep_v4 BertForSequenceClassification from Trong-Nghia +author: John Snow Labs +name: bert_large_uncased_detect_dep_v4 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_detect_dep_v4` is a English model originally trained by Trong-Nghia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_dep_v4_en_5.1.4_3.4_1698196092484.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_dep_v4_en_5.1.4_3.4_1698196092484.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_dep_v4","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_dep_v4","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_detect_dep_v4| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/Trong-Nghia/bert-large-uncased-detect-dep-v4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v5_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v5_en.md new file mode 100644 index 00000000000000..9f06cfb7e1a675 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v5_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_detect_dep_v5 BertForSequenceClassification from Trong-Nghia +author: John Snow Labs +name: bert_large_uncased_detect_dep_v5 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_detect_dep_v5` is a English model originally trained by Trong-Nghia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_dep_v5_en_5.1.4_3.4_1698197305214.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_dep_v5_en_5.1.4_3.4_1698197305214.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_dep_v5","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_dep_v5","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_detect_dep_v5| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/Trong-Nghia/bert-large-uncased-detect-dep-v5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v6_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v6_en.md new file mode 100644 index 00000000000000..2152a2072767a2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v6_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_detect_dep_v6 BertForSequenceClassification from Trong-Nghia +author: John Snow Labs +name: bert_large_uncased_detect_dep_v6 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_detect_dep_v6` is a English model originally trained by Trong-Nghia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_dep_v6_en_5.1.4_3.4_1698197844718.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_dep_v6_en_5.1.4_3.4_1698197844718.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_dep_v6","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_dep_v6","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_detect_dep_v6| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/Trong-Nghia/bert-large-uncased-detect-dep-v6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v7_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v7_en.md new file mode 100644 index 00000000000000..67b9f8352d4544 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v7_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_detect_dep_v7 BertForSequenceClassification from Trong-Nghia +author: John Snow Labs +name: bert_large_uncased_detect_dep_v7 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_detect_dep_v7` is a English model originally trained by Trong-Nghia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_dep_v7_en_5.1.4_3.4_1698199359811.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_dep_v7_en_5.1.4_3.4_1698199359811.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_dep_v7","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_dep_v7","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_detect_dep_v7| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/Trong-Nghia/bert-large-uncased-detect-dep-v7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v8_en.md new file mode 100644 index 00000000000000..0aa14af21905ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_detect_dep_v8 BertForSequenceClassification from Trong-Nghia +author: John Snow Labs +name: bert_large_uncased_detect_dep_v8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_detect_dep_v8` is a English model originally trained by Trong-Nghia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_dep_v8_en_5.1.4_3.4_1698200112307.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_dep_v8_en_5.1.4_3.4_1698200112307.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_dep_v8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_dep_v8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_detect_dep_v8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/Trong-Nghia/bert-large-uncased-detect-dep-v8 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v9_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v9_en.md new file mode 100644 index 00000000000000..248307e5797d98 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_dep_v9_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_detect_dep_v9 BertForSequenceClassification from Trong-Nghia +author: John Snow Labs +name: bert_large_uncased_detect_dep_v9 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_detect_dep_v9` is a English model originally trained by Trong-Nghia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_dep_v9_en_5.1.4_3.4_1698200994577.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_dep_v9_en_5.1.4_3.4_1698200994577.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_dep_v9","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_dep_v9","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_detect_dep_v9| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/Trong-Nghia/bert-large-uncased-detect-dep-v9 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_depression_stage_one_v3_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_depression_stage_one_v3_en.md new file mode 100644 index 00000000000000..fa3e4d62cf706c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_depression_stage_one_v3_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_detect_depression_stage_one_v3 BertForSequenceClassification from hoanghoavienvo +author: John Snow Labs +name: bert_large_uncased_detect_depression_stage_one_v3 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_detect_depression_stage_one_v3` is a English model originally trained by hoanghoavienvo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_depression_stage_one_v3_en_5.1.4_3.4_1698192511564.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_depression_stage_one_v3_en_5.1.4_3.4_1698192511564.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_depression_stage_one_v3","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_depression_stage_one_v3","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_detect_depression_stage_one_v3| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/hoanghoavienvo/bert-large-uncased-detect-depression-stage-one-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_depression_stage_one_v4_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_depression_stage_one_v4_en.md new file mode 100644 index 00000000000000..261fa9a94f8570 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_detect_depression_stage_one_v4_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_detect_depression_stage_one_v4 BertForSequenceClassification from hoanghoavienvo +author: John Snow Labs +name: bert_large_uncased_detect_depression_stage_one_v4 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_detect_depression_stage_one_v4` is a English model originally trained by hoanghoavienvo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_depression_stage_one_v4_en_5.1.4_3.4_1698193240825.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_detect_depression_stage_one_v4_en_5.1.4_3.4_1698193240825.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_depression_stage_one_v4","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_detect_depression_stage_one_v4","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_detect_depression_stage_one_v4| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/hoanghoavienvo/bert-large-uncased-detect-depression-stage-one-v4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_16_13_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_16_13_en.md new file mode 100644 index 00000000000000..42aebe39703aa9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_16_13_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_sst_2_16_13 BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_large_uncased_sst_2_16_13 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_sst_2_16_13` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sst_2_16_13_en_5.1.4_3.4_1698213051929.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sst_2_16_13_en_5.1.4_3.4_1698213051929.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_sst_2_16_13","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_sst_2_16_13","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_sst_2_16_13| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/simonycl/bert-large-uncased-sst-2-16-13 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_32_13_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_32_13_en.md new file mode 100644 index 00000000000000..ddab8271fd3644 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_32_13_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_sst_2_32_13 BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_large_uncased_sst_2_32_13 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_sst_2_32_13` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sst_2_32_13_en_5.1.4_3.4_1698213363304.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sst_2_32_13_en_5.1.4_3.4_1698213363304.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_sst_2_32_13","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_sst_2_32_13","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_sst_2_32_13| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/simonycl/bert-large-uncased-sst-2-32-13 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_32_13_smoothed_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_32_13_smoothed_en.md new file mode 100644 index 00000000000000..01a6edabf1813d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_32_13_smoothed_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_sst_2_32_13_smoothed BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_large_uncased_sst_2_32_13_smoothed +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_sst_2_32_13_smoothed` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sst_2_32_13_smoothed_en_5.1.4_3.4_1698215795624.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sst_2_32_13_smoothed_en_5.1.4_3.4_1698215795624.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_sst_2_32_13_smoothed","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_sst_2_32_13_smoothed","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_sst_2_32_13_smoothed| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/simonycl/bert-large-uncased-sst-2-32-13-smoothed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_64_13_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_64_13_en.md new file mode 100644 index 00000000000000..1d1bd3cc2072b8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_64_13_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_sst_2_64_13 BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_large_uncased_sst_2_64_13 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_sst_2_64_13` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sst_2_64_13_en_5.1.4_3.4_1698213724985.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sst_2_64_13_en_5.1.4_3.4_1698213724985.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_sst_2_64_13","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_sst_2_64_13","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_sst_2_64_13| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/simonycl/bert-large-uncased-sst-2-64-13 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_64_13_smoothed_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_64_13_smoothed_en.md new file mode 100644 index 00000000000000..049a4b9154e80a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_64_13_smoothed_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_sst_2_64_13_smoothed BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_large_uncased_sst_2_64_13_smoothed +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_sst_2_64_13_smoothed` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sst_2_64_13_smoothed_en_5.1.4_3.4_1698216168271.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sst_2_64_13_smoothed_en_5.1.4_3.4_1698216168271.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_sst_2_64_13_smoothed","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_sst_2_64_13_smoothed","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_sst_2_64_13_smoothed| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/simonycl/bert-large-uncased-sst-2-64-13-smoothed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_stereoset_finetuned_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_stereoset_finetuned_en.md new file mode 100644 index 00000000000000..d9eaef2a8af3bf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_stereoset_finetuned_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_stereoset_finetuned BertForSequenceClassification from henryscheible +author: John Snow Labs +name: bert_large_uncased_stereoset_finetuned +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_stereoset_finetuned` is a English model originally trained by henryscheible. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_stereoset_finetuned_en_5.1.4_3.4_1698200996741.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_stereoset_finetuned_en_5.1.4_3.4_1698200996741.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_stereoset_finetuned","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_stereoset_finetuned","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_stereoset_finetuned| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/henryscheible/bert-large-uncased_stereoset_finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_with_preprocess_finetuned_emotion_5_epochs_5e_05_lr_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_with_preprocess_finetuned_emotion_5_epochs_5e_05_lr_en.md new file mode 100644 index 00000000000000..0979b32d51dfe9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_with_preprocess_finetuned_emotion_5_epochs_5e_05_lr_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_with_preprocess_finetuned_emotion_5_epochs_5e_05_lr BertForSequenceClassification from Ioanaaaaaaa +author: John Snow Labs +name: bert_large_uncased_with_preprocess_finetuned_emotion_5_epochs_5e_05_lr +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_with_preprocess_finetuned_emotion_5_epochs_5e_05_lr` is a English model originally trained by Ioanaaaaaaa. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_with_preprocess_finetuned_emotion_5_epochs_5e_05_lr_en_5.1.4_3.4_1698194379064.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_with_preprocess_finetuned_emotion_5_epochs_5e_05_lr_en_5.1.4_3.4_1698194379064.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_with_preprocess_finetuned_emotion_5_epochs_5e_05_lr","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_with_preprocess_finetuned_emotion_5_epochs_5e_05_lr","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_with_preprocess_finetuned_emotion_5_epochs_5e_05_lr| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/Ioanaaaaaaa/bert-large-uncased-with-preprocess-finetuned-emotion-5-epochs-5e-05-lr \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_lf_bond_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_lf_bond_en.md new file mode 100644 index 00000000000000..514f68cabbf9e2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_lf_bond_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_lf_bond BertForSequenceClassification from himanshubeniwal +author: John Snow Labs +name: bert_lf_bond +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_lf_bond` is a English model originally trained by himanshubeniwal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_lf_bond_en_5.1.4_3.4_1698213000387.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_lf_bond_en_5.1.4_3.4_1698213000387.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_lf_bond","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_lf_bond","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_lf_bond| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/himanshubeniwal/bert_lf_bond \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_lf_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_lf_en.md new file mode 100644 index 00000000000000..80d99f1f49cc92 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_lf_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_lf BertForSequenceClassification from himanshubeniwal +author: John Snow Labs +name: bert_lf +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_lf` is a English model originally trained by himanshubeniwal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_lf_en_5.1.4_3.4_1698209907388.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_lf_en_5.1.4_3.4_1698209907388.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_lf","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_lf","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_lf| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/himanshubeniwal/bert_lf \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_multilingual_go_emtions_xx.md b/docs/_posts/ahmedlone127/2023-10-25-bert_multilingual_go_emtions_xx.md new file mode 100644 index 00000000000000..d4bad723fd20dd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_multilingual_go_emtions_xx.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Multilingual bert_multilingual_go_emtions BertForSequenceClassification from SchuylerH +author: John Snow Labs +name: bert_multilingual_go_emtions +date: 2023-10-25 +tags: [bert, xx, open_source, sequence_classification, onnx] +task: Text Classification +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_multilingual_go_emtions` is a Multilingual model originally trained by SchuylerH. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_multilingual_go_emtions_xx_5.1.4_3.4_1698200634121.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_multilingual_go_emtions_xx_5.1.4_3.4_1698200634121.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_multilingual_go_emtions","xx")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_multilingual_go_emtions","xx") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_multilingual_go_emtions| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|xx| +|Size:|667.3 MB| + +## References + +https://huggingface.co/SchuylerH/bert-multilingual-go-emtions \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_multilingual_sentiment_xx.md b/docs/_posts/ahmedlone127/2023-10-25-bert_multilingual_sentiment_xx.md new file mode 100644 index 00000000000000..06334346e1aa5f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_multilingual_sentiment_xx.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Multilingual bert_multilingual_sentiment BertForSequenceClassification from marianna13 +author: John Snow Labs +name: bert_multilingual_sentiment +date: 2023-10-25 +tags: [bert, xx, open_source, sequence_classification, onnx] +task: Text Classification +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_multilingual_sentiment` is a Multilingual model originally trained by marianna13. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_multilingual_sentiment_xx_5.1.4_3.4_1698214796118.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_multilingual_sentiment_xx_5.1.4_3.4_1698214796118.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_multilingual_sentiment","xx")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_multilingual_sentiment","xx") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_multilingual_sentiment| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|xx| +|Size:|627.7 MB| + +## References + +https://huggingface.co/marianna13/bert-multilingual-sentiment \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_regression_dynamic_tokenlimit_v1_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_regression_dynamic_tokenlimit_v1_en.md new file mode 100644 index 00000000000000..24d3d546df2032 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_regression_dynamic_tokenlimit_v1_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_regression_dynamic_tokenlimit_v1 BertForSequenceClassification from OZ1150 +author: John Snow Labs +name: bert_regression_dynamic_tokenlimit_v1 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_regression_dynamic_tokenlimit_v1` is a English model originally trained by OZ1150. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_regression_dynamic_tokenlimit_v1_en_5.1.4_3.4_1698205343608.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_regression_dynamic_tokenlimit_v1_en_5.1.4_3.4_1698205343608.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_regression_dynamic_tokenlimit_v1","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_regression_dynamic_tokenlimit_v1","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_regression_dynamic_tokenlimit_v1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/OZ1150/bert-regression-dynamic-tokenlimit-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_arabic_ar.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_arabic_ar.md new file mode 100644 index 00000000000000..69961195e6eb56 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_arabic_ar.md @@ -0,0 +1,102 @@ +--- +layout: model +title: Arabic BertForSequenceClassification Cased model (from Hate-speech-CNERG) +author: John Snow Labs +name: bert_sequence_classifier_dehate_mono_arabic +date: 2023-10-25 +tags: [ar, open_source, bert, sequence_classification, ner, onnx] +task: Named Entity Recognition +language: ar +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dehatebert-mono-arabic` is a Arabic model originally trained by `Hate-speech-CNERG`. + +## Predicted Entities + +`NON_HATE`, `HATE` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_dehate_mono_arabic_ar_5.1.4_3.4_1698205916789.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_dehate_mono_arabic_ar_5.1.4_3.4_1698205916789.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_dehate_mono_arabic","ar") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_dehate_mono_arabic","ar") + .setInputCols(Array("document", "token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_dehate_mono_arabic| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|ar| +|Size:|627.7 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +References + +- https://huggingface.co/Hate-speech-CNERG/dehatebert-mono-arabic +- https://github.com/punyajoy/DE-LIMIT +- https://arxiv.org/abs/2004.06465 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_english_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_english_en.md new file mode 100644 index 00000000000000..913670c6ea41f1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_english_en.md @@ -0,0 +1,102 @@ +--- +layout: model +title: English BertForSequenceClassification Cased model (from Hate-speech-CNERG) +author: John Snow Labs +name: bert_sequence_classifier_dehate_mono_english +date: 2023-10-25 +tags: [en, open_source, bert, sequence_classification, ner, onnx] +task: Named Entity Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dehatebert-mono-english` is a English model originally trained by `Hate-speech-CNERG`. + +## Predicted Entities + +`NON_HATE`, `HATE` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_dehate_mono_english_en_5.1.4_3.4_1698206347763.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_dehate_mono_english_en_5.1.4_3.4_1698206347763.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_dehate_mono_english","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_dehate_mono_english","en") + .setInputCols(Array("document", "token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_dehate_mono_english| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|en| +|Size:|627.7 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +References + +- https://huggingface.co/Hate-speech-CNERG/dehatebert-mono-english +- https://github.com/punyajoy/DE-LIMIT +- https://arxiv.org/abs/2004.06465 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_french_fr.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_french_fr.md new file mode 100644 index 00000000000000..ca3f72e0baf34a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_french_fr.md @@ -0,0 +1,102 @@ +--- +layout: model +title: French BertForSequenceClassification Cased model (from Hate-speech-CNERG) +author: John Snow Labs +name: bert_sequence_classifier_dehate_mono_french +date: 2023-10-25 +tags: [fr, open_source, bert, sequence_classification, ner, onnx] +task: Named Entity Recognition +language: fr +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dehatebert-mono-french` is a French model originally trained by `Hate-speech-CNERG`. + +## Predicted Entities + +`NON_HATE`, `HATE` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_dehate_mono_french_fr_5.1.4_3.4_1698206729043.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_dehate_mono_french_fr_5.1.4_3.4_1698206729043.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_dehate_mono_french","fr") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_dehate_mono_french","fr") + .setInputCols(Array("document", "token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_dehate_mono_french| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|fr| +|Size:|627.7 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +References + +- https://huggingface.co/Hate-speech-CNERG/dehatebert-mono-french +- https://github.com/punyajoy/DE-LIMIT +- https://arxiv.org/abs/2004.06465 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_german_de.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_german_de.md new file mode 100644 index 00000000000000..a1e3014e6faea9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_german_de.md @@ -0,0 +1,102 @@ +--- +layout: model +title: German BertForSequenceClassification Cased model (from Hate-speech-CNERG) +author: John Snow Labs +name: bert_sequence_classifier_dehate_mono_german +date: 2023-10-25 +tags: [de, open_source, bert, sequence_classification, ner, onnx] +task: Named Entity Recognition +language: de +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dehatebert-mono-german` is a German model originally trained by `Hate-speech-CNERG`. + +## Predicted Entities + +`NON_HATE`, `HATE` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_dehate_mono_german_de_5.1.4_3.4_1698207128997.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_dehate_mono_german_de_5.1.4_3.4_1698207128997.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_dehate_mono_german","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_dehate_mono_german","de") + .setInputCols(Array("document", "token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_dehate_mono_german| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|de| +|Size:|627.7 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +References + +- https://huggingface.co/Hate-speech-CNERG/dehatebert-mono-german +- https://github.com/punyajoy/DE-LIMIT +- https://arxiv.org/abs/2004.06465 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_italian_it.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_italian_it.md new file mode 100644 index 00000000000000..2f54f547c7aacb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_italian_it.md @@ -0,0 +1,102 @@ +--- +layout: model +title: Italian BertForSequenceClassification Cased model (from Hate-speech-CNERG) +author: John Snow Labs +name: bert_sequence_classifier_dehate_mono_italian +date: 2023-10-25 +tags: [it, open_source, bert, sequence_classification, ner, onnx] +task: Named Entity Recognition +language: it +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dehatebert-mono-italian` is a Italian model originally trained by `Hate-speech-CNERG`. + +## Predicted Entities + +`NON_HATE`, `HATE` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_dehate_mono_italian_it_5.1.4_3.4_1698207923030.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_dehate_mono_italian_it_5.1.4_3.4_1698207923030.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_dehate_mono_italian","it") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_dehate_mono_italian","it") + .setInputCols(Array("document", "token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_dehate_mono_italian| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|it| +|Size:|627.7 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +References + +- https://huggingface.co/Hate-speech-CNERG/dehatebert-mono-italian +- https://github.com/punyajoy/DE-LIMIT +- https://arxiv.org/abs/2004.06465 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_polish_pl.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_polish_pl.md new file mode 100644 index 00000000000000..b06094f1d113da --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_polish_pl.md @@ -0,0 +1,102 @@ +--- +layout: model +title: Polish BertForSequenceClassification Cased model (from Hate-speech-CNERG) +author: John Snow Labs +name: bert_sequence_classifier_dehate_mono_polish +date: 2023-10-25 +tags: [pl, open_source, bert, sequence_classification, ner, onnx] +task: Named Entity Recognition +language: pl +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dehatebert-mono-polish` is a Polish model originally trained by `Hate-speech-CNERG`. + +## Predicted Entities + +`NON_HATE`, `HATE` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_dehate_mono_polish_pl_5.1.4_3.4_1698208354124.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_dehate_mono_polish_pl_5.1.4_3.4_1698208354124.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_dehate_mono_polish","pl") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_dehate_mono_polish","pl") + .setInputCols(Array("document", "token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_dehate_mono_polish| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|pl| +|Size:|627.7 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +References + +- https://huggingface.co/Hate-speech-CNERG/dehatebert-mono-polish +- https://github.com/punyajoy/DE-LIMIT +- https://arxiv.org/abs/2004.06465 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_spanish_es.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_spanish_es.md new file mode 100644 index 00000000000000..860003133ded44 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_spanish_es.md @@ -0,0 +1,102 @@ +--- +layout: model +title: Spanish BertForSequenceClassification Cased model (from Hate-speech-CNERG) +author: John Snow Labs +name: bert_sequence_classifier_dehate_mono_spanish +date: 2023-10-25 +tags: [es, open_source, bert, sequence_classification, ner, onnx] +task: Named Entity Recognition +language: es +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dehatebert-mono-spanish` is a Spanish model originally trained by `Hate-speech-CNERG`. + +## Predicted Entities + +`NON_HATE`, `HATE` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_dehate_mono_spanish_es_5.1.4_3.4_1698209098735.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_dehate_mono_spanish_es_5.1.4_3.4_1698209098735.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_dehate_mono_spanish","es") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_dehate_mono_spanish","es") + .setInputCols(Array("document", "token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_dehate_mono_spanish| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|es| +|Size:|627.7 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +References + +- https://huggingface.co/Hate-speech-CNERG/dehatebert-mono-spanish +- https://github.com/punyajoy/DE-LIMIT +- https://arxiv.org/abs/2004.06465 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_persian_farsi_base_uncased_clf_digimag_fa.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_persian_farsi_base_uncased_clf_digimag_fa.md new file mode 100644 index 00000000000000..11ee443db9c24e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_persian_farsi_base_uncased_clf_digimag_fa.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Persian bert_sequence_classifier_persian_farsi_base_uncased_clf_digimag BertForSequenceClassification from HooshvareLab +author: John Snow Labs +name: bert_sequence_classifier_persian_farsi_base_uncased_clf_digimag +date: 2023-10-25 +tags: [bert, fa, open_source, sequence_classification, onnx] +task: Text Classification +language: fa +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_sequence_classifier_persian_farsi_base_uncased_clf_digimag` is a Persian model originally trained by HooshvareLab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_persian_farsi_base_uncased_clf_digimag_fa_5.1.4_3.4_1698209319334.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_persian_farsi_base_uncased_clf_digimag_fa_5.1.4_3.4_1698209319334.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_persian_farsi_base_uncased_clf_digimag","fa")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_persian_farsi_base_uncased_clf_digimag","fa") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_persian_farsi_base_uncased_clf_digimag| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|fa| +|Size:|608.7 MB| + +## References + +https://huggingface.co/HooshvareLab/bert-fa-base-uncased-clf-digimag \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_persian_farsi_base_uncased_clf_persiannews_fa.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_persian_farsi_base_uncased_clf_persiannews_fa.md new file mode 100644 index 00000000000000..d39d71ed0c475f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_persian_farsi_base_uncased_clf_persiannews_fa.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Persian bert_sequence_classifier_persian_farsi_base_uncased_clf_persiannews BertForSequenceClassification from HooshvareLab +author: John Snow Labs +name: bert_sequence_classifier_persian_farsi_base_uncased_clf_persiannews +date: 2023-10-25 +tags: [bert, fa, open_source, sequence_classification, onnx] +task: Text Classification +language: fa +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_sequence_classifier_persian_farsi_base_uncased_clf_persiannews` is a Persian model originally trained by HooshvareLab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_persian_farsi_base_uncased_clf_persiannews_fa_5.1.4_3.4_1698209556757.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_persian_farsi_base_uncased_clf_persiannews_fa_5.1.4_3.4_1698209556757.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_persian_farsi_base_uncased_clf_persiannews","fa")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_persian_farsi_base_uncased_clf_persiannews","fa") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_persian_farsi_base_uncased_clf_persiannews| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|fa| +|Size:|608.7 MB| + +## References + +https://huggingface.co/HooshvareLab/bert-fa-base-uncased-clf-persiannews \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_binary_fa.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_binary_fa.md new file mode 100644 index 00000000000000..17cc4aad219721 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_binary_fa.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Persian bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_binary BertForSequenceClassification from HooshvareLab +author: John Snow Labs +name: bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_binary +date: 2023-10-25 +tags: [bert, fa, open_source, sequence_classification, onnx] +task: Text Classification +language: fa +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_binary` is a Persian model originally trained by HooshvareLab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_binary_fa_5.1.4_3.4_1698209797867.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_binary_fa_5.1.4_3.4_1698209797867.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_binary","fa")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_binary","fa") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_binary| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|fa| +|Size:|608.7 MB| + +## References + +https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_enron_spam_detection_mccoole_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_enron_spam_detection_mccoole_en.md new file mode 100644 index 00000000000000..84fe5dccffb944 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_enron_spam_detection_mccoole_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_tiny_finetuned_enron_spam_detection_mccoole BertForSequenceClassification from mccoole +author: John Snow Labs +name: bert_tiny_finetuned_enron_spam_detection_mccoole +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tiny_finetuned_enron_spam_detection_mccoole` is a English model originally trained by mccoole. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tiny_finetuned_enron_spam_detection_mccoole_en_5.1.4_3.4_1698194499023.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tiny_finetuned_enron_spam_detection_mccoole_en_5.1.4_3.4_1698194499023.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_tiny_finetuned_enron_spam_detection_mccoole","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_tiny_finetuned_enron_spam_detection_mccoole","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tiny_finetuned_enron_spam_detection_mccoole| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/mccoole/bert-tiny-finetuned-enron-spam-detection \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_tweets_semeval_clean_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_tweets_semeval_clean_en.md new file mode 100644 index 00000000000000..dbfdc496183c35 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_tweets_semeval_clean_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_tweets_semeval_clean BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_tweets_semeval_clean +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tweets_semeval_clean` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tweets_semeval_clean_en_5.1.4_3.4_1698201361960.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tweets_semeval_clean_en_5.1.4_3.4_1698201361960.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_tweets_semeval_clean","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_tweets_semeval_clean","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tweets_semeval_clean| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-tweets-semeval-clean \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bertimbau_socioambiental_dandara_en.md b/docs/_posts/ahmedlone127/2023-10-25-bertimbau_socioambiental_dandara_en.md new file mode 100644 index 00000000000000..3faf7331925de6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bertimbau_socioambiental_dandara_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bertimbau_socioambiental_dandara BertForSequenceClassification from Dandara +author: John Snow Labs +name: bertimbau_socioambiental_dandara +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertimbau_socioambiental_dandara` is a English model originally trained by Dandara. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertimbau_socioambiental_dandara_en_5.1.4_3.4_1698193429174.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertimbau_socioambiental_dandara_en_5.1.4_3.4_1698193429174.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bertimbau_socioambiental_dandara","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bertimbau_socioambiental_dandara","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertimbau_socioambiental_dandara| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/Dandara/bertimbau-socioambiental \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-binary_fully_trained_bert_en.md b/docs/_posts/ahmedlone127/2023-10-25-binary_fully_trained_bert_en.md new file mode 100644 index 00000000000000..07619fa7e4dc11 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-binary_fully_trained_bert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English binary_fully_trained_bert BertForSequenceClassification from SporkyToast +author: John Snow Labs +name: binary_fully_trained_bert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`binary_fully_trained_bert` is a English model originally trained by SporkyToast. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/binary_fully_trained_bert_en_5.1.4_3.4_1698217058862.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/binary_fully_trained_bert_en_5.1.4_3.4_1698217058862.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("binary_fully_trained_bert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("binary_fully_trained_bert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|binary_fully_trained_bert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/SporkyToast/binary-fully-trained-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-biobert_base_cased_v1_2_finetuned_textclassification_en.md b/docs/_posts/ahmedlone127/2023-10-25-biobert_base_cased_v1_2_finetuned_textclassification_en.md new file mode 100644 index 00000000000000..e7002773f23e12 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-biobert_base_cased_v1_2_finetuned_textclassification_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English biobert_base_cased_v1_2_finetuned_textclassification BertForSequenceClassification from Kekelilii +author: John Snow Labs +name: biobert_base_cased_v1_2_finetuned_textclassification +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biobert_base_cased_v1_2_finetuned_textclassification` is a English model originally trained by Kekelilii. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biobert_base_cased_v1_2_finetuned_textclassification_en_5.1.4_3.4_1698196715226.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biobert_base_cased_v1_2_finetuned_textclassification_en_5.1.4_3.4_1698196715226.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("biobert_base_cased_v1_2_finetuned_textclassification","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("biobert_base_cased_v1_2_finetuned_textclassification","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biobert_base_cased_v1_2_finetuned_textclassification| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/Kekelilii/biobert-base-cased-v1.2_finetuned_TextClassification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bioclinical_bert_ft_m3_lc_en.md b/docs/_posts/ahmedlone127/2023-10-25-bioclinical_bert_ft_m3_lc_en.md new file mode 100644 index 00000000000000..623070620289b5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bioclinical_bert_ft_m3_lc_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bioclinical_bert_ft_m3_lc BertForSequenceClassification from sarahmiller137 +author: John Snow Labs +name: bioclinical_bert_ft_m3_lc +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bioclinical_bert_ft_m3_lc` is a English model originally trained by sarahmiller137. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bioclinical_bert_ft_m3_lc_en_5.1.4_3.4_1698213779941.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bioclinical_bert_ft_m3_lc_en_5.1.4_3.4_1698213779941.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bioclinical_bert_ft_m3_lc","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bioclinical_bert_ft_m3_lc","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bioclinical_bert_ft_m3_lc| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.5 MB| + +## References + +https://huggingface.co/sarahmiller137/bioclinical-bert-ft-m3-lc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-burmese_bert_imdb_en.md b/docs/_posts/ahmedlone127/2023-10-25-burmese_bert_imdb_en.md new file mode 100644 index 00000000000000..1647260b17dd3c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-burmese_bert_imdb_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English burmese_bert_imdb BertForSequenceClassification from olesya2096 +author: John Snow Labs +name: burmese_bert_imdb +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`burmese_bert_imdb` is a English model originally trained by olesya2096. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/burmese_bert_imdb_en_5.1.4_3.4_1698209910704.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/burmese_bert_imdb_en_5.1.4_3.4_1698209910704.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("burmese_bert_imdb","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("burmese_bert_imdb","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|burmese_bert_imdb| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/olesya2096/my_bert_imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-codenlbert_samoan_en.md b/docs/_posts/ahmedlone127/2023-10-25-codenlbert_samoan_en.md new file mode 100644 index 00000000000000..6d7bf7dbdd7742 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-codenlbert_samoan_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English codenlbert_samoan BertForSequenceClassification from vishnun +author: John Snow Labs +name: codenlbert_samoan +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`codenlbert_samoan` is a English model originally trained by vishnun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/codenlbert_samoan_en_5.1.4_3.4_1698206525278.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/codenlbert_samoan_en_5.1.4_3.4_1698206525278.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("codenlbert_samoan","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("codenlbert_samoan","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|codenlbert_samoan| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|107.9 MB| + +## References + +https://huggingface.co/vishnun/codenlbert-sm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-codenlbert_tiny_en.md b/docs/_posts/ahmedlone127/2023-10-25-codenlbert_tiny_en.md new file mode 100644 index 00000000000000..ecfe9c17439701 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-codenlbert_tiny_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English codenlbert_tiny BertForSequenceClassification from vishnun +author: John Snow Labs +name: codenlbert_tiny +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`codenlbert_tiny` is a English model originally trained by vishnun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/codenlbert_tiny_en_5.1.4_3.4_1698215047894.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/codenlbert_tiny_en_5.1.4_3.4_1698215047894.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("codenlbert_tiny","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("codenlbert_tiny","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|codenlbert_tiny| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/vishnun/codenlbert-tiny \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-covid_bert_e3_b16_v2_w0_01_dev1_en.md b/docs/_posts/ahmedlone127/2023-10-25-covid_bert_e3_b16_v2_w0_01_dev1_en.md new file mode 100644 index 00000000000000..156d554e246d15 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-covid_bert_e3_b16_v2_w0_01_dev1_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English covid_bert_e3_b16_v2_w0_01_dev1 BertForSequenceClassification from JerryYanJiang +author: John Snow Labs +name: covid_bert_e3_b16_v2_w0_01_dev1 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`covid_bert_e3_b16_v2_w0_01_dev1` is a English model originally trained by JerryYanJiang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/covid_bert_e3_b16_v2_w0_01_dev1_en_5.1.4_3.4_1698198314583.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/covid_bert_e3_b16_v2_w0_01_dev1_en_5.1.4_3.4_1698198314583.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("covid_bert_e3_b16_v2_w0_01_dev1","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("covid_bert_e3_b16_v2_w0_01_dev1","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|covid_bert_e3_b16_v2_w0_01_dev1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/JerryYanJiang/covid_bert-e3-b16-v2-w0.01-dev1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-covid_twitter_bert_v2_3_4_2e_05_0_01_en.md b/docs/_posts/ahmedlone127/2023-10-25-covid_twitter_bert_v2_3_4_2e_05_0_01_en.md new file mode 100644 index 00000000000000..6c94172b3aae02 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-covid_twitter_bert_v2_3_4_2e_05_0_01_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English covid_twitter_bert_v2_3_4_2e_05_0_01 BertForSequenceClassification from JerryYanJiang +author: John Snow Labs +name: covid_twitter_bert_v2_3_4_2e_05_0_01 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`covid_twitter_bert_v2_3_4_2e_05_0_01` is a English model originally trained by JerryYanJiang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/covid_twitter_bert_v2_3_4_2e_05_0_01_en_5.1.4_3.4_1698218094936.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/covid_twitter_bert_v2_3_4_2e_05_0_01_en_5.1.4_3.4_1698218094936.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("covid_twitter_bert_v2_3_4_2e_05_0_01","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("covid_twitter_bert_v2_3_4_2e_05_0_01","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|covid_twitter_bert_v2_3_4_2e_05_0_01| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/JerryYanJiang/covid-twitter-bert-v2_3_4_2e-05_0.01 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-covidbert_medmarco_en.md b/docs/_posts/ahmedlone127/2023-10-25-covidbert_medmarco_en.md new file mode 100644 index 00000000000000..9279c7c1c4c9f7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-covidbert_medmarco_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English covidbert_medmarco BertForSequenceClassification from Darkrider +author: John Snow Labs +name: covidbert_medmarco +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`covidbert_medmarco` is a English model originally trained by Darkrider. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/covidbert_medmarco_en_5.1.4_3.4_1698193642559.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/covidbert_medmarco_en_5.1.4_3.4_1698193642559.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("covidbert_medmarco","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("covidbert_medmarco","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|covidbert_medmarco| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Darkrider/covidbert_medmarco \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-cq_bert_model_repo_en.md b/docs/_posts/ahmedlone127/2023-10-25-cq_bert_model_repo_en.md new file mode 100644 index 00000000000000..0ab7e9cf889a79 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-cq_bert_model_repo_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English cq_bert_model_repo BertForSequenceClassification from GD +author: John Snow Labs +name: cq_bert_model_repo +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`cq_bert_model_repo` is a English model originally trained by GD. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/cq_bert_model_repo_en_5.1.4_3.4_1698204629269.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/cq_bert_model_repo_en_5.1.4_3.4_1698204629269.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("cq_bert_model_repo","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("cq_bert_model_repo","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|cq_bert_model_repo| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/GD/cq-bert-model-repo \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-deprem_bert_128k_tr.md b/docs/_posts/ahmedlone127/2023-10-25-deprem_bert_128k_tr.md new file mode 100644 index 00000000000000..8577d498ed5786 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-deprem_bert_128k_tr.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Turkish deprem_bert_128k BertForSequenceClassification from deprem-ml +author: John Snow Labs +name: deprem_bert_128k +date: 2023-10-25 +tags: [bert, tr, open_source, sequence_classification, onnx] +task: Text Classification +language: tr +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`deprem_bert_128k` is a Turkish model originally trained by deprem-ml. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/deprem_bert_128k_tr_5.1.4_3.4_1698207573310.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deprem_bert_128k_tr_5.1.4_3.4_1698207573310.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("deprem_bert_128k","tr")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("deprem_bert_128k","tr") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|deprem_bert_128k| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|tr| +|Size:|691.6 MB| + +## References + +https://huggingface.co/deprem-ml/deprem_bert_128k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-deprem_bert_128k_v13_beta_tr.md b/docs/_posts/ahmedlone127/2023-10-25-deprem_bert_128k_v13_beta_tr.md new file mode 100644 index 00000000000000..e6d50643bdfe97 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-deprem_bert_128k_v13_beta_tr.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Turkish deprem_bert_128k_v13_beta BertForSequenceClassification from deprem-ml +author: John Snow Labs +name: deprem_bert_128k_v13_beta +date: 2023-10-25 +tags: [bert, tr, open_source, sequence_classification, onnx] +task: Text Classification +language: tr +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`deprem_bert_128k_v13_beta` is a Turkish model originally trained by deprem-ml. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/deprem_bert_128k_v13_beta_tr_5.1.4_3.4_1698210154059.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deprem_bert_128k_v13_beta_tr_5.1.4_3.4_1698210154059.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("deprem_bert_128k_v13_beta","tr")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("deprem_bert_128k_v13_beta","tr") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|deprem_bert_128k_v13_beta| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|tr| +|Size:|691.6 MB| + +## References + +https://huggingface.co/deprem-ml/deprem_bert_128k_v13_beta \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-deprem_bert_128k_v2_tr.md b/docs/_posts/ahmedlone127/2023-10-25-deprem_bert_128k_v2_tr.md new file mode 100644 index 00000000000000..8b46e294d08853 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-deprem_bert_128k_v2_tr.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Turkish deprem_bert_128k_v2 BertForSequenceClassification from Ertugrul +author: John Snow Labs +name: deprem_bert_128k_v2 +date: 2023-10-25 +tags: [bert, tr, open_source, sequence_classification, onnx] +task: Text Classification +language: tr +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`deprem_bert_128k_v2` is a Turkish model originally trained by Ertugrul. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/deprem_bert_128k_v2_tr_5.1.4_3.4_1698209721249.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deprem_bert_128k_v2_tr_5.1.4_3.4_1698209721249.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("deprem_bert_128k_v2","tr")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("deprem_bert_128k_v2","tr") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|deprem_bert_128k_v2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|tr| +|Size:|691.6 MB| + +## References + +https://huggingface.co/Ertugrul/deprem_bert_128k_v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-distillprotbert_knots_norwegian_fragments_en.md b/docs/_posts/ahmedlone127/2023-10-25-distillprotbert_knots_norwegian_fragments_en.md new file mode 100644 index 00000000000000..f1d2879604be78 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-distillprotbert_knots_norwegian_fragments_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English distillprotbert_knots_norwegian_fragments BertForSequenceClassification from roa7n +author: John Snow Labs +name: distillprotbert_knots_norwegian_fragments +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`distillprotbert_knots_norwegian_fragments` is a English model originally trained by roa7n. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distillprotbert_knots_norwegian_fragments_en_5.1.4_3.4_1698199593701.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distillprotbert_knots_norwegian_fragments_en_5.1.4_3.4_1698199593701.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("distillprotbert_knots_norwegian_fragments","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("distillprotbert_knots_norwegian_fragments","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|distillprotbert_knots_norwegian_fragments| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|864.5 MB| + +## References + +https://huggingface.co/roa7n/distillprotbert_knots_no_fragments \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-dkbert_hatespeech_detection_da.md b/docs/_posts/ahmedlone127/2023-10-25-dkbert_hatespeech_detection_da.md new file mode 100644 index 00000000000000..ca972e4669cd04 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-dkbert_hatespeech_detection_da.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Danish dkbert_hatespeech_detection BertForSequenceClassification from Guscode +author: John Snow Labs +name: dkbert_hatespeech_detection +date: 2023-10-25 +tags: [bert, da, open_source, sequence_classification, onnx] +task: Text Classification +language: da +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dkbert_hatespeech_detection` is a Danish model originally trained by Guscode. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dkbert_hatespeech_detection_da_5.1.4_3.4_1698205032220.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dkbert_hatespeech_detection_da_5.1.4_3.4_1698205032220.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("dkbert_hatespeech_detection","da")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("dkbert_hatespeech_detection","da") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dkbert_hatespeech_detection| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|da| +|Size:|414.5 MB| + +## References + +https://huggingface.co/Guscode/DKbert-hatespeech-detection \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-dutch_bert_en.md b/docs/_posts/ahmedlone127/2023-10-25-dutch_bert_en.md new file mode 100644 index 00000000000000..2c07f84e18a9e6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-dutch_bert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English dutch_bert BertForSequenceClassification from thearod5 +author: John Snow Labs +name: dutch_bert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dutch_bert` is a English model originally trained by thearod5. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dutch_bert_en_5.1.4_3.4_1698200441893.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dutch_bert_en_5.1.4_3.4_1698200441893.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("dutch_bert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("dutch_bert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dutch_bert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/thearod5/nl-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-elvis_bert_base_en.md b/docs/_posts/ahmedlone127/2023-10-25-elvis_bert_base_en.md new file mode 100644 index 00000000000000..59d151974952cb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-elvis_bert_base_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English elvis_bert_base BertForSequenceClassification from elvis-d +author: John Snow Labs +name: elvis_bert_base +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`elvis_bert_base` is a English model originally trained by elvis-d. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/elvis_bert_base_en_5.1.4_3.4_1698192917559.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/elvis_bert_base_en_5.1.4_3.4_1698192917559.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("elvis_bert_base","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("elvis_bert_base","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|elvis_bert_base| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/elvis-d/elvis_bert_base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-emotion_classification_bert_base_en.md b/docs/_posts/ahmedlone127/2023-10-25-emotion_classification_bert_base_en.md new file mode 100644 index 00000000000000..4b48bb4d18e00a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-emotion_classification_bert_base_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English emotion_classification_bert_base BertForSequenceClassification from EasthShin +author: John Snow Labs +name: emotion_classification_bert_base +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`emotion_classification_bert_base` is a English model originally trained by EasthShin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/emotion_classification_bert_base_en_5.1.4_3.4_1698202284372.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/emotion_classification_bert_base_en_5.1.4_3.4_1698202284372.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("emotion_classification_bert_base","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("emotion_classification_bert_base","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|emotion_classification_bert_base| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/EasthShin/Emotion-Classification-bert-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-esgbert_en.md b/docs/_posts/ahmedlone127/2023-10-25-esgbert_en.md new file mode 100644 index 00000000000000..45ddd7a39d9e87 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-esgbert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English esgbert BertForSequenceClassification from owen198 +author: John Snow Labs +name: esgbert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`esgbert` is a English model originally trained by owen198. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/esgbert_en_5.1.4_3.4_1698218399670.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/esgbert_en_5.1.4_3.4_1698218399670.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("esgbert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("esgbert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|esgbert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/owen198/esgbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-fbert_ai_en.md b/docs/_posts/ahmedlone127/2023-10-25-fbert_ai_en.md new file mode 100644 index 00000000000000..423e422507bb80 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-fbert_ai_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fbert_ai BertForSequenceClassification from wilburchen42 +author: John Snow Labs +name: fbert_ai +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fbert_ai` is a English model originally trained by wilburchen42. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fbert_ai_en_5.1.4_3.4_1698203738918.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fbert_ai_en_5.1.4_3.4_1698203738918.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fbert_ai","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fbert_ai","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fbert_ai| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.6 MB| + +## References + +https://huggingface.co/wilburchen42/fbert-ai \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-fbert_aiclass_en.md b/docs/_posts/ahmedlone127/2023-10-25-fbert_aiclass_en.md new file mode 100644 index 00000000000000..1f24a4bca649d0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-fbert_aiclass_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fbert_aiclass BertForSequenceClassification from wilburchen42 +author: John Snow Labs +name: fbert_aiclass +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fbert_aiclass` is a English model originally trained by wilburchen42. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fbert_aiclass_en_5.1.4_3.4_1698204182166.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fbert_aiclass_en_5.1.4_3.4_1698204182166.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fbert_aiclass","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fbert_aiclass","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fbert_aiclass| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.6 MB| + +## References + +https://huggingface.co/wilburchen42/fbert-aiclass \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-finbert_tsla_en.md b/docs/_posts/ahmedlone127/2023-10-25-finbert_tsla_en.md new file mode 100644 index 00000000000000..176cfc852a7972 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-finbert_tsla_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English finbert_tsla BertForSequenceClassification from Forturne +author: John Snow Labs +name: finbert_tsla +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finbert_tsla` is a English model originally trained by Forturne. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finbert_tsla_en_5.1.4_3.4_1698218209908.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finbert_tsla_en_5.1.4_3.4_1698218209908.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("finbert_tsla","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("finbert_tsla","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finbert_tsla| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.6 MB| + +## References + +https://huggingface.co/Forturne/Finbert_TSLA \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_klue_bert_base_model_11_en.md b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_klue_bert_base_model_11_en.md new file mode 100644 index 00000000000000..978a549659949a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_klue_bert_base_model_11_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fine_tuned_klue_bert_base_model_11 BertForSequenceClassification from Lanvizu +author: John Snow Labs +name: fine_tuned_klue_bert_base_model_11 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tuned_klue_bert_base_model_11` is a English model originally trained by Lanvizu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tuned_klue_bert_base_model_11_en_5.1.4_3.4_1698192014848.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tuned_klue_bert_base_model_11_en_5.1.4_3.4_1698192014848.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_klue_bert_base_model_11","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_klue_bert_base_model_11","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tuned_klue_bert_base_model_11| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Lanvizu/fine-tuned-klue-bert-base_model_11 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-finetune_bert_base_cased_en.md b/docs/_posts/ahmedlone127/2023-10-25-finetune_bert_base_cased_en.md new file mode 100644 index 00000000000000..fb3ed38e59a749 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-finetune_bert_base_cased_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English finetune_bert_base_cased BertForSequenceClassification from LIALLIES +author: John Snow Labs +name: finetune_bert_base_cased +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finetune_bert_base_cased` is a English model originally trained by LIALLIES. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finetune_bert_base_cased_en_5.1.4_3.4_1698211012078.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finetune_bert_base_cased_en_5.1.4_3.4_1698211012078.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("finetune_bert_base_cased","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("finetune_bert_base_cased","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finetune_bert_base_cased| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/LIALLIES/finetune-bert-base-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-finetuned_bert_base_uncased_minoosh_en.md b/docs/_posts/ahmedlone127/2023-10-25-finetuned_bert_base_uncased_minoosh_en.md new file mode 100644 index 00000000000000..f4bb5f1b7b37cf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-finetuned_bert_base_uncased_minoosh_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English finetuned_bert_base_uncased_minoosh BertForSequenceClassification from minoosh +author: John Snow Labs +name: finetuned_bert_base_uncased_minoosh +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finetuned_bert_base_uncased_minoosh` is a English model originally trained by minoosh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finetuned_bert_base_uncased_minoosh_en_5.1.4_3.4_1698199009303.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finetuned_bert_base_uncased_minoosh_en_5.1.4_3.4_1698199009303.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_bert_base_uncased_minoosh","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_bert_base_uncased_minoosh","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finetuned_bert_base_uncased_minoosh| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/minoosh/finetuned_bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-finetuned_sc_indobert_on_indonli_basic_train_en.md b/docs/_posts/ahmedlone127/2023-10-25-finetuned_sc_indobert_on_indonli_basic_train_en.md new file mode 100644 index 00000000000000..beb9316c37f89e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-finetuned_sc_indobert_on_indonli_basic_train_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English finetuned_sc_indobert_on_indonli_basic_train BertForSequenceClassification from muhammadravi251001 +author: John Snow Labs +name: finetuned_sc_indobert_on_indonli_basic_train +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finetuned_sc_indobert_on_indonli_basic_train` is a English model originally trained by muhammadravi251001. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finetuned_sc_indobert_on_indonli_basic_train_en_5.1.4_3.4_1698216396244.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finetuned_sc_indobert_on_indonli_basic_train_en_5.1.4_3.4_1698216396244.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_sc_indobert_on_indonli_basic_train","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_sc_indobert_on_indonli_basic_train","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finetuned_sc_indobert_on_indonli_basic_train| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|413.9 MB| + +## References + +https://huggingface.co/muhammadravi251001/finetuned-SC-indobert-on-indonli_basic-train \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-finetunedfinbert_model_en.md b/docs/_posts/ahmedlone127/2023-10-25-finetunedfinbert_model_en.md new file mode 100644 index 00000000000000..ba61880a841e9f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-finetunedfinbert_model_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English finetunedfinbert_model BertForSequenceClassification from Himanshusingh +author: John Snow Labs +name: finetunedfinbert_model +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finetunedfinbert_model` is a English model originally trained by Himanshusingh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finetunedfinbert_model_en_5.1.4_3.4_1698215223380.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finetunedfinbert_model_en_5.1.4_3.4_1698215223380.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("finetunedfinbert_model","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("finetunedfinbert_model","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finetunedfinbert_model| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Himanshusingh/finetunedfinbert-model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-gikubu_bert_base_en.md b/docs/_posts/ahmedlone127/2023-10-25-gikubu_bert_base_en.md new file mode 100644 index 00000000000000..086a199324709a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-gikubu_bert_base_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English gikubu_bert_base BertForSequenceClassification from Gikubu +author: John Snow Labs +name: gikubu_bert_base +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`gikubu_bert_base` is a English model originally trained by Gikubu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gikubu_bert_base_en_5.1.4_3.4_1698201580816.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gikubu_bert_base_en_5.1.4_3.4_1698201580816.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("gikubu_bert_base","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("gikubu_bert_base","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gikubu_bert_base| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/Gikubu/Gikubu_bert_base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_0_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_0_en.md new file mode 100644 index 00000000000000..86cecc2177ed20 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_0_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_0 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_0 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_0` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_0_en_5.1.4_3.4_1698214940352.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_0_en_5.1.4_3.4_1698214940352.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_0","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_0","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_0| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_10_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_10_en.md new file mode 100644 index 00000000000000..daf4e73e0800a9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_10_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_10 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_10 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_10` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_10_en_5.1.4_3.4_1698215468969.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_10_en_5.1.4_3.4_1698215468969.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_10","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_10","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_10| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_11_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_11_en.md new file mode 100644 index 00000000000000..a80cb2d6ddec10 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_11_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_11 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_11 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_11` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_11_en_5.1.4_3.4_1698216069006.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_11_en_5.1.4_3.4_1698216069006.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_11","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_11","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_11| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-11 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_13_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_13_en.md new file mode 100644 index 00000000000000..bd26d5f765651e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_13_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_13 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_13 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_13` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_13_en_5.1.4_3.4_1698216806137.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_13_en_5.1.4_3.4_1698216806137.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_13","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_13","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_13| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-13 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_15_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_15_en.md new file mode 100644 index 00000000000000..eff3836d2463d5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_15_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_15 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_15 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_15` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_15_en_5.1.4_3.4_1698218006990.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_15_en_5.1.4_3.4_1698218006990.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_15","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_15","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_15| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-15 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_16_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_16_en.md new file mode 100644 index 00000000000000..5e2f6baa8ce8e4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_16_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_16 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_16 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_16` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_16_en_5.1.4_3.4_1698217161066.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_16_en_5.1.4_3.4_1698217161066.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_16","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_16","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_16| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-16 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_17_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_17_en.md new file mode 100644 index 00000000000000..66b75e337a67e8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_17_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_17 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_17 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_17` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_17_en_5.1.4_3.4_1698216972783.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_17_en_5.1.4_3.4_1698216972783.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_17","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_17","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_17| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-17 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_1_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_1_en.md new file mode 100644 index 00000000000000..8cdd2e603280e1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_1_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_1 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_1 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_1` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_1_en_5.1.4_3.4_1698214773723.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_1_en_5.1.4_3.4_1698214773723.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_1","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_1","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_20_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_20_en.md new file mode 100644 index 00000000000000..ee8febc77f995a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_20_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_20 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_20 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_20` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_20_en_5.1.4_3.4_1698217370941.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_20_en_5.1.4_3.4_1698217370941.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_20","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_20","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_20| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_25_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_25_en.md new file mode 100644 index 00000000000000..624ae4afe9d9ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_25_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_25 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_25 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_25` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_25_en_5.1.4_3.4_1698219051243.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_25_en_5.1.4_3.4_1698219051243.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_25","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_25","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_25| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-25 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_27_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_27_en.md new file mode 100644 index 00000000000000..67b503f850af62 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_27_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_27 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_27 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_27` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_27_en_5.1.4_3.4_1698218599491.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_27_en_5.1.4_3.4_1698218599491.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_27","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_27","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_27| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-27 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_28_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_28_en.md new file mode 100644 index 00000000000000..add184fe24151e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_28_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_28 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_28 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_28` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_28_en_5.1.4_3.4_1698218829163.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_28_en_5.1.4_3.4_1698218829163.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_28","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_28","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_28| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_2_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_2_en.md new file mode 100644 index 00000000000000..a4cf05ad5caaab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_2 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_2` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_2_en_5.1.4_3.4_1698214164302.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_2_en_5.1.4_3.4_1698214164302.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_4_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_4_en.md new file mode 100644 index 00000000000000..9a84a8d43abe46 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_4_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_4 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_4 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_4` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_4_en_5.1.4_3.4_1698213968576.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_4_en_5.1.4_3.4_1698213968576.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_4","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_4","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_4| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_5_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_5_en.md new file mode 100644 index 00000000000000..52dd40bcc4dd5c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_5_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_5 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_5 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_5` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_5_en_5.1.4_3.4_1698214559777.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_5_en_5.1.4_3.4_1698214559777.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_5","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_5","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_5| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_6_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_6_en.md new file mode 100644 index 00000000000000..08d563cef1d210 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_6_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_6 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_6 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_6` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_6_en_5.1.4_3.4_1698214361592.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_6_en_5.1.4_3.4_1698214361592.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_6","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_6","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_6| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_7_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_7_en.md new file mode 100644 index 00000000000000..bbdf3063cbbc45 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_7_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_7 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_7 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_7` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_7_en_5.1.4_3.4_1698215109535.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_7_en_5.1.4_3.4_1698215109535.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_7","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_7","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_7| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_8_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_8_en.md new file mode 100644 index 00000000000000..381685395a6aa8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_8_en_5.1.4_3.4_1698215663747.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_8_en_5.1.4_3.4_1698215663747.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-8 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-hate_bert_model_en.md b/docs/_posts/ahmedlone127/2023-10-25-hate_bert_model_en.md new file mode 100644 index 00000000000000..c3ab9f0eed7034 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-hate_bert_model_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English hate_bert_model BertForSequenceClassification from warrior1127 +author: John Snow Labs +name: hate_bert_model +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hate_bert_model` is a English model originally trained by warrior1127. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hate_bert_model_en_5.1.4_3.4_1698197432839.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hate_bert_model_en_5.1.4_3.4_1698197432839.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("hate_bert_model","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("hate_bert_model","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hate_bert_model| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/warrior1127/hate_bert_model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-hate_v1_final_bert_base_spanish_wwm_cased_en.md b/docs/_posts/ahmedlone127/2023-10-25-hate_v1_final_bert_base_spanish_wwm_cased_en.md new file mode 100644 index 00000000000000..ac81c1e69a8277 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-hate_v1_final_bert_base_spanish_wwm_cased_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English hate_v1_final_bert_base_spanish_wwm_cased BertForSequenceClassification from jorgeortizfuentes +author: John Snow Labs +name: hate_v1_final_bert_base_spanish_wwm_cased +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hate_v1_final_bert_base_spanish_wwm_cased` is a English model originally trained by jorgeortizfuentes. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hate_v1_final_bert_base_spanish_wwm_cased_en_5.1.4_3.4_1698209177130.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hate_v1_final_bert_base_spanish_wwm_cased_en_5.1.4_3.4_1698209177130.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("hate_v1_final_bert_base_spanish_wwm_cased","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("hate_v1_final_bert_base_spanish_wwm_cased","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hate_v1_final_bert_base_spanish_wwm_cased| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.7 MB| + +## References + +https://huggingface.co/jorgeortizfuentes/hate_v1_final-bert-base-spanish-wwm-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-hate_v2_final_tulio_chilean_spanish_bert_en.md b/docs/_posts/ahmedlone127/2023-10-25-hate_v2_final_tulio_chilean_spanish_bert_en.md new file mode 100644 index 00000000000000..1cad6d7881d809 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-hate_v2_final_tulio_chilean_spanish_bert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English hate_v2_final_tulio_chilean_spanish_bert BertForSequenceClassification from jorgeortizfuentes +author: John Snow Labs +name: hate_v2_final_tulio_chilean_spanish_bert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hate_v2_final_tulio_chilean_spanish_bert` is a English model originally trained by jorgeortizfuentes. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hate_v2_final_tulio_chilean_spanish_bert_en_5.1.4_3.4_1698209578952.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hate_v2_final_tulio_chilean_spanish_bert_en_5.1.4_3.4_1698209578952.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("hate_v2_final_tulio_chilean_spanish_bert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("hate_v2_final_tulio_chilean_spanish_bert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hate_v2_final_tulio_chilean_spanish_bert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.5 MB| + +## References + +https://huggingface.co/jorgeortizfuentes/hate_v2_final-tulio-chilean-spanish-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-hatebert_ishate_29k_en.md b/docs/_posts/ahmedlone127/2023-10-25-hatebert_ishate_29k_en.md new file mode 100644 index 00000000000000..fe9fb6d3cf007e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-hatebert_ishate_29k_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English hatebert_ishate_29k BertForSequenceClassification from bitsanlp +author: John Snow Labs +name: hatebert_ishate_29k +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hatebert_ishate_29k` is a English model originally trained by bitsanlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hatebert_ishate_29k_en_5.1.4_3.4_1698205841546.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hatebert_ishate_29k_en_5.1.4_3.4_1698205841546.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("hatebert_ishate_29k","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("hatebert_ishate_29k","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hatebert_ishate_29k| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|408.3 MB| + +## References + +https://huggingface.co/bitsanlp/hatebert-ishate-29k \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-human_needs_bert_2_en.md b/docs/_posts/ahmedlone127/2023-10-25-human_needs_bert_2_en.md new file mode 100644 index 00000000000000..fe12b91726415c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-human_needs_bert_2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English human_needs_bert_2 BertForSequenceClassification from zzhifz +author: John Snow Labs +name: human_needs_bert_2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`human_needs_bert_2` is a English model originally trained by zzhifz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/human_needs_bert_2_en_5.1.4_3.4_1698198722764.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/human_needs_bert_2_en_5.1.4_3.4_1698198722764.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("human_needs_bert_2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("human_needs_bert_2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|human_needs_bert_2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|410.0 MB| + +## References + +https://huggingface.co/zzhifz/human-needs-bert-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-human_needs_bert_en.md b/docs/_posts/ahmedlone127/2023-10-25-human_needs_bert_en.md new file mode 100644 index 00000000000000..eb326e33fcc16a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-human_needs_bert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English human_needs_bert BertForSequenceClassification from zzhifz +author: John Snow Labs +name: human_needs_bert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`human_needs_bert` is a English model originally trained by zzhifz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/human_needs_bert_en_5.1.4_3.4_1698196941016.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/human_needs_bert_en_5.1.4_3.4_1698196941016.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("human_needs_bert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("human_needs_bert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|human_needs_bert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/zzhifz/human-needs-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-incivility_v1_final_tulio_chilean_spanish_bert_en.md b/docs/_posts/ahmedlone127/2023-10-25-incivility_v1_final_tulio_chilean_spanish_bert_en.md new file mode 100644 index 00000000000000..975b056ce8c817 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-incivility_v1_final_tulio_chilean_spanish_bert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English incivility_v1_final_tulio_chilean_spanish_bert BertForSequenceClassification from jorgeortizfuentes +author: John Snow Labs +name: incivility_v1_final_tulio_chilean_spanish_bert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`incivility_v1_final_tulio_chilean_spanish_bert` is a English model originally trained by jorgeortizfuentes. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/incivility_v1_final_tulio_chilean_spanish_bert_en_5.1.4_3.4_1698209376203.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/incivility_v1_final_tulio_chilean_spanish_bert_en_5.1.4_3.4_1698209376203.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("incivility_v1_final_tulio_chilean_spanish_bert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("incivility_v1_final_tulio_chilean_spanish_bert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|incivility_v1_final_tulio_chilean_spanish_bert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.5 MB| + +## References + +https://huggingface.co/jorgeortizfuentes/incivility_v1_final-tulio-chilean-spanish-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-indobertnews_en.md b/docs/_posts/ahmedlone127/2023-10-25-indobertnews_en.md new file mode 100644 index 00000000000000..fa30240738f2e3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-indobertnews_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English indobertnews BertForSequenceClassification from mrizalf7 +author: John Snow Labs +name: indobertnews +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indobertnews` is a English model originally trained by mrizalf7. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indobertnews_en_5.1.4_3.4_1698206635949.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indobertnews_en_5.1.4_3.4_1698206635949.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("indobertnews","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("indobertnews","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indobertnews| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.0 MB| + +## References + +https://huggingface.co/mrizalf7/IndobertNews \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-indobertnewstest_dimassamid_en.md b/docs/_posts/ahmedlone127/2023-10-25-indobertnewstest_dimassamid_en.md new file mode 100644 index 00000000000000..aab39d55afa3a8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-indobertnewstest_dimassamid_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English indobertnewstest_dimassamid BertForSequenceClassification from dimassamid +author: John Snow Labs +name: indobertnewstest_dimassamid +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indobertnewstest_dimassamid` is a English model originally trained by dimassamid. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indobertnewstest_dimassamid_en_5.1.4_3.4_1698211212020.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indobertnewstest_dimassamid_en_5.1.4_3.4_1698211212020.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("indobertnewstest_dimassamid","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("indobertnewstest_dimassamid","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indobertnewstest_dimassamid| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.0 MB| + +## References + +https://huggingface.co/dimassamid/IndobertNewsTest \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-indoberttypenews_en.md b/docs/_posts/ahmedlone127/2023-10-25-indoberttypenews_en.md new file mode 100644 index 00000000000000..9afca701315a0f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-indoberttypenews_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English indoberttypenews BertForSequenceClassification from rizalmilyardi +author: John Snow Labs +name: indoberttypenews +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indoberttypenews` is a English model originally trained by rizalmilyardi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indoberttypenews_en_5.1.4_3.4_1698210826631.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indoberttypenews_en_5.1.4_3.4_1698210826631.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("indoberttypenews","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("indoberttypenews","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indoberttypenews| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|413.9 MB| + +## References + +https://huggingface.co/rizalmilyardi/IndobertTypeNews \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-klue_bert_finetuned_nsmc_en.md b/docs/_posts/ahmedlone127/2023-10-25-klue_bert_finetuned_nsmc_en.md new file mode 100644 index 00000000000000..b6f9ca30d96d25 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-klue_bert_finetuned_nsmc_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English klue_bert_finetuned_nsmc BertForSequenceClassification from PlayDev +author: John Snow Labs +name: klue_bert_finetuned_nsmc +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`klue_bert_finetuned_nsmc` is a English model originally trained by PlayDev. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/klue_bert_finetuned_nsmc_en_5.1.4_3.4_1698193370011.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/klue_bert_finetuned_nsmc_en_5.1.4_3.4_1698193370011.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("klue_bert_finetuned_nsmc","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("klue_bert_finetuned_nsmc","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|klue_bert_finetuned_nsmc| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.6 MB| + +## References + +https://huggingface.co/PlayDev/klue-bert-finetuned-nsmc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-knots_protbertbfd_alphafold_evaklimentova_en.md b/docs/_posts/ahmedlone127/2023-10-25-knots_protbertbfd_alphafold_evaklimentova_en.md new file mode 100644 index 00000000000000..0ea896d4c92b78 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-knots_protbertbfd_alphafold_evaklimentova_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English knots_protbertbfd_alphafold_evaklimentova BertForSequenceClassification from EvaKlimentova +author: John Snow Labs +name: knots_protbertbfd_alphafold_evaklimentova +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`knots_protbertbfd_alphafold_evaklimentova` is a English model originally trained by EvaKlimentova. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/knots_protbertbfd_alphafold_evaklimentova_en_5.1.4_3.4_1698198794585.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/knots_protbertbfd_alphafold_evaklimentova_en_5.1.4_3.4_1698198794585.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("knots_protbertbfd_alphafold_evaklimentova","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("knots_protbertbfd_alphafold_evaklimentova","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|knots_protbertbfd_alphafold_evaklimentova| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.6 GB| + +## References + +https://huggingface.co/EvaKlimentova/knots_protbertBFD_alphafold \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-knots_protbertbfd_alphafold_roa7n_en.md b/docs/_posts/ahmedlone127/2023-10-25-knots_protbertbfd_alphafold_roa7n_en.md new file mode 100644 index 00000000000000..82df3a47e922dd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-knots_protbertbfd_alphafold_roa7n_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English knots_protbertbfd_alphafold_roa7n BertForSequenceClassification from roa7n +author: John Snow Labs +name: knots_protbertbfd_alphafold_roa7n +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`knots_protbertbfd_alphafold_roa7n` is a English model originally trained by roa7n. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/knots_protbertbfd_alphafold_roa7n_en_5.1.4_3.4_1698216194692.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/knots_protbertbfd_alphafold_roa7n_en_5.1.4_3.4_1698216194692.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("knots_protbertbfd_alphafold_roa7n","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("knots_protbertbfd_alphafold_roa7n","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|knots_protbertbfd_alphafold_roa7n| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.6 GB| + +## References + +https://huggingface.co/roa7n/knots_protbertBFD_alphafold \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-kobert_esg_en.md b/docs/_posts/ahmedlone127/2023-10-25-kobert_esg_en.md new file mode 100644 index 00000000000000..e423578bfcea39 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-kobert_esg_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English kobert_esg BertForSequenceClassification from keonju +author: John Snow Labs +name: kobert_esg +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kobert_esg` is a English model originally trained by keonju. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kobert_esg_en_5.1.4_3.4_1698213440220.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kobert_esg_en_5.1.4_3.4_1698213440220.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("kobert_esg","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("kobert_esg","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kobert_esg| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|345.1 MB| + +## References + +https://huggingface.co/keonju/kobert_ESG \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-kobert_senti_en.md b/docs/_posts/ahmedlone127/2023-10-25-kobert_senti_en.md new file mode 100644 index 00000000000000..cb16e37e9d3873 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-kobert_senti_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English kobert_senti BertForSequenceClassification from keonju +author: John Snow Labs +name: kobert_senti +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kobert_senti` is a English model originally trained by keonju. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kobert_senti_en_5.1.4_3.4_1698206798000.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kobert_senti_en_5.1.4_3.4_1698206798000.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("kobert_senti","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("kobert_senti","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kobert_senti| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|345.1 MB| + +## References + +https://huggingface.co/keonju/kobert_senti \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-macedonian_bert_sentiment_classification_4labels_en.md b/docs/_posts/ahmedlone127/2023-10-25-macedonian_bert_sentiment_classification_4labels_en.md new file mode 100644 index 00000000000000..a38f01f4e8cf73 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-macedonian_bert_sentiment_classification_4labels_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English macedonian_bert_sentiment_classification_4labels BertForSequenceClassification from mk9165 +author: John Snow Labs +name: macedonian_bert_sentiment_classification_4labels +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`macedonian_bert_sentiment_classification_4labels` is a English model originally trained by mk9165. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/macedonian_bert_sentiment_classification_4labels_en_5.1.4_3.4_1698208948044.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/macedonian_bert_sentiment_classification_4labels_en_5.1.4_3.4_1698208948044.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("macedonian_bert_sentiment_classification_4labels","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("macedonian_bert_sentiment_classification_4labels","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|macedonian_bert_sentiment_classification_4labels| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|443.4 MB| + +## References + +https://huggingface.co/mk9165/mk-bert-sentiment-classification-4labels \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-macedonian_bert_sentiment_classification_en.md b/docs/_posts/ahmedlone127/2023-10-25-macedonian_bert_sentiment_classification_en.md new file mode 100644 index 00000000000000..4b0cd2b74e8ef4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-macedonian_bert_sentiment_classification_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English macedonian_bert_sentiment_classification BertForSequenceClassification from mk9165 +author: John Snow Labs +name: macedonian_bert_sentiment_classification +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`macedonian_bert_sentiment_classification` is a English model originally trained by mk9165. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/macedonian_bert_sentiment_classification_en_5.1.4_3.4_1698196985187.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/macedonian_bert_sentiment_classification_en_5.1.4_3.4_1698196985187.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("macedonian_bert_sentiment_classification","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("macedonian_bert_sentiment_classification","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|macedonian_bert_sentiment_classification| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|443.4 MB| + +## References + +https://huggingface.co/mk9165/mk-bert-sentiment-classification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mbert_base_cased_finetuned_bengali_fakenews_en.md b/docs/_posts/ahmedlone127/2023-10-25-mbert_base_cased_finetuned_bengali_fakenews_en.md new file mode 100644 index 00000000000000..0ad242d94b15b8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mbert_base_cased_finetuned_bengali_fakenews_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mbert_base_cased_finetuned_bengali_fakenews BertForSequenceClassification from DeadBeast +author: John Snow Labs +name: mbert_base_cased_finetuned_bengali_fakenews +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mbert_base_cased_finetuned_bengali_fakenews` is a English model originally trained by DeadBeast. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mbert_base_cased_finetuned_bengali_fakenews_en_5.1.4_3.4_1698193902726.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mbert_base_cased_finetuned_bengali_fakenews_en_5.1.4_3.4_1698193902726.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mbert_base_cased_finetuned_bengali_fakenews","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mbert_base_cased_finetuned_bengali_fakenews","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mbert_base_cased_finetuned_bengali_fakenews| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|667.3 MB| + +## References + +https://huggingface.co/DeadBeast/mbert-base-cased-finetuned-bengali-fakenews \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1_zh.md b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1_zh.md new file mode 100644 index 00000000000000..0d29001ba17803 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1_zh.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Chinese mengzi_bert_base_fin_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: mengzi_bert_base_fin_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1 +date: 2023-10-25 +tags: [bert, zh, open_source, sequence_classification, onnx] +task: Text Classification +language: zh +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mengzi_bert_base_fin_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1` is a Chinese model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1_zh_5.1.4_3.4_1698213915520.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1_zh_5.1.4_3.4_1698213915520.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1","zh")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1","zh") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mengzi_bert_base_fin_finetuning_wallstreetcn_morning_news_market_overview_open_000001sh_v1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|zh| +|Size:|383.1 MB| + +## References + +https://huggingface.co/hw2942/mengzi-bert-base-fin-finetuning-wallstreetcn-morning-news-market-overview-open-000001SH-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mental_bert_base_uncased_finetuned_0505_en.md b/docs/_posts/ahmedlone127/2023-10-25-mental_bert_base_uncased_finetuned_0505_en.md new file mode 100644 index 00000000000000..3a97ef7a3f405a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mental_bert_base_uncased_finetuned_0505_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mental_bert_base_uncased_finetuned_0505 BertForSequenceClassification from YeRyeongLee +author: John Snow Labs +name: mental_bert_base_uncased_finetuned_0505 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mental_bert_base_uncased_finetuned_0505` is a English model originally trained by YeRyeongLee. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mental_bert_base_uncased_finetuned_0505_en_5.1.4_3.4_1698201411316.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mental_bert_base_uncased_finetuned_0505_en_5.1.4_3.4_1698201411316.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mental_bert_base_uncased_finetuned_0505","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mental_bert_base_uncased_finetuned_0505","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mental_bert_base_uncased_finetuned_0505| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|408.8 MB| + +## References + +https://huggingface.co/YeRyeongLee/mental-bert-base-uncased-finetuned-0505 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-ml_ns_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-10-25-ml_ns_bert_base_uncased_en.md new file mode 100644 index 00000000000000..2d5811fc5ce2ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-ml_ns_bert_base_uncased_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English ml_ns_bert_base_uncased BertForSequenceClassification from sara-nabhani +author: John Snow Labs +name: ml_ns_bert_base_uncased +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ml_ns_bert_base_uncased` is a English model originally trained by sara-nabhani. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ml_ns_bert_base_uncased_en_5.1.4_3.4_1698194262921.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ml_ns_bert_base_uncased_en_5.1.4_3.4_1698194262921.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("ml_ns_bert_base_uncased","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("ml_ns_bert_base_uncased","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ml_ns_bert_base_uncased| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/sara-nabhani/ML-ns-bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-ml_ns_finbert_en.md b/docs/_posts/ahmedlone127/2023-10-25-ml_ns_finbert_en.md new file mode 100644 index 00000000000000..781fa13d4c9cf0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-ml_ns_finbert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English ml_ns_finbert BertForSequenceClassification from sara-nabhani +author: John Snow Labs +name: ml_ns_finbert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ml_ns_finbert` is a English model originally trained by sara-nabhani. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ml_ns_finbert_en_5.1.4_3.4_1698194077828.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ml_ns_finbert_en_5.1.4_3.4_1698194077828.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("ml_ns_finbert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("ml_ns_finbert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ml_ns_finbert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/sara-nabhani/ML-ns-finbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mpe_bert_l_en.md b/docs/_posts/ahmedlone127/2023-10-25-mpe_bert_l_en.md new file mode 100644 index 00000000000000..a6a6d101b119d9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mpe_bert_l_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mpe_bert_l BertForSequenceClassification from veronica320 +author: John Snow Labs +name: mpe_bert_l +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpe_bert_l` is a English model originally trained by veronica320. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mpe_bert_l_en_5.1.4_3.4_1698198372650.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpe_bert_l_en_5.1.4_3.4_1698198372650.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mpe_bert_l","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mpe_bert_l","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mpe_bert_l| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/veronica320/MPE_bert-l \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-multicite_multilabel_scibert_en.md b/docs/_posts/ahmedlone127/2023-10-25-multicite_multilabel_scibert_en.md new file mode 100644 index 00000000000000..6cd937cb2f1156 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-multicite_multilabel_scibert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English multicite_multilabel_scibert BertForSequenceClassification from allenai +author: John Snow Labs +name: multicite_multilabel_scibert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multicite_multilabel_scibert` is a English model originally trained by allenai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/multicite_multilabel_scibert_en_5.1.4_3.4_1698202659603.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multicite_multilabel_scibert_en_5.1.4_3.4_1698202659603.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("multicite_multilabel_scibert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("multicite_multilabel_scibert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|multicite_multilabel_scibert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|412.1 MB| + +## References + +https://huggingface.co/allenai/multicite-multilabel-scibert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-multilabel_earthquake_tweet_intent_bert_base_turkish_cased_tr.md b/docs/_posts/ahmedlone127/2023-10-25-multilabel_earthquake_tweet_intent_bert_base_turkish_cased_tr.md new file mode 100644 index 00000000000000..17056f5ecf68b4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-multilabel_earthquake_tweet_intent_bert_base_turkish_cased_tr.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Turkish multilabel_earthquake_tweet_intent_bert_base_turkish_cased BertForSequenceClassification from deprem-ml +author: John Snow Labs +name: multilabel_earthquake_tweet_intent_bert_base_turkish_cased +date: 2023-10-25 +tags: [bert, tr, open_source, sequence_classification, onnx] +task: Text Classification +language: tr +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multilabel_earthquake_tweet_intent_bert_base_turkish_cased` is a Turkish model originally trained by deprem-ml. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/multilabel_earthquake_tweet_intent_bert_base_turkish_cased_tr_5.1.4_3.4_1698200263892.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multilabel_earthquake_tweet_intent_bert_base_turkish_cased_tr_5.1.4_3.4_1698200263892.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("multilabel_earthquake_tweet_intent_bert_base_turkish_cased","tr")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("multilabel_earthquake_tweet_intent_bert_base_turkish_cased","tr") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|multilabel_earthquake_tweet_intent_bert_base_turkish_cased| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|tr| +|Size:|414.5 MB| + +## References + +https://huggingface.co/deprem-ml/multilabel_earthquake_tweet_intent_bert_base_turkish_cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_no.md b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_no.md new file mode 100644 index 00000000000000..4856cf5837f5a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_no.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Norwegian norbert2_sentiment_norec BertForSequenceClassification from NTCAL +author: John Snow Labs +name: norbert2_sentiment_norec +date: 2023-10-25 +tags: [bert, "no", open_source, sequence_classification, onnx] +task: Text Classification +language: "no" +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`norbert2_sentiment_norec` is a Norwegian model originally trained by NTCAL. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_no_5.1.4_3.4_1698195161375.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_no_5.1.4_3.4_1698195161375.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec","no")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec","no") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|norbert2_sentiment_norec| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|no| +|Size:|467.4 MB| + +## References + +https://huggingface.co/NTCAL/norbert2_sentiment_norec \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-northern_sami_bert_en.md b/docs/_posts/ahmedlone127/2023-10-25-northern_sami_bert_en.md new file mode 100644 index 00000000000000..f406ec79495580 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-northern_sami_bert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English northern_sami_bert BertForSequenceClassification from thearod5 +author: John Snow Labs +name: northern_sami_bert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`northern_sami_bert` is a English model originally trained by thearod5. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/northern_sami_bert_en_5.1.4_3.4_1698201246750.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/northern_sami_bert_en_5.1.4_3.4_1698201246750.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("northern_sami_bert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("northern_sami_bert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|northern_sami_bert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/thearod5/se-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-nosql_identifier_bert_en.md b/docs/_posts/ahmedlone127/2023-10-25-nosql_identifier_bert_en.md new file mode 100644 index 00000000000000..58a0b5fabc99ea --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-nosql_identifier_bert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English nosql_identifier_bert BertForSequenceClassification from ankush-003 +author: John Snow Labs +name: nosql_identifier_bert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`nosql_identifier_bert` is a English model originally trained by ankush-003. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/nosql_identifier_bert_en_5.1.4_3.4_1698201204944.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/nosql_identifier_bert_en_5.1.4_3.4_1698201204944.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("nosql_identifier_bert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("nosql_identifier_bert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|nosql_identifier_bert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/ankush-003/nosql-identifier-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-platzi_distilroberta_base_mrpc_glue_en.md b/docs/_posts/ahmedlone127/2023-10-25-platzi_distilroberta_base_mrpc_glue_en.md new file mode 100644 index 00000000000000..af3b8ce66b0268 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-platzi_distilroberta_base_mrpc_glue_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English platzi_distilroberta_base_mrpc_glue BertForSequenceClassification from eormeno12 +author: John Snow Labs +name: platzi_distilroberta_base_mrpc_glue +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`platzi_distilroberta_base_mrpc_glue` is a English model originally trained by eormeno12. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/platzi_distilroberta_base_mrpc_glue_en_5.1.4_3.4_1698201428077.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/platzi_distilroberta_base_mrpc_glue_en_5.1.4_3.4_1698201428077.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("platzi_distilroberta_base_mrpc_glue","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("platzi_distilroberta_base_mrpc_glue","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|platzi_distilroberta_base_mrpc_glue| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/eormeno12/platzi-distilroberta-base-mrpc-glue \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-prot_bert_finetuned_localization_hazel0707_en.md b/docs/_posts/ahmedlone127/2023-10-25-prot_bert_finetuned_localization_hazel0707_en.md new file mode 100644 index 00000000000000..f004e6767bc5be --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-prot_bert_finetuned_localization_hazel0707_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English prot_bert_finetuned_localization_hazel0707 BertForSequenceClassification from Hazel0707 +author: John Snow Labs +name: prot_bert_finetuned_localization_hazel0707 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`prot_bert_finetuned_localization_hazel0707` is a English model originally trained by Hazel0707. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/prot_bert_finetuned_localization_hazel0707_en_5.1.4_3.4_1698205163369.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/prot_bert_finetuned_localization_hazel0707_en_5.1.4_3.4_1698205163369.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("prot_bert_finetuned_localization_hazel0707","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("prot_bert_finetuned_localization_hazel0707","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|prot_bert_finetuned_localization_hazel0707| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.6 GB| + +## References + +https://huggingface.co/Hazel0707/prot_bert-finetuned-localization \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-rubert_base_emotion_russian_cedr_m7_en.md b/docs/_posts/ahmedlone127/2023-10-25-rubert_base_emotion_russian_cedr_m7_en.md new file mode 100644 index 00000000000000..2a9676fa5046d2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-rubert_base_emotion_russian_cedr_m7_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English rubert_base_emotion_russian_cedr_m7 BertForSequenceClassification from Aniemore +author: John Snow Labs +name: rubert_base_emotion_russian_cedr_m7 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`rubert_base_emotion_russian_cedr_m7` is a English model originally trained by Aniemore. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/rubert_base_emotion_russian_cedr_m7_en_5.1.4_3.4_1698207995037.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/rubert_base_emotion_russian_cedr_m7_en_5.1.4_3.4_1698207995037.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("rubert_base_emotion_russian_cedr_m7","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("rubert_base_emotion_russian_cedr_m7","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rubert_base_emotion_russian_cedr_m7| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|666.5 MB| + +## References + +https://huggingface.co/Aniemore/rubert-base-emotion-russian-cedr-m7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-rubert_tiny_emotion_russian_cedr_m7_en.md b/docs/_posts/ahmedlone127/2023-10-25-rubert_tiny_emotion_russian_cedr_m7_en.md new file mode 100644 index 00000000000000..9b2fd20f1d5a44 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-rubert_tiny_emotion_russian_cedr_m7_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English rubert_tiny_emotion_russian_cedr_m7 BertForSequenceClassification from Aniemore +author: John Snow Labs +name: rubert_tiny_emotion_russian_cedr_m7 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`rubert_tiny_emotion_russian_cedr_m7` is a English model originally trained by Aniemore. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/rubert_tiny_emotion_russian_cedr_m7_en_5.1.4_3.4_1698207727419.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/rubert_tiny_emotion_russian_cedr_m7_en_5.1.4_3.4_1698207727419.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("rubert_tiny_emotion_russian_cedr_m7","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("rubert_tiny_emotion_russian_cedr_m7","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rubert_tiny_emotion_russian_cedr_m7| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|109.5 MB| + +## References + +https://huggingface.co/Aniemore/rubert-tiny-emotion-russian-cedr-m7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-rubert_tweetsentiment_en.md b/docs/_posts/ahmedlone127/2023-10-25-rubert_tweetsentiment_en.md new file mode 100644 index 00000000000000..f700d0e9809590 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-rubert_tweetsentiment_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English rubert_tweetsentiment BertForSequenceClassification from EMBEDDIA +author: John Snow Labs +name: rubert_tweetsentiment +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`rubert_tweetsentiment` is a English model originally trained by EMBEDDIA. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/rubert_tweetsentiment_en_5.1.4_3.4_1698202113288.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/rubert_tweetsentiment_en_5.1.4_3.4_1698202113288.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("rubert_tweetsentiment","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("rubert_tweetsentiment","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rubert_tweetsentiment| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|666.5 MB| + +## References + +https://huggingface.co/EMBEDDIA/rubert-tweetsentiment \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-scibert_finetuned_dagpap22_en.md b/docs/_posts/ahmedlone127/2023-10-25-scibert_finetuned_dagpap22_en.md new file mode 100644 index 00000000000000..6232c4c3edc170 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-scibert_finetuned_dagpap22_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English scibert_finetuned_dagpap22 BertForSequenceClassification from domenicrosati +author: John Snow Labs +name: scibert_finetuned_dagpap22 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scibert_finetuned_dagpap22` is a English model originally trained by domenicrosati. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scibert_finetuned_dagpap22_en_5.1.4_3.4_1698211484008.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scibert_finetuned_dagpap22_en_5.1.4_3.4_1698211484008.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("scibert_finetuned_dagpap22","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("scibert_finetuned_dagpap22","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scibert_finetuned_dagpap22| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|412.2 MB| + +## References + +https://huggingface.co/domenicrosati/scibert-finetuned-DAGPap22 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-scotus_bert_chiragasarpota_en.md b/docs/_posts/ahmedlone127/2023-10-25-scotus_bert_chiragasarpota_en.md new file mode 100644 index 00000000000000..a10a1eec0a6f4c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-scotus_bert_chiragasarpota_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English scotus_bert_chiragasarpota BertForSequenceClassification from chiragasarpota +author: John Snow Labs +name: scotus_bert_chiragasarpota +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scotus_bert_chiragasarpota` is a English model originally trained by chiragasarpota. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scotus_bert_chiragasarpota_en_5.1.4_3.4_1698192913047.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scotus_bert_chiragasarpota_en_5.1.4_3.4_1698192913047.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("scotus_bert_chiragasarpota","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("scotus_bert_chiragasarpota","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scotus_bert_chiragasarpota| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/chiragasarpota/scotus-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-sikubert_finetuned_poetry_en.md b/docs/_posts/ahmedlone127/2023-10-25-sikubert_finetuned_poetry_en.md new file mode 100644 index 00000000000000..24a66e93420267 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-sikubert_finetuned_poetry_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English sikubert_finetuned_poetry BertForSequenceClassification from synpjh +author: John Snow Labs +name: sikubert_finetuned_poetry +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sikubert_finetuned_poetry` is a English model originally trained by synpjh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sikubert_finetuned_poetry_en_5.1.4_3.4_1698205521845.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sikubert_finetuned_poetry_en_5.1.4_3.4_1698205521845.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("sikubert_finetuned_poetry","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("sikubert_finetuned_poetry","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sikubert_finetuned_poetry| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|408.9 MB| + +## References + +https://huggingface.co/synpjh/sikubert-finetuned-poetry \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-sqlclassification_normal_bert_model_en.md b/docs/_posts/ahmedlone127/2023-10-25-sqlclassification_normal_bert_model_en.md new file mode 100644 index 00000000000000..e19b3a5eb70245 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-sqlclassification_normal_bert_model_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English sqlclassification_normal_bert_model BertForSequenceClassification from flagship +author: John Snow Labs +name: sqlclassification_normal_bert_model +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sqlclassification_normal_bert_model` is a English model originally trained by flagship. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sqlclassification_normal_bert_model_en_5.1.4_3.4_1698214173523.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sqlclassification_normal_bert_model_en_5.1.4_3.4_1698214173523.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("sqlclassification_normal_bert_model","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("sqlclassification_normal_bert_model","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sqlclassification_normal_bert_model| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/flagship/sqlclassification_normal_bert_model \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_0_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_0_en.md new file mode 100644 index 00000000000000..1adcc9b6675ed6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_0_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_0 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_0 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_0` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_0_en_5.1.4_3.4_1698194100114.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_0_en_5.1.4_3.4_1698194100114.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_0","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_0","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_0| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_10_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_10_en.md new file mode 100644 index 00000000000000..fda2fb8a71d9d2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_10_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_10 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_10 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_10` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_10_en_5.1.4_3.4_1698204826153.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_10_en_5.1.4_3.4_1698204826153.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_10","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_10","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_10| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_11_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_11_en.md new file mode 100644 index 00000000000000..1a9a05034ea083 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_11_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_11 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_11 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_11` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_11_en_5.1.4_3.4_1698196289627.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_11_en_5.1.4_3.4_1698196289627.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_11","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_11","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_11| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-11 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_12_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_12_en.md new file mode 100644 index 00000000000000..f879298658eb0b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_12_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_12 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_12 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_12` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_12_en_5.1.4_3.4_1698202946217.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_12_en_5.1.4_3.4_1698202946217.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_12","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_12","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_12| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-12 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_13_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_13_en.md new file mode 100644 index 00000000000000..b04be0adf54361 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_13_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_13 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_13 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_13` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_13_en_5.1.4_3.4_1698202339295.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_13_en_5.1.4_3.4_1698202339295.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_13","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_13","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_13| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-13 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_14_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_14_en.md new file mode 100644 index 00000000000000..ce7c94f6764be6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_14_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_14 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_14 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_14` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_14_en_5.1.4_3.4_1698206251020.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_14_en_5.1.4_3.4_1698206251020.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_14","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_14","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_14| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-14 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_15_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_15_en.md new file mode 100644 index 00000000000000..d02e3d614c3282 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_15_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_15 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_15 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_15` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_15_en_5.1.4_3.4_1698201749425.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_15_en_5.1.4_3.4_1698201749425.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_15","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_15","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_15| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-15 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_17_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_17_en.md new file mode 100644 index 00000000000000..192f7fa3a0e8e2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_17_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_17 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_17 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_17` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_17_en_5.1.4_3.4_1698194512012.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_17_en_5.1.4_3.4_1698194512012.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_17","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_17","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_17| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-17 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_18_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_18_en.md new file mode 100644 index 00000000000000..0d1b09cef1df6a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_18_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_18 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_18 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_18` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_18_en_5.1.4_3.4_1698197910979.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_18_en_5.1.4_3.4_1698197910979.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_18","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_18","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_18| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-18 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_19_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_19_en.md new file mode 100644 index 00000000000000..74d8b69357ec03 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_19_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_19 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_19 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_19` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_19_en_5.1.4_3.4_1698193304132.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_19_en_5.1.4_3.4_1698193304132.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_19","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_19","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_19| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-19 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_1_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_1_en.md new file mode 100644 index 00000000000000..559b5f827c2a77 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_1_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_1 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_1 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_1` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_1_en_5.1.4_3.4_1698206917592.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_1_en_5.1.4_3.4_1698206917592.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_1","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_1","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_20_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_20_en.md new file mode 100644 index 00000000000000..6d3420e09b91ba --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_20_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_20 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_20 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_20` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_20_en_5.1.4_3.4_1698200494489.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_20_en_5.1.4_3.4_1698200494489.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_20","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_20","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_20| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_21_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_21_en.md new file mode 100644 index 00000000000000..1e462eeaaf5b43 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_21_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_21 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_21 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_21` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_21_en_5.1.4_3.4_1698206393610.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_21_en_5.1.4_3.4_1698206393610.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_21","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_21","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_21| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-21 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_22_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_22_en.md new file mode 100644 index 00000000000000..1b2bde12d799ff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_22_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_22 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_22 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_22` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_22_en_5.1.4_3.4_1698194286917.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_22_en_5.1.4_3.4_1698194286917.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_22","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_22","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_22| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-22 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_23_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_23_en.md new file mode 100644 index 00000000000000..9c833232fcdc08 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_23_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_23 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_23 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_23` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_23_en_5.1.4_3.4_1698207111260.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_23_en_5.1.4_3.4_1698207111260.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_23","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_23","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_23| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-23 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_24_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_24_en.md new file mode 100644 index 00000000000000..17740e1a45d10c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_24_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_24 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_24 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_24` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_24_en_5.1.4_3.4_1698196718644.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_24_en_5.1.4_3.4_1698196718644.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_24","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_24","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_24| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-24 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_25_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_25_en.md new file mode 100644 index 00000000000000..4c15aea843aaa8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_25_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_25 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_25 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_25` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_25_en_5.1.4_3.4_1698207290480.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_25_en_5.1.4_3.4_1698207290480.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_25","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_25","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_25| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-25 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_26_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_26_en.md new file mode 100644 index 00000000000000..a894cc3daaaa75 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_26_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_26 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_26 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_26` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_26_en_5.1.4_3.4_1698195522947.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_26_en_5.1.4_3.4_1698195522947.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_26","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_26","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_26| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-26 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_27_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_27_en.md new file mode 100644 index 00000000000000..9158184da7520b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_27_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_27 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_27 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_27` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_27_en_5.1.4_3.4_1698201949807.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_27_en_5.1.4_3.4_1698201949807.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_27","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_27","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_27| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-27 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_28_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_28_en.md new file mode 100644 index 00000000000000..4519dba0ea4a0c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_28_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_28 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_28 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_28` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_28_en_5.1.4_3.4_1698194746151.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_28_en_5.1.4_3.4_1698194746151.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_28","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_28","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_28| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_29_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_29_en.md new file mode 100644 index 00000000000000..9dfaa454811515 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_29_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_29 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_29 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_29` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_29_en_5.1.4_3.4_1698195887691.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_29_en_5.1.4_3.4_1698195887691.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_29","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_29","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_29| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-29 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_2_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_2_en.md new file mode 100644 index 00000000000000..1cad5a9413a1bf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_2 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_2` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_2_en_5.1.4_3.4_1698207970966.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_2_en_5.1.4_3.4_1698207970966.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_30_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_30_en.md new file mode 100644 index 00000000000000..2394c652a4f7b1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_30_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_30 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_30 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_30` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_30_en_5.1.4_3.4_1698195333470.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_30_en_5.1.4_3.4_1698195333470.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_30","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_30","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_30| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-30 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_31_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_31_en.md new file mode 100644 index 00000000000000..375545b18455f9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_31_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_31 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_31 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_31` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_31_en_5.1.4_3.4_1698205575224.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_31_en_5.1.4_3.4_1698205575224.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_31","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_31","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_31| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-31 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_32_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_32_en.md new file mode 100644 index 00000000000000..c1e0673254dd35 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_32_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_32 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_32 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_32` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_32_en_5.1.4_3.4_1698193896004.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_32_en_5.1.4_3.4_1698193896004.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_32","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_32","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_32| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-32 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_33_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_33_en.md new file mode 100644 index 00000000000000..7978abf4163fe3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_33_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_33 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_33 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_33` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_33_en_5.1.4_3.4_1698195707858.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_33_en_5.1.4_3.4_1698195707858.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_33","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_33","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_33| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-33 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_34_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_34_en.md new file mode 100644 index 00000000000000..042cdbaaa58d55 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_34_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_34 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_34 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_34` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_34_en_5.1.4_3.4_1698197715006.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_34_en_5.1.4_3.4_1698197715006.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_34","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_34","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_34| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-34 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_35_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_35_en.md new file mode 100644 index 00000000000000..b9a510c5c9e96e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_35_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_35 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_35 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_35` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_35_en_5.1.4_3.4_1698194923423.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_35_en_5.1.4_3.4_1698194923423.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_35","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_35","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_35| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-35 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_36_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_36_en.md new file mode 100644 index 00000000000000..b73e3d6d34f591 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_36_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_36 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_36 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_36` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_36_en_5.1.4_3.4_1698196482406.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_36_en_5.1.4_3.4_1698196482406.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_36","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_36","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_36| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-36 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_37_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_37_en.md new file mode 100644 index 00000000000000..f6a0972782f41f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_37_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_37 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_37 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_37` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_37_en_5.1.4_3.4_1698199974008.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_37_en_5.1.4_3.4_1698199974008.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_37","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_37","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_37| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-37 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_38_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_38_en.md new file mode 100644 index 00000000000000..d6b84a580a847c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_38_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_38 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_38 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_38` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_38_en_5.1.4_3.4_1698205728278.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_38_en_5.1.4_3.4_1698205728278.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_38","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_38","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_38| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-38 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_39_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_39_en.md new file mode 100644 index 00000000000000..39b71cf94deade --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_39_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_39 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_39 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_39` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_39_en_5.1.4_3.4_1698193524584.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_39_en_5.1.4_3.4_1698193524584.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_39","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_39","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_39| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-39 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_3_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_3_en.md new file mode 100644 index 00000000000000..e74293a7b63972 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_3_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_3 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_3 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_3` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_3_en_5.1.4_3.4_1698204625280.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_3_en_5.1.4_3.4_1698204625280.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_3","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_3","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_3| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_40_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_40_en.md new file mode 100644 index 00000000000000..8302ab3f592ff8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_40_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_40 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_40 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_40` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_40_en_5.1.4_3.4_1698195158475.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_40_en_5.1.4_3.4_1698195158475.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_40","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_40","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_40| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-40 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_41_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_41_en.md new file mode 100644 index 00000000000000..cd5b58d034e15b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_41_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_41 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_41 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_41` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_41_en_5.1.4_3.4_1698206559892.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_41_en_5.1.4_3.4_1698206559892.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_41","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_41","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_41| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-41 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_42_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_42_en.md new file mode 100644 index 00000000000000..c3dcba2d3209a0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_42_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_42 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_42 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_42` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_42_en_5.1.4_3.4_1698199210250.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_42_en_5.1.4_3.4_1698199210250.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_42","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_42","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_42| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-42 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_43_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_43_en.md new file mode 100644 index 00000000000000..ba4088de428822 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_43_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_43 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_43 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_43` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_43_en_5.1.4_3.4_1698205039126.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_43_en_5.1.4_3.4_1698205039126.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_43","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_43","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_43| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-43 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_44_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_44_en.md new file mode 100644 index 00000000000000..c49008bbcfec3e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_44_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_44 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_44 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_44` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_44_en_5.1.4_3.4_1698196940511.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_44_en_5.1.4_3.4_1698196940511.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_44","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_44","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_44| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-44 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_45_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_45_en.md new file mode 100644 index 00000000000000..61d2b8627b0073 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_45_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_45 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_45 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_45` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_45_en_5.1.4_3.4_1698211090662.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_45_en_5.1.4_3.4_1698211090662.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_45","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_45","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_45| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-45 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_46_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_46_en.md new file mode 100644 index 00000000000000..33154a332d9539 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_46_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_46 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_46 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_46` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_46_en_5.1.4_3.4_1698197337466.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_46_en_5.1.4_3.4_1698197337466.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_46","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_46","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_46| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-46 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_47_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_47_en.md new file mode 100644 index 00000000000000..2ff186b1ad4dd4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_47_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_47 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_47 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_47` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_47_en_5.1.4_3.4_1698201157917.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_47_en_5.1.4_3.4_1698201157917.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_47","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_47","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_47| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-47 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_48_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_48_en.md new file mode 100644 index 00000000000000..eb7c32881ea6a1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_48_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_48 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_48 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_48` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_48_en_5.1.4_3.4_1698203742663.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_48_en_5.1.4_3.4_1698203742663.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_48","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_48","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_48| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-48 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_49_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_49_en.md new file mode 100644 index 00000000000000..2d67edd2064171 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_49_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_49 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_49 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_49` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_49_en_5.1.4_3.4_1698197139808.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_49_en_5.1.4_3.4_1698197139808.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_49","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_49","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_49| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-49 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_4_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_4_en.md new file mode 100644 index 00000000000000..f1c1d592762c6b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_4_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_4 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_4 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_4` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_4_en_5.1.4_3.4_1698193722580.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_4_en_5.1.4_3.4_1698193722580.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_4","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_4","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_4| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_50_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_50_en.md new file mode 100644 index 00000000000000..80e2a3d9f3af1b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_50_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_50 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_50 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_50` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_50_en_5.1.4_3.4_1698201549968.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_50_en_5.1.4_3.4_1698201549968.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_50","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_50","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_50| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-50 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_51_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_51_en.md new file mode 100644 index 00000000000000..76752fe8578b53 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_51_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_51 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_51 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_51` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_51_en_5.1.4_3.4_1698203578389.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_51_en_5.1.4_3.4_1698203578389.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_51","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_51","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_51| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-51 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_52_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_52_en.md new file mode 100644 index 00000000000000..6d6582a3874903 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_52_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_52 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_52 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_52` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_52_en_5.1.4_3.4_1698198979075.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_52_en_5.1.4_3.4_1698198979075.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_52","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_52","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_52| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-52 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_53_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_53_en.md new file mode 100644 index 00000000000000..d751eb41c5e1c0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_53_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_53 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_53 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_53` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_53_en_5.1.4_3.4_1698207734508.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_53_en_5.1.4_3.4_1698207734508.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_53","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_53","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_53| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-53 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_54_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_54_en.md new file mode 100644 index 00000000000000..7ae3210c157b6d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_54_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_54 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_54 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_54` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_54_en_5.1.4_3.4_1698205408269.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_54_en_5.1.4_3.4_1698205408269.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_54","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_54","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_54| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-54 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_55_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_55_en.md new file mode 100644 index 00000000000000..9a5baf9401f29b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_55_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_55 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_55 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_55` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_55_en_5.1.4_3.4_1698196108440.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_55_en_5.1.4_3.4_1698196108440.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_55","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_55","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_55| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-55 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_56_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_56_en.md new file mode 100644 index 00000000000000..2e46a4ebe5b8e3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_56_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_56 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_56 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_56` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_56_en_5.1.4_3.4_1698202740922.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_56_en_5.1.4_3.4_1698202740922.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_56","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_56","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_56| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-56 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_57_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_57_en.md new file mode 100644 index 00000000000000..75d27135e74a28 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_57_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_57 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_57 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_57` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_57_en_5.1.4_3.4_1698198595004.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_57_en_5.1.4_3.4_1698198595004.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_57","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_57","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_57| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-57 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_58_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_58_en.md new file mode 100644 index 00000000000000..dd7b8f1d51abc4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_58_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_58 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_58 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_58` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_58_en_5.1.4_3.4_1698200937531.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_58_en_5.1.4_3.4_1698200937531.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_58","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_58","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_58| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-58 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_59_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_59_en.md new file mode 100644 index 00000000000000..3b9abbbd5334ed --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_59_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_59 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_59 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_59` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_59_en_5.1.4_3.4_1698205221969.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_59_en_5.1.4_3.4_1698205221969.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_59","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_59","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_59| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-59 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_5_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_5_en.md new file mode 100644 index 00000000000000..3ed4e1111eaaa1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_5_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_5 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_5 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_5` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_5_en_5.1.4_3.4_1698203161321.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_5_en_5.1.4_3.4_1698203161321.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_5","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_5","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_5| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_60_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_60_en.md new file mode 100644 index 00000000000000..a609c4a7f55f95 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_60_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_60 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_60 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_60` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_60_en_5.1.4_3.4_1698201369330.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_60_en_5.1.4_3.4_1698201369330.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_60","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_60","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_60| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-60 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_61_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_61_en.md new file mode 100644 index 00000000000000..0f1d6d9d7993f5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_61_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_61 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_61 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_61` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_61_en_5.1.4_3.4_1698203921006.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_61_en_5.1.4_3.4_1698203921006.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_61","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_61","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_61| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-61 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_63_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_63_en.md new file mode 100644 index 00000000000000..5035af9364286f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_63_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_63 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_63 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_63` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_63_en_5.1.4_3.4_1698200696666.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_63_en_5.1.4_3.4_1698200696666.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_63","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_63","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_63| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-63 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_64_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_64_en.md new file mode 100644 index 00000000000000..0c35026ebd6e86 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_64_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_64 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_64 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_64` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_64_en_5.1.4_3.4_1698205893853.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_64_en_5.1.4_3.4_1698205893853.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_64","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_64","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_64| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-64 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_65_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_65_en.md new file mode 100644 index 00000000000000..060b6d67371d9e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_65_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_65 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_65 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_65` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_65_en_5.1.4_3.4_1698198791587.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_65_en_5.1.4_3.4_1698198791587.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_65","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_65","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_65| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-65 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_66_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_66_en.md new file mode 100644 index 00000000000000..65a219b4a3f9ec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_66_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_66 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_66 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_66` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_66_en_5.1.4_3.4_1698204114463.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_66_en_5.1.4_3.4_1698204114463.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_66","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_66","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_66| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-66 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_67_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_67_en.md new file mode 100644 index 00000000000000..155f6505e56aad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_67_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_67 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_67 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_67` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_67_en_5.1.4_3.4_1698203354474.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_67_en_5.1.4_3.4_1698203354474.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_67","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_67","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_67| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-67 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_68_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_68_en.md new file mode 100644 index 00000000000000..18875852e3848e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_68_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_68 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_68 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_68` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_68_en_5.1.4_3.4_1698200320551.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_68_en_5.1.4_3.4_1698200320551.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_68","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_68","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_68| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-68 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_69_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_69_en.md new file mode 100644 index 00000000000000..93b4fb67e14519 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_69_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_69 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_69 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_69` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_69_en_5.1.4_3.4_1698198162433.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_69_en_5.1.4_3.4_1698198162433.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_69","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_69","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_69| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-69 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_6_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_6_en.md new file mode 100644 index 00000000000000..8b9b933e7c1716 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_6_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_6 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_6 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_6` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_6_en_5.1.4_3.4_1698200139565.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_6_en_5.1.4_3.4_1698200139565.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_6","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_6","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_6| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_70_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_70_en.md new file mode 100644 index 00000000000000..ab94bc213554e3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_70_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_70 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_70 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_70` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_70_en_5.1.4_3.4_1698199412068.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_70_en_5.1.4_3.4_1698199412068.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_70","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_70","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_70| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-70 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_71_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_71_en.md new file mode 100644 index 00000000000000..50333ab5f0cdc6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_71_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_71 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_71 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_71` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_71_en_5.1.4_3.4_1698204297121.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_71_en_5.1.4_3.4_1698204297121.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_71","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_71","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_71| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-71 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_72_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_72_en.md new file mode 100644 index 00000000000000..6dcb4251c24949 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_72_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_72 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_72 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_72` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_72_en_5.1.4_3.4_1698198386848.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_72_en_5.1.4_3.4_1698198386848.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_72","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_72","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_72| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-72 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_73_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_73_en.md new file mode 100644 index 00000000000000..bee46117f59f79 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_73_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_73 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_73 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_73` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_73_en_5.1.4_3.4_1698208395070.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_73_en_5.1.4_3.4_1698208395070.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_73","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_73","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_73| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-73 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_74_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_74_en.md new file mode 100644 index 00000000000000..efbf82564f6168 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_74_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_74 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_74 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_74` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_74_en_5.1.4_3.4_1698208587674.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_74_en_5.1.4_3.4_1698208587674.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_74","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_74","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_74| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-74 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_75_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_75_en.md new file mode 100644 index 00000000000000..fd59ebf063d91c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_75_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_75 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_75 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_75` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_75_en_5.1.4_3.4_1698208214707.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_75_en_5.1.4_3.4_1698208214707.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_75","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_75","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_75| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-75 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_76_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_76_en.md new file mode 100644 index 00000000000000..3e2d8848a452d1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_76_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_76 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_76 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_76` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_76_en_5.1.4_3.4_1698206767477.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_76_en_5.1.4_3.4_1698206767477.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_76","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_76","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_76| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-76 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_77_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_77_en.md new file mode 100644 index 00000000000000..01414a577a0a57 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_77_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_77 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_77 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_77` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_77_en_5.1.4_3.4_1698199785215.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_77_en_5.1.4_3.4_1698199785215.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_77","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_77","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_77| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-77 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_78_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_78_en.md new file mode 100644 index 00000000000000..f972186928014b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_78_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_78 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_78 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_78` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_78_en_5.1.4_3.4_1698206075918.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_78_en_5.1.4_3.4_1698206075918.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_78","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_78","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_78| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-78 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_79_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_79_en.md new file mode 100644 index 00000000000000..c4db1c974f4592 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_79_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_79 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_79 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_79` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_79_en_5.1.4_3.4_1698207537777.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_79_en_5.1.4_3.4_1698207537777.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_79","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_79","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_79| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-79 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_7_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_7_en.md new file mode 100644 index 00000000000000..55d6e9a94b6d0d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_7_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_7 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_7 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_7` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_7_en_5.1.4_3.4_1698202539959.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_7_en_5.1.4_3.4_1698202539959.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_7","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_7","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_7| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_8_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_8_en.md new file mode 100644 index 00000000000000..a3e3388d1bb7d4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_8_en_5.1.4_3.4_1698197524932.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_8_en_5.1.4_3.4_1698197524932.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-8 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_9_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_9_en.md new file mode 100644 index 00000000000000..310e2cf7c7afc3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_9_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_9 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_9 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_9` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_9_en_5.1.4_3.4_1698199592174.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_9_en_5.1.4_3.4_1698199592174.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_9","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_9","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_9| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-9 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-subsimulator_bert_en.md b/docs/_posts/ahmedlone127/2023-10-25-subsimulator_bert_en.md new file mode 100644 index 00000000000000..6a05cde2f2cb9d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-subsimulator_bert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English subsimulator_bert BertForSequenceClassification from helliun +author: John Snow Labs +name: subsimulator_bert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`subsimulator_bert` is a English model originally trained by helliun. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/subsimulator_bert_en_5.1.4_3.4_1698210463463.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/subsimulator_bert_en_5.1.4_3.4_1698210463463.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("subsimulator_bert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("subsimulator_bert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|subsimulator_bert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/helliun/subsimulator-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-swmh4_bert_en.md b/docs/_posts/ahmedlone127/2023-10-25-swmh4_bert_en.md new file mode 100644 index 00000000000000..c950024b9295e4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-swmh4_bert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English swmh4_bert BertForSequenceClassification from tiya1012 +author: John Snow Labs +name: swmh4_bert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`swmh4_bert` is a English model originally trained by tiya1012. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/swmh4_bert_en_5.1.4_3.4_1698197603467.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/swmh4_bert_en_5.1.4_3.4_1698197603467.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("swmh4_bert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("swmh4_bert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|swmh4_bert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/tiya1012/swmh4_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-symps_disease_bert_v3_c41_en.md b/docs/_posts/ahmedlone127/2023-10-25-symps_disease_bert_v3_c41_en.md new file mode 100644 index 00000000000000..4b534cb4cecd19 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-symps_disease_bert_v3_c41_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English symps_disease_bert_v3_c41 BertForSequenceClassification from shanover +author: John Snow Labs +name: symps_disease_bert_v3_c41 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`symps_disease_bert_v3_c41` is a English model originally trained by shanover. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/symps_disease_bert_v3_c41_en_5.1.4_3.4_1698193526004.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/symps_disease_bert_v3_c41_en_5.1.4_3.4_1698193526004.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("symps_disease_bert_v3_c41","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("symps_disease_bert_v3_c41","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|symps_disease_bert_v3_c41| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/shanover/symps_disease_bert_v3_c41 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-task_implicit_task__model_bert__aug_method_gm_en.md b/docs/_posts/ahmedlone127/2023-10-25-task_implicit_task__model_bert__aug_method_gm_en.md new file mode 100644 index 00000000000000..aba81338430b9b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-task_implicit_task__model_bert__aug_method_gm_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English task_implicit_task__model_bert__aug_method_gm BertForSequenceClassification from BenjaminOcampo +author: John Snow Labs +name: task_implicit_task__model_bert__aug_method_gm +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`task_implicit_task__model_bert__aug_method_gm` is a English model originally trained by BenjaminOcampo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/task_implicit_task__model_bert__aug_method_gm_en_5.1.4_3.4_1698196152482.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/task_implicit_task__model_bert__aug_method_gm_en_5.1.4_3.4_1698196152482.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("task_implicit_task__model_bert__aug_method_gm","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("task_implicit_task__model_bert__aug_method_gm","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|task_implicit_task__model_bert__aug_method_gm| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/BenjaminOcampo/task-implicit_task__model-bert__aug_method-gm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-task_implicit_task__model_bert__aug_method_ra_en.md b/docs/_posts/ahmedlone127/2023-10-25-task_implicit_task__model_bert__aug_method_ra_en.md new file mode 100644 index 00000000000000..3e5c83db481921 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-task_implicit_task__model_bert__aug_method_ra_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English task_implicit_task__model_bert__aug_method_ra BertForSequenceClassification from BenjaminOcampo +author: John Snow Labs +name: task_implicit_task__model_bert__aug_method_ra +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`task_implicit_task__model_bert__aug_method_ra` is a English model originally trained by BenjaminOcampo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/task_implicit_task__model_bert__aug_method_ra_en_5.1.4_3.4_1698196596903.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/task_implicit_task__model_bert__aug_method_ra_en_5.1.4_3.4_1698196596903.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("task_implicit_task__model_bert__aug_method_ra","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("task_implicit_task__model_bert__aug_method_ra","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|task_implicit_task__model_bert__aug_method_ra| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/BenjaminOcampo/task-implicit_task__model-bert__aug_method-ra \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-task_subtle_task__model_bert__aug_method_gm_en.md b/docs/_posts/ahmedlone127/2023-10-25-task_subtle_task__model_bert__aug_method_gm_en.md new file mode 100644 index 00000000000000..3502876f9532ad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-task_subtle_task__model_bert__aug_method_gm_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English task_subtle_task__model_bert__aug_method_gm BertForSequenceClassification from BenjaminOcampo +author: John Snow Labs +name: task_subtle_task__model_bert__aug_method_gm +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`task_subtle_task__model_bert__aug_method_gm` is a English model originally trained by BenjaminOcampo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/task_subtle_task__model_bert__aug_method_gm_en_5.1.4_3.4_1698196330798.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/task_subtle_task__model_bert__aug_method_gm_en_5.1.4_3.4_1698196330798.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("task_subtle_task__model_bert__aug_method_gm","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("task_subtle_task__model_bert__aug_method_gm","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|task_subtle_task__model_bert__aug_method_gm| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/BenjaminOcampo/task-subtle_task__model-bert__aug_method-gm \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-test_bert_training_en.md b/docs/_posts/ahmedlone127/2023-10-25-test_bert_training_en.md new file mode 100644 index 00000000000000..42d708d76199ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-test_bert_training_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English test_bert_training BertForSequenceClassification from andfanilo +author: John Snow Labs +name: test_bert_training +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`test_bert_training` is a English model originally trained by andfanilo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/test_bert_training_en_5.1.4_3.4_1698209381719.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/test_bert_training_en_5.1.4_3.4_1698209381719.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("test_bert_training","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("test_bert_training","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|test_bert_training| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/andfanilo/test-bert-training \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-thermo_predictor_thermo_evotuning_prot_bert_en.md b/docs/_posts/ahmedlone127/2023-10-25-thermo_predictor_thermo_evotuning_prot_bert_en.md new file mode 100644 index 00000000000000..614435197e4c67 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-thermo_predictor_thermo_evotuning_prot_bert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English thermo_predictor_thermo_evotuning_prot_bert BertForSequenceClassification from cradle-bio +author: John Snow Labs +name: thermo_predictor_thermo_evotuning_prot_bert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`thermo_predictor_thermo_evotuning_prot_bert` is a English model originally trained by cradle-bio. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/thermo_predictor_thermo_evotuning_prot_bert_en_5.1.4_3.4_1698201988248.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/thermo_predictor_thermo_evotuning_prot_bert_en_5.1.4_3.4_1698201988248.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("thermo_predictor_thermo_evotuning_prot_bert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("thermo_predictor_thermo_evotuning_prot_bert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|thermo_predictor_thermo_evotuning_prot_bert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.6 GB| + +## References + +https://huggingface.co/cradle-bio/thermo-predictor-thermo-evotuning-prot_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_cola_distilled_en.md b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_cola_distilled_en.md new file mode 100644 index 00000000000000..1d11bd5e37be49 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_cola_distilled_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_cola_distilled BertForSequenceClassification from Sayan01 +author: John Snow Labs +name: tiny_bert_cola_distilled +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_cola_distilled` is a English model originally trained by Sayan01. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_cola_distilled_en_5.1.4_3.4_1698192433149.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_cola_distilled_en_5.1.4_3.4_1698192433149.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_cola_distilled","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_cola_distilled","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_cola_distilled| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|54.2 MB| + +## References + +https://huggingface.co/Sayan01/tiny-bert-cola-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_mnli_distilled_en.md b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_mnli_distilled_en.md new file mode 100644 index 00000000000000..f965b0939d9eeb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_mnli_distilled_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_mnli_distilled BertForSequenceClassification from Sayan01 +author: John Snow Labs +name: tiny_bert_mnli_distilled +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_mnli_distilled` is a English model originally trained by Sayan01. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_mnli_distilled_en_5.1.4_3.4_1698208686168.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_mnli_distilled_en_5.1.4_3.4_1698208686168.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_mnli_distilled","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_mnli_distilled","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_mnli_distilled| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/Sayan01/tiny-bert-mnli-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_qnli_distilled_en.md b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_qnli_distilled_en.md new file mode 100644 index 00000000000000..5bf40203bc9d4e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_qnli_distilled_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_qnli_distilled BertForSequenceClassification from Sayan01 +author: John Snow Labs +name: tiny_bert_qnli_distilled +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_qnli_distilled` is a English model originally trained by Sayan01. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_qnli_distilled_en_5.1.4_3.4_1698211808577.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_qnli_distilled_en_5.1.4_3.4_1698211808577.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_qnli_distilled","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_qnli_distilled","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_qnli_distilled| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|54.2 MB| + +## References + +https://huggingface.co/Sayan01/tiny-bert-qnli-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_rte_distilled_en.md b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_rte_distilled_en.md new file mode 100644 index 00000000000000..a146a48ddc0f31 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_rte_distilled_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_rte_distilled BertForSequenceClassification from Sayan01 +author: John Snow Labs +name: tiny_bert_rte_distilled +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_rte_distilled` is a English model originally trained by Sayan01. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_rte_distilled_en_5.1.4_3.4_1698210066696.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_rte_distilled_en_5.1.4_3.4_1698210066696.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_rte_distilled","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_rte_distilled","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_rte_distilled| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|54.2 MB| + +## References + +https://huggingface.co/Sayan01/tiny-bert-rte-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_kaakekhan_en.md b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_kaakekhan_en.md new file mode 100644 index 00000000000000..bd625d12052856 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_kaakekhan_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_sst2_distilled_kaakekhan BertForSequenceClassification from kaakekhan +author: John Snow Labs +name: tiny_bert_sst2_distilled_kaakekhan +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_sst2_distilled_kaakekhan` is a English model originally trained by kaakekhan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_sst2_distilled_kaakekhan_en_5.1.4_3.4_1698203758116.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_sst2_distilled_kaakekhan_en_5.1.4_3.4_1698203758116.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_sst2_distilled_kaakekhan","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_sst2_distilled_kaakekhan","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_sst2_distilled_kaakekhan| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/kaakekhan/tiny-bert-sst2-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_sayan01_en.md b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_sayan01_en.md new file mode 100644 index 00000000000000..8f6291197995f2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_sayan01_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_sst2_distilled_sayan01 BertForSequenceClassification from Sayan01 +author: John Snow Labs +name: tiny_bert_sst2_distilled_sayan01 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_sst2_distilled_sayan01` is a English model originally trained by Sayan01. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_sst2_distilled_sayan01_en_5.1.4_3.4_1698192322663.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_sst2_distilled_sayan01_en_5.1.4_3.4_1698192322663.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_sst2_distilled_sayan01","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_sst2_distilled_sayan01","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_sst2_distilled_sayan01| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|54.2 MB| + +## References + +https://huggingface.co/Sayan01/tiny-bert-sst2-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_smith123_en.md b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_smith123_en.md new file mode 100644 index 00000000000000..63e50b39464580 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_smith123_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_sst2_distilled_smith123 BertForSequenceClassification from Smith123 +author: John Snow Labs +name: tiny_bert_sst2_distilled_smith123 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_sst2_distilled_smith123` is a English model originally trained by Smith123. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_sst2_distilled_smith123_en_5.1.4_3.4_1698210672627.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_sst2_distilled_smith123_en_5.1.4_3.4_1698210672627.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_sst2_distilled_smith123","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_sst2_distilled_smith123","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_sst2_distilled_smith123| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|42.1 MB| + +## References + +https://huggingface.co/Smith123/tiny-bert-sst2-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_youssef320_en.md b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_youssef320_en.md new file mode 100644 index 00000000000000..0ecb380b17dfe7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_youssef320_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_sst2_distilled_youssef320 BertForSequenceClassification from Youssef320 +author: John Snow Labs +name: tiny_bert_sst2_distilled_youssef320 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_sst2_distilled_youssef320` is a English model originally trained by Youssef320. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_sst2_distilled_youssef320_en_5.1.4_3.4_1698212716468.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_sst2_distilled_youssef320_en_5.1.4_3.4_1698212716468.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_sst2_distilled_youssef320","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_sst2_distilled_youssef320","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_sst2_distilled_youssef320| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/Youssef320/tiny-bert-sst2-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_wnli_distilled_en.md b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_wnli_distilled_en.md new file mode 100644 index 00000000000000..46549eb6781535 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_wnli_distilled_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_wnli_distilled BertForSequenceClassification from Sayan01 +author: John Snow Labs +name: tiny_bert_wnli_distilled +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_wnli_distilled` is a English model originally trained by Sayan01. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_wnli_distilled_en_5.1.4_3.4_1698211599601.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_wnli_distilled_en_5.1.4_3.4_1698211599601.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_wnli_distilled","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_wnli_distilled","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_wnli_distilled| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|54.2 MB| + +## References + +https://huggingface.co/Sayan01/tiny-bert-wnli-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tweet_sentiments_analysis_bert_jedida_en.md b/docs/_posts/ahmedlone127/2023-10-25-tweet_sentiments_analysis_bert_jedida_en.md new file mode 100644 index 00000000000000..dd97e9eeef963b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tweet_sentiments_analysis_bert_jedida_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tweet_sentiments_analysis_bert_jedida BertForSequenceClassification from Jedida +author: John Snow Labs +name: tweet_sentiments_analysis_bert_jedida +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tweet_sentiments_analysis_bert_jedida` is a English model originally trained by Jedida. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tweet_sentiments_analysis_bert_jedida_en_5.1.4_3.4_1698199772330.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tweet_sentiments_analysis_bert_jedida_en_5.1.4_3.4_1698199772330.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tweet_sentiments_analysis_bert_jedida","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tweet_sentiments_analysis_bert_jedida","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tweet_sentiments_analysis_bert_jedida| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/Jedida/tweet_sentiments_analysis_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tweet_sentiments_analysis_bert_kingasiedu_en.md b/docs/_posts/ahmedlone127/2023-10-25-tweet_sentiments_analysis_bert_kingasiedu_en.md new file mode 100644 index 00000000000000..04b4ce374eba38 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tweet_sentiments_analysis_bert_kingasiedu_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tweet_sentiments_analysis_bert_kingasiedu BertForSequenceClassification from KingAsiedu +author: John Snow Labs +name: tweet_sentiments_analysis_bert_kingasiedu +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tweet_sentiments_analysis_bert_kingasiedu` is a English model originally trained by KingAsiedu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tweet_sentiments_analysis_bert_kingasiedu_en_5.1.4_3.4_1698198506026.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tweet_sentiments_analysis_bert_kingasiedu_en_5.1.4_3.4_1698198506026.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tweet_sentiments_analysis_bert_kingasiedu","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tweet_sentiments_analysis_bert_kingasiedu","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tweet_sentiments_analysis_bert_kingasiedu| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/KingAsiedu/tweet_sentiments_analysis_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tweet_sentiments_analysis_bert_penscola_en.md b/docs/_posts/ahmedlone127/2023-10-25-tweet_sentiments_analysis_bert_penscola_en.md new file mode 100644 index 00000000000000..ed126b9689a5cb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tweet_sentiments_analysis_bert_penscola_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tweet_sentiments_analysis_bert_penscola BertForSequenceClassification from penscola +author: John Snow Labs +name: tweet_sentiments_analysis_bert_penscola +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tweet_sentiments_analysis_bert_penscola` is a English model originally trained by penscola. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tweet_sentiments_analysis_bert_penscola_en_5.1.4_3.4_1698197498186.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tweet_sentiments_analysis_bert_penscola_en_5.1.4_3.4_1698197498186.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tweet_sentiments_analysis_bert_penscola","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tweet_sentiments_analysis_bert_penscola","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tweet_sentiments_analysis_bert_penscola| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/penscola/tweet_sentiments_analysis_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tweet_sentiments_analysis_bert_uholodala_en.md b/docs/_posts/ahmedlone127/2023-10-25-tweet_sentiments_analysis_bert_uholodala_en.md new file mode 100644 index 00000000000000..b0dc567b573372 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tweet_sentiments_analysis_bert_uholodala_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tweet_sentiments_analysis_bert_uholodala BertForSequenceClassification from UholoDala +author: John Snow Labs +name: tweet_sentiments_analysis_bert_uholodala +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tweet_sentiments_analysis_bert_uholodala` is a English model originally trained by UholoDala. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tweet_sentiments_analysis_bert_uholodala_en_5.1.4_3.4_1698193855869.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tweet_sentiments_analysis_bert_uholodala_en_5.1.4_3.4_1698193855869.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tweet_sentiments_analysis_bert_uholodala","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tweet_sentiments_analysis_bert_uholodala","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tweet_sentiments_analysis_bert_uholodala| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/UholoDala/tweet_sentiments_analysis_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-xlm_bert_go_emotions_en.md b/docs/_posts/ahmedlone127/2023-10-25-xlm_bert_go_emotions_en.md new file mode 100644 index 00000000000000..3a3add25fea6f1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-xlm_bert_go_emotions_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English xlm_bert_go_emotions BertForSequenceClassification from SchuylerH +author: John Snow Labs +name: xlm_bert_go_emotions +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`xlm_bert_go_emotions` is a English model originally trained by SchuylerH. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_bert_go_emotions_en_5.1.4_3.4_1698204430255.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_bert_go_emotions_en_5.1.4_3.4_1698204430255.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("xlm_bert_go_emotions","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("xlm_bert_go_emotions","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|xlm_bert_go_emotions| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|667.3 MB| + +## References + +https://huggingface.co/SchuylerH/xlm-bert-go-emotions \ No newline at end of file From 824a221aa2bb4ac98dfb4091a819e39c5fdff73e Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Wed, 25 Oct 2023 22:03:30 +0700 Subject: [PATCH 54/55] 2023-10-25-bert_ft_qqp_79_jeevesh8_en (#14040) * Add model 2023-10-25-bert_ft_qqp_68_jeevesh8_en * Add model 2023-10-25-bert_base_uncased_sst_2_32_13_30_en * Add model 2023-10-25-boss_sentiment_bert_base_uncased_en * Add model 2023-10-25-bert_mini_finetuned_sst2_en * Add model 2023-10-25-bert_ft_qqp_95_jeevesh8_en * Add model 2023-10-25-bert_tiny_finetuned_mnli_en * Add model 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2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v22_en * Add model 2023-10-25-bert_sequence_classifier_persian_farsi_base_uncased_sentiment_digikala_fa * Add model 2023-10-25-fine_tuned_indonli_basic_with_indobert_base_uncased_muhammadravi251001_en * Add model 2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_1_en * Add model 2023-10-25-goog_bert_ft_cola_40_en * Add model 2023-10-25-goog_bert_ft_cola_50_en * Add model 2023-10-25-goog_bert_ft_cola_12_en * Add model 2023-10-25-bert_base_uncased_finetuned_hateful_meme_en * Add model 2023-10-25-bert_ft_cola_96_en * Add model 2023-10-25-bert_ft_qqp_72_jeevesh8_en * Add model 2023-10-25-goog_bert_ft_cola_22_en * Add model 2023-10-25-bert_wikipedia_sst2_en * Add model 2023-10-25-goog_bert_ft_cola_26_en * Add model 2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_2_en * Add model 2023-10-25-bert_deepfake_bulgarian_multiclass_bg * Add model 2023-10-25-bert_ft_qqp_52_jeevesh8_en * Add model 2023-10-25-bert_ft_qqp_56_jeevesh8_en * Add model 2023-10-25-goog_bert_ft_cola_49_en * Add model 2023-10-25-goog_bert_ft_cola_64_en * Add model 2023-10-25-indobertnewstest_rizalmilyardi_en * Add model 2023-10-25-a01_suicide_bert_huggingface_finetune_en * Add model 2023-10-25-bert_base_arabic_camelbert_msa_finetuned_arabic_dialect_identification_model_1_ar * Add model 2023-10-25-bert_base_uncased_sst_2_16_13_30_en * Add model 2023-10-25-bert_ft_cola_97_en * Add model 2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_3_en * Add model 2023-10-25-bert_ft_qqp_45_jeevesh8_en * Add model 2023-10-25-goog_bert_ft_cola_32_en * Add model 2023-10-25-goog_bert_ft_cola_74_en * Add model 2023-10-25-norbert2_sentiment_norec_2_en * Add model 2023-10-25-biobert_finetuned_genetic_mutation_en * Add model 2023-10-25-bert_sequence_classifier_paraphrase_pt * Add model 2023-10-25-bert_sim_doc_en * Add model 2023-10-25-goog_bert_ft_cola_70_en * Add model 2023-10-25-goog_bert_ft_cola_42_en * Add model 2023-10-25-bert_finetuning_test_itcastai_en * Add model 2023-10-25-bert_ft_cola_98_en * Add model 2023-10-25-goog_bert_ft_cola_18_en * Add model 2023-10-25-baseline_bert_base_cased_epoch3_batch4_lr2e_05_w0_01_en * Add model 2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_4_en * Add model 2023-10-25-bert_ft_qqp_99_jeevesh8_en * Add model 2023-10-25-bert_classifier_dehate_mono_indonesian_en * Add model 2023-10-25-bert_italian_hate_speech_en * Add model 2023-10-25-bert_classifier_base_uncased_qnli_en * Add model 2023-10-25-goog_bert_ft_cola_21_en * Add model 2023-10-25-goog_bert_ft_cola_31_en * Add model 2023-10-25-goog_bert_ft_cola_67_en * Add model 2023-10-25-a02_suicide_bert_huggingface_finetune_en * Add model 2023-10-25-bert_classifier_finbert_en * Add model 2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_5_en * Add model 2023-10-25-bert_ft_cola_99_en * Add model 2023-10-25-bert_ft_qqp_82_jeevesh8_en * Add model 2023-10-25-goog_bert_ft_cola_3_en * Add model 2023-10-25-bert_base_chinese_finetuned_binary_best_en * Add model 2023-10-25-bert_classification_1500samples_en * Add model 2023-10-25-goog_bert_ft_cola_35_en * Add model 2023-10-25-tiny_bert_sst2_distilled_kushaljoseph_en * Add model 2023-10-25-goog_bert_ft_cola_65_en * Add model 2023-10-25-chinese_roberta_wwm_ext_finetuned_binary_en * Add model 2023-10-25-goog_bert_ft_cola_58_en * Add model 2023-10-25-norbert2_sentiment_norec_4_en * Add model 2023-10-25-bert_classifier_russian_base_srl_ru * Add model 2023-10-25-goog_bert_ft_cola_59_en * Add model 2023-10-25-covid_twitter_bert_v2_mnli_nli_sts_crossencoder_covid_hera_en * Add model 2023-10-25-norbert2_sentiment_norec_6_en * Add model 2023-10-25-legal_bert_tpb_clause_class_en * Add model 2023-10-25-goog_bert_ft_cola_61_en * Add model 2023-10-25-twitter_disaster_bert_large_en * Add model 2023-10-25-goog_bert_ft_cola_62_en * Add model 2023-10-25-bert_large_uncased_mnli_ofirzaf_en * Add model 2023-10-25-norbert2_sentiment_norec_7_en * Add model 2023-10-25-bert_classifier_riad_finetuned_mrpc_en * Add model 2023-10-25-bert_base_cased_finetuned_cola_sreyang_nvidia_en * Add model 2023-10-25-bert_base_uncased_research_articles_multilabel_en * Add model 2023-10-25-goog_bert_ft_cola_66_en * Add model 2023-10-25-mini_bert_distilled_en * Add model 2023-10-25-bert_fom_job_description_assignment_en * Add model 2023-10-25-goog_bert_ft_cola_76_en * Add model 2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_6_en * Add model 2023-10-25-finbert_finetuned_fg_single_sentence_news_weighted_en * Add model 2023-10-25-bert_cl_cf_1700_en * Add model 2023-10-25-goog_bert_ft_cola_77_en * Add model 2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_7_en * Add model 2023-10-25-bert_classifier_prot_bfd_localization_en * Add model 2023-10-25-bert_cl_g_1700_en * Add model 2023-10-25-bert_sentence_classifier_en * Add model 2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_8_en * Add model 2023-10-25-goog_bert_ft_cola_81_en * Add model 2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_9_en * Add model 2023-10-25-rubert_rusentitweet_sismetanin_en * Add model 2023-10-25-goog_bert_ft_cola_91_en * Add model 2023-10-25-norbert2_sentiment_norec_8_en * Add model 2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_10_en * Add model 2023-10-25-goog_bert_ft_cola_89_en * Add model 2023-10-25-bert_base_uncased_random_weights_s42_en * Add model 2023-10-25-norbert2_sentiment_norec_9_en * Add model 2023-10-25-bert_base_uncased_finetuned_cola_ruizhou_en * Add model 2023-10-25-fine_tuned_koreanindonli_kornli_with_bert_base_en * Add model 2023-10-25-goog_bert_ft_cola_82_en * Add model 2023-10-25-finbert_fls_en * Add model 2023-10-25-bert_base_uncased_finetuned_mrpc_ruizhou_en * Add model 2023-10-25-bert_cnn_news_en --------- Co-authored-by: ahmedlone127 --- ...01_suicide_bert_huggingface_finetune_en.md | 97 ++++++++++++++++ ...02_suicide_bert_huggingface_finetune_en.md | 97 ++++++++++++++++ ...2023-10-25-ag_news_bert_base_uncased_en.md | 97 ++++++++++++++++ ...rain_bert_wikipedia_sst_2_1034235513_en.md | 97 ++++++++++++++++ ...se_cased_epoch3_batch4_lr2e_05_w0_01_en.md | 97 ++++++++++++++++ ...ge_cased_epoch3_batch4_lr2e_05_w0_01_en.md | 97 ++++++++++++++++ ...rabic_dialect_identification_model_1_ar.md | 97 ++++++++++++++++ ..._sixteenth_xnli_finetuned_sarahadnan_en.md | 97 ++++++++++++++++ ..._cased_finetuned_cola_sreyang_nvidia_en.md | 97 ++++++++++++++++ ...t_base_chinese_finetuned_binary_best_en.md | 97 ++++++++++++++++ ...news_market_overview_open_ssec_f1_v1_en.md | 97 ++++++++++++++++ ...news_market_overview_open_ssec_f1_v2_en.md | 97 ++++++++++++++++ ...ng_news_market_overview_open_ssec_v2_en.md 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b/docs/_posts/ahmedlone127/2023-10-25-a01_suicide_bert_huggingface_finetune_en.md new file mode 100644 index 00000000000000..b6f2767b0f2302 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-a01_suicide_bert_huggingface_finetune_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English a01_suicide_bert_huggingface_finetune BertForSequenceClassification from Ariffnaz +author: John Snow Labs +name: a01_suicide_bert_huggingface_finetune +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`a01_suicide_bert_huggingface_finetune` is a English model originally trained by Ariffnaz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/a01_suicide_bert_huggingface_finetune_en_5.1.4_3.4_1698231600777.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/a01_suicide_bert_huggingface_finetune_en_5.1.4_3.4_1698231600777.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("a01_suicide_bert_huggingface_finetune","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("a01_suicide_bert_huggingface_finetune","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|a01_suicide_bert_huggingface_finetune| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Ariffnaz/a01-suicide-bert-huggingface-finetune \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-a02_suicide_bert_huggingface_finetune_en.md b/docs/_posts/ahmedlone127/2023-10-25-a02_suicide_bert_huggingface_finetune_en.md new file mode 100644 index 00000000000000..257a39a4621e78 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-a02_suicide_bert_huggingface_finetune_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English a02_suicide_bert_huggingface_finetune BertForSequenceClassification from Ariffnaz +author: John Snow Labs +name: a02_suicide_bert_huggingface_finetune +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`a02_suicide_bert_huggingface_finetune` is a English model originally trained by Ariffnaz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/a02_suicide_bert_huggingface_finetune_en_5.1.4_3.4_1698233704009.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/a02_suicide_bert_huggingface_finetune_en_5.1.4_3.4_1698233704009.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("a02_suicide_bert_huggingface_finetune","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("a02_suicide_bert_huggingface_finetune","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|a02_suicide_bert_huggingface_finetune| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Ariffnaz/a02-suicide-bert-huggingface-finetune \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-ag_news_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-10-25-ag_news_bert_base_uncased_en.md new file mode 100644 index 00000000000000..716d441fbee0e2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-ag_news_bert_base_uncased_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English ag_news_bert_base_uncased BertForSequenceClassification from Kyle1668 +author: John Snow Labs +name: ag_news_bert_base_uncased +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ag_news_bert_base_uncased` is a English model originally trained by Kyle1668. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ag_news_bert_base_uncased_en_5.1.4_3.4_1698221722413.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ag_news_bert_base_uncased_en_5.1.4_3.4_1698221722413.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("ag_news_bert_base_uncased","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("ag_news_bert_base_uncased","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ag_news_bert_base_uncased| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Kyle1668/ag-news-bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-autotrain_bert_wikipedia_sst_2_1034235513_en.md b/docs/_posts/ahmedlone127/2023-10-25-autotrain_bert_wikipedia_sst_2_1034235513_en.md new file mode 100644 index 00000000000000..29142f1ea13ac5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-autotrain_bert_wikipedia_sst_2_1034235513_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English autotrain_bert_wikipedia_sst_2_1034235513 BertForSequenceClassification from deepesh0x +author: John Snow Labs +name: autotrain_bert_wikipedia_sst_2_1034235513 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`autotrain_bert_wikipedia_sst_2_1034235513` is a English model originally trained by deepesh0x. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/autotrain_bert_wikipedia_sst_2_1034235513_en_5.1.4_3.4_1698212254160.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/autotrain_bert_wikipedia_sst_2_1034235513_en_5.1.4_3.4_1698212254160.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("autotrain_bert_wikipedia_sst_2_1034235513","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("autotrain_bert_wikipedia_sst_2_1034235513","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|autotrain_bert_wikipedia_sst_2_1034235513| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/deepesh0x/autotrain-bert_wikipedia_sst_2-1034235513 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-baseline_bert_base_cased_epoch3_batch4_lr2e_05_w0_01_en.md b/docs/_posts/ahmedlone127/2023-10-25-baseline_bert_base_cased_epoch3_batch4_lr2e_05_w0_01_en.md new file mode 100644 index 00000000000000..4df20a4bbda04e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-baseline_bert_base_cased_epoch3_batch4_lr2e_05_w0_01_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English baseline_bert_base_cased_epoch3_batch4_lr2e_05_w0_01 BertForSequenceClassification from JerryYanJiang +author: John Snow Labs +name: baseline_bert_base_cased_epoch3_batch4_lr2e_05_w0_01 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`baseline_bert_base_cased_epoch3_batch4_lr2e_05_w0_01` is a English model originally trained by JerryYanJiang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/baseline_bert_base_cased_epoch3_batch4_lr2e_05_w0_01_en_5.1.4_3.4_1698214827245.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/baseline_bert_base_cased_epoch3_batch4_lr2e_05_w0_01_en_5.1.4_3.4_1698214827245.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("baseline_bert_base_cased_epoch3_batch4_lr2e_05_w0_01","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("baseline_bert_base_cased_epoch3_batch4_lr2e_05_w0_01","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|baseline_bert_base_cased_epoch3_batch4_lr2e_05_w0_01| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/JerryYanJiang/baseline_bert-base-cased_epoch3_batch4_lr2e-05_w0.01 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-baseline_bert_large_cased_epoch3_batch4_lr2e_05_w0_01_en.md b/docs/_posts/ahmedlone127/2023-10-25-baseline_bert_large_cased_epoch3_batch4_lr2e_05_w0_01_en.md new file mode 100644 index 00000000000000..2f38d8481bb9a1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-baseline_bert_large_cased_epoch3_batch4_lr2e_05_w0_01_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English baseline_bert_large_cased_epoch3_batch4_lr2e_05_w0_01 BertForSequenceClassification from JerryYanJiang +author: John Snow Labs +name: baseline_bert_large_cased_epoch3_batch4_lr2e_05_w0_01 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`baseline_bert_large_cased_epoch3_batch4_lr2e_05_w0_01` is a English model originally trained by JerryYanJiang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/baseline_bert_large_cased_epoch3_batch4_lr2e_05_w0_01_en_5.1.4_3.4_1698217405966.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/baseline_bert_large_cased_epoch3_batch4_lr2e_05_w0_01_en_5.1.4_3.4_1698217405966.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("baseline_bert_large_cased_epoch3_batch4_lr2e_05_w0_01","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("baseline_bert_large_cased_epoch3_batch4_lr2e_05_w0_01","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|baseline_bert_large_cased_epoch3_batch4_lr2e_05_w0_01| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.2 GB| + +## References + +https://huggingface.co/JerryYanJiang/baseline_bert-large-cased_epoch3_batch4_lr2e-05_w0.01 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabic_camelbert_msa_finetuned_arabic_dialect_identification_model_1_ar.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabic_camelbert_msa_finetuned_arabic_dialect_identification_model_1_ar.md new file mode 100644 index 00000000000000..9e7c77824e37aa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabic_camelbert_msa_finetuned_arabic_dialect_identification_model_1_ar.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Arabic bert_base_arabic_camelbert_msa_finetuned_arabic_dialect_identification_model_1 BertForSequenceClassification from Abdelrahman-Rezk +author: John Snow Labs +name: bert_base_arabic_camelbert_msa_finetuned_arabic_dialect_identification_model_1 +date: 2023-10-25 +tags: [bert, ar, open_source, sequence_classification, onnx] +task: Text Classification +language: ar +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabic_camelbert_msa_finetuned_arabic_dialect_identification_model_1` is a Arabic model originally trained by Abdelrahman-Rezk. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_msa_finetuned_arabic_dialect_identification_model_1_ar_5.1.4_3.4_1698213574208.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_msa_finetuned_arabic_dialect_identification_model_1_ar_5.1.4_3.4_1698213574208.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabic_camelbert_msa_finetuned_arabic_dialect_identification_model_1","ar")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabic_camelbert_msa_finetuned_arabic_dialect_identification_model_1","ar") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabic_camelbert_msa_finetuned_arabic_dialect_identification_model_1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|ar| +|Size:|408.6 MB| + +## References + +https://huggingface.co/Abdelrahman-Rezk/bert-base-arabic-camelbert-msa-finetuned-Arabic_Dialect_Identification_model_1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_sarahadnan_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_sarahadnan_en.md new file mode 100644 index 00000000000000..32f1070e740dba --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_sarahadnan_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_sarahadnan BertForSequenceClassification from SarahAdnan +author: John Snow Labs +name: bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_sarahadnan +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_sarahadnan` is a English model originally trained by SarahAdnan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_sarahadnan_en_5.1.4_3.4_1698210534868.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_sarahadnan_en_5.1.4_3.4_1698210534868.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_sarahadnan","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_sarahadnan","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabic_camelbert_msa_sixteenth_xnli_finetuned_sarahadnan| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|507.3 MB| + +## References + +https://huggingface.co/SarahAdnan/bert-base-arabic-camelbert-msa-sixteenth-xnli-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_finetuned_cola_sreyang_nvidia_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_finetuned_cola_sreyang_nvidia_en.md new file mode 100644 index 00000000000000..416af5ec23cb6c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_finetuned_cola_sreyang_nvidia_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_cased_finetuned_cola_sreyang_nvidia BertForSequenceClassification from SreyanG-NVIDIA +author: John Snow Labs +name: bert_base_cased_finetuned_cola_sreyang_nvidia +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_finetuned_cola_sreyang_nvidia` is a English model originally trained by SreyanG-NVIDIA. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_cola_sreyang_nvidia_en_5.1.4_3.4_1698239875860.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_finetuned_cola_sreyang_nvidia_en_5.1.4_3.4_1698239875860.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_finetuned_cola_sreyang_nvidia","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_finetuned_cola_sreyang_nvidia","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_finetuned_cola_sreyang_nvidia| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/SreyanG-NVIDIA/bert-base-cased-finetuned-cola \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_finetuned_binary_best_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_finetuned_binary_best_en.md new file mode 100644 index 00000000000000..3c21357daf4f37 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_finetuned_binary_best_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_finetuned_binary_best BertForSequenceClassification from Raychanan +author: John Snow Labs +name: bert_base_chinese_finetuned_binary_best +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_finetuned_binary_best` is a English model originally trained by Raychanan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_finetuned_binary_best_en_5.1.4_3.4_1698234417943.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_finetuned_binary_best_en_5.1.4_3.4_1698234417943.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_finetuned_binary_best","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_finetuned_binary_best","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_finetuned_binary_best| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/Raychanan/bert-base-chinese-FineTuned-Binary-Best \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1_en.md new file mode 100644 index 00000000000000..4ec59ff1d51af6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1_en_5.1.4_3.4_1698219578005.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1_en_5.1.4_3.4_1698219578005.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-open-SSEC-f1-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v2_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v2_en.md new file mode 100644 index 00000000000000..54e7e89e90dfaf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v2 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v2` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v2_en_5.1.4_3.4_1698222739007.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v2_en_5.1.4_3.4_1698222739007.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_f1_v2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-open-SSEC-f1-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_v2_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_v2_en.md new file mode 100644 index 00000000000000..ad82bdcfa957ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_v2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_v2 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_v2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_v2` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_v2_en_5.1.4_3.4_1698220941999.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_v2_en_5.1.4_3.4_1698220941999.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_v2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_v2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_open_ssec_v2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-open-SSEC-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_10_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_10_en.md new file mode 100644 index 00000000000000..ce8994b8d36642 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_10_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_10 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_10 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_10` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_10_en_5.1.4_3.4_1698244428839.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_10_en_5.1.4_3.4_1698244428839.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_10","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_10","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_10| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSEC-10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_1_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_1_en.md new file mode 100644 index 00000000000000..1c9ac7f7c442c3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_1_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_1 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_1 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_1` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_1_en_5.1.4_3.4_1698231415062.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_1_en_5.1.4_3.4_1698231415062.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_1","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_1","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSEC-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_2_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_2_en.md new file mode 100644 index 00000000000000..f37188738f45cf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_2 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_2` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_2_en_5.1.4_3.4_1698232117390.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_2_en_5.1.4_3.4_1698232117390.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSEC-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_3_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_3_en.md new file mode 100644 index 00000000000000..d68f331ff6c819 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_3_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_3 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_3 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_3` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_3_en_5.1.4_3.4_1698232786971.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_3_en_5.1.4_3.4_1698232786971.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_3","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_3","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_3| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSEC-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_4_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_4_en.md new file mode 100644 index 00000000000000..2798c1896a7de9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_4_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_4 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_4 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_4` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_4_en_5.1.4_3.4_1698233527930.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_4_en_5.1.4_3.4_1698233527930.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_4","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_4","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_4| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSEC-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_5_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_5_en.md new file mode 100644 index 00000000000000..f5c416b40d8d47 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_5_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_5 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_5 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_5` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_5_en_5.1.4_3.4_1698234159300.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_5_en_5.1.4_3.4_1698234159300.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_5","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_5","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_5| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSEC-5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_6_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_6_en.md new file mode 100644 index 00000000000000..1267a8354a009b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_6_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_6 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_6 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_6` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_6_en_5.1.4_3.4_1698240802038.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_6_en_5.1.4_3.4_1698240802038.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_6","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_6","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_6| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSEC-6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_7_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_7_en.md new file mode 100644 index 00000000000000..20f18fe0923de3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_7_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_7 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_7 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_7` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_7_en_5.1.4_3.4_1698241641367.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_7_en_5.1.4_3.4_1698241641367.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_7","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_7","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_7| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSEC-7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_8_en.md new file mode 100644 index 00000000000000..4ebd1ef10fd525 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_8 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_8` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_8_en_5.1.4_3.4_1698242416129.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_8_en_5.1.4_3.4_1698242416129.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSEC-8 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_9_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_9_en.md new file mode 100644 index 00000000000000..950fda31680853 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_9_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_9 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_9 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_9` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_9_en_5.1.4_3.4_1698243365653.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_9_en_5.1.4_3.4_1698243365653.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_9","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_9","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_9| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSEC-9 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_dutch_cased_finetuned_dt_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_dutch_cased_finetuned_dt_en.md new file mode 100644 index 00000000000000..1ba10f1a6edf3b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_dutch_cased_finetuned_dt_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_dutch_cased_finetuned_dt BertForSequenceClassification from ArjanvD95 +author: John Snow Labs +name: bert_base_dutch_cased_finetuned_dt +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_dutch_cased_finetuned_dt` is a English model originally trained by ArjanvD95. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_dutch_cased_finetuned_dt_en_5.1.4_3.4_1698212262096.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_dutch_cased_finetuned_dt_en_5.1.4_3.4_1698212262096.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_dutch_cased_finetuned_dt","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_dutch_cased_finetuned_dt","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_dutch_cased_finetuned_dt| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.0 MB| + +## References + +https://huggingface.co/ArjanvD95/bert-base-dutch-cased-finetuned-dt \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_mdoc_bm25_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_mdoc_bm25_en.md new file mode 100644 index 00000000000000..8d013fc00ce1ce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_mdoc_bm25_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_mdoc_bm25 BertForSequenceClassification from Luyu +author: John Snow Labs +name: bert_base_mdoc_bm25 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_mdoc_bm25` is a English model originally trained by Luyu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_mdoc_bm25_en_5.1.4_3.4_1698221725395.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_mdoc_bm25_en_5.1.4_3.4_1698221725395.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_mdoc_bm25","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_mdoc_bm25","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_mdoc_bm25| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Luyu/bert-base-mdoc-bm25 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_mdoc_hdct_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_mdoc_hdct_en.md new file mode 100644 index 00000000000000..f23947e624bdd2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_mdoc_hdct_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_mdoc_hdct BertForSequenceClassification from Luyu +author: John Snow Labs +name: bert_base_mdoc_hdct +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_mdoc_hdct` is a English model originally trained by Luyu. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_mdoc_hdct_en_5.1.4_3.4_1698221922423.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_mdoc_hdct_en_5.1.4_3.4_1698221922423.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_mdoc_hdct","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_mdoc_hdct","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_mdoc_hdct| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Luyu/bert-base-mdoc-hdct \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_multilingual_cased_mrpc_glue_xx.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_multilingual_cased_mrpc_glue_xx.md new file mode 100644 index 00000000000000..1a2fecef6fe686 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_multilingual_cased_mrpc_glue_xx.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Multilingual bert_base_multilingual_cased_mrpc_glue BertForSequenceClassification from rriverar75 +author: John Snow Labs +name: bert_base_multilingual_cased_mrpc_glue +date: 2023-10-25 +tags: [bert, xx, open_source, sequence_classification, onnx] +task: Text Classification +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_multilingual_cased_mrpc_glue` is a Multilingual model originally trained by rriverar75. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_mrpc_glue_xx_5.1.4_3.4_1698230620198.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_multilingual_cased_mrpc_glue_xx_5.1.4_3.4_1698230620198.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_multilingual_cased_mrpc_glue","xx")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_multilingual_cased_mrpc_glue","xx") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_multilingual_cased_mrpc_glue| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|xx| +|Size:|667.3 MB| + +## References + +https://huggingface.co/rriverar75/bert-base-multilingual-cased-mrpc-glue \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_ade_ade_corpus_v2_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_ade_ade_corpus_v2_en.md new file mode 100644 index 00000000000000..78fae46dc05da7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_ade_ade_corpus_v2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_ade_ade_corpus_v2 BertForSequenceClassification from Jorgeutd +author: John Snow Labs +name: bert_base_uncased_ade_ade_corpus_v2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_ade_ade_corpus_v2` is a English model originally trained by Jorgeutd. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_ade_ade_corpus_v2_en_5.1.4_3.4_1698219322282.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_ade_ade_corpus_v2_en_5.1.4_3.4_1698219322282.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_ade_ade_corpus_v2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_ade_ade_corpus_v2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_ade_ade_corpus_v2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jorgeutd/bert-base-uncased-ade-Ade-corpus-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_ag_news_finetuned_dwnews_categories_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_ag_news_finetuned_dwnews_categories_en.md new file mode 100644 index 00000000000000..fdfa063057616a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_ag_news_finetuned_dwnews_categories_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_ag_news_finetuned_dwnews_categories BertForSequenceClassification from tillschwoerer +author: John Snow Labs +name: bert_base_uncased_ag_news_finetuned_dwnews_categories +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_ag_news_finetuned_dwnews_categories` is a English model originally trained by tillschwoerer. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_ag_news_finetuned_dwnews_categories_en_5.1.4_3.4_1698217799442.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_ag_news_finetuned_dwnews_categories_en_5.1.4_3.4_1698217799442.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_ag_news_finetuned_dwnews_categories","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_ag_news_finetuned_dwnews_categories","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_ag_news_finetuned_dwnews_categories| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/tillschwoerer/bert-base-uncased-ag-news-finetuned-dwnews-categories \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_fine_tuned_on_clinc_oos_dataset_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_fine_tuned_on_clinc_oos_dataset_en.md new file mode 100644 index 00000000000000..abc4019940e9ef --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_fine_tuned_on_clinc_oos_dataset_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_fine_tuned_on_clinc_oos_dataset BertForSequenceClassification from itzo +author: John Snow Labs +name: bert_base_uncased_fine_tuned_on_clinc_oos_dataset +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_fine_tuned_on_clinc_oos_dataset` is a English model originally trained by itzo. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_fine_tuned_on_clinc_oos_dataset_en_5.1.4_3.4_1698212599389.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_fine_tuned_on_clinc_oos_dataset_en_5.1.4_3.4_1698212599389.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_fine_tuned_on_clinc_oos_dataset","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_fine_tuned_on_clinc_oos_dataset","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_fine_tuned_on_clinc_oos_dataset| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/itzo/bert-base-uncased-fine-tuned-on-clinc_oos-dataset \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_cola_ruizhou_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_cola_ruizhou_en.md new file mode 100644 index 00000000000000..f4c5c411b5d34a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_cola_ruizhou_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_cola_ruizhou BertForSequenceClassification from Ruizhou +author: John Snow Labs +name: bert_base_uncased_finetuned_cola_ruizhou +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_cola_ruizhou` is a English model originally trained by Ruizhou. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_cola_ruizhou_en_5.1.4_3.4_1698245116739.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_cola_ruizhou_en_5.1.4_3.4_1698245116739.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_cola_ruizhou","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_cola_ruizhou","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_cola_ruizhou| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Ruizhou/bert-base-uncased-finetuned-cola \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_hateful_meme_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_hateful_meme_en.md new file mode 100644 index 00000000000000..981f61563c0a60 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_hateful_meme_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_hateful_meme BertForSequenceClassification from tommilyjones +author: John Snow Labs +name: bert_base_uncased_finetuned_hateful_meme +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_hateful_meme` is a English model originally trained by tommilyjones. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_hateful_meme_en_5.1.4_3.4_1698218460479.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_hateful_meme_en_5.1.4_3.4_1698218460479.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_hateful_meme","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_hateful_meme","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_hateful_meme| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/tommilyjones/bert-base-uncased-finetuned-hateful-meme \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap2_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap2_en.md new file mode 100644 index 00000000000000..e36ecf8fb073f3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_iemocap2 BertForSequenceClassification from Zahra99 +author: John Snow Labs +name: bert_base_uncased_finetuned_iemocap2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_iemocap2` is a English model originally trained by Zahra99. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap2_en_5.1.4_3.4_1698214367259.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap2_en_5.1.4_3.4_1698214367259.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_iemocap2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Zahra99/bert-base-uncased-finetuned-iemocap2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap4_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap4_en.md new file mode 100644 index 00000000000000..cd56cc076f64b3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap4_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_iemocap4 BertForSequenceClassification from Zahra99 +author: John Snow Labs +name: bert_base_uncased_finetuned_iemocap4 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_iemocap4` is a English model originally trained by Zahra99. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap4_en_5.1.4_3.4_1698215069499.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap4_en_5.1.4_3.4_1698215069499.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap4","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap4","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_iemocap4| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Zahra99/bert-base-uncased-finetuned-iemocap4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap_uptuna2_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap_uptuna2_en.md new file mode 100644 index 00000000000000..c3f77f2231298a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_iemocap_uptuna2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_iemocap_uptuna2 BertForSequenceClassification from Zahra99 +author: John Snow Labs +name: bert_base_uncased_finetuned_iemocap_uptuna2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_iemocap_uptuna2` is a English model originally trained by Zahra99. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap_uptuna2_en_5.1.4_3.4_1698213797050.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_iemocap_uptuna2_en_5.1.4_3.4_1698213797050.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap_uptuna2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_iemocap_uptuna2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_iemocap_uptuna2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Zahra99/bert-base-uncased-finetuned-iemocap-uptuna2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_mrpc_hilariooliveira_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_mrpc_hilariooliveira_en.md new file mode 100644 index 00000000000000..7a312b503d735c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_mrpc_hilariooliveira_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_mrpc_hilariooliveira BertForSequenceClassification from hilariooliveira +author: John Snow Labs +name: bert_base_uncased_finetuned_mrpc_hilariooliveira +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_mrpc_hilariooliveira` is a English model originally trained by hilariooliveira. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_mrpc_hilariooliveira_en_5.1.4_3.4_1698219891498.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_mrpc_hilariooliveira_en_5.1.4_3.4_1698219891498.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_mrpc_hilariooliveira","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_mrpc_hilariooliveira","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_mrpc_hilariooliveira| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/hilariooliveira/bert-base-uncased-finetuned-mrpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_mrpc_ruizhou_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_mrpc_ruizhou_en.md new file mode 100644 index 00000000000000..3ffc3b947b8ed0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_mrpc_ruizhou_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_mrpc_ruizhou BertForSequenceClassification from Ruizhou +author: John Snow Labs +name: bert_base_uncased_finetuned_mrpc_ruizhou +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_mrpc_ruizhou` is a English model originally trained by Ruizhou. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_mrpc_ruizhou_en_5.1.4_3.4_1698245908228.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_mrpc_ruizhou_en_5.1.4_3.4_1698245908228.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_mrpc_ruizhou","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_mrpc_ruizhou","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_mrpc_ruizhou| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Ruizhou/bert-base-uncased-finetuned-mrpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_question_v_statement_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_question_v_statement_en.md new file mode 100644 index 00000000000000..a64c63361d443f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_question_v_statement_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_question_v_statement BertForSequenceClassification from mafwalter +author: John Snow Labs +name: bert_base_uncased_finetuned_question_v_statement +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_question_v_statement` is a English model originally trained by mafwalter. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_question_v_statement_en_5.1.4_3.4_1698222726543.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_question_v_statement_en_5.1.4_3.4_1698222726543.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_question_v_statement","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_question_v_statement","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_question_v_statement| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/mafwalter/bert-base-uncased-finetuned-question-v-statement \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_sufficiency_ukp_balanced_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_sufficiency_ukp_balanced_en.md new file mode 100644 index 00000000000000..f721c063d5e0af --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_sufficiency_ukp_balanced_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_sufficiency_ukp_balanced BertForSequenceClassification from jakub014 +author: John Snow Labs +name: bert_base_uncased_finetuned_sufficiency_ukp_balanced +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_sufficiency_ukp_balanced` is a English model originally trained by jakub014. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_sufficiency_ukp_balanced_en_5.1.4_3.4_1698218620317.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_sufficiency_ukp_balanced_en_5.1.4_3.4_1698218620317.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_sufficiency_ukp_balanced","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_sufficiency_ukp_balanced","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_sufficiency_ukp_balanced| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/jakub014/bert-base-uncased-finetuned-sufficiency-ukp-balanced \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_mnli_sparse_70_unstructured_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_mnli_sparse_70_unstructured_en.md new file mode 100644 index 00000000000000..57d101ad1b8671 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_mnli_sparse_70_unstructured_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_mnli_sparse_70_unstructured BertForSequenceClassification from Intel +author: John Snow Labs +name: bert_base_uncased_mnli_sparse_70_unstructured +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_mnli_sparse_70_unstructured` is a English model originally trained by Intel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mnli_sparse_70_unstructured_en_5.1.4_3.4_1698211070392.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mnli_sparse_70_unstructured_en_5.1.4_3.4_1698211070392.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_mnli_sparse_70_unstructured","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_mnli_sparse_70_unstructured","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_mnli_sparse_70_unstructured| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|228.2 MB| + +## References + +https://huggingface.co/Intel/bert-base-uncased-mnli-sparse-70-unstructured \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_nisadibipolar_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_nisadibipolar_en.md new file mode 100644 index 00000000000000..53fa91cdff27dc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_nisadibipolar_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_nisadibipolar BertForSequenceClassification from Joom +author: John Snow Labs +name: bert_base_uncased_nisadibipolar +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_nisadibipolar` is a English model originally trained by Joom. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_nisadibipolar_en_5.1.4_3.4_1698228126069.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_nisadibipolar_en_5.1.4_3.4_1698228126069.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_nisadibipolar","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_nisadibipolar","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_nisadibipolar| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Joom/bert-base-uncased-NisadiBipolar \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_random_weights_s42_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_random_weights_s42_en.md new file mode 100644 index 00000000000000..7fa0fc6ddb3e1e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_random_weights_s42_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_random_weights_s42 BertForSequenceClassification from EhsanAghazadeh +author: John Snow Labs +name: bert_base_uncased_random_weights_s42 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_random_weights_s42` is a English model originally trained by EhsanAghazadeh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_random_weights_s42_en_5.1.4_3.4_1698244533509.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_random_weights_s42_en_5.1.4_3.4_1698244533509.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_random_weights_s42","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_random_weights_s42","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_random_weights_s42| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/EhsanAghazadeh/bert-base-uncased-random-weights-S42 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_regression_edmunds_car_reviews_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_regression_edmunds_car_reviews_en.md new file mode 100644 index 00000000000000..d2d2a42e9df806 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_regression_edmunds_car_reviews_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_regression_edmunds_car_reviews BertForSequenceClassification from DunnBC22 +author: John Snow Labs +name: bert_base_uncased_regression_edmunds_car_reviews +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_regression_edmunds_car_reviews` is a English model originally trained by DunnBC22. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_regression_edmunds_car_reviews_en_5.1.4_3.4_1698210378387.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_regression_edmunds_car_reviews_en_5.1.4_3.4_1698210378387.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_regression_edmunds_car_reviews","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_regression_edmunds_car_reviews","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_regression_edmunds_car_reviews| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DunnBC22/bert-base-uncased-Regression-Edmunds_Car_Reviews \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_research_articles_multilabel_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_research_articles_multilabel_en.md new file mode 100644 index 00000000000000..410d858526df84 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_research_articles_multilabel_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_research_articles_multilabel BertForSequenceClassification from DunnBC22 +author: John Snow Labs +name: bert_base_uncased_research_articles_multilabel +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_research_articles_multilabel` is a English model originally trained by DunnBC22. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_research_articles_multilabel_en_5.1.4_3.4_1698239982874.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_research_articles_multilabel_en_5.1.4_3.4_1698239982874.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_research_articles_multilabel","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_research_articles_multilabel","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_research_articles_multilabel| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DunnBC22/bert-base-uncased-Research_Articles_Multilabel \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_reviews_4_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_reviews_4_en.md new file mode 100644 index 00000000000000..489547802d9d39 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_reviews_4_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_reviews_4 BertForSequenceClassification from insaf +author: John Snow Labs +name: bert_base_uncased_reviews_4 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_reviews_4` is a English model originally trained by insaf. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_reviews_4_en_5.1.4_3.4_1698229934185.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_reviews_4_en_5.1.4_3.4_1698229934185.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_reviews_4","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_reviews_4","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_reviews_4| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/insaf/bert-base-uncased-reviews-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_16_100_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_16_100_en.md new file mode 100644 index 00000000000000..5b7ae90489115b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_16_100_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_sst_2_16_100 BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_base_uncased_sst_2_16_100 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sst_2_16_100` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_16_100_en_5.1.4_3.4_1698211268902.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_16_100_en_5.1.4_3.4_1698211268902.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_16_100","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_16_100","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sst_2_16_100| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/simonycl/bert-base-uncased-sst-2-16-100 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_16_13_30_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_16_13_30_en.md new file mode 100644 index 00000000000000..e23331bdfb588a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_16_13_30_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_sst_2_16_13_30 BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_base_uncased_sst_2_16_13_30 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sst_2_16_13_30` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_16_13_30_en_5.1.4_3.4_1698222121181.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_16_13_30_en_5.1.4_3.4_1698222121181.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_16_13_30","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_16_13_30","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sst_2_16_13_30| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/simonycl/bert-base-uncased-sst-2-16-13-30 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_13_30_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_13_30_en.md new file mode 100644 index 00000000000000..d62f2db4724571 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_13_30_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_sst_2_32_13_30 BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_base_uncased_sst_2_32_13_30 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sst_2_32_13_30` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_32_13_30_en_5.1.4_3.4_1698222331550.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_32_13_30_en_5.1.4_3.4_1698222331550.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_32_13_30","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_32_13_30","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sst_2_32_13_30| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/simonycl/bert-base-uncased-sst-2-32-13-30 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_13_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_13_en.md new file mode 100644 index 00000000000000..18322a37c1ee8b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_32_13_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_sst_2_32_13 BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_base_uncased_sst_2_32_13 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sst_2_32_13` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_32_13_en_5.1.4_3.4_1698211493421.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_32_13_en_5.1.4_3.4_1698211493421.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_32_13","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_32_13","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sst_2_32_13| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/simonycl/bert-base-uncased-sst-2-32-13 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_64_13_30_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_64_13_30_en.md new file mode 100644 index 00000000000000..da87b695bc24bf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_sst_2_64_13_30_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_sst_2_64_13_30 BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_base_uncased_sst_2_64_13_30 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_sst_2_64_13_30` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_64_13_30_en_5.1.4_3.4_1698222554412.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_sst_2_64_13_30_en_5.1.4_3.4_1698222554412.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_64_13_30","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_sst_2_64_13_30","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_sst_2_64_13_30| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/simonycl/bert-base-uncased-sst-2-64-13-30 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_cased_sst2_finetuned_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_cased_sst2_finetuned_en.md new file mode 100644 index 00000000000000..98824407c260e8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_cased_sst2_finetuned_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_cased_sst2_finetuned BertForSequenceClassification from ElcKeT +author: John Snow Labs +name: bert_cased_sst2_finetuned +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_cased_sst2_finetuned` is a English model originally trained by ElcKeT. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_cased_sst2_finetuned_en_5.1.4_3.4_1698221932937.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_cased_sst2_finetuned_en_5.1.4_3.4_1698221932937.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_cased_sst2_finetuned","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_cased_sst2_finetuned","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_cased_sst2_finetuned| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/ElcKeT/bert-cased-sst2-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_cl_cf_1700_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_cl_cf_1700_en.md new file mode 100644 index 00000000000000..a54ad51438a87a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_cl_cf_1700_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_cl_cf_1700 BertForSequenceClassification from himanshubeniwal +author: John Snow Labs +name: bert_cl_cf_1700 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_cl_cf_1700` is a English model originally trained by himanshubeniwal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_cl_cf_1700_en_5.1.4_3.4_1698241368584.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_cl_cf_1700_en_5.1.4_3.4_1698241368584.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_cl_cf_1700","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_cl_cf_1700","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_cl_cf_1700| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/himanshubeniwal/bert_cl_cf_1700 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_cl_g_1700_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_cl_g_1700_en.md new file mode 100644 index 00000000000000..7aa7dcd944fc42 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_cl_g_1700_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_cl_g_1700 BertForSequenceClassification from himanshubeniwal +author: John Snow Labs +name: bert_cl_g_1700 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_cl_g_1700` is a English model originally trained by himanshubeniwal. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_cl_g_1700_en_5.1.4_3.4_1698242314807.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_cl_g_1700_en_5.1.4_3.4_1698242314807.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_cl_g_1700","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_cl_g_1700","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_cl_g_1700| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/himanshubeniwal/bert_cl_g_1700 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classification_10ksamples_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classification_10ksamples_en.md new file mode 100644 index 00000000000000..d3230ed49ce898 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classification_10ksamples_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_classification_10ksamples BertForSequenceClassification from jayavibhav +author: John Snow Labs +name: bert_classification_10ksamples +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_classification_10ksamples` is a English model originally trained by jayavibhav. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classification_10ksamples_en_5.1.4_3.4_1698223539762.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classification_10ksamples_en_5.1.4_3.4_1698223539762.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_classification_10ksamples","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_classification_10ksamples","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classification_10ksamples| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/jayavibhav/bert-classification-10ksamples \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classification_1500samples_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classification_1500samples_en.md new file mode 100644 index 00000000000000..96cdd384b00952 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classification_1500samples_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_classification_1500samples BertForSequenceClassification from jayavibhav +author: John Snow Labs +name: bert_classification_1500samples +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_classification_1500samples` is a English model originally trained by jayavibhav. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classification_1500samples_en_5.1.4_3.4_1698221153308.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classification_1500samples_en_5.1.4_3.4_1698221153308.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_classification_1500samples","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_classification_1500samples","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classification_1500samples| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/jayavibhav/bert-classification-1500samples \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classification_5ksamples_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classification_5ksamples_en.md new file mode 100644 index 00000000000000..bc784121a2d604 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classification_5ksamples_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_classification_5ksamples BertForSequenceClassification from jayavibhav +author: John Snow Labs +name: bert_classification_5ksamples +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_classification_5ksamples` is a English model originally trained by jayavibhav. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classification_5ksamples_en_5.1.4_3.4_1698223323561.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classification_5ksamples_en_5.1.4_3.4_1698223323561.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_classification_5ksamples","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_classification_5ksamples","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classification_5ksamples| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/jayavibhav/bert-classification-5ksamples \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_ara_multiclass_news_ar.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_ara_multiclass_news_ar.md new file mode 100644 index 00000000000000..902ac68b082fa5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_ara_multiclass_news_ar.md @@ -0,0 +1,100 @@ +--- +layout: model +title: Arabic BertForSequenceClassification Cased model (from M47Labs) +author: John Snow Labs +name: bert_classifier_ara_multiclass_news +date: 2023-10-25 +tags: [bert, sequence_classification, classification, open_source, ar, onnx] +task: Text Classification +language: ar +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `arabert_multiclass_news` is a Arabic model originally trained by `M47Labs`. + +## Predicted Entities + +`sports`, `politics`, `culture`, `tech`, `religion`, `medical`, `finance` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_ara_multiclass_news_ar_5.1.4_3.4_1698223354357.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_ara_multiclass_news_ar_5.1.4_3.4_1698223354357.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_ara_multiclass_news","ar") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["أنا أحب الشرارة NLP"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_ara_multiclass_news","ar") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("أنا أحب الشرارة NLP").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_ara_multiclass_news| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|ar| +|Size:|414.2 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/M47Labs/arabert_multiclass_news \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_autotrain_wikipedia_sst_2_1034235509_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_autotrain_wikipedia_sst_2_1034235509_en.md new file mode 100644 index 00000000000000..d4bceb039d04a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_autotrain_wikipedia_sst_2_1034235509_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: English BertForSequenceClassification Cased model (from deepesh0x) +author: John Snow Labs +name: bert_classifier_autotrain_wikipedia_sst_2_1034235509 +date: 2023-10-25 +tags: [bert, sequence_classification, classification, open_source, en, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `autotrain-bert_wikipedia_sst_2-1034235509` is a English model originally trained by `deepesh0x`. + +## Predicted Entities + +`negative`, `positive` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_autotrain_wikipedia_sst_2_1034235509_en_5.1.4_3.4_1698212046365.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_autotrain_wikipedia_sst_2_1034235509_en_5.1.4_3.4_1698212046365.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_autotrain_wikipedia_sst_2_1034235509","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_autotrain_wikipedia_sst_2_1034235509","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.by_deepesh0x").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_autotrain_wikipedia_sst_2_1034235509| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/deepesh0x/autotrain-bert_wikipedia_sst_2-1034235509 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_base_uncased_qnli_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_base_uncased_qnli_en.md new file mode 100644 index 00000000000000..97cfc0ea4ffd8f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_base_uncased_qnli_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: English BertForSequenceClassification Base Uncased model (from Li) +author: John Snow Labs +name: bert_classifier_base_uncased_qnli +date: 2023-10-25 +tags: [bert, sequence_classification, classification, open_source, en, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-uncased-qnli` is a English model originally trained by `Li`. + +## Predicted Entities + +`entailment`, `not_entailment` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_base_uncased_qnli_en_5.1.4_3.4_1698220628861.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_base_uncased_qnli_en_5.1.4_3.4_1698220628861.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_base_uncased_qnli","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_base_uncased_qnli","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.uncased_base.by_li").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_base_uncased_qnli| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| +|Case sensitive:|false| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/Li/bert-base-uncased-qnli +- https://paperswithcode.com/dataset/qnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_bert_base_german_cased_gnad10_de.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_bert_base_german_cased_gnad10_de.md new file mode 100644 index 00000000000000..85d5d53ee2be45 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_bert_base_german_cased_gnad10_de.md @@ -0,0 +1,106 @@ +--- +layout: model +title: German BertForSequenceClassification Base Cased model (from Mathking) +author: John Snow Labs +name: bert_classifier_bert_base_german_cased_gnad10 +date: 2023-10-25 +tags: [de, open_source, bert, sequence_classification, classification, onnx] +task: Text Classification +language: de +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-german-cased-gnad10` is a German model originally trained by `Mathking`. + +## Predicted Entities + +`Wirtschaft`, `Panorama`, `Web`, `Inland`, `Etat`, `Wissenschaft`, `International`, `Sport`, `Kultur` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_bert_base_german_cased_gnad10_de_5.1.4_3.4_1698224767090.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_bert_base_german_cased_gnad10_de_5.1.4_3.4_1698224767090.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_bert_base_german_cased_gnad10","de") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_bert_base_german_cased_gnad10","de") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("de.classify.bert.cased_base.by_mathking").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_bert_base_german_cased_gnad10| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|de| +|Size:|409.1 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/Mathking/bert-base-german-cased-gnad10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_dehate_mono_indonesian_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_dehate_mono_indonesian_en.md new file mode 100644 index 00000000000000..3abbbab1c37120 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_dehate_mono_indonesian_en.md @@ -0,0 +1,108 @@ +--- +layout: model +title: English BertForSequenceClassification Cased model (from Hate-speech-CNERG) +author: John Snow Labs +name: bert_classifier_dehate_mono_indonesian +date: 2023-10-25 +tags: [bert, sequence_classification, classification, open_source, en, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dehatebert-mono-indonesian` is a English model originally trained by `Hate-speech-CNERG`. + +## Predicted Entities + +`HATE`, `NON_HATE` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_dehate_mono_indonesian_en_5.1.4_3.4_1698207541153.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_dehate_mono_indonesian_en_5.1.4_3.4_1698207541153.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_dehate_mono_indonesian","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_dehate_mono_indonesian","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.hate.mono_indonesian.by_hate_speech_cnerg").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_dehate_mono_indonesian| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|627.7 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/Hate-speech-CNERG/dehatebert-mono-indonesian +- https://github.com/punyajoy/DE-LIMIT +- https://arxiv.org/abs/2004.06465 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_finbert_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_finbert_en.md new file mode 100644 index 00000000000000..6cfa5cff102591 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_finbert_en.md @@ -0,0 +1,109 @@ +--- +layout: model +title: English BertForSequenceClassification Cased model (from ProsusAI) +author: John Snow Labs +name: bert_classifier_finbert +date: 2023-10-25 +tags: [en, open_source, bert, sequence_classification, classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `finbert` is a English model originally trained by `ProsusAI`. + +## Predicted Entities + +`neutral`, `positive`, `negative` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_finbert_en_5.1.4_3.4_1698233630506.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_finbert_en_5.1.4_3.4_1698233630506.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_finbert","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_finbert","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.by_prosusai").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_finbert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/ProsusAI/finbert +- https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts +- https://arxiv.org/abs/1908.10063 +- https://medium.com/prosus-ai-tech-blog/finbert-financial-sentiment-analysis-with-bert-b277a3607101 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_prot_bfd_localization_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_prot_bfd_localization_en.md new file mode 100644 index 00000000000000..636458196bfb2d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_prot_bfd_localization_en.md @@ -0,0 +1,106 @@ +--- +layout: model +title: English BertForSequenceClassification Cased model (from Rostlab) +author: John Snow Labs +name: bert_classifier_prot_bfd_localization +date: 2023-10-25 +tags: [bert, sequence_classification, classification, open_source, en, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `prot_bert_bfd_localization` is a English model originally trained by `Rostlab`. + +## Predicted Entities + +`Mitochondrion`, `Plastid`, `Extracellular`, `Golgi.apparatus`, `Lysosome/Vacuole`, `Endoplasmic.reticulum`, `Cell.membrane`, `Cytoplasm`, `Peroxisome`, `Nucleus` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_prot_bfd_localization_en_5.1.4_3.4_1698242237342.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_prot_bfd_localization_en_5.1.4_3.4_1698242237342.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_prot_bfd_localization","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_prot_bfd_localization","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.prot_bfd_localization.bert.by_rostlab").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_prot_bfd_localization| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.6 GB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/Rostlab/prot_bert_bfd_localization \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_riad_finetuned_mrpc_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_riad_finetuned_mrpc_en.md new file mode 100644 index 00000000000000..a9f8b2152d0c4c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_riad_finetuned_mrpc_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: English BertForSequenceClassification Cased model (from Riad) +author: John Snow Labs +name: bert_classifier_riad_finetuned_mrpc +date: 2023-10-25 +tags: [bert, sequence_classification, classification, open_source, en, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `finetuned-bert-mrpc` is a English model originally trained by `Riad`. + +## Predicted Entities + +`equivalent`, `not equivalent` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_riad_finetuned_mrpc_en_5.1.4_3.4_1698239870016.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_riad_finetuned_mrpc_en_5.1.4_3.4_1698239870016.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_riad_finetuned_mrpc","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_riad_finetuned_mrpc","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.glue.finetuned.by_Riad").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_riad_finetuned_mrpc| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/Riad/finetuned-bert-mrpc +- https://paperswithcode.com/sota?task=Text+Classification&dataset=glue \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_russian_base_srl_ru.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_russian_base_srl_ru.md new file mode 100644 index 00000000000000..a77f5d7f698f53 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_russian_base_srl_ru.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Russian bert_classifier_russian_base_srl BertForSequenceClassification from Rexhaif +author: John Snow Labs +name: bert_classifier_russian_base_srl +date: 2023-10-25 +tags: [bert, ru, open_source, sequence_classification, onnx] +task: Text Classification +language: ru +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_classifier_russian_base_srl` is a Russian model originally trained by Rexhaif. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_russian_base_srl_ru_5.1.4_3.4_1698236889529.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_russian_base_srl_ru_5.1.4_3.4_1698236889529.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_classifier_russian_base_srl","ru")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_classifier_russian_base_srl","ru") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_russian_base_srl| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|ru| +|Size:|669.3 MB| + +## References + +https://huggingface.co/Rexhaif/rubert-base-srl \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_cn_finetunning_jovenpai_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_cn_finetunning_jovenpai_en.md new file mode 100644 index 00000000000000..05260f273ec9ed --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_cn_finetunning_jovenpai_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_cn_finetunning_jovenpai BertForSequenceClassification from JovenPai +author: John Snow Labs +name: bert_cn_finetunning_jovenpai +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_cn_finetunning_jovenpai` is a English model originally trained by JovenPai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_cn_finetunning_jovenpai_en_5.1.4_3.4_1698219841571.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_cn_finetunning_jovenpai_en_5.1.4_3.4_1698219841571.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_cn_finetunning_jovenpai","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_cn_finetunning_jovenpai","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_cn_finetunning_jovenpai| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/JovenPai/bert_cn_finetunning \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_cnn_news_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_cnn_news_en.md new file mode 100644 index 00000000000000..8c7b8108a52125 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_cnn_news_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_cnn_news BertForSequenceClassification from AyoubChLin +author: John Snow Labs +name: bert_cnn_news +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_cnn_news` is a English model originally trained by AyoubChLin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_cnn_news_en_5.1.4_3.4_1698245908185.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_cnn_news_en_5.1.4_3.4_1698245908185.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_cnn_news","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_cnn_news","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_cnn_news| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/AyoubChLin/bert_cnn_news \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_deepfake_bulgarian_multiclass_bg.md b/docs/_posts/ahmedlone127/2023-10-25-bert_deepfake_bulgarian_multiclass_bg.md new file mode 100644 index 00000000000000..e73f8e0fd9bfe6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_deepfake_bulgarian_multiclass_bg.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Bulgarian bert_deepfake_bulgarian_multiclass BertForSequenceClassification from usmiva +author: John Snow Labs +name: bert_deepfake_bulgarian_multiclass +date: 2023-10-25 +tags: [bert, bg, open_source, sequence_classification, onnx] +task: Text Classification +language: bg +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_deepfake_bulgarian_multiclass` is a Bulgarian model originally trained by usmiva. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_deepfake_bulgarian_multiclass_bg_5.1.4_3.4_1698212606456.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_deepfake_bulgarian_multiclass_bg_5.1.4_3.4_1698212606456.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_deepfake_bulgarian_multiclass","bg")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_deepfake_bulgarian_multiclass","bg") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_deepfake_bulgarian_multiclass| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|bg| +|Size:|409.1 MB| + +## References + +https://huggingface.co/usmiva/bert-deepfake-bg-multiclass \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_fakenews_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_fakenews_en.md new file mode 100644 index 00000000000000..12621cd2dc0b79 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_fakenews_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_fakenews BertForSequenceClassification from JKKANG +author: John Snow Labs +name: bert_fakenews +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_fakenews` is a English model originally trained by JKKANG. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_fakenews_en_5.1.4_3.4_1698221382925.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_fakenews_en_5.1.4_3.4_1698221382925.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_fakenews","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_fakenews","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_fakenews| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.6 MB| + +## References + +https://huggingface.co/JKKANG/bert-fakenews \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_mrpc_tiansiyuan_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_mrpc_tiansiyuan_en.md new file mode 100644 index 00000000000000..2fd92f028df865 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_mrpc_tiansiyuan_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetuned_mrpc_tiansiyuan BertForSequenceClassification from tiansiyuan +author: John Snow Labs +name: bert_finetuned_mrpc_tiansiyuan +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuned_mrpc_tiansiyuan` is a English model originally trained by tiansiyuan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuned_mrpc_tiansiyuan_en_5.1.4_3.4_1698223815854.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuned_mrpc_tiansiyuan_en_5.1.4_3.4_1698223815854.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_mrpc_tiansiyuan","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_mrpc_tiansiyuan","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuned_mrpc_tiansiyuan| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/tiansiyuan/bert-finetuned-mrpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_winogrande_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_winogrande_en.md new file mode 100644 index 00000000000000..da788c8034df82 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_winogrande_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetuned_winogrande BertForSequenceClassification from Kalslice +author: John Snow Labs +name: bert_finetuned_winogrande +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuned_winogrande` is a English model originally trained by Kalslice. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuned_winogrande_en_5.1.4_3.4_1698230827620.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuned_winogrande_en_5.1.4_3.4_1698230827620.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_winogrande","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_winogrande","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuned_winogrande| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Kalslice/bert-finetuned-winogrande \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_finetuning_test_itcastai_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuning_test_itcastai_en.md new file mode 100644 index 00000000000000..f0e2e41f58598e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuning_test_itcastai_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetuning_test_itcastai BertForSequenceClassification from ItcastAI +author: John Snow Labs +name: bert_finetuning_test_itcastai +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuning_test_itcastai` is a English model originally trained by ItcastAI. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuning_test_itcastai_en_5.1.4_3.4_1698211632257.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuning_test_itcastai_en_5.1.4_3.4_1698211632257.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuning_test_itcastai","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuning_test_itcastai","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuning_test_itcastai| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/ItcastAI/bert_finetuning_test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_finetuning_test_milian_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuning_test_milian_en.md new file mode 100644 index 00000000000000..8b245b1435a894 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuning_test_milian_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetuning_test_milian BertForSequenceClassification from Milian +author: John Snow Labs +name: bert_finetuning_test_milian +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuning_test_milian` is a English model originally trained by Milian. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuning_test_milian_en_5.1.4_3.4_1698225530685.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuning_test_milian_en_5.1.4_3.4_1698225530685.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuning_test_milian","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuning_test_milian","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuning_test_milian| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Milian/bert_finetuning_test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_finetunning_test_itcastai_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_finetunning_test_itcastai_en.md new file mode 100644 index 00000000000000..9340812df09e45 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_finetunning_test_itcastai_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetunning_test_itcastai BertForSequenceClassification from ItcastAI +author: John Snow Labs +name: bert_finetunning_test_itcastai +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetunning_test_itcastai` is a English model originally trained by ItcastAI. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetunning_test_itcastai_en_5.1.4_3.4_1698211833653.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetunning_test_itcastai_en_5.1.4_3.4_1698211833653.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetunning_test_itcastai","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetunning_test_itcastai","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetunning_test_itcastai| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/ItcastAI/bert_finetunning_test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_finetunning_test_jovenpai_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_finetunning_test_jovenpai_en.md new file mode 100644 index 00000000000000..ef5149b8cd4d3c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_finetunning_test_jovenpai_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetunning_test_jovenpai BertForSequenceClassification from JovenPai +author: John Snow Labs +name: bert_finetunning_test_jovenpai +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetunning_test_jovenpai` is a English model originally trained by JovenPai. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetunning_test_jovenpai_en_5.1.4_3.4_1698220026934.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetunning_test_jovenpai_en_5.1.4_3.4_1698220026934.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetunning_test_jovenpai","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetunning_test_jovenpai","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetunning_test_jovenpai| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/JovenPai/bert_finetunning_test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_fom_job_description_assignment_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_fom_job_description_assignment_en.md new file mode 100644 index 00000000000000..659f75aa08ef65 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_fom_job_description_assignment_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_fom_job_description_assignment BertForSequenceClassification from Pazel +author: John Snow Labs +name: bert_fom_job_description_assignment +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_fom_job_description_assignment` is a English model originally trained by Pazel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_fom_job_description_assignment_en_5.1.4_3.4_1698240255635.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_fom_job_description_assignment_en_5.1.4_3.4_1698240255635.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_fom_job_description_assignment","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_fom_job_description_assignment","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_fom_job_description_assignment| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|627.7 MB| + +## References + +https://huggingface.co/Pazel/bert-fom-job-description-assignment \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_80_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_80_en.md new file mode 100644 index 00000000000000..a11758a4334828 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_80_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_80 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_80 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_80` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_80_en_5.1.4_3.4_1698223321437.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_80_en_5.1.4_3.4_1698223321437.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_80","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_80","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_80| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-80 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_81_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_81_en.md new file mode 100644 index 00000000000000..8dca927e9d1e3b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_81_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_81 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_81 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_81` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_81_en_5.1.4_3.4_1698223539173.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_81_en_5.1.4_3.4_1698223539173.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_81","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_81","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_81| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-81 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_82_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_82_en.md new file mode 100644 index 00000000000000..5eef4285deaf00 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_82_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_82 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_82 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_82` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_82_en_5.1.4_3.4_1698223738328.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_82_en_5.1.4_3.4_1698223738328.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_82","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_82","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_82| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-82 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_83_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_83_en.md new file mode 100644 index 00000000000000..a3ca40a8535b14 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_83_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_83 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_83 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_83` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_83_en_5.1.4_3.4_1698223981519.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_83_en_5.1.4_3.4_1698223981519.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_83","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_83","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_83| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-83 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_84_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_84_en.md new file mode 100644 index 00000000000000..bba56c7c70c7c7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_84_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_84 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_84 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_84` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_84_en_5.1.4_3.4_1698224151148.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_84_en_5.1.4_3.4_1698224151148.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_84","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_84","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_84| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-84 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_85_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_85_en.md new file mode 100644 index 00000000000000..2c535953295c5e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_85_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_85 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_85 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_85` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_85_en_5.1.4_3.4_1698224389127.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_85_en_5.1.4_3.4_1698224389127.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_85","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_85","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_85| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-85 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_86_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_86_en.md new file mode 100644 index 00000000000000..04c994e1e652fc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_86_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_86 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_86 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_86` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_86_en_5.1.4_3.4_1698224587586.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_86_en_5.1.4_3.4_1698224587586.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_86","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_86","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_86| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-86 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_87_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_87_en.md new file mode 100644 index 00000000000000..0892b6a6eb38ac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_87_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_87 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_87 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_87` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_87_en_5.1.4_3.4_1698224817220.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_87_en_5.1.4_3.4_1698224817220.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_87","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_87","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_87| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-87 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_88_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_88_en.md new file mode 100644 index 00000000000000..273bb864799e6e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_88_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_88 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_88 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_88` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_88_en_5.1.4_3.4_1698225043797.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_88_en_5.1.4_3.4_1698225043797.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_88","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_88","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_88| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-88 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_89_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_89_en.md new file mode 100644 index 00000000000000..3d97ca2002d05c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_89_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_89 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_89 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_89` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_89_en_5.1.4_3.4_1698225941627.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_89_en_5.1.4_3.4_1698225941627.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_89","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_89","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_89| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-89 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_90_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_90_en.md new file mode 100644 index 00000000000000..0dbfdf0e30c5e1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_90_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_90 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_90 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_90` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_90_en_5.1.4_3.4_1698226789555.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_90_en_5.1.4_3.4_1698226789555.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_90","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_90","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_90| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-90 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_91_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_91_en.md new file mode 100644 index 00000000000000..03b02307627fca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_91_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_91 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_91 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_91` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_91_en_5.1.4_3.4_1698227620626.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_91_en_5.1.4_3.4_1698227620626.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_91","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_91","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_91| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-91 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_92_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_92_en.md new file mode 100644 index 00000000000000..399837865a7ada --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_92_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_92 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_92 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_92` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_92_en_5.1.4_3.4_1698228298225.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_92_en_5.1.4_3.4_1698228298225.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_92","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_92","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_92| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-92 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_93_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_93_en.md new file mode 100644 index 00000000000000..6aa06d646ead45 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_93_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_93 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_93 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_93` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_93_en_5.1.4_3.4_1698229260438.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_93_en_5.1.4_3.4_1698229260438.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_93","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_93","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_93| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-93 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_94_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_94_en.md new file mode 100644 index 00000000000000..b42bb56a7fe310 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_94_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_94 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_94 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_94` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_94_en_5.1.4_3.4_1698230141395.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_94_en_5.1.4_3.4_1698230141395.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_94","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_94","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_94| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-94 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_95_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_95_en.md new file mode 100644 index 00000000000000..429efe7c6a856a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_95_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_95 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_95 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_95` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_95_en_5.1.4_3.4_1698230908873.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_95_en_5.1.4_3.4_1698230908873.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_95","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_95","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_95| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-95 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_96_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_96_en.md new file mode 100644 index 00000000000000..ad2da76485c85f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_96_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_96 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_96 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_96` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_96_en_5.1.4_3.4_1698231828253.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_96_en_5.1.4_3.4_1698231828253.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_96","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_96","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_96| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-96 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_97_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_97_en.md new file mode 100644 index 00000000000000..3fe7a26782da02 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_97_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_97 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_97 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_97` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_97_en_5.1.4_3.4_1698232593733.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_97_en_5.1.4_3.4_1698232593733.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_97","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_97","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_97| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-97 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_98_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_98_en.md new file mode 100644 index 00000000000000..66a9f195cc2db1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_98_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_98 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_98 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_98` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_98_en_5.1.4_3.4_1698233424850.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_98_en_5.1.4_3.4_1698233424850.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_98","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_98","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_98| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-98 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_99_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_99_en.md new file mode 100644 index 00000000000000..628c3fa60b2503 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_cola_99_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_cola_99 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_cola_99 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_cola_99` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_cola_99_en_5.1.4_3.4_1698234234091.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_cola_99_en_5.1.4_3.4_1698234234091.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_99","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_cola_99","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_cola_99| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_cola-99 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_24_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_24_jeevesh8_en.md new file mode 100644 index 00000000000000..148e66ddfc49ad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_24_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_24_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_24_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_24_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_24_jeevesh8_en_5.1.4_3.4_1698208744152.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_24_jeevesh8_en_5.1.4_3.4_1698208744152.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_24_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_24_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_24_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-24 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_31_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_31_jeevesh8_en.md new file mode 100644 index 00000000000000..18fca0704094bb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_31_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_31_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_31_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_31_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_31_jeevesh8_en_5.1.4_3.4_1698209991407.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_31_jeevesh8_en_5.1.4_3.4_1698209991407.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_31_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_31_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_31_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-31 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_33_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_33_jeevesh8_en.md new file mode 100644 index 00000000000000..f1e443cdd98700 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_33_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_33_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_33_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_33_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_33_jeevesh8_en_5.1.4_3.4_1698210374266.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_33_jeevesh8_en_5.1.4_3.4_1698210374266.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_33_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_33_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_33_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-33 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_35_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_35_jeevesh8_en.md new file mode 100644 index 00000000000000..5255680f088f1c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_35_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_35_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_35_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_35_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_35_jeevesh8_en_5.1.4_3.4_1698210737122.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_35_jeevesh8_en_5.1.4_3.4_1698210737122.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_35_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_35_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_35_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-35 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_36_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_36_jeevesh8_en.md new file mode 100644 index 00000000000000..fd58f173f8acd5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_36_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_36_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_36_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_36_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_36_jeevesh8_en_5.1.4_3.4_1698210931104.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_36_jeevesh8_en_5.1.4_3.4_1698210931104.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_36_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_36_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_36_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-36 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_38_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_38_jeevesh8_en.md new file mode 100644 index 00000000000000..66c21265c34ccb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_38_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_38_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_38_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_38_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_38_jeevesh8_en_5.1.4_3.4_1698211283214.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_38_jeevesh8_en_5.1.4_3.4_1698211283214.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_38_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_38_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_38_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-38 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_42_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_42_jeevesh8_en.md new file mode 100644 index 00000000000000..6e8cd8ba73b5c0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_42_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_42_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_42_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_42_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_42_jeevesh8_en_5.1.4_3.4_1698212046210.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_42_jeevesh8_en_5.1.4_3.4_1698212046210.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_42_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_42_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_42_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-42 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_45_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_45_jeevesh8_en.md new file mode 100644 index 00000000000000..76a4bbff36664a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_45_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_45_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_45_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_45_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_45_jeevesh8_en_5.1.4_3.4_1698212646870.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_45_jeevesh8_en_5.1.4_3.4_1698212646870.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_45_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_45_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_45_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-45 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_52_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_52_jeevesh8_en.md new file mode 100644 index 00000000000000..cfc8b90c280ca1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_52_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_52_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_52_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_52_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_52_jeevesh8_en_5.1.4_3.4_1698213920269.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_52_jeevesh8_en_5.1.4_3.4_1698213920269.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_52_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_52_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_52_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-52 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_55_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_55_jeevesh8_en.md new file mode 100644 index 00000000000000..010d33b0f4687c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_55_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_55_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_55_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_55_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_55_jeevesh8_en_5.1.4_3.4_1698214456511.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_55_jeevesh8_en_5.1.4_3.4_1698214456511.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_55_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_55_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_55_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-55 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_56_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_56_jeevesh8_en.md new file mode 100644 index 00000000000000..c733153ce00af4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_56_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_56_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_56_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_56_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_56_jeevesh8_en_5.1.4_3.4_1698214624241.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_56_jeevesh8_en_5.1.4_3.4_1698214624241.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_56_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_56_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_56_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-56 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_62_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_62_jeevesh8_en.md new file mode 100644 index 00000000000000..77b77be0520796 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_62_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_62_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_62_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_62_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_62_jeevesh8_en_5.1.4_3.4_1698215835889.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_62_jeevesh8_en_5.1.4_3.4_1698215835889.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_62_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_62_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_62_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-62 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_64_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_64_jeevesh8_en.md new file mode 100644 index 00000000000000..41b955f83abbaa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_64_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_64_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_64_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_64_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_64_jeevesh8_en_5.1.4_3.4_1698216257062.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_64_jeevesh8_en_5.1.4_3.4_1698216257062.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_64_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_64_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_64_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-64 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_68_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_68_jeevesh8_en.md new file mode 100644 index 00000000000000..06c843a9e74b16 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_68_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_68_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_68_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_68_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_68_jeevesh8_en_5.1.4_3.4_1698217001117.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_68_jeevesh8_en_5.1.4_3.4_1698217001117.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_68_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_68_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_68_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-68 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_6_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_6_jeevesh8_en.md new file mode 100644 index 00000000000000..68ec5ec38c6f42 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_6_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_6_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_6_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_6_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_6_jeevesh8_en_5.1.4_3.4_1698205246513.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_6_jeevesh8_en_5.1.4_3.4_1698205246513.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_6_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_6_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_6_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_72_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_72_jeevesh8_en.md new file mode 100644 index 00000000000000..825dee7b6c2872 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_72_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_72_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_72_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_72_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_72_jeevesh8_en_5.1.4_3.4_1698217803909.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_72_jeevesh8_en_5.1.4_3.4_1698217803909.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_72_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_72_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_72_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-72 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_73_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_73_jeevesh8_en.md new file mode 100644 index 00000000000000..44602a69184f7c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_73_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_73_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_73_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_73_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_73_jeevesh8_en_5.1.4_3.4_1698218011258.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_73_jeevesh8_en_5.1.4_3.4_1698218011258.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_73_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_73_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_73_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-73 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_75_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_75_jeevesh8_en.md new file mode 100644 index 00000000000000..43eb52676b0869 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_75_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_75_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_75_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_75_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_75_jeevesh8_en_5.1.4_3.4_1698218407337.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_75_jeevesh8_en_5.1.4_3.4_1698218407337.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_75_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_75_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_75_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-75 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_77_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_77_jeevesh8_en.md new file mode 100644 index 00000000000000..e286b0611a9ece --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_77_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_77_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_77_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_77_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_77_jeevesh8_en_5.1.4_3.4_1698218842115.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_77_jeevesh8_en_5.1.4_3.4_1698218842115.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_77_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_77_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_77_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-77 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_79_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_79_jeevesh8_en.md new file mode 100644 index 00000000000000..4c5c45e5978045 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_79_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_79_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_79_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_79_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_79_jeevesh8_en_5.1.4_3.4_1698219225696.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_79_jeevesh8_en_5.1.4_3.4_1698219225696.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_79_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_79_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_79_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-79 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_80_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_80_jeevesh8_en.md new file mode 100644 index 00000000000000..d6625282269809 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_80_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_80_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_80_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_80_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_80_jeevesh8_en_5.1.4_3.4_1698219407321.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_80_jeevesh8_en_5.1.4_3.4_1698219407321.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_80_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_80_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_80_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-80 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_81_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_81_jeevesh8_en.md new file mode 100644 index 00000000000000..ed95812dd8dbe0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_81_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_81_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_81_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_81_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_81_jeevesh8_en_5.1.4_3.4_1698219587212.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_81_jeevesh8_en_5.1.4_3.4_1698219587212.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_81_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_81_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_81_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-81 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_82_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_82_jeevesh8_en.md new file mode 100644 index 00000000000000..fed0cb005da5c4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_82_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_82_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_82_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_82_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_82_jeevesh8_en_5.1.4_3.4_1698219788884.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_82_jeevesh8_en_5.1.4_3.4_1698219788884.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_82_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_82_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_82_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-82 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_83_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_83_jeevesh8_en.md new file mode 100644 index 00000000000000..d199f7ff7379dc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_83_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_83_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_83_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_83_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_83_jeevesh8_en_5.1.4_3.4_1698219977145.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_83_jeevesh8_en_5.1.4_3.4_1698219977145.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_83_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_83_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_83_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-83 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_84_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_84_jeevesh8_en.md new file mode 100644 index 00000000000000..db8311cb113793 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_84_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_84_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_84_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_84_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_84_jeevesh8_en_5.1.4_3.4_1698220142773.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_84_jeevesh8_en_5.1.4_3.4_1698220142773.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_84_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_84_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_84_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-84 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_85_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_85_jeevesh8_en.md new file mode 100644 index 00000000000000..b6b91b836dafc4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_85_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_85_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_85_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_85_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_85_jeevesh8_en_5.1.4_3.4_1698220376391.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_85_jeevesh8_en_5.1.4_3.4_1698220376391.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_85_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_85_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_85_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-85 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_86_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_86_jeevesh8_en.md new file mode 100644 index 00000000000000..6c1a72f86b478d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_86_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_86_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_86_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_86_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_86_jeevesh8_en_5.1.4_3.4_1698220562838.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_86_jeevesh8_en_5.1.4_3.4_1698220562838.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_86_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_86_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_86_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-86 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_87_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_87_jeevesh8_en.md new file mode 100644 index 00000000000000..bec5d19ff4e595 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_87_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_87_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_87_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_87_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_87_jeevesh8_en_5.1.4_3.4_1698220763383.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_87_jeevesh8_en_5.1.4_3.4_1698220763383.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_87_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_87_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_87_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-87 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_88_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_88_jeevesh8_en.md new file mode 100644 index 00000000000000..e8a257a0cec721 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_88_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_88_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_88_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_88_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_88_jeevesh8_en_5.1.4_3.4_1698220946763.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_88_jeevesh8_en_5.1.4_3.4_1698220946763.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_88_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_88_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_88_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-88 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_89_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_89_jeevesh8_en.md new file mode 100644 index 00000000000000..154c0757ad2f48 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_89_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_89_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_89_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_89_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_89_jeevesh8_en_5.1.4_3.4_1698221153353.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_89_jeevesh8_en_5.1.4_3.4_1698221153353.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_89_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_89_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_89_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-89 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_90_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_90_jeevesh8_en.md new file mode 100644 index 00000000000000..b87c08c17e7577 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_90_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_90_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_90_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_90_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_90_jeevesh8_en_5.1.4_3.4_1698221354382.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_90_jeevesh8_en_5.1.4_3.4_1698221354382.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_90_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_90_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_90_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-90 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_91_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_91_jeevesh8_en.md new file mode 100644 index 00000000000000..5d572e035ecb56 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_91_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_91_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_91_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_91_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_91_jeevesh8_en_5.1.4_3.4_1698221523166.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_91_jeevesh8_en_5.1.4_3.4_1698221523166.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_91_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_91_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_91_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-91 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_92_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_92_jeevesh8_en.md new file mode 100644 index 00000000000000..dc9a643130d58b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_92_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_92_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_92_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_92_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_92_jeevesh8_en_5.1.4_3.4_1698221750750.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_92_jeevesh8_en_5.1.4_3.4_1698221750750.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_92_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_92_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_92_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-92 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_93_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_93_jeevesh8_en.md new file mode 100644 index 00000000000000..5ad503ad756a17 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_93_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_93_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_93_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_93_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_93_jeevesh8_en_5.1.4_3.4_1698221927847.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_93_jeevesh8_en_5.1.4_3.4_1698221927847.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_93_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_93_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_93_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-93 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_94_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_94_jeevesh8_en.md new file mode 100644 index 00000000000000..2482eea032a407 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_94_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_94_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_94_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_94_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_94_jeevesh8_en_5.1.4_3.4_1698222116709.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_94_jeevesh8_en_5.1.4_3.4_1698222116709.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_94_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_94_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_94_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-94 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_95_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_95_jeevesh8_en.md new file mode 100644 index 00000000000000..aed86d7d67f013 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_95_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_95_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_95_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_95_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_95_jeevesh8_en_5.1.4_3.4_1698222330820.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_95_jeevesh8_en_5.1.4_3.4_1698222330820.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_95_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_95_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_95_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-95 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_96_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_96_jeevesh8_en.md new file mode 100644 index 00000000000000..6a4460720fd2e1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_96_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_96_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_96_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_96_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_96_jeevesh8_en_5.1.4_3.4_1698222552917.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_96_jeevesh8_en_5.1.4_3.4_1698222552917.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_96_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_96_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_96_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-96 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_97_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_97_jeevesh8_en.md new file mode 100644 index 00000000000000..903dcfcb641ca0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_97_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_97_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_97_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_97_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_97_jeevesh8_en_5.1.4_3.4_1698222743039.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_97_jeevesh8_en_5.1.4_3.4_1698222743039.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_97_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_97_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_97_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-97 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_98_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_98_jeevesh8_en.md new file mode 100644 index 00000000000000..a26be80333e374 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_98_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_98_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_98_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_98_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_98_jeevesh8_en_5.1.4_3.4_1698222930954.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_98_jeevesh8_en_5.1.4_3.4_1698222930954.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_98_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_98_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_98_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-98 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_99_jeevesh8_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_99_jeevesh8_en.md new file mode 100644 index 00000000000000..cf663bca885066 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_ft_qqp_99_jeevesh8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_ft_qqp_99_jeevesh8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: bert_ft_qqp_99_jeevesh8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_ft_qqp_99_jeevesh8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_99_jeevesh8_en_5.1.4_3.4_1698223118980.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_ft_qqp_99_jeevesh8_en_5.1.4_3.4_1698223118980.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_99_jeevesh8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_ft_qqp_99_jeevesh8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_ft_qqp_99_jeevesh8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/bert_ft_qqp-99 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_italian_emotion_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_italian_emotion_en.md new file mode 100644 index 00000000000000..b76d0f2c0bd357 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_italian_emotion_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_italian_emotion BertForSequenceClassification from pysentimiento +author: John Snow Labs +name: bert_italian_emotion +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_italian_emotion` is a English model originally trained by pysentimiento. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_italian_emotion_en_5.1.4_3.4_1698221965080.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_italian_emotion_en_5.1.4_3.4_1698221965080.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_italian_emotion","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_italian_emotion","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_italian_emotion| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.9 MB| + +## References + +https://huggingface.co/pysentimiento/bert-it-emotion \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_italian_hate_speech_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_italian_hate_speech_en.md new file mode 100644 index 00000000000000..4ecf9e8b7aa01c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_italian_hate_speech_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_italian_hate_speech BertForSequenceClassification from pysentimiento +author: John Snow Labs +name: bert_italian_hate_speech +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_italian_hate_speech` is a English model originally trained by pysentimiento. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_italian_hate_speech_en_5.1.4_3.4_1698222151141.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_italian_hate_speech_en_5.1.4_3.4_1698222151141.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_italian_hate_speech","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_italian_hate_speech","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_italian_hate_speech| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.9 MB| + +## References + +https://huggingface.co/pysentimiento/bert-it-hate-speech \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_italian_irony_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_italian_irony_en.md new file mode 100644 index 00000000000000..8ecdc13462ecf9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_italian_irony_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_italian_irony BertForSequenceClassification from pysentimiento +author: John Snow Labs +name: bert_italian_irony +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_italian_irony` is a English model originally trained by pysentimiento. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_italian_irony_en_5.1.4_3.4_1698222335196.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_italian_irony_en_5.1.4_3.4_1698222335196.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_italian_irony","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_italian_irony","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_italian_irony| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.9 MB| + +## References + +https://huggingface.co/pysentimiento/bert-it-irony \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_italian_sentiment_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_italian_sentiment_en.md new file mode 100644 index 00000000000000..6b6a48ceebb185 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_italian_sentiment_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_italian_sentiment BertForSequenceClassification from pysentimiento +author: John Snow Labs +name: bert_italian_sentiment +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_italian_sentiment` is a English model originally trained by pysentimiento. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_italian_sentiment_en_5.1.4_3.4_1698221577302.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_italian_sentiment_en_5.1.4_3.4_1698221577302.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_italian_sentiment","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_italian_sentiment","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_italian_sentiment| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.9 MB| + +## References + +https://huggingface.co/pysentimiento/bert-it-sentiment \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_cola_b_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_cola_b_en.md new file mode 100644 index 00000000000000..4a7a2833858a43 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_cola_b_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_cola_b BertForSequenceClassification from EhsanAghazadeh +author: John Snow Labs +name: bert_large_uncased_cola_b +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_cola_b` is a English model originally trained by EhsanAghazadeh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_cola_b_en_5.1.4_3.4_1698204080875.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_cola_b_en_5.1.4_3.4_1698204080875.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_cola_b","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_cola_b","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_cola_b| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/EhsanAghazadeh/bert-large-uncased-CoLA_B \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_hoax_classifier_v1_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_hoax_classifier_v1_en.md new file mode 100644 index 00000000000000..d70f7e5053f647 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_hoax_classifier_v1_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_hoax_classifier_v1 BertForSequenceClassification from research-dump +author: John Snow Labs +name: bert_large_uncased_hoax_classifier_v1 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_hoax_classifier_v1` is a English model originally trained by research-dump. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_hoax_classifier_v1_en_5.1.4_3.4_1698229551553.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_hoax_classifier_v1_en_5.1.4_3.4_1698229551553.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_hoax_classifier_v1","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_hoax_classifier_v1","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_hoax_classifier_v1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/research-dump/bert-large-uncased_hoax_classifier_v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_mnli_ofirzaf_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_mnli_ofirzaf_en.md new file mode 100644 index 00000000000000..63886fb14ffb4e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_mnli_ofirzaf_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_mnli_ofirzaf BertForSequenceClassification from ofirzaf +author: John Snow Labs +name: bert_large_uncased_mnli_ofirzaf +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_mnli_ofirzaf` is a English model originally trained by ofirzaf. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_mnli_ofirzaf_en_5.1.4_3.4_1698239030775.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_mnli_ofirzaf_en_5.1.4_3.4_1698239030775.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_mnli_ofirzaf","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_mnli_ofirzaf","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_mnli_ofirzaf| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/ofirzaf/bert-large-uncased-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_16_13_30_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_16_13_30_en.md new file mode 100644 index 00000000000000..0f548ac7d8a7ca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_16_13_30_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_sst_2_16_13_30 BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_large_uncased_sst_2_16_13_30 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_sst_2_16_13_30` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sst_2_16_13_30_en_5.1.4_3.4_1698224647052.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sst_2_16_13_30_en_5.1.4_3.4_1698224647052.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_sst_2_16_13_30","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_sst_2_16_13_30","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_sst_2_16_13_30| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/simonycl/bert-large-uncased-sst-2-16-13-30 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_16_13_smoothed_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_16_13_smoothed_en.md new file mode 100644 index 00000000000000..3f4e8dd0f028f6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_16_13_smoothed_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_sst_2_16_13_smoothed BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_large_uncased_sst_2_16_13_smoothed +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_sst_2_16_13_smoothed` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sst_2_16_13_smoothed_en_5.1.4_3.4_1698220491868.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sst_2_16_13_smoothed_en_5.1.4_3.4_1698220491868.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_sst_2_16_13_smoothed","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_sst_2_16_13_smoothed","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_sst_2_16_13_smoothed| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/simonycl/bert-large-uncased-sst-2-16-13-smoothed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_32_13_30_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_32_13_30_en.md new file mode 100644 index 00000000000000..10834a73e981af --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_32_13_30_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_sst_2_32_13_30 BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_large_uncased_sst_2_32_13_30 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_sst_2_32_13_30` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sst_2_32_13_30_en_5.1.4_3.4_1698225008323.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sst_2_32_13_30_en_5.1.4_3.4_1698225008323.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_sst_2_32_13_30","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_sst_2_32_13_30","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_sst_2_32_13_30| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/simonycl/bert-large-uncased-sst-2-32-13-30 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_64_13_30_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_64_13_30_en.md new file mode 100644 index 00000000000000..7647d4ca3a2aab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_sst_2_64_13_30_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_sst_2_64_13_30 BertForSequenceClassification from simonycl +author: John Snow Labs +name: bert_large_uncased_sst_2_64_13_30 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_sst_2_64_13_30` is a English model originally trained by simonycl. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sst_2_64_13_30_en_5.1.4_3.4_1698226238586.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_sst_2_64_13_30_en_5.1.4_3.4_1698226238586.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_sst_2_64_13_30","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_sst_2_64_13_30","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_sst_2_64_13_30| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/simonycl/bert-large-uncased-sst-2-64-13-30 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_mini_finetuned_mnli_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_mini_finetuned_mnli_en.md new file mode 100644 index 00000000000000..c86eb14f202c19 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_mini_finetuned_mnli_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_mini_finetuned_mnli BertForSequenceClassification from M-FAC +author: John Snow Labs +name: bert_mini_finetuned_mnli +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_mini_finetuned_mnli` is a English model originally trained by M-FAC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_mini_finetuned_mnli_en_5.1.4_3.4_1698222045987.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_mini_finetuned_mnli_en_5.1.4_3.4_1698222045987.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_mini_finetuned_mnli","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_mini_finetuned_mnli","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_mini_finetuned_mnli| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|42.1 MB| + +## References + +https://huggingface.co/M-FAC/bert-mini-finetuned-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_mini_finetuned_mrpc_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_mini_finetuned_mrpc_en.md new file mode 100644 index 00000000000000..4d6737a21da254 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_mini_finetuned_mrpc_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_mini_finetuned_mrpc BertForSequenceClassification from M-FAC +author: John Snow Labs +name: bert_mini_finetuned_mrpc +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_mini_finetuned_mrpc` is a English model originally trained by M-FAC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_mini_finetuned_mrpc_en_5.1.4_3.4_1698222142012.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_mini_finetuned_mrpc_en_5.1.4_3.4_1698222142012.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_mini_finetuned_mrpc","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_mini_finetuned_mrpc","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_mini_finetuned_mrpc| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|42.1 MB| + +## References + +https://huggingface.co/M-FAC/bert-mini-finetuned-mrpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_mini_finetuned_qnli_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_mini_finetuned_qnli_en.md new file mode 100644 index 00000000000000..06edc3f29f8485 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_mini_finetuned_qnli_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_mini_finetuned_qnli BertForSequenceClassification from M-FAC +author: John Snow Labs +name: bert_mini_finetuned_qnli +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_mini_finetuned_qnli` is a English model originally trained by M-FAC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_mini_finetuned_qnli_en_5.1.4_3.4_1698222259511.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_mini_finetuned_qnli_en_5.1.4_3.4_1698222259511.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_mini_finetuned_qnli","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_mini_finetuned_qnli","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_mini_finetuned_qnli| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|42.1 MB| + +## References + +https://huggingface.co/M-FAC/bert-mini-finetuned-qnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_mini_finetuned_qqp_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_mini_finetuned_qqp_en.md new file mode 100644 index 00000000000000..7dc7272d8455d0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_mini_finetuned_qqp_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_mini_finetuned_qqp BertForSequenceClassification from M-FAC +author: John Snow Labs +name: bert_mini_finetuned_qqp +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_mini_finetuned_qqp` is a English model originally trained by M-FAC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_mini_finetuned_qqp_en_5.1.4_3.4_1698222353761.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_mini_finetuned_qqp_en_5.1.4_3.4_1698222353761.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_mini_finetuned_qqp","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_mini_finetuned_qqp","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_mini_finetuned_qqp| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|42.1 MB| + +## References + +https://huggingface.co/M-FAC/bert-mini-finetuned-qqp \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_mini_finetuned_sst2_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_mini_finetuned_sst2_en.md new file mode 100644 index 00000000000000..5647461caea8d4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_mini_finetuned_sst2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_mini_finetuned_sst2 BertForSequenceClassification from M-FAC +author: John Snow Labs +name: bert_mini_finetuned_sst2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_mini_finetuned_sst2` is a English model originally trained by M-FAC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_mini_finetuned_sst2_en_5.1.4_3.4_1698222448691.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_mini_finetuned_sst2_en_5.1.4_3.4_1698222448691.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_mini_finetuned_sst2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_mini_finetuned_sst2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_mini_finetuned_sst2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|42.1 MB| + +## References + +https://huggingface.co/M-FAC/bert-mini-finetuned-sst2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_mini_finetuned_stsb_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_mini_finetuned_stsb_en.md new file mode 100644 index 00000000000000..34b3b2d4bac057 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_mini_finetuned_stsb_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_mini_finetuned_stsb BertForSequenceClassification from M-FAC +author: John Snow Labs +name: bert_mini_finetuned_stsb +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_mini_finetuned_stsb` is a English model originally trained by M-FAC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_mini_finetuned_stsb_en_5.1.4_3.4_1698222538855.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_mini_finetuned_stsb_en_5.1.4_3.4_1698222538855.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_mini_finetuned_stsb","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_mini_finetuned_stsb","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_mini_finetuned_stsb| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|42.1 MB| + +## References + +https://huggingface.co/M-FAC/bert-mini-finetuned-stsb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_mixed_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_mixed_en.md new file mode 100644 index 00000000000000..49aa9d877edc14 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_mixed_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_mixed BertForSequenceClassification from PravallikaMyneni +author: John Snow Labs +name: bert_mixed +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_mixed` is a English model originally trained by PravallikaMyneni. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_mixed_en_5.1.4_3.4_1698222545171.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_mixed_en_5.1.4_3.4_1698222545171.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_mixed","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_mixed","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_mixed| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/PravallikaMyneni/bert_mixed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_pre_doc_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_pre_doc_en.md new file mode 100644 index 00000000000000..75e5c6f1d1b618 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_pre_doc_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_pre_doc BertForSequenceClassification from LilaBoualili +author: John Snow Labs +name: bert_pre_doc +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_pre_doc` is a English model originally trained by LilaBoualili. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_pre_doc_en_5.1.4_3.4_1698220828401.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_pre_doc_en_5.1.4_3.4_1698220828401.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_pre_doc","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_pre_doc","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_pre_doc| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/LilaBoualili/bert-pre-doc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_pre_pair_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_pre_pair_en.md new file mode 100644 index 00000000000000..8c09fb2a1ae7e0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_pre_pair_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_pre_pair BertForSequenceClassification from LilaBoualili +author: John Snow Labs +name: bert_pre_pair +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_pre_pair` is a English model originally trained by LilaBoualili. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_pre_pair_en_5.1.4_3.4_1698221017879.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_pre_pair_en_5.1.4_3.4_1698221017879.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_pre_pair","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_pre_pair","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_pre_pair| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/LilaBoualili/bert-pre-pair \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sdg_french_fr.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sdg_french_fr.md new file mode 100644 index 00000000000000..ada1dbaeb13105 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sdg_french_fr.md @@ -0,0 +1,97 @@ +--- +layout: model +title: French bert_sdg_french BertForSequenceClassification from ilovebots +author: John Snow Labs +name: bert_sdg_french +date: 2023-10-25 +tags: [bert, fr, open_source, sequence_classification, onnx] +task: Text Classification +language: fr +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_sdg_french` is a French model originally trained by ilovebots. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sdg_french_fr_5.1.4_3.4_1698214647394.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sdg_french_fr_5.1.4_3.4_1698214647394.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sdg_french","fr")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sdg_french","fr") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sdg_french| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|fr| +|Size:|414.6 MB| + +## References + +https://huggingface.co/ilovebots/bert-sdg-french \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sentence_classifier_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sentence_classifier_en.md new file mode 100644 index 00000000000000..611b1a83e0f076 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sentence_classifier_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_sentence_classifier BertForSequenceClassification from Paleontolog +author: John Snow Labs +name: bert_sentence_classifier +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_sentence_classifier` is a English model originally trained by Paleontolog. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sentence_classifier_en_5.1.4_3.4_1698242352864.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sentence_classifier_en_5.1.4_3.4_1698242352864.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sentence_classifier","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sentence_classifier","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sentence_classifier| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|667.3 MB| + +## References + +https://huggingface.co/Paleontolog/bert_sentence_classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sequce_classifier_paraphrase_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sequce_classifier_paraphrase_en.md new file mode 100644 index 00000000000000..01140afc54eaa6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sequce_classifier_paraphrase_en.md @@ -0,0 +1,100 @@ +--- +layout: model +title: English BertForSequenceClassification Cased model (from Prompsit) +author: John Snow Labs +name: bert_sequce_classifier_paraphrase +date: 2023-10-25 +tags: [en, open_source, bert, sequence_classification, ner, onnx] +task: Named Entity Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `paraphrase-bert-en` is a English model originally trained by `Prompsit`. + +## Predicted Entities + +`Not Paraphrase`, `Paraphrase` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequce_classifier_paraphrase_en_5.1.4_3.4_1698231258424.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequce_classifier_paraphrase_en_5.1.4_3.4_1698231258424.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequce_classifier_paraphrase","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequce_classifier_paraphrase","en") + .setInputCols(Array("document", "token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequce_classifier_paraphrase| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|en| +|Size:|409.4 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +References + +- https://huggingface.co/Prompsit/paraphrase-bert-en \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_base_uncased_finetuned_surveyclassification_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_base_uncased_finetuned_surveyclassification_en.md new file mode 100644 index 00000000000000..49d58adbd7092c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_base_uncased_finetuned_surveyclassification_en.md @@ -0,0 +1,100 @@ +--- +layout: model +title: English BertForSequenceClassification Base Uncased model (from Jorgeutd) +author: John Snow Labs +name: bert_sequence_classifier_base_uncased_finetuned_surveyclassification +date: 2023-10-25 +tags: [en, open_source, bert, sequence_classification, ner, onnx] +task: Named Entity Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-uncased-finetuned-surveyclassification` is a English model originally trained by `Jorgeutd`. + +## Predicted Entities + +`positive`, `neutral`, `negative` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_uncased_finetuned_surveyclassification_en_5.1.4_3.4_1698219648617.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_uncased_finetuned_surveyclassification_en_5.1.4_3.4_1698219648617.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_uncased_finetuned_surveyclassification","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_uncased_finetuned_surveyclassification","en") + .setInputCols(Array("document", "token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_base_uncased_finetuned_surveyclassification| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|en| +|Size:|409.4 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +References + +- https://huggingface.co/Jorgeutd/bert-base-uncased-finetuned-surveyclassification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_base_uncased_sst2_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_base_uncased_sst2_en.md new file mode 100644 index 00000000000000..bf377d771b78ab --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_base_uncased_sst2_en.md @@ -0,0 +1,101 @@ +--- +layout: model +title: English BertForSequenceClassification Base Uncased model (from JeremiahZ) +author: John Snow Labs +name: bert_sequence_classifier_base_uncased_sst2 +date: 2023-10-25 +tags: [en, open_source, bert, sequence_classification, ner, onnx] +task: Named Entity Recognition +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-uncased-sst2` is a English model originally trained by `JeremiahZ`. + +## Predicted Entities + +`positive`, `negative` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_uncased_sst2_en_5.1.4_3.4_1698208956258.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_base_uncased_sst2_en_5.1.4_3.4_1698208956258.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_uncased_sst2","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_uncased_sst2","en") + .setInputCols(Array("document", "token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_base_uncased_sst2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|en| +|Size:|409.4 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +References + +- https://huggingface.co/JeremiahZ/bert-base-uncased-sst2 +- https://paperswithcode.com/sota?task=Text+Classification&dataset=GLUE+SST2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_portugese_pt.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_portugese_pt.md new file mode 100644 index 00000000000000..5d054e173420dd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_dehate_mono_portugese_pt.md @@ -0,0 +1,102 @@ +--- +layout: model +title: Portuguese BertForSequenceClassification Cased model (from Hate-speech-CNERG) +author: John Snow Labs +name: bert_sequence_classifier_dehate_mono_portugese +date: 2023-10-25 +tags: [pt, open_source, bert, sequence_classification, ner, onnx] +task: Named Entity Recognition +language: pt +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dehatebert-mono-portugese` is a Portuguese model originally trained by `Hate-speech-CNERG`. + +## Predicted Entities + +`NON_HATE`, `HATE` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_dehate_mono_portugese_pt_5.1.4_3.4_1698208710232.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_dehate_mono_portugese_pt_5.1.4_3.4_1698208710232.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_dehate_mono_portugese","pt") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_dehate_mono_portugese","pt") + .setInputCols(Array("document", "token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_dehate_mono_portugese| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|pt| +|Size:|627.7 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +References + +- https://huggingface.co/Hate-speech-CNERG/dehatebert-mono-portugese +- https://github.com/punyajoy/DE-LIMIT +- https://arxiv.org/abs/2004.06465 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_paraphrase_pt.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_paraphrase_pt.md new file mode 100644 index 00000000000000..c5ff6d7364ff75 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_paraphrase_pt.md @@ -0,0 +1,100 @@ +--- +layout: model +title: Portuguese BertForSequenceClassification Cased model (from Prompsit) +author: John Snow Labs +name: bert_sequence_classifier_paraphrase +date: 2023-10-25 +tags: [pt, open_source, bert, sequence_classification, ner, onnx] +task: Named Entity Recognition +language: pt +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `paraphrase-bert-pt` is a Portuguese model originally trained by `Prompsit`. + +## Predicted Entities + +`Not Paraphrase`, `Paraphrase` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_paraphrase_pt_5.1.4_3.4_1698232661863.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_paraphrase_pt_5.1.4_3.4_1698232661863.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_paraphrase","pt") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_paraphrase","pt") + .setInputCols(Array("document", "token")) + .setOutputCol("ner") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_paraphrase| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|pt| +|Size:|408.2 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +References + +- https://huggingface.co/Prompsit/paraphrase-bert-pt \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_multi_fa.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_multi_fa.md new file mode 100644 index 00000000000000..9feda5ef5fc54a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_multi_fa.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Persian bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_multi BertForSequenceClassification from HooshvareLab +author: John Snow Labs +name: bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_multi +date: 2023-10-25 +tags: [bert, fa, open_source, sequence_classification, onnx] +task: Text Classification +language: fa +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_multi` is a Persian model originally trained by HooshvareLab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_multi_fa_5.1.4_3.4_1698210004394.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_multi_fa_5.1.4_3.4_1698210004394.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_multi","fa")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_multi","fa") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_persian_farsi_base_uncased_sentiment_deepsentipers_multi| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|fa| +|Size:|608.7 MB| + +## References + +https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-multi \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_persian_farsi_base_uncased_sentiment_digikala_fa.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_persian_farsi_base_uncased_sentiment_digikala_fa.md new file mode 100644 index 00000000000000..343b06b8e5ed38 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_persian_farsi_base_uncased_sentiment_digikala_fa.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Persian bert_sequence_classifier_persian_farsi_base_uncased_sentiment_digikala BertForSequenceClassification from HooshvareLab +author: John Snow Labs +name: bert_sequence_classifier_persian_farsi_base_uncased_sentiment_digikala +date: 2023-10-25 +tags: [bert, fa, open_source, sequence_classification, onnx] +task: Text Classification +language: fa +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_sequence_classifier_persian_farsi_base_uncased_sentiment_digikala` is a Persian model originally trained by HooshvareLab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_persian_farsi_base_uncased_sentiment_digikala_fa_5.1.4_3.4_1698210264996.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_persian_farsi_base_uncased_sentiment_digikala_fa_5.1.4_3.4_1698210264996.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_persian_farsi_base_uncased_sentiment_digikala","fa")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_persian_farsi_base_uncased_sentiment_digikala","fa") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_persian_farsi_base_uncased_sentiment_digikala| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|fa| +|Size:|608.7 MB| + +## References + +https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-digikala \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_persian_farsi_base_uncased_sentiment_snappfood_fa.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_persian_farsi_base_uncased_sentiment_snappfood_fa.md new file mode 100644 index 00000000000000..85056d89c3ccd6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sequence_classifier_persian_farsi_base_uncased_sentiment_snappfood_fa.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Persian bert_sequence_classifier_persian_farsi_base_uncased_sentiment_snappfood BertForSequenceClassification from HooshvareLab +author: John Snow Labs +name: bert_sequence_classifier_persian_farsi_base_uncased_sentiment_snappfood +date: 2023-10-25 +tags: [bert, fa, open_source, sequence_classification, onnx] +task: Text Classification +language: fa +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_sequence_classifier_persian_farsi_base_uncased_sentiment_snappfood` is a Persian model originally trained by HooshvareLab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_persian_farsi_base_uncased_sentiment_snappfood_fa_5.1.4_3.4_1698210508316.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sequence_classifier_persian_farsi_base_uncased_sentiment_snappfood_fa_5.1.4_3.4_1698210508316.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_persian_farsi_base_uncased_sentiment_snappfood","fa")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_persian_farsi_base_uncased_sentiment_snappfood","fa") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sequence_classifier_persian_farsi_base_uncased_sentiment_snappfood| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|fa| +|Size:|608.7 MB| + +## References + +https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-snappfood \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sim_doc_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sim_doc_en.md new file mode 100644 index 00000000000000..b597749fd4d9d9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sim_doc_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_sim_doc BertForSequenceClassification from LilaBoualili +author: John Snow Labs +name: bert_sim_doc +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_sim_doc` is a English model originally trained by LilaBoualili. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sim_doc_en_5.1.4_3.4_1698221193643.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sim_doc_en_5.1.4_3.4_1698221193643.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sim_doc","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sim_doc","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sim_doc| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/LilaBoualili/bert-sim-doc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_sim_pair_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_sim_pair_en.md new file mode 100644 index 00000000000000..c3ebb2668186f8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_sim_pair_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_sim_pair BertForSequenceClassification from LilaBoualili +author: John Snow Labs +name: bert_sim_pair +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_sim_pair` is a English model originally trained by LilaBoualili. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_sim_pair_en_5.1.4_3.4_1698221374777.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_sim_pair_en_5.1.4_3.4_1698221374777.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_sim_pair","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sim_pair","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_sim_pair| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/LilaBoualili/bert-sim-pair \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_mnli_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_mnli_en.md new file mode 100644 index 00000000000000..4ce35b5fb49fc7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_mnli_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_tiny_finetuned_mnli BertForSequenceClassification from M-FAC +author: John Snow Labs +name: bert_tiny_finetuned_mnli +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tiny_finetuned_mnli` is a English model originally trained by M-FAC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tiny_finetuned_mnli_en_5.1.4_3.4_1698222635613.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tiny_finetuned_mnli_en_5.1.4_3.4_1698222635613.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_tiny_finetuned_mnli","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_tiny_finetuned_mnli","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tiny_finetuned_mnli| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/M-FAC/bert-tiny-finetuned-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_mrpc_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_mrpc_en.md new file mode 100644 index 00000000000000..bb433413792c4c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_mrpc_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_tiny_finetuned_mrpc BertForSequenceClassification from M-FAC +author: John Snow Labs +name: bert_tiny_finetuned_mrpc +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tiny_finetuned_mrpc` is a English model originally trained by M-FAC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tiny_finetuned_mrpc_en_5.1.4_3.4_1698222721542.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tiny_finetuned_mrpc_en_5.1.4_3.4_1698222721542.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_tiny_finetuned_mrpc","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_tiny_finetuned_mrpc","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tiny_finetuned_mrpc| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/M-FAC/bert-tiny-finetuned-mrpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_qnli_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_qnli_en.md new file mode 100644 index 00000000000000..4ea066c989469b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_qnli_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_tiny_finetuned_qnli BertForSequenceClassification from M-FAC +author: John Snow Labs +name: bert_tiny_finetuned_qnli +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tiny_finetuned_qnli` is a English model originally trained by M-FAC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tiny_finetuned_qnli_en_5.1.4_3.4_1698222815267.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tiny_finetuned_qnli_en_5.1.4_3.4_1698222815267.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_tiny_finetuned_qnli","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_tiny_finetuned_qnli","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tiny_finetuned_qnli| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/M-FAC/bert-tiny-finetuned-qnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_qqp_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_qqp_en.md new file mode 100644 index 00000000000000..ffe2e048cb4561 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_qqp_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_tiny_finetuned_qqp BertForSequenceClassification from M-FAC +author: John Snow Labs +name: bert_tiny_finetuned_qqp +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tiny_finetuned_qqp` is a English model originally trained by M-FAC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tiny_finetuned_qqp_en_5.1.4_3.4_1698222915759.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tiny_finetuned_qqp_en_5.1.4_3.4_1698222915759.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_tiny_finetuned_qqp","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_tiny_finetuned_qqp","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tiny_finetuned_qqp| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/M-FAC/bert-tiny-finetuned-qqp \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_sst2_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_sst2_en.md new file mode 100644 index 00000000000000..119e41dac1f850 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_sst2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_tiny_finetuned_sst2 BertForSequenceClassification from M-FAC +author: John Snow Labs +name: bert_tiny_finetuned_sst2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tiny_finetuned_sst2` is a English model originally trained by M-FAC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tiny_finetuned_sst2_en_5.1.4_3.4_1698223010239.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tiny_finetuned_sst2_en_5.1.4_3.4_1698223010239.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_tiny_finetuned_sst2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_tiny_finetuned_sst2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tiny_finetuned_sst2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/M-FAC/bert-tiny-finetuned-sst2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_stsb_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_stsb_en.md new file mode 100644 index 00000000000000..b625afd2dd6355 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_finetuned_stsb_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_tiny_finetuned_stsb BertForSequenceClassification from M-FAC +author: John Snow Labs +name: bert_tiny_finetuned_stsb +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tiny_finetuned_stsb` is a English model originally trained by M-FAC. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tiny_finetuned_stsb_en_5.1.4_3.4_1698223101321.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tiny_finetuned_stsb_en_5.1.4_3.4_1698223101321.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_tiny_finetuned_stsb","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_tiny_finetuned_stsb","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tiny_finetuned_stsb| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/M-FAC/bert-tiny-finetuned-stsb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_tweets_semeval_unclean_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_tweets_semeval_unclean_en.md new file mode 100644 index 00000000000000..797d04bc672bb2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_tweets_semeval_unclean_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_tweets_semeval_unclean BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_tweets_semeval_unclean +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tweets_semeval_unclean` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tweets_semeval_unclean_en_5.1.4_3.4_1698201555184.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tweets_semeval_unclean_en_5.1.4_3.4_1698201555184.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_tweets_semeval_unclean","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_tweets_semeval_unclean","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tweets_semeval_unclean| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-tweets-semeval-unclean \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_vanilla_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_vanilla_en.md new file mode 100644 index 00000000000000..b0cec13a6b38c5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_vanilla_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_vanilla BertForSequenceClassification from LilaBoualili +author: John Snow Labs +name: bert_vanilla +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_vanilla` is a English model originally trained by LilaBoualili. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_vanilla_en_5.1.4_3.4_1698221552184.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_vanilla_en_5.1.4_3.4_1698221552184.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_vanilla","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_vanilla","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_vanilla| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/LilaBoualili/bert-vanilla \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_wiki_comments_finetuned_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_wiki_comments_finetuned_en.md new file mode 100644 index 00000000000000..255d344248db6b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_wiki_comments_finetuned_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_wiki_comments_finetuned BertForSequenceClassification from DoyyingFace +author: John Snow Labs +name: bert_wiki_comments_finetuned +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_wiki_comments_finetuned` is a English model originally trained by DoyyingFace. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_wiki_comments_finetuned_en_5.1.4_3.4_1698201749015.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_wiki_comments_finetuned_en_5.1.4_3.4_1698201749015.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_wiki_comments_finetuned","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_wiki_comments_finetuned","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_wiki_comments_finetuned| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DoyyingFace/bert-wiki-comments-finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_wikipedia_sst2_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_wikipedia_sst2_en.md new file mode 100644 index 00000000000000..0decb892f3ea53 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_wikipedia_sst2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_wikipedia_sst2 BertForSequenceClassification from deepesh0x +author: John Snow Labs +name: bert_wikipedia_sst2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_wikipedia_sst2` is a English model originally trained by deepesh0x. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_wikipedia_sst2_en_5.1.4_3.4_1698211274392.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_wikipedia_sst2_en_5.1.4_3.4_1698211274392.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_wikipedia_sst2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_wikipedia_sst2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_wikipedia_sst2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/deepesh0x/bert_wikipedia_sst2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bertabaporu_portuguese_irony_en.md b/docs/_posts/ahmedlone127/2023-10-25-bertabaporu_portuguese_irony_en.md new file mode 100644 index 00000000000000..9f393d39e88ebc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bertabaporu_portuguese_irony_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bertabaporu_portuguese_irony BertForSequenceClassification from pysentimiento +author: John Snow Labs +name: bertabaporu_portuguese_irony +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertabaporu_portuguese_irony` is a English model originally trained by pysentimiento. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertabaporu_portuguese_irony_en_5.1.4_3.4_1698227283769.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertabaporu_portuguese_irony_en_5.1.4_3.4_1698227283769.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bertabaporu_portuguese_irony","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bertabaporu_portuguese_irony","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertabaporu_portuguese_irony| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|507.0 MB| + +## References + +https://huggingface.co/pysentimiento/bertabaporu-pt-irony \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-biobert_finetuned_genetic_mutation_en.md b/docs/_posts/ahmedlone127/2023-10-25-biobert_finetuned_genetic_mutation_en.md new file mode 100644 index 00000000000000..9a1ce98a172d31 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-biobert_finetuned_genetic_mutation_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English biobert_finetuned_genetic_mutation BertForSequenceClassification from wahdan99 +author: John Snow Labs +name: biobert_finetuned_genetic_mutation +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`biobert_finetuned_genetic_mutation` is a English model originally trained by wahdan99. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/biobert_finetuned_genetic_mutation_en_5.1.4_3.4_1698224837394.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/biobert_finetuned_genetic_mutation_en_5.1.4_3.4_1698224837394.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("biobert_finetuned_genetic_mutation","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("biobert_finetuned_genetic_mutation","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|biobert_finetuned_genetic_mutation| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.3 MB| + +## References + +https://huggingface.co/wahdan99/biobert-finetuned-genetic-mutation \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-boss_sentiment_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-10-25-boss_sentiment_bert_base_uncased_en.md new file mode 100644 index 00000000000000..13522eff0e11e1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-boss_sentiment_bert_base_uncased_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English boss_sentiment_bert_base_uncased BertForSequenceClassification from Kyle1668 +author: John Snow Labs +name: boss_sentiment_bert_base_uncased +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`boss_sentiment_bert_base_uncased` is a English model originally trained by Kyle1668. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/boss_sentiment_bert_base_uncased_en_5.1.4_3.4_1698221348012.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/boss_sentiment_bert_base_uncased_en_5.1.4_3.4_1698221348012.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("boss_sentiment_bert_base_uncased","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("boss_sentiment_bert_base_uncased","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|boss_sentiment_bert_base_uncased| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Kyle1668/boss-sentiment-bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-boss_toxicity_bert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-10-25-boss_toxicity_bert_base_uncased_en.md new file mode 100644 index 00000000000000..376157fbf627b7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-boss_toxicity_bert_base_uncased_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English boss_toxicity_bert_base_uncased BertForSequenceClassification from Kyle1668 +author: John Snow Labs +name: boss_toxicity_bert_base_uncased +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`boss_toxicity_bert_base_uncased` is a English model originally trained by Kyle1668. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/boss_toxicity_bert_base_uncased_en_5.1.4_3.4_1698221527242.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/boss_toxicity_bert_base_uncased_en_5.1.4_3.4_1698221527242.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("boss_toxicity_bert_base_uncased","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("boss_toxicity_bert_base_uncased","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|boss_toxicity_bert_base_uncased| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Kyle1668/boss-toxicity-bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-chinese_roberta_wwm_ext_finetuned_binary_en.md b/docs/_posts/ahmedlone127/2023-10-25-chinese_roberta_wwm_ext_finetuned_binary_en.md new file mode 100644 index 00000000000000..c4d3f7d814634f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-chinese_roberta_wwm_ext_finetuned_binary_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English chinese_roberta_wwm_ext_finetuned_binary BertForSequenceClassification from Raychanan +author: John Snow Labs +name: chinese_roberta_wwm_ext_finetuned_binary +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`chinese_roberta_wwm_ext_finetuned_binary` is a English model originally trained by Raychanan. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/chinese_roberta_wwm_ext_finetuned_binary_en_5.1.4_3.4_1698235230260.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/chinese_roberta_wwm_ext_finetuned_binary_en_5.1.4_3.4_1698235230260.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("chinese_roberta_wwm_ext_finetuned_binary","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("chinese_roberta_wwm_ext_finetuned_binary","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|chinese_roberta_wwm_ext_finetuned_binary| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.2 MB| + +## References + +https://huggingface.co/Raychanan/chinese-roberta-wwm-ext-FineTuned-Binary \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-covid_twitter_bert_v2_1_4_2e_05_0_01_en.md b/docs/_posts/ahmedlone127/2023-10-25-covid_twitter_bert_v2_1_4_2e_05_0_01_en.md new file mode 100644 index 00000000000000..266876cec82bdd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-covid_twitter_bert_v2_1_4_2e_05_0_01_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English covid_twitter_bert_v2_1_4_2e_05_0_01 BertForSequenceClassification from JerryYanJiang +author: John Snow Labs +name: covid_twitter_bert_v2_1_4_2e_05_0_01 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`covid_twitter_bert_v2_1_4_2e_05_0_01` is a English model originally trained by JerryYanJiang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/covid_twitter_bert_v2_1_4_2e_05_0_01_en_5.1.4_3.4_1698219195720.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/covid_twitter_bert_v2_1_4_2e_05_0_01_en_5.1.4_3.4_1698219195720.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("covid_twitter_bert_v2_1_4_2e_05_0_01","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("covid_twitter_bert_v2_1_4_2e_05_0_01","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|covid_twitter_bert_v2_1_4_2e_05_0_01| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/JerryYanJiang/covid-twitter-bert-v2_1_4_2e-05_0.01 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-covid_twitter_bert_v2_epoch3_batch4_lr2e_05_w0_01_en.md b/docs/_posts/ahmedlone127/2023-10-25-covid_twitter_bert_v2_epoch3_batch4_lr2e_05_w0_01_en.md new file mode 100644 index 00000000000000..5376352c5b5c55 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-covid_twitter_bert_v2_epoch3_batch4_lr2e_05_w0_01_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English covid_twitter_bert_v2_epoch3_batch4_lr2e_05_w0_01 BertForSequenceClassification from JerryYanJiang +author: John Snow Labs +name: covid_twitter_bert_v2_epoch3_batch4_lr2e_05_w0_01 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`covid_twitter_bert_v2_epoch3_batch4_lr2e_05_w0_01` is a English model originally trained by JerryYanJiang. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/covid_twitter_bert_v2_epoch3_batch4_lr2e_05_w0_01_en_5.1.4_3.4_1698217732233.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/covid_twitter_bert_v2_epoch3_batch4_lr2e_05_w0_01_en_5.1.4_3.4_1698217732233.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("covid_twitter_bert_v2_epoch3_batch4_lr2e_05_w0_01","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("covid_twitter_bert_v2_epoch3_batch4_lr2e_05_w0_01","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|covid_twitter_bert_v2_epoch3_batch4_lr2e_05_w0_01| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/JerryYanJiang/covid-twitter-bert-v2_epoch3_batch4_lr2e-05_w0.01 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-covid_twitter_bert_v2_mnli_nli_sts_crossencoder_covid_hera_en.md b/docs/_posts/ahmedlone127/2023-10-25-covid_twitter_bert_v2_mnli_nli_sts_crossencoder_covid_hera_en.md new file mode 100644 index 00000000000000..64d2a043d42408 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-covid_twitter_bert_v2_mnli_nli_sts_crossencoder_covid_hera_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English covid_twitter_bert_v2_mnli_nli_sts_crossencoder_covid_hera BertForSequenceClassification from liyijing024 +author: John Snow Labs +name: covid_twitter_bert_v2_mnli_nli_sts_crossencoder_covid_hera +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`covid_twitter_bert_v2_mnli_nli_sts_crossencoder_covid_hera` is a English model originally trained by liyijing024. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/covid_twitter_bert_v2_mnli_nli_sts_crossencoder_covid_hera_en_5.1.4_3.4_1698237356534.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/covid_twitter_bert_v2_mnli_nli_sts_crossencoder_covid_hera_en_5.1.4_3.4_1698237356534.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("covid_twitter_bert_v2_mnli_nli_sts_crossencoder_covid_hera","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("covid_twitter_bert_v2_mnli_nli_sts_crossencoder_covid_hera","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|covid_twitter_bert_v2_mnli_nli_sts_crossencoder_covid_hera| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/liyijing024/covid-twitter-bert-v2-mnli-NLI-STS-CrossEncoder-Covid-HeRA \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-deprem_berturk_binary_tr.md b/docs/_posts/ahmedlone127/2023-10-25-deprem_berturk_binary_tr.md new file mode 100644 index 00000000000000..93a6c5af8e4e43 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-deprem_berturk_binary_tr.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Turkish deprem_berturk_binary BertForSequenceClassification from ctoraman +author: John Snow Labs +name: deprem_berturk_binary +date: 2023-10-25 +tags: [bert, tr, open_source, sequence_classification, onnx] +task: Text Classification +language: tr +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`deprem_berturk_binary` is a Turkish model originally trained by ctoraman. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/deprem_berturk_binary_tr_5.1.4_3.4_1698218881162.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deprem_berturk_binary_tr_5.1.4_3.4_1698218881162.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("deprem_berturk_binary","tr")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("deprem_berturk_binary","tr") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|deprem_berturk_binary| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|tr| +|Size:|414.5 MB| + +## References + +https://huggingface.co/ctoraman/deprem-berturk-binary \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-dk_emotion_bert_2_en.md b/docs/_posts/ahmedlone127/2023-10-25-dk_emotion_bert_2_en.md new file mode 100644 index 00000000000000..4953bc34c7f514 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-dk_emotion_bert_2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English dk_emotion_bert_2 BertForSequenceClassification from Only-Mike +author: John Snow Labs +name: dk_emotion_bert_2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dk_emotion_bert_2` is a English model originally trained by Only-Mike. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dk_emotion_bert_2_en_5.1.4_3.4_1698223071802.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dk_emotion_bert_2_en_5.1.4_3.4_1698223071802.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("dk_emotion_bert_2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("dk_emotion_bert_2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dk_emotion_bert_2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.6 MB| + +## References + +https://huggingface.co/Only-Mike/dk_emotion_bert_2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-dk_emotion_bert_class_en.md b/docs/_posts/ahmedlone127/2023-10-25-dk_emotion_bert_class_en.md new file mode 100644 index 00000000000000..5bb80989303491 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-dk_emotion_bert_class_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English dk_emotion_bert_class BertForSequenceClassification from Korsholm22 +author: John Snow Labs +name: dk_emotion_bert_class +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dk_emotion_bert_class` is a English model originally trained by Korsholm22. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dk_emotion_bert_class_en_5.1.4_3.4_1698222898858.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dk_emotion_bert_class_en_5.1.4_3.4_1698222898858.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("dk_emotion_bert_class","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("dk_emotion_bert_class","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dk_emotion_bert_class| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.6 MB| + +## References + +https://huggingface.co/Korsholm22/dk_emotion_bert_class \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-esg_bert_sector_classifier_en.md b/docs/_posts/ahmedlone127/2023-10-25-esg_bert_sector_classifier_en.md new file mode 100644 index 00000000000000..9d2530e3eb06d4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-esg_bert_sector_classifier_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English esg_bert_sector_classifier BertForSequenceClassification from ppsingh +author: John Snow Labs +name: esg_bert_sector_classifier +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`esg_bert_sector_classifier` is a English model originally trained by ppsingh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/esg_bert_sector_classifier_en_5.1.4_3.4_1698229025440.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/esg_bert_sector_classifier_en_5.1.4_3.4_1698229025440.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("esg_bert_sector_classifier","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("esg_bert_sector_classifier","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|esg_bert_sector_classifier| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.8 MB| + +## References + +https://huggingface.co/ppsingh/esg-bert-sector-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-finbert_finetuned_fg_single_sentence_news_weighted_en.md b/docs/_posts/ahmedlone127/2023-10-25-finbert_finetuned_fg_single_sentence_news_weighted_en.md new file mode 100644 index 00000000000000..5e6e199c7e098c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-finbert_finetuned_fg_single_sentence_news_weighted_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English finbert_finetuned_fg_single_sentence_news_weighted BertForSequenceClassification from lucaordronneau +author: John Snow Labs +name: finbert_finetuned_fg_single_sentence_news_weighted +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finbert_finetuned_fg_single_sentence_news_weighted` is a English model originally trained by lucaordronneau. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finbert_finetuned_fg_single_sentence_news_weighted_en_5.1.4_3.4_1698241050659.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finbert_finetuned_fg_single_sentence_news_weighted_en_5.1.4_3.4_1698241050659.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("finbert_finetuned_fg_single_sentence_news_weighted","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("finbert_finetuned_fg_single_sentence_news_weighted","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finbert_finetuned_fg_single_sentence_news_weighted| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/lucaordronneau/finbert-finetuned-FG-SINGLE_SENTENCE-NEWS-WEIGHTED \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-finbert_fls_en.md b/docs/_posts/ahmedlone127/2023-10-25-finbert_fls_en.md new file mode 100644 index 00000000000000..db3949ce9f2f7a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-finbert_fls_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English finbert_fls BertForSequenceClassification from yiyanghkust +author: John Snow Labs +name: finbert_fls +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finbert_fls` is a English model originally trained by yiyanghkust. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finbert_fls_en_5.1.4_3.4_1698245276476.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finbert_fls_en_5.1.4_3.4_1698245276476.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("finbert_fls","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("finbert_fls","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finbert_fls| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.6 MB| + +## References + +https://huggingface.co/yiyanghkust/finbert-fls \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_augmented_with_indobert_base_uncased_afaji_en.md b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_augmented_with_indobert_base_uncased_afaji_en.md new file mode 100644 index 00000000000000..3980e5051c15b1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_augmented_with_indobert_base_uncased_afaji_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fine_tuned_indonli_augmented_with_indobert_base_uncased_afaji BertForSequenceClassification from afaji +author: John Snow Labs +name: fine_tuned_indonli_augmented_with_indobert_base_uncased_afaji +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tuned_indonli_augmented_with_indobert_base_uncased_afaji` is a English model originally trained by afaji. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_augmented_with_indobert_base_uncased_afaji_en_5.1.4_3.4_1698221048030.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_augmented_with_indobert_base_uncased_afaji_en_5.1.4_3.4_1698221048030.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_augmented_with_indobert_base_uncased_afaji","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_augmented_with_indobert_base_uncased_afaji","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tuned_indonli_augmented_with_indobert_base_uncased_afaji| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|413.9 MB| + +## References + +https://huggingface.co/afaji/fine-tuned-IndoNLI-Augmented-with-indobert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_augmented_with_indobert_base_uncased_muhammadravi251001_en.md b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_augmented_with_indobert_base_uncased_muhammadravi251001_en.md new file mode 100644 index 00000000000000..99fe6e43b50a22 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_augmented_with_indobert_base_uncased_muhammadravi251001_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fine_tuned_indonli_augmented_with_indobert_base_uncased_muhammadravi251001 BertForSequenceClassification from muhammadravi251001 +author: John Snow Labs +name: fine_tuned_indonli_augmented_with_indobert_base_uncased_muhammadravi251001 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tuned_indonli_augmented_with_indobert_base_uncased_muhammadravi251001` is a English model originally trained by muhammadravi251001. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_augmented_with_indobert_base_uncased_muhammadravi251001_en_5.1.4_3.4_1698225941984.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_augmented_with_indobert_base_uncased_muhammadravi251001_en_5.1.4_3.4_1698225941984.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_augmented_with_indobert_base_uncased_muhammadravi251001","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_augmented_with_indobert_base_uncased_muhammadravi251001","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tuned_indonli_augmented_with_indobert_base_uncased_muhammadravi251001| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|413.9 MB| + +## References + +https://huggingface.co/muhammadravi251001/fine-tuned-IndoNLI-Augmented-with-indobert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_augmented_with_indobert_large_p2_afaji_en.md b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_augmented_with_indobert_large_p2_afaji_en.md new file mode 100644 index 00000000000000..621d35e533a7d6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_augmented_with_indobert_large_p2_afaji_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fine_tuned_indonli_augmented_with_indobert_large_p2_afaji BertForSequenceClassification from afaji +author: John Snow Labs +name: fine_tuned_indonli_augmented_with_indobert_large_p2_afaji +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tuned_indonli_augmented_with_indobert_large_p2_afaji` is a English model originally trained by afaji. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_augmented_with_indobert_large_p2_afaji_en_5.1.4_3.4_1698220859599.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_augmented_with_indobert_large_p2_afaji_en_5.1.4_3.4_1698220859599.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_augmented_with_indobert_large_p2_afaji","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_augmented_with_indobert_large_p2_afaji","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tuned_indonli_augmented_with_indobert_large_p2_afaji| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/afaji/fine-tuned-IndoNLI-Augmented-with-indobert-large-p2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_augmented_with_indobert_large_p2_muhammadravi251001_en.md b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_augmented_with_indobert_large_p2_muhammadravi251001_en.md new file mode 100644 index 00000000000000..94bba1b2d3c5b6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_augmented_with_indobert_large_p2_muhammadravi251001_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fine_tuned_indonli_augmented_with_indobert_large_p2_muhammadravi251001 BertForSequenceClassification from muhammadravi251001 +author: John Snow Labs +name: fine_tuned_indonli_augmented_with_indobert_large_p2_muhammadravi251001 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tuned_indonli_augmented_with_indobert_large_p2_muhammadravi251001` is a English model originally trained by muhammadravi251001. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_augmented_with_indobert_large_p2_muhammadravi251001_en_5.1.4_3.4_1698223677212.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_augmented_with_indobert_large_p2_muhammadravi251001_en_5.1.4_3.4_1698223677212.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_augmented_with_indobert_large_p2_muhammadravi251001","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_augmented_with_indobert_large_p2_muhammadravi251001","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tuned_indonli_augmented_with_indobert_large_p2_muhammadravi251001| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/muhammadravi251001/fine-tuned-IndoNLI-Augmented-with-indobert-large-p2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_basic_with_indobert_base_uncased_afaji_en.md b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_basic_with_indobert_base_uncased_afaji_en.md new file mode 100644 index 00000000000000..c8a49a6f970488 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_basic_with_indobert_base_uncased_afaji_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fine_tuned_indonli_basic_with_indobert_base_uncased_afaji BertForSequenceClassification from afaji +author: John Snow Labs +name: fine_tuned_indonli_basic_with_indobert_base_uncased_afaji +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tuned_indonli_basic_with_indobert_base_uncased_afaji` is a English model originally trained by afaji. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_basic_with_indobert_base_uncased_afaji_en_5.1.4_3.4_1698219271386.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_basic_with_indobert_base_uncased_afaji_en_5.1.4_3.4_1698219271386.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_basic_with_indobert_base_uncased_afaji","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_basic_with_indobert_base_uncased_afaji","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tuned_indonli_basic_with_indobert_base_uncased_afaji| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|413.9 MB| + +## References + +https://huggingface.co/afaji/fine-tuned-IndoNLI-Basic-with-indobert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_basic_with_indobert_base_uncased_muhammadravi251001_en.md b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_basic_with_indobert_base_uncased_muhammadravi251001_en.md new file mode 100644 index 00000000000000..ff7efc7423853a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_basic_with_indobert_base_uncased_muhammadravi251001_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fine_tuned_indonli_basic_with_indobert_base_uncased_muhammadravi251001 BertForSequenceClassification from muhammadravi251001 +author: John Snow Labs +name: fine_tuned_indonli_basic_with_indobert_base_uncased_muhammadravi251001 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tuned_indonli_basic_with_indobert_base_uncased_muhammadravi251001` is a English model originally trained by muhammadravi251001. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_basic_with_indobert_base_uncased_muhammadravi251001_en_5.1.4_3.4_1698225044555.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_basic_with_indobert_base_uncased_muhammadravi251001_en_5.1.4_3.4_1698225044555.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_basic_with_indobert_base_uncased_muhammadravi251001","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_basic_with_indobert_base_uncased_muhammadravi251001","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tuned_indonli_basic_with_indobert_base_uncased_muhammadravi251001| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|413.9 MB| + +## References + +https://huggingface.co/muhammadravi251001/fine-tuned-IndoNLI-Basic-with-indobert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_basic_with_indobert_large_p2_afaji_en.md b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_basic_with_indobert_large_p2_afaji_en.md new file mode 100644 index 00000000000000..60ae6ef7cfe365 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_basic_with_indobert_large_p2_afaji_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fine_tuned_indonli_basic_with_indobert_large_p2_afaji BertForSequenceClassification from afaji +author: John Snow Labs +name: fine_tuned_indonli_basic_with_indobert_large_p2_afaji +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tuned_indonli_basic_with_indobert_large_p2_afaji` is a English model originally trained by afaji. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_basic_with_indobert_large_p2_afaji_en_5.1.4_3.4_1698219934268.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_basic_with_indobert_large_p2_afaji_en_5.1.4_3.4_1698219934268.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_basic_with_indobert_large_p2_afaji","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_basic_with_indobert_large_p2_afaji","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tuned_indonli_basic_with_indobert_large_p2_afaji| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/afaji/fine-tuned-IndoNLI-Basic-with-indobert-large-p2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_basic_with_indobert_large_p2_muhammadravi251001_en.md b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_basic_with_indobert_large_p2_muhammadravi251001_en.md new file mode 100644 index 00000000000000..4fc489ec386c5c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_basic_with_indobert_large_p2_muhammadravi251001_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fine_tuned_indonli_basic_with_indobert_large_p2_muhammadravi251001 BertForSequenceClassification from muhammadravi251001 +author: John Snow Labs +name: fine_tuned_indonli_basic_with_indobert_large_p2_muhammadravi251001 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tuned_indonli_basic_with_indobert_large_p2_muhammadravi251001` is a English model originally trained by muhammadravi251001. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_basic_with_indobert_large_p2_muhammadravi251001_en_5.1.4_3.4_1698224046216.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_basic_with_indobert_large_p2_muhammadravi251001_en_5.1.4_3.4_1698224046216.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_basic_with_indobert_large_p2_muhammadravi251001","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_basic_with_indobert_large_p2_muhammadravi251001","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tuned_indonli_basic_with_indobert_large_p2_muhammadravi251001| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/muhammadravi251001/fine-tuned-IndoNLI-Basic-with-indobert-large-p2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_translated_with_indobert_base_uncased_afaji_en.md b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_translated_with_indobert_base_uncased_afaji_en.md new file mode 100644 index 00000000000000..b6bb021fec9193 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_translated_with_indobert_base_uncased_afaji_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fine_tuned_indonli_translated_with_indobert_base_uncased_afaji BertForSequenceClassification from afaji +author: John Snow Labs +name: fine_tuned_indonli_translated_with_indobert_base_uncased_afaji +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tuned_indonli_translated_with_indobert_base_uncased_afaji` is a English model originally trained by afaji. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_translated_with_indobert_base_uncased_afaji_en_5.1.4_3.4_1698219456786.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_translated_with_indobert_base_uncased_afaji_en_5.1.4_3.4_1698219456786.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_translated_with_indobert_base_uncased_afaji","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_translated_with_indobert_base_uncased_afaji","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tuned_indonli_translated_with_indobert_base_uncased_afaji| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|413.9 MB| + +## References + +https://huggingface.co/afaji/fine-tuned-IndoNLI-Translated-with-indobert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_translated_with_indobert_base_uncased_muhammadravi251001_en.md b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_translated_with_indobert_base_uncased_muhammadravi251001_en.md new file mode 100644 index 00000000000000..ef47c1fe1b46f2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_translated_with_indobert_base_uncased_muhammadravi251001_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fine_tuned_indonli_translated_with_indobert_base_uncased_muhammadravi251001 BertForSequenceClassification from muhammadravi251001 +author: John Snow Labs +name: fine_tuned_indonli_translated_with_indobert_base_uncased_muhammadravi251001 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tuned_indonli_translated_with_indobert_base_uncased_muhammadravi251001` is a English model originally trained by muhammadravi251001. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_translated_with_indobert_base_uncased_muhammadravi251001_en_5.1.4_3.4_1698219097750.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_translated_with_indobert_base_uncased_muhammadravi251001_en_5.1.4_3.4_1698219097750.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_translated_with_indobert_base_uncased_muhammadravi251001","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_translated_with_indobert_base_uncased_muhammadravi251001","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tuned_indonli_translated_with_indobert_base_uncased_muhammadravi251001| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|413.9 MB| + +## References + +https://huggingface.co/muhammadravi251001/fine-tuned-IndoNLI-Translated-with-indobert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_translated_with_indobert_large_p2_en.md b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_translated_with_indobert_large_p2_en.md new file mode 100644 index 00000000000000..37b51526c73bce --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_indonli_translated_with_indobert_large_p2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fine_tuned_indonli_translated_with_indobert_large_p2 BertForSequenceClassification from afaji +author: John Snow Labs +name: fine_tuned_indonli_translated_with_indobert_large_p2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tuned_indonli_translated_with_indobert_large_p2` is a English model originally trained by afaji. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_translated_with_indobert_large_p2_en_5.1.4_3.4_1698220492900.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tuned_indonli_translated_with_indobert_large_p2_en_5.1.4_3.4_1698220492900.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_translated_with_indobert_large_p2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_indonli_translated_with_indobert_large_p2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tuned_indonli_translated_with_indobert_large_p2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/afaji/fine-tuned-IndoNLI-Translated-with-indobert-large-p2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_koreanindonli_kornli_with_bert_base_en.md b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_koreanindonli_kornli_with_bert_base_en.md new file mode 100644 index 00000000000000..c609724838520b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_koreanindonli_kornli_with_bert_base_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fine_tuned_koreanindonli_kornli_with_bert_base BertForSequenceClassification from muhammadravi251001 +author: John Snow Labs +name: fine_tuned_koreanindonli_kornli_with_bert_base +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tuned_koreanindonli_kornli_with_bert_base` is a English model originally trained by muhammadravi251001. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tuned_koreanindonli_kornli_with_bert_base_en_5.1.4_3.4_1698245173848.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tuned_koreanindonli_kornli_with_bert_base_en_5.1.4_3.4_1698245173848.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_koreanindonli_kornli_with_bert_base","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_koreanindonli_kornli_with_bert_base","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tuned_koreanindonli_kornli_with_bert_base| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.6 MB| + +## References + +https://huggingface.co/muhammadravi251001/fine-tuned-KoreanIndoNLI-KorNLI-with-bert-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_koreanindonli_kornli_with_bert_kor_base_en.md b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_koreanindonli_kornli_with_bert_kor_base_en.md new file mode 100644 index 00000000000000..afb80453f32e98 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-fine_tuned_koreanindonli_kornli_with_bert_kor_base_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fine_tuned_koreanindonli_kornli_with_bert_kor_base BertForSequenceClassification from muhammadravi251001 +author: John Snow Labs +name: fine_tuned_koreanindonli_kornli_with_bert_kor_base +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tuned_koreanindonli_kornli_with_bert_kor_base` is a English model originally trained by muhammadravi251001. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tuned_koreanindonli_kornli_with_bert_kor_base_en_5.1.4_3.4_1698228339319.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tuned_koreanindonli_kornli_with_bert_kor_base_en_5.1.4_3.4_1698228339319.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_koreanindonli_kornli_with_bert_kor_base","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fine_tuned_koreanindonli_kornli_with_bert_kor_base","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tuned_koreanindonli_kornli_with_bert_kor_base| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|443.4 MB| + +## References + +https://huggingface.co/muhammadravi251001/fine-tuned-KoreanIndoNLI-KorNLI-with-bert-kor-base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-finetuned_bert_mrpc_ndugar_en.md b/docs/_posts/ahmedlone127/2023-10-25-finetuned_bert_mrpc_ndugar_en.md new file mode 100644 index 00000000000000..92a237b46c2e5b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-finetuned_bert_mrpc_ndugar_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English finetuned_bert_mrpc_ndugar BertForSequenceClassification from NDugar +author: John Snow Labs +name: finetuned_bert_mrpc_ndugar +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finetuned_bert_mrpc_ndugar` is a English model originally trained by NDugar. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finetuned_bert_mrpc_ndugar_en_5.1.4_3.4_1698226789566.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finetuned_bert_mrpc_ndugar_en_5.1.4_3.4_1698226789566.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_bert_mrpc_ndugar","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_bert_mrpc_ndugar","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finetuned_bert_mrpc_ndugar| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/NDugar/finetuned-bert-mrpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_12_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_12_en.md new file mode 100644 index 00000000000000..d407570ebbe125 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_12_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_12 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_12 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_12` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_12_en_5.1.4_3.4_1698216256946.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_12_en_5.1.4_3.4_1698216256946.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_12","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_12","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_12| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-12 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_14_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_14_en.md new file mode 100644 index 00000000000000..926c43f8080c6f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_14_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_14 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_14 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_14` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_14_en_5.1.4_3.4_1698216461612.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_14_en_5.1.4_3.4_1698216461612.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_14","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_14","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_14| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-14 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_18_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_18_en.md new file mode 100644 index 00000000000000..24ed3f394e1b7a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_18_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_18 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_18 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_18` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_18_en_5.1.4_3.4_1698216649878.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_18_en_5.1.4_3.4_1698216649878.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_18","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_18","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_18| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-18 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_19_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_19_en.md new file mode 100644 index 00000000000000..eff358c5fb7547 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_19_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_19 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_19 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_19` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_19_en_5.1.4_3.4_1698218217176.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_19_en_5.1.4_3.4_1698218217176.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_19","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_19","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_19| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-19 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_21_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_21_en.md new file mode 100644 index 00000000000000..f3a087bdcfa5b5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_21_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_21 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_21 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_21` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_21_en_5.1.4_3.4_1698217595519.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_21_en_5.1.4_3.4_1698217595519.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_21","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_21","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_21| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-21 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_22_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_22_en.md new file mode 100644 index 00000000000000..b40c94252c46a3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_22_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_22 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_22 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_22` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_22_en_5.1.4_3.4_1698217792710.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_22_en_5.1.4_3.4_1698217792710.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_22","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_22","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_22| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-22 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_23_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_23_en.md new file mode 100644 index 00000000000000..10f7b5ba369f76 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_23_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_23 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_23 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_23` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_23_en_5.1.4_3.4_1698222310863.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_23_en_5.1.4_3.4_1698222310863.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_23","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_23","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_23| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-23 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_24_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_24_en.md new file mode 100644 index 00000000000000..80f0e899b951eb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_24_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_24 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_24 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_24` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_24_en_5.1.4_3.4_1698222516029.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_24_en_5.1.4_3.4_1698222516029.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_24","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_24","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_24| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-24 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_26_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_26_en.md new file mode 100644 index 00000000000000..5ad2b7a3ce6c60 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_26_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_26 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_26 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_26` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_26_en_5.1.4_3.4_1698219644965.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_26_en_5.1.4_3.4_1698219644965.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_26","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_26","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_26| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-26 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_30_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_30_en.md new file mode 100644 index 00000000000000..6ddedc444a2eea --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_30_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_30 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_30 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_30` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_30_en_5.1.4_3.4_1698219245865.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_30_en_5.1.4_3.4_1698219245865.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_30","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_30","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_30| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-30 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_31_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_31_en.md new file mode 100644 index 00000000000000..9164a916b483c0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_31_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_31 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_31 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_31` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_31_en_5.1.4_3.4_1698218399794.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_31_en_5.1.4_3.4_1698218399794.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_31","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_31","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_31| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-31 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_32_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_32_en.md new file mode 100644 index 00000000000000..64ea0587bf2874 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_32_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_32 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_32 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_32` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_32_en_5.1.4_3.4_1698219435732.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_32_en_5.1.4_3.4_1698219435732.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_32","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_32","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_32| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-32 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_33_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_33_en.md new file mode 100644 index 00000000000000..ee7679c3eca54d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_33_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_33 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_33 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_33` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_33_en_5.1.4_3.4_1698219845008.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_33_en_5.1.4_3.4_1698219845008.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_33","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_33","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_33| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-33 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_34_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_34_en.md new file mode 100644 index 00000000000000..d1e5636311de44 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_34_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_34 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_34 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_34` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_34_en_5.1.4_3.4_1698220231904.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_34_en_5.1.4_3.4_1698220231904.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_34","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_34","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_34| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-34 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_35_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_35_en.md new file mode 100644 index 00000000000000..da0bd10c0c1f63 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_35_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_35 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_35 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_35` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_35_en_5.1.4_3.4_1698220033273.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_35_en_5.1.4_3.4_1698220033273.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_35","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_35","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_35| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-35 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_36_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_36_en.md new file mode 100644 index 00000000000000..cfcef8587f4844 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_36_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_36 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_36 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_36` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_36_en_5.1.4_3.4_1698220575602.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_36_en_5.1.4_3.4_1698220575602.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_36","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_36","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_36| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-36 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_37_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_37_en.md new file mode 100644 index 00000000000000..02cdb814d6ab38 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_37_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_37 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_37 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_37` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_37_en_5.1.4_3.4_1698220419732.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_37_en_5.1.4_3.4_1698220419732.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_37","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_37","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_37| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-37 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_38_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_38_en.md new file mode 100644 index 00000000000000..b82bfd6d3288aa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_38_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_38 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_38 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_38` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_38_en_5.1.4_3.4_1698220977338.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_38_en_5.1.4_3.4_1698220977338.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_38","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_38","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_38| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-38 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_39_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_39_en.md new file mode 100644 index 00000000000000..76d2b0e394ff87 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_39_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_39 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_39 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_39` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_39_en_5.1.4_3.4_1698221378949.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_39_en_5.1.4_3.4_1698221378949.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_39","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_39","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_39| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-39 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_3_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_3_en.md new file mode 100644 index 00000000000000..8150098741b8bf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_3_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_3 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_3 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_3` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_3_en_5.1.4_3.4_1698215297155.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_3_en_5.1.4_3.4_1698215297155.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_3","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_3","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_3| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_40_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_40_en.md new file mode 100644 index 00000000000000..cb7ef6fdf253d1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_40_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_40 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_40 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_40` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_40_en_5.1.4_3.4_1698221167152.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_40_en_5.1.4_3.4_1698221167152.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_40","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_40","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_40| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-40 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_41_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_41_en.md new file mode 100644 index 00000000000000..98b5a18461d1ed --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_41_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_41 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_41 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_41` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_41_en_5.1.4_3.4_1698220788822.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_41_en_5.1.4_3.4_1698220788822.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_41","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_41","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_41| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-41 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_42_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_42_en.md new file mode 100644 index 00000000000000..208db8035d1ad9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_42_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_42 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_42 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_42` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_42_en_5.1.4_3.4_1698221947943.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_42_en_5.1.4_3.4_1698221947943.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_42","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_42","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_42| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-42 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_43_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_43_en.md new file mode 100644 index 00000000000000..98b58fc5562163 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_43_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_43 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_43 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_43` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_43_en_5.1.4_3.4_1698221771690.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_43_en_5.1.4_3.4_1698221771690.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_43","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_43","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_43| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-43 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_44_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_44_en.md new file mode 100644 index 00000000000000..86685d30bbac38 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_44_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_44 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_44 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_44` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_44_en_5.1.4_3.4_1698222126299.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_44_en_5.1.4_3.4_1698222126299.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_44","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_44","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_44| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-44 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_45_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_45_en.md new file mode 100644 index 00000000000000..9e80699e8a0ae9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_45_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_45 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_45 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_45` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_45_en_5.1.4_3.4_1698221572355.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_45_en_5.1.4_3.4_1698221572355.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_45","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_45","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_45| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-45 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_46_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_46_en.md new file mode 100644 index 00000000000000..4a5551ef179908 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_46_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_46 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_46 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_46` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_46_en_5.1.4_3.4_1698222695220.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_46_en_5.1.4_3.4_1698222695220.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_46","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_46","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_46| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-46 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_47_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_47_en.md new file mode 100644 index 00000000000000..abe8336f4ea9a2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_47_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_47 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_47 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_47` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_47_en_5.1.4_3.4_1698222867221.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_47_en_5.1.4_3.4_1698222867221.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_47","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_47","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_47| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-47 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_48_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_48_en.md new file mode 100644 index 00000000000000..abce1cdf1dcc68 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_48_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_48 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_48 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_48` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_48_en_5.1.4_3.4_1698225719514.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_48_en_5.1.4_3.4_1698225719514.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_48","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_48","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_48| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-48 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_49_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_49_en.md new file mode 100644 index 00000000000000..9f0c57140f6d0d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_49_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_49 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_49 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_49` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_49_en_5.1.4_3.4_1698229026818.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_49_en_5.1.4_3.4_1698229026818.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_49","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_49","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_49| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-49 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_50_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_50_en.md new file mode 100644 index 00000000000000..cd94e000f51da6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_50_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_50 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_50 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_50` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_50_en_5.1.4_3.4_1698231600698.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_50_en_5.1.4_3.4_1698231600698.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_50","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_50","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_50| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-50 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_51_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_51_en.md new file mode 100644 index 00000000000000..18bc0c70d31135 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_51_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_51 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_51 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_51` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_51_en_5.1.4_3.4_1698227400253.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_51_en_5.1.4_3.4_1698227400253.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_51","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_51","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_51| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-51 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_52_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_52_en.md new file mode 100644 index 00000000000000..0213934506a26a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_52_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_52 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_52 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_52` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_52_en_5.1.4_3.4_1698224786799.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_52_en_5.1.4_3.4_1698224786799.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_52","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_52","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_52| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-52 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_53_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_53_en.md new file mode 100644 index 00000000000000..71f33047ff8ca0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_53_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_53 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_53 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_53` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_53_en_5.1.4_3.4_1698225000509.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_53_en_5.1.4_3.4_1698225000509.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_53","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_53","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_53| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-53 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_54_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_54_en.md new file mode 100644 index 00000000000000..2390033ee09458 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_54_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_54 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_54 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_54` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_54_en_5.1.4_3.4_1698230803166.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_54_en_5.1.4_3.4_1698230803166.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_54","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_54","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_54| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-54 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_55_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_55_en.md new file mode 100644 index 00000000000000..d273e6a62e91d0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_55_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_55 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_55 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_55` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_55_en_5.1.4_3.4_1698229934190.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_55_en_5.1.4_3.4_1698229934190.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_55","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_55","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_55| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-55 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_56_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_56_en.md new file mode 100644 index 00000000000000..3cca9930f87549 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_56_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_56 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_56 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_56` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_56_en_5.1.4_3.4_1698226620229.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_56_en_5.1.4_3.4_1698226620229.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_56","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_56","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_56| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-56 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_57_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_57_en.md new file mode 100644 index 00000000000000..6333ebd3b81807 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_57_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_57 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_57 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_57` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_57_en_5.1.4_3.4_1698228126156.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_57_en_5.1.4_3.4_1698228126156.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_57","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_57","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_57| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-57 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_58_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_58_en.md new file mode 100644 index 00000000000000..d6d1a9450b8f47 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_58_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_58 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_58 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_58` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_58_en_5.1.4_3.4_1698235996382.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_58_en_5.1.4_3.4_1698235996382.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_58","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_58","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_58| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-58 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_59_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_59_en.md new file mode 100644 index 00000000000000..0fa6ae8ccc1213 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_59_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_59 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_59 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_59` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_59_en_5.1.4_3.4_1698236948806.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_59_en_5.1.4_3.4_1698236948806.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_59","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_59","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_59| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-59 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_60_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_60_en.md new file mode 100644 index 00000000000000..8cbdb3d1be1f56 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_60_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_60 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_60 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_60` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_60_en_5.1.4_3.4_1698224363748.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_60_en_5.1.4_3.4_1698224363748.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_60","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_60","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_60| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-60 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_61_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_61_en.md new file mode 100644 index 00000000000000..1b7d5299d6c7dd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_61_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_61 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_61 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_61` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_61_en_5.1.4_3.4_1698237883855.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_61_en_5.1.4_3.4_1698237883855.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_61","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_61","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_61| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-61 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_62_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_62_en.md new file mode 100644 index 00000000000000..6e1ab25b09c2e1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_62_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_62 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_62 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_62` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_62_en_5.1.4_3.4_1698239026933.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_62_en_5.1.4_3.4_1698239026933.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_62","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_62","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_62| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-62 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_63_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_63_en.md new file mode 100644 index 00000000000000..37e917d2144fad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_63_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_63 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_63 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_63` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_63_en_5.1.4_3.4_1698224150303.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_63_en_5.1.4_3.4_1698224150303.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_63","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_63","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_63| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-63 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_64_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_64_en.md new file mode 100644 index 00000000000000..fc8b6cad2d4af7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_64_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_64 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_64 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_64` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_64_en_5.1.4_3.4_1698232386985.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_64_en_5.1.4_3.4_1698232386985.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_64","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_64","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_64| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-64 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_65_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_65_en.md new file mode 100644 index 00000000000000..3c511abdf76e74 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_65_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_65 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_65 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_65` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_65_en_5.1.4_3.4_1698234886083.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_65_en_5.1.4_3.4_1698234886083.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_65","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_65","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_65| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-65 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_66_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_66_en.md new file mode 100644 index 00000000000000..58193b59a8aa0d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_66_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_66 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_66 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_66` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_66_en_5.1.4_3.4_1698239876018.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_66_en_5.1.4_3.4_1698239876018.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_66","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_66","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_66| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-66 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_67_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_67_en.md new file mode 100644 index 00000000000000..fa12338d9dd42a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_67_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_67 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_67 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_67` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_67_en_5.1.4_3.4_1698233984646.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_67_en_5.1.4_3.4_1698233984646.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_67","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_67","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_67| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-67 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_68_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_68_en.md new file mode 100644 index 00000000000000..a6dddfecc8d91c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_68_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_68 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_68 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_68` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_68_en_5.1.4_3.4_1698223587499.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_68_en_5.1.4_3.4_1698223587499.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_68","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_68","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_68| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-68 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_69_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_69_en.md new file mode 100644 index 00000000000000..124ef297147464 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_69_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_69 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_69 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_69` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_69_en_5.1.4_3.4_1698224583571.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_69_en_5.1.4_3.4_1698224583571.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_69","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_69","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_69| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-69 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_70_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_70_en.md new file mode 100644 index 00000000000000..b609c5b69696b2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_70_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_70 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_70 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_70` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_70_en_5.1.4_3.4_1698233231183.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_70_en_5.1.4_3.4_1698233231183.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_70","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_70","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_70| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-70 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_71_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_71_en.md new file mode 100644 index 00000000000000..9a6ee5a84ab843 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_71_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_71 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_71 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_71` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_71_en_5.1.4_3.4_1698223045460.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_71_en_5.1.4_3.4_1698223045460.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_71","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_71","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_71| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-71 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_72_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_72_en.md new file mode 100644 index 00000000000000..8e8a4e73a26404 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_72_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_72 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_72 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_72` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_72_en_5.1.4_3.4_1698223744727.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_72_en_5.1.4_3.4_1698223744727.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_72","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_72","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_72| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-72 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_73_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_73_en.md new file mode 100644 index 00000000000000..d3a2d9ac149662 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_73_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_73 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_73 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_73` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_73_en_5.1.4_3.4_1698223219896.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_73_en_5.1.4_3.4_1698223219896.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_73","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_73","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_73| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-73 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_74_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_74_en.md new file mode 100644 index 00000000000000..2d4bc553a6b360 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_74_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_74 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_74 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_74` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_74_en_5.1.4_3.4_1698223397781.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_74_en_5.1.4_3.4_1698223397781.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_74","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_74","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_74| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-74 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_75_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_75_en.md new file mode 100644 index 00000000000000..c6b7e2cf9c1edd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_75_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_75 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_75 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_75` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_75_en_5.1.4_3.4_1698223965234.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_75_en_5.1.4_3.4_1698223965234.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_75","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_75","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_75| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-75 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_76_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_76_en.md new file mode 100644 index 00000000000000..0084c059fa22af --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_76_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_76 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_76 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_76` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_76_en_5.1.4_3.4_1698240706542.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_76_en_5.1.4_3.4_1698240706542.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_76","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_76","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_76| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-76 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_77_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_77_en.md new file mode 100644 index 00000000000000..24e5ce5121f174 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_77_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_77 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_77 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_77` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_77_en_5.1.4_3.4_1698241641782.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_77_en_5.1.4_3.4_1698241641782.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_77","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_77","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_77| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-77 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_81_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_81_en.md new file mode 100644 index 00000000000000..11f590ad3c0e39 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_81_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_81 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_81 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_81` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_81_en_5.1.4_3.4_1698242418276.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_81_en_5.1.4_3.4_1698242418276.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_81","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_81","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_81| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-81 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_82_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_82_en.md new file mode 100644 index 00000000000000..c90ed243a1122c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_82_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_82 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_82 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_82` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_82_en_5.1.4_3.4_1698245210556.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_82_en_5.1.4_3.4_1698245210556.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_82","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_82","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_82| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-82 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_89_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_89_en.md new file mode 100644 index 00000000000000..104864c31bf932 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_89_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_89 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_89 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_89` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_89_en_5.1.4_3.4_1698244522753.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_89_en_5.1.4_3.4_1698244522753.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_89","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_89","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_89| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-89 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_91_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_91_en.md new file mode 100644 index 00000000000000..f9779e0c363f8b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_91_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_91 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_91 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_91` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_91_en_5.1.4_3.4_1698243759493.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_91_en_5.1.4_3.4_1698243759493.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_91","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_91","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_91| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-91 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_9_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_9_en.md new file mode 100644 index 00000000000000..f63cd5d3f30d5c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_9_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_9 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_9 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_9` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_9_en_5.1.4_3.4_1698215840044.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_9_en_5.1.4_3.4_1698215840044.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_9","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_9","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_9| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-9 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-hindi_const21_hibert_final_en.md b/docs/_posts/ahmedlone127/2023-10-25-hindi_const21_hibert_final_en.md new file mode 100644 index 00000000000000..67a0c24f3195c4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-hindi_const21_hibert_final_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English hindi_const21_hibert_final BertForSequenceClassification from Maha +author: John Snow Labs +name: hindi_const21_hibert_final +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hindi_const21_hibert_final` is a English model originally trained by Maha. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/hindi_const21_hibert_final_en_5.1.4_3.4_1698224332652.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hindi_const21_hibert_final_en_5.1.4_3.4_1698224332652.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("hindi_const21_hibert_final","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("hindi_const21_hibert_final","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|hindi_const21_hibert_final| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|611.4 MB| + +## References + +https://huggingface.co/Maha/hi-const21-hibert_final \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-incivility_v2_final_tulio_chilean_spanish_bert_en.md b/docs/_posts/ahmedlone127/2023-10-25-incivility_v2_final_tulio_chilean_spanish_bert_en.md new file mode 100644 index 00000000000000..b2d9d776af8270 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-incivility_v2_final_tulio_chilean_spanish_bert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English incivility_v2_final_tulio_chilean_spanish_bert BertForSequenceClassification from jorgeortizfuentes +author: John Snow Labs +name: incivility_v2_final_tulio_chilean_spanish_bert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`incivility_v2_final_tulio_chilean_spanish_bert` is a English model originally trained by jorgeortizfuentes. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/incivility_v2_final_tulio_chilean_spanish_bert_en_5.1.4_3.4_1698209746645.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/incivility_v2_final_tulio_chilean_spanish_bert_en_5.1.4_3.4_1698209746645.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("incivility_v2_final_tulio_chilean_spanish_bert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("incivility_v2_final_tulio_chilean_spanish_bert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|incivility_v2_final_tulio_chilean_spanish_bert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.5 MB| + +## References + +https://huggingface.co/jorgeortizfuentes/incivility_v2_final-tulio-chilean-spanish-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-indobertnewstest_rizalmilyardi_en.md b/docs/_posts/ahmedlone127/2023-10-25-indobertnewstest_rizalmilyardi_en.md new file mode 100644 index 00000000000000..461c447d6d40dc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-indobertnewstest_rizalmilyardi_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English indobertnewstest_rizalmilyardi BertForSequenceClassification from rizalmilyardi +author: John Snow Labs +name: indobertnewstest_rizalmilyardi +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`indobertnewstest_rizalmilyardi` is a English model originally trained by rizalmilyardi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/indobertnewstest_rizalmilyardi_en_5.1.4_3.4_1698211956208.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/indobertnewstest_rizalmilyardi_en_5.1.4_3.4_1698211956208.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("indobertnewstest_rizalmilyardi","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("indobertnewstest_rizalmilyardi","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|indobertnewstest_rizalmilyardi| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.0 MB| + +## References + +https://huggingface.co/rizalmilyardi/IndobertNewsTest \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-legal_bert_tpb_clause_class_en.md b/docs/_posts/ahmedlone127/2023-10-25-legal_bert_tpb_clause_class_en.md new file mode 100644 index 00000000000000..db9dffa14be5d4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-legal_bert_tpb_clause_class_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English legal_bert_tpb_clause_class BertForSequenceClassification from frankkuete +author: John Snow Labs +name: legal_bert_tpb_clause_class +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legal_bert_tpb_clause_class` is a English model originally trained by frankkuete. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legal_bert_tpb_clause_class_en_5.1.4_3.4_1698237815681.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legal_bert_tpb_clause_class_en_5.1.4_3.4_1698237815681.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("legal_bert_tpb_clause_class","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("legal_bert_tpb_clause_class","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legal_bert_tpb_clause_class| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/frankkuete/legal-bert-tpb-clause-class \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_ssec_en.md b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_ssec_en.md new file mode 100644 index 00000000000000..9ad40abc891aba --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_ssec_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mengzi_bert_base_fin_ssec BertForSequenceClassification from hw2942 +author: John Snow Labs +name: mengzi_bert_base_fin_ssec +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mengzi_bert_base_fin_ssec` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_ssec_en_5.1.4_3.4_1698222925679.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_ssec_en_5.1.4_3.4_1698222925679.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_ssec","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_ssec","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mengzi_bert_base_fin_ssec| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.1 MB| + +## References + +https://huggingface.co/hw2942/mengzi-bert-base-fin-SSEC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1_en.md b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1_en.md new file mode 100644 index 00000000000000..7609c326cdf263 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1_en_5.1.4_3.4_1698219379071.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1_en_5.1.4_3.4_1698219379071.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.1 MB| + +## References + +https://huggingface.co/hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-open-SSEC-f1-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v22_en.md b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v22_en.md new file mode 100644 index 00000000000000..3cff0c16472972 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v22_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v22 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v22 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v22` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v22_en_5.1.4_3.4_1698223114906.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v22_en_5.1.4_3.4_1698223114906.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v22","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v22","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_f1_v22| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.1 MB| + +## References + +https://huggingface.co/hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-open-SSEC-f1-v22 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_v2_en.md b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_v2_en.md new file mode 100644 index 00000000000000..db46416dce0017 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_v2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_v2 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_v2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_v2` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_v2_en_5.1.4_3.4_1698220731080.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_v2_en_5.1.4_3.4_1698220731080.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_v2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_v2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_open_ssec_v2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.1 MB| + +## References + +https://huggingface.co/hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-open-SSEC-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mini_bert_distilled_en.md b/docs/_posts/ahmedlone127/2023-10-25-mini_bert_distilled_en.md new file mode 100644 index 00000000000000..19b3cb261a694c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mini_bert_distilled_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mini_bert_distilled BertForSequenceClassification from moshew +author: John Snow Labs +name: mini_bert_distilled +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mini_bert_distilled` is a English model originally trained by moshew. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mini_bert_distilled_en_5.1.4_3.4_1698240255617.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mini_bert_distilled_en_5.1.4_3.4_1698240255617.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mini_bert_distilled","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mini_bert_distilled","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mini_bert_distilled| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|42.2 MB| + +## References + +https://huggingface.co/moshew/Mini-bert-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_2_en.md b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_2_en.md new file mode 100644 index 00000000000000..0485e80e906c86 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English norbert2_sentiment_norec_2 BertForSequenceClassification from NTCAL +author: John Snow Labs +name: norbert2_sentiment_norec_2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`norbert2_sentiment_norec_2` is a English model originally trained by NTCAL. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_2_en_5.1.4_3.4_1698233003739.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_2_en_5.1.4_3.4_1698233003739.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|norbert2_sentiment_norec_2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/NTCAL/norbert2_sentiment_norec_2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_4_en.md b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_4_en.md new file mode 100644 index 00000000000000..f7cec3abb9b5ee --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_4_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English norbert2_sentiment_norec_4 BertForSequenceClassification from NTCAL +author: John Snow Labs +name: norbert2_sentiment_norec_4 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`norbert2_sentiment_norec_4` is a English model originally trained by NTCAL. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_4_en_5.1.4_3.4_1698235996431.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_4_en_5.1.4_3.4_1698235996431.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_4","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_4","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|norbert2_sentiment_norec_4| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/NTCAL/norbert2_sentiment_norec_4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_6_en.md b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_6_en.md new file mode 100644 index 00000000000000..60212355c661b3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_6_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English norbert2_sentiment_norec_6 BertForSequenceClassification from NTCAL +author: John Snow Labs +name: norbert2_sentiment_norec_6 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`norbert2_sentiment_norec_6` is a English model originally trained by NTCAL. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_6_en_5.1.4_3.4_1698237683293.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_6_en_5.1.4_3.4_1698237683293.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_6","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_6","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|norbert2_sentiment_norec_6| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/NTCAL/norbert2_sentiment_norec_6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_7_en.md b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_7_en.md new file mode 100644 index 00000000000000..1927f729a20ea6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_7_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English norbert2_sentiment_norec_7 BertForSequenceClassification from NTCAL +author: John Snow Labs +name: norbert2_sentiment_norec_7 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`norbert2_sentiment_norec_7` is a English model originally trained by NTCAL. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_7_en_5.1.4_3.4_1698239342573.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_7_en_5.1.4_3.4_1698239342573.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_7","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_7","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|norbert2_sentiment_norec_7| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/NTCAL/norbert2_sentiment_norec_7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_8_en.md b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_8_en.md new file mode 100644 index 00000000000000..fe27aa1b1fce53 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English norbert2_sentiment_norec_8 BertForSequenceClassification from NTCAL +author: John Snow Labs +name: norbert2_sentiment_norec_8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`norbert2_sentiment_norec_8` is a English model originally trained by NTCAL. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_8_en_5.1.4_3.4_1698244059994.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_8_en_5.1.4_3.4_1698244059994.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|norbert2_sentiment_norec_8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/NTCAL/norbert2_sentiment_norec_8 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_9_en.md b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_9_en.md new file mode 100644 index 00000000000000..13708009c12d63 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_9_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English norbert2_sentiment_norec_9 BertForSequenceClassification from NTCAL +author: John Snow Labs +name: norbert2_sentiment_norec_9 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`norbert2_sentiment_norec_9` is a English model originally trained by NTCAL. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_9_en_5.1.4_3.4_1698245023007.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_9_en_5.1.4_3.4_1698245023007.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_9","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_9","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|norbert2_sentiment_norec_9| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|467.4 MB| + +## References + +https://huggingface.co/NTCAL/norbert2_sentiment_norec_9 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-norsk_bert_fintuned_en.md b/docs/_posts/ahmedlone127/2023-10-25-norsk_bert_fintuned_en.md new file mode 100644 index 00000000000000..840c67aedb048e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-norsk_bert_fintuned_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English norsk_bert_fintuned BertForSequenceClassification from kirisums +author: John Snow Labs +name: norsk_bert_fintuned +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`norsk_bert_fintuned` is a English model originally trained by kirisums. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/norsk_bert_fintuned_en_5.1.4_3.4_1698224676169.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/norsk_bert_fintuned_en_5.1.4_3.4_1698224676169.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("norsk_bert_fintuned","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("norsk_bert_fintuned","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|norsk_bert_fintuned| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|668.4 MB| + +## References + +https://huggingface.co/kirisums/norsk-bert-fintuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-ogbv_gender_bert_hindi_english_hasoc20a_fin_en.md b/docs/_posts/ahmedlone127/2023-10-25-ogbv_gender_bert_hindi_english_hasoc20a_fin_en.md new file mode 100644 index 00000000000000..0cadc0726cfaa2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-ogbv_gender_bert_hindi_english_hasoc20a_fin_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English ogbv_gender_bert_hindi_english_hasoc20a_fin BertForSequenceClassification from Maha +author: John Snow Labs +name: ogbv_gender_bert_hindi_english_hasoc20a_fin +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ogbv_gender_bert_hindi_english_hasoc20a_fin` is a English model originally trained by Maha. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ogbv_gender_bert_hindi_english_hasoc20a_fin_en_5.1.4_3.4_1698224066734.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ogbv_gender_bert_hindi_english_hasoc20a_fin_en_5.1.4_3.4_1698224066734.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("ogbv_gender_bert_hindi_english_hasoc20a_fin","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("ogbv_gender_bert_hindi_english_hasoc20a_fin","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ogbv_gender_bert_hindi_english_hasoc20a_fin| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|667.3 MB| + +## References + +https://huggingface.co/Maha/OGBV-gender-bert-hi-en-hasoc20a-fin \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-pipp_finder_bert_base_cased_en.md b/docs/_posts/ahmedlone127/2023-10-25-pipp_finder_bert_base_cased_en.md new file mode 100644 index 00000000000000..2ce48d13b73d85 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-pipp_finder_bert_base_cased_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English pipp_finder_bert_base_cased BertForSequenceClassification from cgpotts +author: John Snow Labs +name: pipp_finder_bert_base_cased +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`pipp_finder_bert_base_cased` is a English model originally trained by cgpotts. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/pipp_finder_bert_base_cased_en_5.1.4_3.4_1698220093324.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/pipp_finder_bert_base_cased_en_5.1.4_3.4_1698220093324.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("pipp_finder_bert_base_cased","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("pipp_finder_bert_base_cased","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|pipp_finder_bert_base_cased| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/cgpotts/pipp-finder-bert-base-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-roberta_fake_real_en.md b/docs/_posts/ahmedlone127/2023-10-25-roberta_fake_real_en.md new file mode 100644 index 00000000000000..ff3001149544cc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-roberta_fake_real_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English roberta_fake_real BertForSequenceClassification from PravallikaMyneni +author: John Snow Labs +name: roberta_fake_real +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`roberta_fake_real` is a English model originally trained by PravallikaMyneni. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/roberta_fake_real_en_5.1.4_3.4_1698221775747.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_fake_real_en_5.1.4_3.4_1698221775747.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("roberta_fake_real","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("roberta_fake_real","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|roberta_fake_real| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/PravallikaMyneni/roberta_fake_real \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-rubert_large_emotion_russian_cedr_m7_en.md b/docs/_posts/ahmedlone127/2023-10-25-rubert_large_emotion_russian_cedr_m7_en.md new file mode 100644 index 00000000000000..f9b4f7acf93a44 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-rubert_large_emotion_russian_cedr_m7_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English rubert_large_emotion_russian_cedr_m7 BertForSequenceClassification from Aniemore +author: John Snow Labs +name: rubert_large_emotion_russian_cedr_m7 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`rubert_large_emotion_russian_cedr_m7` is a English model originally trained by Aniemore. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/rubert_large_emotion_russian_cedr_m7_en_5.1.4_3.4_1698208388605.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/rubert_large_emotion_russian_cedr_m7_en_5.1.4_3.4_1698208388605.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("rubert_large_emotion_russian_cedr_m7","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("rubert_large_emotion_russian_cedr_m7","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rubert_large_emotion_russian_cedr_m7| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.6 GB| + +## References + +https://huggingface.co/Aniemore/rubert-large-emotion-russian-cedr-m7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-rubert_rusentitweet_sismetanin_en.md b/docs/_posts/ahmedlone127/2023-10-25-rubert_rusentitweet_sismetanin_en.md new file mode 100644 index 00000000000000..e9f32b9b16863f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-rubert_rusentitweet_sismetanin_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English rubert_rusentitweet_sismetanin BertForSequenceClassification from sismetanin +author: John Snow Labs +name: rubert_rusentitweet_sismetanin +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`rubert_rusentitweet_sismetanin` is a English model originally trained by sismetanin. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/rubert_rusentitweet_sismetanin_en_5.1.4_3.4_1698243502522.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/rubert_rusentitweet_sismetanin_en_5.1.4_3.4_1698243502522.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("rubert_rusentitweet_sismetanin","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("rubert_rusentitweet_sismetanin","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rubert_rusentitweet_sismetanin| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|666.5 MB| + +## References + +https://huggingface.co/sismetanin/rubert-rusentitweet \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-samyarn_bert_base_multilingual_cased_xx.md b/docs/_posts/ahmedlone127/2023-10-25-samyarn_bert_base_multilingual_cased_xx.md new file mode 100644 index 00000000000000..ea42b4ff0221b2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-samyarn_bert_base_multilingual_cased_xx.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Multilingual samyarn_bert_base_multilingual_cased BertForSequenceClassification from Kao +author: John Snow Labs +name: samyarn_bert_base_multilingual_cased +date: 2023-10-25 +tags: [bert, xx, open_source, sequence_classification, onnx] +task: Text Classification +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`samyarn_bert_base_multilingual_cased` is a Multilingual model originally trained by Kao. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/samyarn_bert_base_multilingual_cased_xx_5.1.4_3.4_1698220388695.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/samyarn_bert_base_multilingual_cased_xx_5.1.4_3.4_1698220388695.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("samyarn_bert_base_multilingual_cased","xx")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("samyarn_bert_base_multilingual_cased","xx") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|samyarn_bert_base_multilingual_cased| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|xx| +|Size:|667.3 MB| + +## References + +https://huggingface.co/Kao/samyarn-bert-base-multilingual-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-sentiment_hts2_hubert_hungarian_hu.md b/docs/_posts/ahmedlone127/2023-10-25-sentiment_hts2_hubert_hungarian_hu.md new file mode 100644 index 00000000000000..76fb842f567620 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-sentiment_hts2_hubert_hungarian_hu.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Hungarian sentiment_hts2_hubert_hungarian BertForSequenceClassification from NYTK +author: John Snow Labs +name: sentiment_hts2_hubert_hungarian +date: 2023-10-25 +tags: [bert, hu, open_source, sequence_classification, onnx] +task: Text Classification +language: hu +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sentiment_hts2_hubert_hungarian` is a Hungarian model originally trained by NYTK. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sentiment_hts2_hubert_hungarian_hu_5.1.4_3.4_1698227753323.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sentiment_hts2_hubert_hungarian_hu_5.1.4_3.4_1698227753323.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("sentiment_hts2_hubert_hungarian","hu")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("sentiment_hts2_hubert_hungarian","hu") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sentiment_hts2_hubert_hungarian| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|hu| +|Size:|414.7 MB| + +## References + +https://huggingface.co/NYTK/sentiment-hts2-hubert-hungarian \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-sentiment_hts5_hubert_hungarian_hu.md b/docs/_posts/ahmedlone127/2023-10-25-sentiment_hts5_hubert_hungarian_hu.md new file mode 100644 index 00000000000000..025fc5dbefc631 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-sentiment_hts5_hubert_hungarian_hu.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Hungarian sentiment_hts5_hubert_hungarian BertForSequenceClassification from NYTK +author: John Snow Labs +name: sentiment_hts5_hubert_hungarian +date: 2023-10-25 +tags: [bert, hu, open_source, sequence_classification, onnx] +task: Text Classification +language: hu +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sentiment_hts5_hubert_hungarian` is a Hungarian model originally trained by NYTK. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sentiment_hts5_hubert_hungarian_hu_5.1.4_3.4_1698228545238.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sentiment_hts5_hubert_hungarian_hu_5.1.4_3.4_1698228545238.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("sentiment_hts5_hubert_hungarian","hu")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("sentiment_hts5_hubert_hungarian","hu") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sentiment_hts5_hubert_hungarian| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|hu| +|Size:|414.7 MB| + +## References + +https://huggingface.co/NYTK/sentiment-hts5-hubert-hungarian \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-solved_finbert_tone_en.md b/docs/_posts/ahmedlone127/2023-10-25-solved_finbert_tone_en.md new file mode 100644 index 00000000000000..7c6d502cfcf9f8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-solved_finbert_tone_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English solved_finbert_tone BertForSequenceClassification from ldeb +author: John Snow Labs +name: solved_finbert_tone +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`solved_finbert_tone` is a English model originally trained by ldeb. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/solved_finbert_tone_en_5.1.4_3.4_1698211497295.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/solved_finbert_tone_en_5.1.4_3.4_1698211497295.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("solved_finbert_tone","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("solved_finbert_tone","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|solved_finbert_tone| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.6 MB| + +## References + +https://huggingface.co/ldeb/solved-finbert-tone \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_16_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_16_en.md new file mode 100644 index 00000000000000..c0542f65b189d9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_16_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_16 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_16 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_16` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_16_en_5.1.4_3.4_1698202138259.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_16_en_5.1.4_3.4_1698202138259.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_16","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_16","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_16| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-16 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_62_en.md b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_62_en.md new file mode 100644 index 00000000000000..2f78fd9098a28c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-std_0pnt2_bert_ft_cola_62_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English std_0pnt2_bert_ft_cola_62 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: std_0pnt2_bert_ft_cola_62 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`std_0pnt2_bert_ft_cola_62` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_62_en_5.1.4_3.4_1698204456665.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/std_0pnt2_bert_ft_cola_62_en_5.1.4_3.4_1698204456665.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_62","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("std_0pnt2_bert_ft_cola_62","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|std_0pnt2_bert_ft_cola_62| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-62 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_mrpc_distilled_en.md b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_mrpc_distilled_en.md new file mode 100644 index 00000000000000..80a136cb63b766 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_mrpc_distilled_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_mrpc_distilled BertForSequenceClassification from Sayan01 +author: John Snow Labs +name: tiny_bert_mrpc_distilled +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_mrpc_distilled` is a English model originally trained by Sayan01. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_mrpc_distilled_en_5.1.4_3.4_1698209972204.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_mrpc_distilled_en_5.1.4_3.4_1698209972204.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_mrpc_distilled","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_mrpc_distilled","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_mrpc_distilled| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|54.2 MB| + +## References + +https://huggingface.co/Sayan01/tiny-bert-mrpc-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_kushaljoseph_en.md b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_kushaljoseph_en.md new file mode 100644 index 00000000000000..720694324591fb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_kushaljoseph_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_sst2_distilled_kushaljoseph BertForSequenceClassification from kushaljoseph +author: John Snow Labs +name: tiny_bert_sst2_distilled_kushaljoseph +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_sst2_distilled_kushaljoseph` is a English model originally trained by kushaljoseph. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_sst2_distilled_kushaljoseph_en_5.1.4_3.4_1698234651704.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_sst2_distilled_kushaljoseph_en_5.1.4_3.4_1698234651704.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_sst2_distilled_kushaljoseph","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_sst2_distilled_kushaljoseph","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_sst2_distilled_kushaljoseph| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/kushaljoseph/tiny-bert-sst2-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tonely_bert_en.md b/docs/_posts/ahmedlone127/2023-10-25-tonely_bert_en.md new file mode 100644 index 00000000000000..d28bb22dad1362 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tonely_bert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tonely_bert BertForSequenceClassification from francheska-vicente +author: John Snow Labs +name: tonely_bert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tonely_bert` is a English model originally trained by francheska-vicente. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tonely_bert_en_5.1.4_3.4_1698223349625.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tonely_bert_en_5.1.4_3.4_1698223349625.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tonely_bert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tonely_bert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tonely_bert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|406.0 MB| + +## References + +https://huggingface.co/francheska-vicente/tonely-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-twitter_disaster_bert_large_en.md b/docs/_posts/ahmedlone127/2023-10-25-twitter_disaster_bert_large_en.md new file mode 100644 index 00000000000000..54e97698c2eb2c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-twitter_disaster_bert_large_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English twitter_disaster_bert_large BertForSequenceClassification from ReynaQuita +author: John Snow Labs +name: twitter_disaster_bert_large +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`twitter_disaster_bert_large` is a English model originally trained by ReynaQuita. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/twitter_disaster_bert_large_en_5.1.4_3.4_1698238716080.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/twitter_disaster_bert_large_en_5.1.4_3.4_1698238716080.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("twitter_disaster_bert_large","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("twitter_disaster_bert_large","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|twitter_disaster_bert_large| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/ReynaQuita/twitter_disaster_bert_large \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-vashkontrol_sentiment_rubert_ru.md b/docs/_posts/ahmedlone127/2023-10-25-vashkontrol_sentiment_rubert_ru.md new file mode 100644 index 00000000000000..2c70fdca761862 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-vashkontrol_sentiment_rubert_ru.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Russian vashkontrol_sentiment_rubert BertForSequenceClassification from kartashoffv +author: John Snow Labs +name: vashkontrol_sentiment_rubert +date: 2023-10-25 +tags: [bert, ru, open_source, sequence_classification, onnx] +task: Text Classification +language: ru +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`vashkontrol_sentiment_rubert` is a Russian model originally trained by kartashoffv. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/vashkontrol_sentiment_rubert_ru_5.1.4_3.4_1698206312526.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/vashkontrol_sentiment_rubert_ru_5.1.4_3.4_1698206312526.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("vashkontrol_sentiment_rubert","ru")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("vashkontrol_sentiment_rubert","ru") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|vashkontrol_sentiment_rubert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|ru| +|Size:|666.5 MB| + +## References + +https://huggingface.co/kartashoffv/vashkontrol-sentiment-rubert \ No newline at end of file From 7066a172c69932e00abdb908db11272e6540287e Mon Sep 17 00:00:00 2001 From: jsl-models <74001263+jsl-models@users.noreply.github.com> Date: Thu, 26 Oct 2023 15:49:51 +0700 Subject: [PATCH 55/55] 2023-10-25-bert_classifier_finbert_esg_en (#14041) * Add model 2023-10-25-bert_classifier_finbert_esg_en * Add model 2023-10-25-imdb_bert_base_uncased_kyle1668_en * Add model 2023-10-25-bert_base_uncased_finetuned_rte_ruizhou_en * Add model 2023-10-25-goog_bert_ft_cola_88_en * Add model 2023-10-25-emobert_single_binary_en * Add model 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* Add model 2023-10-26-6ep_bert_ft_cola_39_en * Add model 2023-10-26-model3_marabertv2_t2_ws_a100_en * Add model 2023-10-26-bert_large_uncased_fine_tune_winogrande_ep_8_en * Add model 2023-10-26-bert_finetuned_char_classification_e8_en * Add model 2023-10-26-bert_base_uncased_finetuned_cola_anirudh21_en * Add model 2023-10-26-6ep_bert_ft_cola_40_en * Add model 2023-10-26-bert_large_uncased_finetuned_winogrande_en * Add model 2023-10-26-bert_vast_binary_en * Add model 2023-10-26-6ep_bert_ft_cola_41_en * Add model 2023-10-26-bert_base_uncased_finetuned_mrpc_anirudh21_en * Add model 2023-10-26-finbert_slow_en * Add model 2023-10-26-bert_finetuned_char_classification_e81_en * Add model 2023-10-26-bert_base_uncased_finetuned_qnli_anirudh21_en * Add model 2023-10-26-6ep_bert_ft_cola_42_en * Add model 2023-10-26-bert_classifier_base_cased_chuvash_studio_name_medium_en * Add model 2023-10-26-kd_roberta_1lbert_lambda50_en * Add model 2023-10-26-tiny_bert_qnli_128_distilled_en * Add model 2023-10-26-bert_base_uncased_finetuned_rte_anirudh21_en * Add model 2023-10-26-6ep_bert_ft_cola_43_en * Add model 2023-10-26-tiny_bert_cola_128_distilled_en * Add model 2023-10-26-bert_large_uncased_fine_tune_winogrande_ep_8_original_2e_en * Add model 2023-10-26-tiny_bert_qnli128_distilled_en * Add model 2023-10-26-bert_base_uncased_finetuned_wnli_anirudh21_en * Add model 2023-10-26-6ep_bert_ft_cola_44_en * Add model 2023-10-26-bert_base_uncased_fine_tune_winogrande_ep_8_original_2e_en * Add model 2023-10-26-bert_workforce_en * Add model 2023-10-26-bert_base_uncased_finetuned_addresso_en * Add model 2023-10-26-6ep_bert_ft_cola_45_en * Add model 2023-10-26-tinybertje_v2_en * Add model 2023-10-26-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v1_en * Add model 2023-10-26-bert_base_uncased_finetuned_coda19_en * Add model 2023-10-26-6ep_bert_ft_cola_46_en * Add model 2023-10-26-filbert_en * Add model 2023-10-26-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v2_en * Add model 2023-10-26-scibert_uncased_finetuned_coda19_en * Add model 2023-10-26-6ep_bert_ft_cola_47_en * Add model 2023-10-26-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v3_en * Add model 2023-10-26-6ep_bert_ft_cola_48_en * Add model 2023-10-26-bert_base_emotion_en --------- Co-authored-by: ahmedlone127 --- ...3-10-25-22_languages_bert_base_cased_en.md | 97 +++++++++++++++ .../2023-10-25-6ep_bert_ft_cola_0_en.md | 97 +++++++++++++++ .../2023-10-25-6ep_bert_ft_cola_10_en.md | 97 +++++++++++++++ .../2023-10-25-6ep_bert_ft_cola_11_en.md | 97 +++++++++++++++ .../2023-10-25-6ep_bert_ft_cola_12_en.md | 97 +++++++++++++++ .../2023-10-25-6ep_bert_ft_cola_13_en.md | 97 +++++++++++++++ .../2023-10-25-6ep_bert_ft_cola_14_en.md | 97 +++++++++++++++ .../2023-10-25-6ep_bert_ft_cola_15_en.md | 97 +++++++++++++++ .../2023-10-25-6ep_bert_ft_cola_16_en.md | 97 +++++++++++++++ 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diff --git a/docs/_posts/ahmedlone127/2023-10-25-22_languages_bert_base_cased_en.md b/docs/_posts/ahmedlone127/2023-10-25-22_languages_bert_base_cased_en.md new file mode 100644 index 00000000000000..b4110c4c8f24bb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-22_languages_bert_base_cased_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 22_languages_bert_base_cased BertForSequenceClassification from SharanSMenon +author: John Snow Labs +name: 22_languages_bert_base_cased +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`22_languages_bert_base_cased` is a English model originally trained by SharanSMenon. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/22_languages_bert_base_cased_en_5.1.4_3.4_1698247449271.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/22_languages_bert_base_cased_en_5.1.4_3.4_1698247449271.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("22_languages_bert_base_cased","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("22_languages_bert_base_cased","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|22_languages_bert_base_cased| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/SharanSMenon/22-languages-bert-base-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_0_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_0_en.md new file mode 100644 index 00000000000000..008224d969811c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_0_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_0 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_0 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_0` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_0_en_5.1.4_3.4_1698253166052.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_0_en_5.1.4_3.4_1698253166052.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_0","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_0","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_0| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-0 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_10_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_10_en.md new file mode 100644 index 00000000000000..03d248f8f78ec3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_10_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_10 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_10 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_10` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_10_en_5.1.4_3.4_1698261608790.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_10_en_5.1.4_3.4_1698261608790.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_10","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_10","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_10| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_11_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_11_en.md new file mode 100644 index 00000000000000..a0fba4985e93b5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_11_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_11 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_11 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_11` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_11_en_5.1.4_3.4_1698262418067.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_11_en_5.1.4_3.4_1698262418067.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_11","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_11","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_11| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-11 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_12_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_12_en.md new file mode 100644 index 00000000000000..9e35e262e1a52a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_12_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_12 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_12 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_12` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_12_en_5.1.4_3.4_1698263175775.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_12_en_5.1.4_3.4_1698263175775.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_12","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_12","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_12| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-12 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_13_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_13_en.md new file mode 100644 index 00000000000000..c3fe1d17ae549b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_13_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_13 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_13 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_13` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_13_en_5.1.4_3.4_1698263891012.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_13_en_5.1.4_3.4_1698263891012.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_13","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_13","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_13| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-13 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_14_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_14_en.md new file mode 100644 index 00000000000000..4cb602e0cfec9b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_14_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_14 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_14 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_14` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_14_en_5.1.4_3.4_1698264650124.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_14_en_5.1.4_3.4_1698264650124.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_14","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_14","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_14| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-14 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_15_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_15_en.md new file mode 100644 index 00000000000000..c415519bdb500c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_15_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_15 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_15 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_15` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_15_en_5.1.4_3.4_1698265490438.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_15_en_5.1.4_3.4_1698265490438.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_15","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_15","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_15| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-15 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_16_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_16_en.md new file mode 100644 index 00000000000000..c5a039c5e644c2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_16_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_16 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_16 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_16` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_16_en_5.1.4_3.4_1698266482423.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_16_en_5.1.4_3.4_1698266482423.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_16","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_16","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_16| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-16 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_17_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_17_en.md new file mode 100644 index 00000000000000..2d0e48b0d26d75 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_17_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_17 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_17 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_17` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_17_en_5.1.4_3.4_1698267365486.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_17_en_5.1.4_3.4_1698267365486.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_17","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_17","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_17| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-17 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_18_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_18_en.md new file mode 100644 index 00000000000000..403105fe96451a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_18_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_18 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_18 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_18` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_18_en_5.1.4_3.4_1698268241788.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_18_en_5.1.4_3.4_1698268241788.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_18","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_18","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_18| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-18 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_19_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_19_en.md new file mode 100644 index 00000000000000..6d93adb9d6bdd1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_19_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_19 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_19 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_19` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_19_en_5.1.4_3.4_1698269068968.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_19_en_5.1.4_3.4_1698269068968.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_19","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_19","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_19| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-19 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_1_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_1_en.md new file mode 100644 index 00000000000000..56218223570386 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_1_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_1 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_1 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_1` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_1_en_5.1.4_3.4_1698253969671.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_1_en_5.1.4_3.4_1698253969671.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_1","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_1","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_20_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_20_en.md new file mode 100644 index 00000000000000..7c435c5b86f1fb --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_20_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_20 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_20 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_20` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_20_en_5.1.4_3.4_1698269802842.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_20_en_5.1.4_3.4_1698269802842.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_20","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_20","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_20| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_21_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_21_en.md new file mode 100644 index 00000000000000..9e9e2d5658bfcf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_21_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_21 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_21 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_21` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_21_en_5.1.4_3.4_1698270716210.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_21_en_5.1.4_3.4_1698270716210.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_21","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_21","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_21| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-21 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_22_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_22_en.md new file mode 100644 index 00000000000000..6c71e3e098ae30 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_22_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_22 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_22 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_22` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_22_en_5.1.4_3.4_1698271464883.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_22_en_5.1.4_3.4_1698271464883.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_22","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_22","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_22| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-22 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_23_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_23_en.md new file mode 100644 index 00000000000000..43bca9fa97e0c5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_23_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_23 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_23 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_23` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_23_en_5.1.4_3.4_1698272325215.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_23_en_5.1.4_3.4_1698272325215.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_23","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_23","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_23| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-23 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_24_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_24_en.md new file mode 100644 index 00000000000000..b44f0b890f0f40 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_24_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_24 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_24 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_24` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_24_en_5.1.4_3.4_1698273091954.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_24_en_5.1.4_3.4_1698273091954.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_24","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_24","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_24| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-24 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_25_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_25_en.md new file mode 100644 index 00000000000000..c594762801ec84 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_25_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_25 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_25 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_25` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_25_en_5.1.4_3.4_1698274055819.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_25_en_5.1.4_3.4_1698274055819.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_25","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_25","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_25| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-25 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_26_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_26_en.md new file mode 100644 index 00000000000000..374be3e244ee7c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_26_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_26 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_26 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_26` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_26_en_5.1.4_3.4_1698274824916.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_26_en_5.1.4_3.4_1698274824916.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_26","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_26","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_26| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-26 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_27_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_27_en.md new file mode 100644 index 00000000000000..a866bff8c59cb8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_27_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_27 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_27 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_27` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_27_en_5.1.4_3.4_1698275884617.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_27_en_5.1.4_3.4_1698275884617.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_27","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_27","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_27| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-27 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_28_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_28_en.md new file mode 100644 index 00000000000000..7e6013de5b3078 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_28_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_28 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_28 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_28` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_28_en_5.1.4_3.4_1698276743401.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_28_en_5.1.4_3.4_1698276743401.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_28","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_28","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_28| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-28 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_29_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_29_en.md new file mode 100644 index 00000000000000..2327a5ca943659 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_29_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_29 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_29 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_29` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_29_en_5.1.4_3.4_1698277388273.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_29_en_5.1.4_3.4_1698277388273.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_29","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_29","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_29| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-29 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_2_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_2_en.md new file mode 100644 index 00000000000000..ed93540bf488f3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_2 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_2` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_2_en_5.1.4_3.4_1698254514952.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_2_en_5.1.4_3.4_1698254514952.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_30_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_30_en.md new file mode 100644 index 00000000000000..82db308ee89551 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_30_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_30 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_30 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_30` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_30_en_5.1.4_3.4_1698278240367.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_30_en_5.1.4_3.4_1698278240367.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_30","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_30","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_30| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-30 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_3_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_3_en.md new file mode 100644 index 00000000000000..077ce9c4048a57 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_3_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_3 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_3 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_3` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_3_en_5.1.4_3.4_1698255296378.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_3_en_5.1.4_3.4_1698255296378.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_3","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_3","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_3| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_4_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_4_en.md new file mode 100644 index 00000000000000..e47453def8355c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_4_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_4 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_4 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_4` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_4_en_5.1.4_3.4_1698256192915.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_4_en_5.1.4_3.4_1698256192915.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_4","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_4","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_4| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_5_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_5_en.md new file mode 100644 index 00000000000000..31b621cbb49941 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_5_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_5 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_5 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_5` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_5_en_5.1.4_3.4_1698257229791.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_5_en_5.1.4_3.4_1698257229791.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_5","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_5","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_5| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_6_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_6_en.md new file mode 100644 index 00000000000000..b6fca803be6766 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_6_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_6 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_6 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_6` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_6_en_5.1.4_3.4_1698258140211.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_6_en_5.1.4_3.4_1698258140211.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_6","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_6","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_6| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_7_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_7_en.md new file mode 100644 index 00000000000000..7db57fb483e055 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_7_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_7 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_7 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_7` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_7_en_5.1.4_3.4_1698258839246.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_7_en_5.1.4_3.4_1698258839246.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_7","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_7","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_7| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_8_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_8_en.md new file mode 100644 index 00000000000000..e748bc4f7c0edc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_8 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_8` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_8_en_5.1.4_3.4_1698259697258.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_8_en_5.1.4_3.4_1698259697258.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-8 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_9_en.md b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_9_en.md new file mode 100644 index 00000000000000..38a4cc641d9614 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-6ep_bert_ft_cola_9_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_9 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_9 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_9` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_9_en_5.1.4_3.4_1698260656905.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_9_en_5.1.4_3.4_1698260656905.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_9","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_9","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_9| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-9 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-autotrain_test_4_macbert_1071837613_en.md b/docs/_posts/ahmedlone127/2023-10-25-autotrain_test_4_macbert_1071837613_en.md new file mode 100644 index 00000000000000..28e8165beadbe7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-autotrain_test_4_macbert_1071837613_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English autotrain_test_4_macbert_1071837613 BertForSequenceClassification from Luojike +author: John Snow Labs +name: autotrain_test_4_macbert_1071837613 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`autotrain_test_4_macbert_1071837613` is a English model originally trained by Luojike. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/autotrain_test_4_macbert_1071837613_en_5.1.4_3.4_1698267335416.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/autotrain_test_4_macbert_1071837613_en_5.1.4_3.4_1698267335416.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("autotrain_test_4_macbert_1071837613","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("autotrain_test_4_macbert_1071837613","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|autotrain_test_4_macbert_1071837613| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/Luojike/autotrain-test-4-macbert-1071837613 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bangla_fake_news_mbert_en.md b/docs/_posts/ahmedlone127/2023-10-25-bangla_fake_news_mbert_en.md new file mode 100644 index 00000000000000..71c43ad7b09081 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bangla_fake_news_mbert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bangla_fake_news_mbert BertForSequenceClassification from Tahsin-Mayeesha +author: John Snow Labs +name: bangla_fake_news_mbert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bangla_fake_news_mbert` is a English model originally trained by Tahsin-Mayeesha. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bangla_fake_news_mbert_en_5.1.4_3.4_1698250864297.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bangla_fake_news_mbert_en_5.1.4_3.4_1698250864297.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bangla_fake_news_mbert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bangla_fake_news_mbert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bangla_fake_news_mbert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|667.3 MB| + +## References + +https://huggingface.co/Tahsin-Mayeesha/bangla-fake-news-mbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv02_twitter_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv02_twitter_en.md new file mode 100644 index 00000000000000..63f1350bd99439 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv02_twitter_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_arabertv02_twitter BertForSequenceClassification from nouman-10 +author: John Snow Labs +name: bert_base_arabertv02_twitter +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabertv02_twitter` is a English model originally trained by nouman-10. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabertv02_twitter_en_5.1.4_3.4_1698252082816.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabertv02_twitter_en_5.1.4_3.4_1698252082816.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabertv02_twitter","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabertv02_twitter","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabertv02_twitter| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|507.3 MB| + +## References + +https://huggingface.co/nouman-10/bert-base-arabertv02-twitter \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv22a_preprocessed_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv22a_preprocessed_en.md new file mode 100644 index 00000000000000..503e8d15dcff61 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv22a_preprocessed_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_arabertv22a_preprocessed BertForSequenceClassification from Jehadoumer +author: John Snow Labs +name: bert_base_arabertv22a_preprocessed +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabertv22a_preprocessed` is a English model originally trained by Jehadoumer. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabertv22a_preprocessed_en_5.1.4_3.4_1698248197330.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabertv22a_preprocessed_en_5.1.4_3.4_1698248197330.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabertv22a_preprocessed","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabertv22a_preprocessed","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabertv22a_preprocessed| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|507.1 MB| + +## References + +https://huggingface.co/Jehadoumer/bert-base-arabertv22a-preprocessed \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv2_jehadoumer_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv2_jehadoumer_en.md new file mode 100644 index 00000000000000..aff780285b9835 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv2_jehadoumer_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_arabertv2_jehadoumer BertForSequenceClassification from Jehadoumer +author: John Snow Labs +name: bert_base_arabertv2_jehadoumer +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabertv2_jehadoumer` is a English model originally trained by Jehadoumer. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabertv2_jehadoumer_en_5.1.4_3.4_1698247360873.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabertv2_jehadoumer_en_5.1.4_3.4_1698247360873.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabertv2_jehadoumer","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabertv2_jehadoumer","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabertv2_jehadoumer| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|507.1 MB| + +## References + +https://huggingface.co/Jehadoumer/bert-base-arabertv2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv2_nouman_10_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv2_nouman_10_en.md new file mode 100644 index 00000000000000..2c815623cac192 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv2_nouman_10_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_arabertv2_nouman_10 BertForSequenceClassification from nouman-10 +author: John Snow Labs +name: bert_base_arabertv2_nouman_10 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabertv2_nouman_10` is a English model originally trained by nouman-10. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabertv2_nouman_10_en_5.1.4_3.4_1698249224530.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabertv2_nouman_10_en_5.1.4_3.4_1698249224530.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabertv2_nouman_10","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabertv2_nouman_10","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabertv2_nouman_10| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|507.1 MB| + +## References + +https://huggingface.co/nouman-10/bert-base-arabertv2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv2test_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv2test_en.md new file mode 100644 index 00000000000000..8d621abac5d519 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_arabertv2test_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_arabertv2test BertForSequenceClassification from nouman-10 +author: John Snow Labs +name: bert_base_arabertv2test +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_arabertv2test` is a English model originally trained by nouman-10. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_arabertv2test_en_5.1.4_3.4_1698252993807.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_arabertv2test_en_5.1.4_3.4_1698252993807.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabertv2test","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_arabertv2test","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_arabertv2test| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|507.1 MB| + +## References + +https://huggingface.co/nouman-10/bert-base-arabertv2test \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_banking77_pt2_shahzay_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_banking77_pt2_shahzay_en.md new file mode 100644 index 00000000000000..cde9393c012c99 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_banking77_pt2_shahzay_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_banking77_pt2_shahzay BertForSequenceClassification from Shahzay +author: John Snow Labs +name: bert_base_banking77_pt2_shahzay +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_banking77_pt2_shahzay` is a English model originally trained by Shahzay. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_banking77_pt2_shahzay_en_5.1.4_3.4_1698251201329.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_banking77_pt2_shahzay_en_5.1.4_3.4_1698251201329.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_banking77_pt2_shahzay","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_banking77_pt2_shahzay","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_banking77_pt2_shahzay| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.6 MB| + +## References + +https://huggingface.co/Shahzay/bert-base-banking77-pt2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_avg_cola_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_avg_cola_en.md new file mode 100644 index 00000000000000..bc50664366521b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_avg_cola_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_cased_avg_cola BertForSequenceClassification from TehranNLP-org +author: John Snow Labs +name: bert_base_cased_avg_cola +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_avg_cola` is a English model originally trained by TehranNLP-org. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_avg_cola_en_5.1.4_3.4_1698254382799.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_avg_cola_en_5.1.4_3.4_1698254382799.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_avg_cola","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_avg_cola","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_avg_cola| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/TehranNLP-org/bert-base-cased-avg-cola \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_avg_mnli_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_avg_mnli_en.md new file mode 100644 index 00000000000000..21fadcd92682c8 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_avg_mnli_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_cased_avg_mnli BertForSequenceClassification from TehranNLP-org +author: John Snow Labs +name: bert_base_cased_avg_mnli +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_avg_mnli` is a English model originally trained by TehranNLP-org. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_avg_mnli_en_5.1.4_3.4_1698255296405.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_avg_mnli_en_5.1.4_3.4_1698255296405.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_avg_mnli","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_avg_mnli","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_avg_mnli| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/TehranNLP-org/bert-base-cased-avg-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_fake_real_2_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_fake_real_2_en.md new file mode 100644 index 00000000000000..6fdf09d0f493a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_fake_real_2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_cased_fake_real_2 BertForSequenceClassification from VA777 +author: John Snow Labs +name: bert_base_cased_fake_real_2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_fake_real_2` is a English model originally trained by VA777. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_fake_real_2_en_5.1.4_3.4_1698252013476.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_fake_real_2_en_5.1.4_3.4_1698252013476.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_fake_real_2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_fake_real_2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_fake_real_2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/VA777/bert_base_cased_fake_real_2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_fake_real_3_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_fake_real_3_en.md new file mode 100644 index 00000000000000..0bafe338ef6e3d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_fake_real_3_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_cased_fake_real_3 BertForSequenceClassification from VA777 +author: John Snow Labs +name: bert_base_cased_fake_real_3 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_fake_real_3` is a English model originally trained by VA777. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_fake_real_3_en_5.1.4_3.4_1698252780645.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_fake_real_3_en_5.1.4_3.4_1698252780645.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_fake_real_3","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_fake_real_3","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_fake_real_3| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/VA777/bert_base_cased_fake_real_3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_fake_real_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_fake_real_en.md new file mode 100644 index 00000000000000..2fab8ccaf3e512 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_fake_real_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_cased_fake_real BertForSequenceClassification from VA777 +author: John Snow Labs +name: bert_base_cased_fake_real +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_fake_real` is a English model originally trained by VA777. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_fake_real_en_5.1.4_3.4_1698250505726.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_fake_real_en_5.1.4_3.4_1698250505726.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_fake_real","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_fake_real","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_fake_real| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.8 MB| + +## References + +https://huggingface.co/VA777/bert_base_cased_fake_real \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_mnli_tehrannlp_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_mnli_tehrannlp_en.md new file mode 100644 index 00000000000000..b74689e0a2bc90 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_mnli_tehrannlp_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_cased_mnli_tehrannlp BertForSequenceClassification from TehranNLP +author: John Snow Labs +name: bert_base_cased_mnli_tehrannlp +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_mnli_tehrannlp` is a English model originally trained by TehranNLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_mnli_tehrannlp_en_5.1.4_3.4_1698252780573.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_mnli_tehrannlp_en_5.1.4_3.4_1698252780573.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_mnli_tehrannlp","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_mnli_tehrannlp","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_mnli_tehrannlp| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.8 MB| + +## References + +https://huggingface.co/TehranNLP/bert-base-cased-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_resume_classification_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_resume_classification_en.md new file mode 100644 index 00000000000000..4b1ccd25f0643a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_resume_classification_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_cased_resume_classification BertForSequenceClassification from Kowshik24 +author: John Snow Labs +name: bert_base_cased_resume_classification +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_resume_classification` is a English model originally trained by Kowshik24. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_resume_classification_en_5.1.4_3.4_1698256194279.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_resume_classification_en_5.1.4_3.4_1698256194279.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_resume_classification","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_resume_classification","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_resume_classification| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/Kowshik24/bert-base-cased-resume-classification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_twitter_sentiment_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_twitter_sentiment_en.md new file mode 100644 index 00000000000000..b27d37442de8c6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_cased_twitter_sentiment_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_cased_twitter_sentiment BertForSequenceClassification from Theivaprakasham +author: John Snow Labs +name: bert_base_cased_twitter_sentiment +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_twitter_sentiment` is a English model originally trained by Theivaprakasham. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_cased_twitter_sentiment_en_5.1.4_3.4_1698268240508.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_twitter_sentiment_en_5.1.4_3.4_1698268240508.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_twitter_sentiment","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_twitter_sentiment","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_cased_twitter_sentiment| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/Theivaprakasham/bert-base-cased-twitter_sentiment \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_finetuned_mosei1_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_finetuned_mosei1_en.md new file mode 100644 index 00000000000000..f771a1538b28bc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_finetuned_mosei1_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_finetuned_mosei1 BertForSequenceClassification from luckydog +author: John Snow Labs +name: bert_base_chinese_finetuned_mosei1 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_finetuned_mosei1` is a English model originally trained by luckydog. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_finetuned_mosei1_en_5.1.4_3.4_1698249306584.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_finetuned_mosei1_en_5.1.4_3.4_1698249306584.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_finetuned_mosei1","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_finetuned_mosei1","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_finetuned_mosei1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/luckydog/bert-base-chinese-finetuned-mosei1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_finetuned_mosei_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_finetuned_mosei_en.md new file mode 100644 index 00000000000000..ecf31d4ceacfd7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_finetuned_mosei_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_finetuned_mosei BertForSequenceClassification from luckydog +author: John Snow Labs +name: bert_base_chinese_finetuned_mosei +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_finetuned_mosei` is a English model originally trained by luckydog. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_finetuned_mosei_en_5.1.4_3.4_1698247667699.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_finetuned_mosei_en_5.1.4_3.4_1698247667699.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_finetuned_mosei","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_finetuned_mosei","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_finetuned_mosei| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/luckydog/bert-base-chinese-finetuned-mosei \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_ssec_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_ssec_en.md new file mode 100644 index 00000000000000..4fd1270ccf666e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_ssec_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_ssec BertForSequenceClassification from hw2942 +author: John Snow Labs +name: bert_base_chinese_ssec +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_ssec` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_ssec_en_5.1.4_3.4_1698263128720.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_ssec_en_5.1.4_3.4_1698263128720.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_ssec","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_ssec","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_ssec| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/hw2942/bert-base-chinese-SSEC \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v1_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v1_en.md new file mode 100644 index 00000000000000..36f48b29723b83 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v1_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v1 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v1 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v1` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v1_en_5.1.4_3.4_1698261759300.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v1_en_5.1.4_3.4_1698261759300.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v1","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v1","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSEC-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v2_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v2_en.md new file mode 100644 index 00000000000000..70041a049463c4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v2 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v2` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v2_en_5.1.4_3.4_1698262490366.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v2_en_5.1.4_3.4_1698262490366.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSEC-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v4_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v4_en.md new file mode 100644 index 00000000000000..a3ec53fb413fb5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v4_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v4 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v4 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v4` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v4_en_5.1.4_3.4_1698263835406.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v4_en_5.1.4_3.4_1698263835406.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v4","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v4","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v4| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSEC-v4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v5_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v5_en.md new file mode 100644 index 00000000000000..1362bd9eec0b79 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v5_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v5 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v5 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v5` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v5_en_5.1.4_3.4_1698264650096.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v5_en_5.1.4_3.4_1698264650096.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v5","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v5","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_v5| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.3 MB| + +## References + +https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSEC-v5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncase_contracts_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncase_contracts_en.md new file mode 100644 index 00000000000000..06a32d6678f25b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncase_contracts_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncase_contracts BertForSequenceClassification from amanbawa96 +author: John Snow Labs +name: bert_base_uncase_contracts +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncase_contracts` is a English model originally trained by amanbawa96. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncase_contracts_en_5.1.4_3.4_1698263890692.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncase_contracts_en_5.1.4_3.4_1698263890692.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncase_contracts","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncase_contracts","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncase_contracts| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/amanbawa96/bert-base-uncase-contracts \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_cola_2e_5_21_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_cola_2e_5_21_en.md new file mode 100644 index 00000000000000..edd7d6a8b48232 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_cola_2e_5_21_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_avg_cola_2e_5_21 BertForSequenceClassification from TehranNLP-org +author: John Snow Labs +name: bert_base_uncased_avg_cola_2e_5_21 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_avg_cola_2e_5_21` is a English model originally trained by TehranNLP-org. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_avg_cola_2e_5_21_en_5.1.4_3.4_1698256190441.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_avg_cola_2e_5_21_en_5.1.4_3.4_1698256190441.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_avg_cola_2e_5_21","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_avg_cola_2e_5_21","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_avg_cola_2e_5_21| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/TehranNLP-org/bert-base-uncased-avg-cola-2e-5-21 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_cola_2e_5_42_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_cola_2e_5_42_en.md new file mode 100644 index 00000000000000..82a0600fa2e057 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_cola_2e_5_42_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_avg_cola_2e_5_42 BertForSequenceClassification from TehranNLP-org +author: John Snow Labs +name: bert_base_uncased_avg_cola_2e_5_42 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_avg_cola_2e_5_42` is a English model originally trained by TehranNLP-org. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_avg_cola_2e_5_42_en_5.1.4_3.4_1698257229443.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_avg_cola_2e_5_42_en_5.1.4_3.4_1698257229443.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_avg_cola_2e_5_42","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_avg_cola_2e_5_42","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_avg_cola_2e_5_42| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/TehranNLP-org/bert-base-uncased-avg-cola-2e-5-42 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_cola_2e_5_63_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_cola_2e_5_63_en.md new file mode 100644 index 00000000000000..9e99ff5bd17be5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_cola_2e_5_63_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_avg_cola_2e_5_63 BertForSequenceClassification from TehranNLP-org +author: John Snow Labs +name: bert_base_uncased_avg_cola_2e_5_63 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_avg_cola_2e_5_63` is a English model originally trained by TehranNLP-org. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_avg_cola_2e_5_63_en_5.1.4_3.4_1698258140167.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_avg_cola_2e_5_63_en_5.1.4_3.4_1698258140167.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_avg_cola_2e_5_63","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_avg_cola_2e_5_63","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_avg_cola_2e_5_63| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/TehranNLP-org/bert-base-uncased-avg-cola-2e-5-63 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_mnli_2e_5_21_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_mnli_2e_5_21_en.md new file mode 100644 index 00000000000000..3865df2545f283 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_mnli_2e_5_21_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_avg_mnli_2e_5_21 BertForSequenceClassification from TehranNLP-org +author: John Snow Labs +name: bert_base_uncased_avg_mnli_2e_5_21 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_avg_mnli_2e_5_21` is a English model originally trained by TehranNLP-org. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_avg_mnli_2e_5_21_en_5.1.4_3.4_1698259076415.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_avg_mnli_2e_5_21_en_5.1.4_3.4_1698259076415.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_avg_mnli_2e_5_21","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_avg_mnli_2e_5_21","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_avg_mnli_2e_5_21| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/TehranNLP-org/bert-base-uncased-avg-mnli-2e-5-21 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_mnli_2e_5_63_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_mnli_2e_5_63_en.md new file mode 100644 index 00000000000000..f46d48b303686b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_mnli_2e_5_63_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_avg_mnli_2e_5_63 BertForSequenceClassification from TehranNLP-org +author: John Snow Labs +name: bert_base_uncased_avg_mnli_2e_5_63 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_avg_mnli_2e_5_63` is a English model originally trained by TehranNLP-org. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_avg_mnli_2e_5_63_en_5.1.4_3.4_1698259748434.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_avg_mnli_2e_5_63_en_5.1.4_3.4_1698259748434.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_avg_mnli_2e_5_63","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_avg_mnli_2e_5_63","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_avg_mnli_2e_5_63| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/TehranNLP-org/bert-base-uncased-avg-mnli-2e-5-63 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_mnli_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_mnli_en.md new file mode 100644 index 00000000000000..5c021fab151414 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_mnli_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_avg_mnli BertForSequenceClassification from TehranNLP-org +author: John Snow Labs +name: bert_base_uncased_avg_mnli +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_avg_mnli` is a English model originally trained by TehranNLP-org. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_avg_mnli_en_5.1.4_3.4_1698260656942.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_avg_mnli_en_5.1.4_3.4_1698260656942.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_avg_mnli","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_avg_mnli","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_avg_mnli| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/TehranNLP-org/bert-base-uncased-avg-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_sst2_2e_5_21_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_sst2_2e_5_21_en.md new file mode 100644 index 00000000000000..63856260d44f74 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_sst2_2e_5_21_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_avg_sst2_2e_5_21 BertForSequenceClassification from TehranNLP-org +author: John Snow Labs +name: bert_base_uncased_avg_sst2_2e_5_21 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_avg_sst2_2e_5_21` is a English model originally trained by TehranNLP-org. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_avg_sst2_2e_5_21_en_5.1.4_3.4_1698261608243.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_avg_sst2_2e_5_21_en_5.1.4_3.4_1698261608243.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_avg_sst2_2e_5_21","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_avg_sst2_2e_5_21","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_avg_sst2_2e_5_21| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/TehranNLP-org/bert-base-uncased-avg-sst2-2e-5-21 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_sst2_2e_5_42_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_sst2_2e_5_42_en.md new file mode 100644 index 00000000000000..0c48df8116e3b3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_sst2_2e_5_42_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_avg_sst2_2e_5_42 BertForSequenceClassification from TehranNLP-org +author: John Snow Labs +name: bert_base_uncased_avg_sst2_2e_5_42 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_avg_sst2_2e_5_42` is a English model originally trained by TehranNLP-org. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_avg_sst2_2e_5_42_en_5.1.4_3.4_1698262417809.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_avg_sst2_2e_5_42_en_5.1.4_3.4_1698262417809.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_avg_sst2_2e_5_42","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_avg_sst2_2e_5_42","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_avg_sst2_2e_5_42| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/TehranNLP-org/bert-base-uncased-avg-sst2-2e-5-42 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_sst2_2e_5_63_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_sst2_2e_5_63_en.md new file mode 100644 index 00000000000000..572000aa72b2ad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_avg_sst2_2e_5_63_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_avg_sst2_2e_5_63 BertForSequenceClassification from TehranNLP-org +author: John Snow Labs +name: bert_base_uncased_avg_sst2_2e_5_63 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_avg_sst2_2e_5_63` is a English model originally trained by TehranNLP-org. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_avg_sst2_2e_5_63_en_5.1.4_3.4_1698263251300.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_avg_sst2_2e_5_63_en_5.1.4_3.4_1698263251300.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_avg_sst2_2e_5_63","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_avg_sst2_2e_5_63","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_avg_sst2_2e_5_63| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/TehranNLP-org/bert-base-uncased-avg-sst2-2e-5-63 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_cls_hatexplain_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_cls_hatexplain_en.md new file mode 100644 index 00000000000000..c540ccd75a84f9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_cls_hatexplain_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_cls_hatexplain BertForSequenceClassification from TehranNLP-org +author: John Snow Labs +name: bert_base_uncased_cls_hatexplain +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_cls_hatexplain` is a English model originally trained by TehranNLP-org. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_cls_hatexplain_en_5.1.4_3.4_1698264097575.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_cls_hatexplain_en_5.1.4_3.4_1698264097575.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_cls_hatexplain","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_cls_hatexplain","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_cls_hatexplain| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/TehranNLP-org/bert-base-uncased-cls-hatexplain \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_cls_mnli_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_cls_mnli_en.md new file mode 100644 index 00000000000000..c475b751051246 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_cls_mnli_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_cls_mnli BertForSequenceClassification from TehranNLP-org +author: John Snow Labs +name: bert_base_uncased_cls_mnli +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_cls_mnli` is a English model originally trained by TehranNLP-org. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_cls_mnli_en_5.1.4_3.4_1698264715258.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_cls_mnli_en_5.1.4_3.4_1698264715258.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_cls_mnli","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_cls_mnli","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_cls_mnli| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/TehranNLP-org/bert-base-uncased-cls-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_cls_sst2_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_cls_sst2_en.md new file mode 100644 index 00000000000000..b794fc15525399 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_cls_sst2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_cls_sst2 BertForSequenceClassification from TehranNLP-org +author: John Snow Labs +name: bert_base_uncased_cls_sst2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_cls_sst2` is a English model originally trained by TehranNLP-org. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_cls_sst2_en_5.1.4_3.4_1698265490203.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_cls_sst2_en_5.1.4_3.4_1698265490203.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_cls_sst2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_cls_sst2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_cls_sst2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/TehranNLP-org/bert-base-uncased-cls-sst2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_emotion_damamsaketh19_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_emotion_damamsaketh19_en.md new file mode 100644 index 00000000000000..234225fc44a696 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_emotion_damamsaketh19_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_emotion_damamsaketh19 BertForSequenceClassification from damamsaketh19 +author: John Snow Labs +name: bert_base_uncased_emotion_damamsaketh19 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_emotion_damamsaketh19` is a English model originally trained by damamsaketh19. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_emotion_damamsaketh19_en_5.1.4_3.4_1698268726136.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_emotion_damamsaketh19_en_5.1.4_3.4_1698268726136.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_emotion_damamsaketh19","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_emotion_damamsaketh19","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_emotion_damamsaketh19| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/damamsaketh19/bert-base-uncased-emotion \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_binary_classification_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_binary_classification_en.md new file mode 100644 index 00000000000000..1b3b285f15d434 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_binary_classification_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_binary_classification BertForSequenceClassification from nielsr +author: John Snow Labs +name: bert_base_uncased_finetuned_binary_classification +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_binary_classification` is a English model originally trained by nielsr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_binary_classification_en_5.1.4_3.4_1698259651090.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_binary_classification_en_5.1.4_3.4_1698259651090.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_binary_classification","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_binary_classification","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_binary_classification| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/nielsr/bert-base-uncased-finetuned-binary-classification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_cola_ajrae_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_cola_ajrae_en.md new file mode 100644 index 00000000000000..f2cbbab4d23c01 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_cola_ajrae_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_cola_ajrae BertForSequenceClassification from ajrae +author: John Snow Labs +name: bert_base_uncased_finetuned_cola_ajrae +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_cola_ajrae` is a English model originally trained by ajrae. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_cola_ajrae_en_5.1.4_3.4_1698278372008.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_cola_ajrae_en_5.1.4_3.4_1698278372008.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_cola_ajrae","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_cola_ajrae","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_cola_ajrae| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/ajrae/bert-base-uncased-finetuned-cola \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_osdg_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_osdg_en.md new file mode 100644 index 00000000000000..169987921a7ef2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_osdg_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_osdg BertForSequenceClassification from ppsingh +author: John Snow Labs +name: bert_base_uncased_finetuned_osdg +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_osdg` is a English model originally trained by ppsingh. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_osdg_en_5.1.4_3.4_1698278330539.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_osdg_en_5.1.4_3.4_1698278330539.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_osdg","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_osdg","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_osdg| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/ppsingh/bert-base-uncased-finetuned-osdg \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_rte_ruizhou_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_rte_ruizhou_en.md new file mode 100644 index 00000000000000..83da10bd745d40 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_rte_ruizhou_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_rte_ruizhou BertForSequenceClassification from Ruizhou +author: John Snow Labs +name: bert_base_uncased_finetuned_rte_ruizhou +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_rte_ruizhou` is a English model originally trained by Ruizhou. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_rte_ruizhou_en_5.1.4_3.4_1698246671592.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_rte_ruizhou_en_5.1.4_3.4_1698246671592.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_rte_ruizhou","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_rte_ruizhou","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_rte_ruizhou| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Ruizhou/bert-base-uncased-finetuned-rte \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_winogrande_debiased_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_winogrande_debiased_en.md new file mode 100644 index 00000000000000..8fc2d0b1d0efb6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_winogrande_debiased_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_winogrande_debiased BertForSequenceClassification from bpark5233 +author: John Snow Labs +name: bert_base_uncased_finetuned_winogrande_debiased +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_winogrande_debiased` is a English model originally trained by bpark5233. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_winogrande_debiased_en_5.1.4_3.4_1698262418020.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_winogrande_debiased_en_5.1.4_3.4_1698262418020.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_winogrande_debiased","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_winogrande_debiased","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_winogrande_debiased| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/bpark5233/bert-base-uncased-finetuned-winogrande_debiased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_wnli_sumaia_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_wnli_sumaia_en.md new file mode 100644 index 00000000000000..dbb3ec7081a8e9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_finetuned_wnli_sumaia_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_wnli_sumaia BertForSequenceClassification from Sumaia +author: John Snow Labs +name: bert_base_uncased_finetuned_wnli_sumaia +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_wnli_sumaia` is a English model originally trained by Sumaia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wnli_sumaia_en_5.1.4_3.4_1698264480283.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wnli_sumaia_en_5.1.4_3.4_1698264480283.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_wnli_sumaia","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_wnli_sumaia","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_wnli_sumaia| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Sumaia/bert-base-uncased-finetuned-wnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_ft_m3_lc_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_ft_m3_lc_en.md new file mode 100644 index 00000000000000..1a29e7d01b6ffa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_ft_m3_lc_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_ft_m3_lc BertForSequenceClassification from sarahmiller137 +author: John Snow Labs +name: bert_base_uncased_ft_m3_lc +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_ft_m3_lc` is a English model originally trained by sarahmiller137. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_ft_m3_lc_en_5.1.4_3.4_1698263229908.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_ft_m3_lc_en_5.1.4_3.4_1698263229908.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_ft_m3_lc","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_ft_m3_lc","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_ft_m3_lc| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/sarahmiller137/bert-base-uncased-ft-m3-lc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_hate_offensive_oriya_normal_speech_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_hate_offensive_oriya_normal_speech_en.md new file mode 100644 index 00000000000000..64586fad689258 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_hate_offensive_oriya_normal_speech_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_hate_offensive_oriya_normal_speech BertForSequenceClassification from DunnBC22 +author: John Snow Labs +name: bert_base_uncased_hate_offensive_oriya_normal_speech +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_hate_offensive_oriya_normal_speech` is a English model originally trained by DunnBC22. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_hate_offensive_oriya_normal_speech_en_5.1.4_3.4_1698247127712.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_hate_offensive_oriya_normal_speech_en_5.1.4_3.4_1698247127712.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_hate_offensive_oriya_normal_speech","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_hate_offensive_oriya_normal_speech","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_hate_offensive_oriya_normal_speech| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/DunnBC22/bert-base-uncased-Hate_Offensive_or_Normal_Speech \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_hoax_classifier_fulltext_v1_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_hoax_classifier_fulltext_v1_en.md new file mode 100644 index 00000000000000..1a54c21505e047 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_hoax_classifier_fulltext_v1_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_hoax_classifier_fulltext_v1 BertForSequenceClassification from research-dump +author: John Snow Labs +name: bert_base_uncased_hoax_classifier_fulltext_v1 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_hoax_classifier_fulltext_v1` is a English model originally trained by research-dump. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_hoax_classifier_fulltext_v1_en_5.1.4_3.4_1698254236956.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_hoax_classifier_fulltext_v1_en_5.1.4_3.4_1698254236956.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_hoax_classifier_fulltext_v1","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_hoax_classifier_fulltext_v1","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_hoax_classifier_fulltext_v1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/research-dump/bert-base-uncased_hoax_classifier_fulltext_v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_mnli_tehrannlp_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_mnli_tehrannlp_en.md new file mode 100644 index 00000000000000..76818f5249d1bc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_mnli_tehrannlp_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_mnli_tehrannlp BertForSequenceClassification from TehranNLP +author: John Snow Labs +name: bert_base_uncased_mnli_tehrannlp +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_mnli_tehrannlp` is a English model originally trained by TehranNLP. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mnli_tehrannlp_en_5.1.4_3.4_1698253664529.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mnli_tehrannlp_en_5.1.4_3.4_1698253664529.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_mnli_tehrannlp","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_mnli_tehrannlp","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_mnli_tehrannlp| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.3 MB| + +## References + +https://huggingface.co/TehranNLP/bert-base-uncased-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_mrpc_2e_5_42_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_mrpc_2e_5_42_en.md new file mode 100644 index 00000000000000..b07b164bba46da --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_mrpc_2e_5_42_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_mrpc_2e_5_42 BertForSequenceClassification from TehranNLP-org +author: John Snow Labs +name: bert_base_uncased_mrpc_2e_5_42 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_mrpc_2e_5_42` is a English model originally trained by TehranNLP-org. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mrpc_2e_5_42_en_5.1.4_3.4_1698266482405.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_mrpc_2e_5_42_en_5.1.4_3.4_1698266482405.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_mrpc_2e_5_42","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_mrpc_2e_5_42","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_mrpc_2e_5_42| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/TehranNLP-org/bert-base-uncased-mrpc-2e-5-42 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_qqp_2e_5_42_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_qqp_2e_5_42_en.md new file mode 100644 index 00000000000000..a7f00f3464ad0f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_base_uncased_qqp_2e_5_42_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_qqp_2e_5_42 BertForSequenceClassification from TehranNLP-org +author: John Snow Labs +name: bert_base_uncased_qqp_2e_5_42 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_qqp_2e_5_42` is a English model originally trained by TehranNLP-org. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_qqp_2e_5_42_en_5.1.4_3.4_1698267365286.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_qqp_2e_5_42_en_5.1.4_3.4_1698267365286.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_qqp_2e_5_42","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_qqp_2e_5_42","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_qqp_2e_5_42| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/TehranNLP-org/bert-base-uncased-qqp-2e-5-42 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_claimcoherence_mini_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_claimcoherence_mini_en.md new file mode 100644 index 00000000000000..bd41ef08954856 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_claimcoherence_mini_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_claimcoherence_mini BertForSequenceClassification from whispAI +author: John Snow Labs +name: bert_claimcoherence_mini +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_claimcoherence_mini` is a English model originally trained by whispAI. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_claimcoherence_mini_en_5.1.4_3.4_1698266362091.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_claimcoherence_mini_en_5.1.4_3.4_1698266362091.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_claimcoherence_mini","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_claimcoherence_mini","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_claimcoherence_mini| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/whispAI/bert-claimcoherence-mini \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_base_styleclassification_subjective_neutral_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_base_styleclassification_subjective_neutral_en.md new file mode 100644 index 00000000000000..dd1a16367eca07 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_base_styleclassification_subjective_neutral_en.md @@ -0,0 +1,111 @@ +--- +layout: model +title: English BertForSequenceClassification Base Cased model (from cffl) +author: John Snow Labs +name: bert_classifier_base_styleclassification_subjective_neutral +date: 2023-10-25 +tags: [bert, sequence_classification, classification, open_source, en, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-styleclassification-subjective-neutral` is a English model originally trained by `cffl`. + +## Predicted Entities + +`NEUTRAL`, `SUBJECTIVE` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_base_styleclassification_subjective_neutral_en_5.1.4_3.4_1698268337507.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_base_styleclassification_subjective_neutral_en_5.1.4_3.4_1698268337507.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_base_styleclassification_subjective_neutral","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_base_styleclassification_subjective_neutral","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.base.by_cffl").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_base_styleclassification_subjective_neutral| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/cffl/bert-base-styleclassification-subjective-neutral +- https://arxiv.org/pdf/1911.09709.pdf +- https://arxiv.org/pdf/1703.01365.pdf +- https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases +- https://github.com/fastforwardlabs/text-style-transfer/blob/main/scripts/train/classifier/train_classifier.py +- https://github.com/fastforwardlabs/text-style-transfer/blob/main/notebooks/WNC_full_style_classifier_evaluation.ipynb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_distil_base_uncased_finetuned_emotion_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_distil_base_uncased_finetuned_emotion_en.md new file mode 100644 index 00000000000000..537fa80c9e5471 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_distil_base_uncased_finetuned_emotion_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: English BertForSequenceClassification Base Uncased model (from Tahsin) +author: John Snow Labs +name: bert_classifier_distil_base_uncased_finetuned_emotion +date: 2023-10-25 +tags: [bert, sequence_classification, classification, open_source, en, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `distilbert-base-uncased-finetuned-emotion` is a English model originally trained by `Tahsin`. + +## Predicted Entities + +`sadness`, `joy`, `love`, `anger`, `surprise`, `fear` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_distil_base_uncased_finetuned_emotion_en_5.1.4_3.4_1698249889650.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_distil_base_uncased_finetuned_emotion_en_5.1.4_3.4_1698249889650.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_distil_base_uncased_finetuned_emotion","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_distil_base_uncased_finetuned_emotion","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.emotion.distilled_uncased_base_finetuned").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_distil_base_uncased_finetuned_emotion| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| +|Case sensitive:|false| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/Tahsin/distilbert-base-uncased-finetuned-emotion +- https://paperswithcode.com/sota?task=Text+Classification&dataset=emotion \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_finbert_esg_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_finbert_esg_en.md new file mode 100644 index 00000000000000..c7bfb278e41602 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_finbert_esg_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: English BertForSequenceClassification Cased model (from yiyanghkust) +author: John Snow Labs +name: bert_classifier_finbert_esg +date: 2023-10-25 +tags: [en, open_source, bert, sequence_classification, classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `finbert-esg` is a English model originally trained by `yiyanghkust`. + +## Predicted Entities + +`None`, `Governance`, `Environmental`, `Social` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_finbert_esg_en_5.1.4_3.4_1698246278299.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_finbert_esg_en_5.1.4_3.4_1698246278299.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_finbert_esg","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCols(Array("text")) + .setOutputCols(Array("document")) + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_finbert_esg","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.esg.bert.by_yiyanghkust").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_finbert_esg| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.6 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/yiyanghkust/finbert-esg +- https://finbert.ai/ \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_hashtag_tonga_tonga_islands_hashtag_20_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_hashtag_tonga_tonga_islands_hashtag_20_en.md new file mode 100644 index 00000000000000..253eea800bf39d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_hashtag_tonga_tonga_islands_hashtag_20_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_classifier_hashtag_tonga_tonga_islands_hashtag_20 BertForSequenceClassification from Bryan0123 +author: John Snow Labs +name: bert_classifier_hashtag_tonga_tonga_islands_hashtag_20 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_classifier_hashtag_tonga_tonga_islands_hashtag_20` is a English model originally trained by Bryan0123. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_hashtag_tonga_tonga_islands_hashtag_20_en_5.1.4_3.4_1698250323134.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_hashtag_tonga_tonga_islands_hashtag_20_en_5.1.4_3.4_1698250323134.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_classifier_hashtag_tonga_tonga_islands_hashtag_20","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_classifier_hashtag_tonga_tonga_islands_hashtag_20","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_hashtag_tonga_tonga_islands_hashtag_20| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Bryan0123/bert-hashtag-to-hashtag-20 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_shahma_finetuned_mrpc_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_shahma_finetuned_mrpc_en.md new file mode 100644 index 00000000000000..346eff587dc41c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_classifier_shahma_finetuned_mrpc_en.md @@ -0,0 +1,107 @@ +--- +layout: model +title: English BertForSequenceClassification Cased model (from shahma) +author: John Snow Labs +name: bert_classifier_shahma_finetuned_mrpc +date: 2023-10-25 +tags: [bert, sequence_classification, classification, open_source, en, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `finetuned-bert-mrpc` is a English model originally trained by `shahma`. + +## Predicted Entities + +`equivalent`, `not equivalent` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_shahma_finetuned_mrpc_en_5.1.4_3.4_1698262498901.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_shahma_finetuned_mrpc_en_5.1.4_3.4_1698262498901.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +documentAssembler = DocumentAssembler() \ + .setInputCol("text") \ + .setOutputCol("document") + +tokenizer = Tokenizer() \ + .setInputCols("document") \ + .setOutputCol("token") + +sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_shahma_finetuned_mrpc","en") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) +``` +```scala +val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols(Array("document")) + .setOutputCol("token") + +val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_shahma_finetuned_mrpc","en") + .setInputCols(Array("document", "token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded)) + +val data = Seq("PUT YOUR STRING HERE").toDF("text") + +val result = pipeline.fit(data).transform(data) +``` + +{:.nlu-block} +```python +import nlu +nlu.load("en.classify.bert.glue.finetuned.by_shahma").predict("""PUT YOUR STRING HERE""") +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_shahma_finetuned_mrpc| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| +|Case sensitive:|true| +|Max sentence length:|256| + +## References + +References + +- https://huggingface.co/shahma/finetuned-bert-mrpc +- https://paperswithcode.com/sota?task=Text+Classification&dataset=glue \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_char_classification_e15_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_char_classification_e15_en.md new file mode 100644 index 00000000000000..f8ba95b835b3f2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_char_classification_e15_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetuned_char_classification_e15 BertForSequenceClassification from bhagasra-saurav +author: John Snow Labs +name: bert_finetuned_char_classification_e15 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuned_char_classification_e15` is a English model originally trained by bhagasra-saurav. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuned_char_classification_e15_en_5.1.4_3.4_1698277567602.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuned_char_classification_e15_en_5.1.4_3.4_1698277567602.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_char_classification_e15","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_char_classification_e15","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuned_char_classification_e15| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/bhagasra-saurav/bert-finetuned-char-classification-e15 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_char_classification_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_char_classification_en.md new file mode 100644 index 00000000000000..9de3f7adae0e32 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_char_classification_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetuned_char_classification BertForSequenceClassification from bhagasra-saurav +author: John Snow Labs +name: bert_finetuned_char_classification +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuned_char_classification` is a English model originally trained by bhagasra-saurav. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuned_char_classification_en_5.1.4_3.4_1698267584576.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuned_char_classification_en_5.1.4_3.4_1698267584576.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_char_classification","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_char_classification","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuned_char_classification| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.5 MB| + +## References + +https://huggingface.co/bhagasra-saurav/bert-finetuned-char-classification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_mrpc_trainerclass_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_mrpc_trainerclass_en.md new file mode 100644 index 00000000000000..a8b274e1666e24 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_mrpc_trainerclass_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetuned_mrpc_trainerclass BertForSequenceClassification from sara98 +author: John Snow Labs +name: bert_finetuned_mrpc_trainerclass +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuned_mrpc_trainerclass` is a English model originally trained by sara98. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuned_mrpc_trainerclass_en_5.1.4_3.4_1698265407693.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuned_mrpc_trainerclass_en_5.1.4_3.4_1698265407693.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_mrpc_trainerclass","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_mrpc_trainerclass","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuned_mrpc_trainerclass| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/sara98/bert-finetuned-mrpc-trainerclass \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_winogrande83e_058_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_winogrande83e_058_en.md new file mode 100644 index 00000000000000..8fed34946f3565 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_finetuned_winogrande83e_058_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetuned_winogrande83e_058 BertForSequenceClassification from Kalslice +author: John Snow Labs +name: bert_finetuned_winogrande83e_058 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuned_winogrande83e_058` is a English model originally trained by Kalslice. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuned_winogrande83e_058_en_5.1.4_3.4_1698248111414.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuned_winogrande83e_058_en_5.1.4_3.4_1698248111414.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_winogrande83e_058","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_winogrande83e_058","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuned_winogrande83e_058| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Kalslice/bert-finetuned-winogrande83e-058 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_crows_pairs_classifieronly_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_crows_pairs_classifieronly_en.md new file mode 100644 index 00000000000000..8c7f307bd80806 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_crows_pairs_classifieronly_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_crows_pairs_classifieronly BertForSequenceClassification from henryscheible +author: John Snow Labs +name: bert_large_uncased_crows_pairs_classifieronly +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_crows_pairs_classifieronly` is a English model originally trained by henryscheible. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_crows_pairs_classifieronly_en_5.1.4_3.4_1698256326716.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_crows_pairs_classifieronly_en_5.1.4_3.4_1698256326716.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_crows_pairs_classifieronly","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_crows_pairs_classifieronly","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_crows_pairs_classifieronly| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/henryscheible/bert-large-uncased_crows_pairs_classifieronly \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_crows_pairs_finetuned_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_crows_pairs_finetuned_en.md new file mode 100644 index 00000000000000..678438bdcdfdd1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_crows_pairs_finetuned_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_crows_pairs_finetuned BertForSequenceClassification from henryscheible +author: John Snow Labs +name: bert_large_uncased_crows_pairs_finetuned +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_crows_pairs_finetuned` is a English model originally trained by henryscheible. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_crows_pairs_finetuned_en_5.1.4_3.4_1698257524318.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_crows_pairs_finetuned_en_5.1.4_3.4_1698257524318.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_crows_pairs_finetuned","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_crows_pairs_finetuned","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_crows_pairs_finetuned| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/henryscheible/bert-large-uncased_crows_pairs_finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_stereoset_classifieronly_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_stereoset_classifieronly_en.md new file mode 100644 index 00000000000000..8db133d915d1f0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_stereoset_classifieronly_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_stereoset_classifieronly BertForSequenceClassification from henryscheible +author: John Snow Labs +name: bert_large_uncased_stereoset_classifieronly +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_stereoset_classifieronly` is a English model originally trained by henryscheible. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_stereoset_classifieronly_en_5.1.4_3.4_1698258967203.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_stereoset_classifieronly_en_5.1.4_3.4_1698258967203.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_stereoset_classifieronly","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_stereoset_classifieronly","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_stereoset_classifieronly| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/henryscheible/bert-large-uncased_stereoset_classifieronly \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_whole_word_masking_finetuned_squad_finetuned_polifact_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_whole_word_masking_finetuned_squad_finetuned_polifact_en.md new file mode 100644 index 00000000000000..400050e606034d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_whole_word_masking_finetuned_squad_finetuned_polifact_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_whole_word_masking_finetuned_squad_finetuned_polifact BertForSequenceClassification from anuj55 +author: John Snow Labs +name: bert_large_uncased_whole_word_masking_finetuned_squad_finetuned_polifact +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_whole_word_masking_finetuned_squad_finetuned_polifact` is a English model originally trained by anuj55. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_whole_word_masking_finetuned_squad_finetuned_polifact_en_5.1.4_3.4_1698272933947.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_whole_word_masking_finetuned_squad_finetuned_polifact_en_5.1.4_3.4_1698272933947.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_whole_word_masking_finetuned_squad_finetuned_polifact","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_whole_word_masking_finetuned_squad_finetuned_polifact","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_whole_word_masking_finetuned_squad_finetuned_polifact| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/anuj55/bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-polifact \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_winobias_classifieronly_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_winobias_classifieronly_en.md new file mode 100644 index 00000000000000..1e22f3de8c730e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_winobias_classifieronly_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_winobias_classifieronly BertForSequenceClassification from henryscheible +author: John Snow Labs +name: bert_large_uncased_winobias_classifieronly +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_winobias_classifieronly` is a English model originally trained by henryscheible. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_winobias_classifieronly_en_5.1.4_3.4_1698261699082.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_winobias_classifieronly_en_5.1.4_3.4_1698261699082.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_winobias_classifieronly","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_winobias_classifieronly","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_winobias_classifieronly| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/henryscheible/bert-large-uncased_winobias_classifieronly \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_winobias_finetuned_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_winobias_finetuned_en.md new file mode 100644 index 00000000000000..e7cc50de66b3e2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_large_uncased_winobias_finetuned_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_winobias_finetuned BertForSequenceClassification from henryscheible +author: John Snow Labs +name: bert_large_uncased_winobias_finetuned +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_winobias_finetuned` is a English model originally trained by henryscheible. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_winobias_finetuned_en_5.1.4_3.4_1698260379138.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_winobias_finetuned_en_5.1.4_3.4_1698260379138.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_winobias_finetuned","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_winobias_finetuned","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_winobias_finetuned| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/henryscheible/bert-large-uncased_winobias_finetuned \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_multilingual_xx.md b/docs/_posts/ahmedlone127/2023-10-25-bert_multilingual_xx.md new file mode 100644 index 00000000000000..673fa29afa83d0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_multilingual_xx.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Multilingual bert_multilingual BertForSequenceClassification from jiiyy +author: John Snow Labs +name: bert_multilingual +date: 2023-10-25 +tags: [bert, xx, open_source, sequence_classification, onnx] +task: Text Classification +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_multilingual` is a Multilingual model originally trained by jiiyy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_multilingual_xx_5.1.4_3.4_1698258613797.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_multilingual_xx_5.1.4_3.4_1698258613797.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_multilingual","xx")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_multilingual","xx") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_multilingual| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|xx| +|Size:|667.3 MB| + +## References + +https://huggingface.co/jiiyy/bert_multilingual \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_goodreads_wandb_en.md b/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_goodreads_wandb_en.md new file mode 100644 index 00000000000000..0438af78995fcc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bert_tiny_goodreads_wandb_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_tiny_goodreads_wandb BertForSequenceClassification from dhmeltzer +author: John Snow Labs +name: bert_tiny_goodreads_wandb +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_tiny_goodreads_wandb` is a English model originally trained by dhmeltzer. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_tiny_goodreads_wandb_en_5.1.4_3.4_1698263734400.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_tiny_goodreads_wandb_en_5.1.4_3.4_1698263734400.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_tiny_goodreads_wandb","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_tiny_goodreads_wandb","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_tiny_goodreads_wandb| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/dhmeltzer/bert-tiny-goodreads-wandb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bertimbau_hate_speech_en.md b/docs/_posts/ahmedlone127/2023-10-25-bertimbau_hate_speech_en.md new file mode 100644 index 00000000000000..b63f0e8192f2ad --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bertimbau_hate_speech_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bertimbau_hate_speech BertForSequenceClassification from Nelci +author: John Snow Labs +name: bertimbau_hate_speech +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertimbau_hate_speech` is a English model originally trained by Nelci. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bertimbau_hate_speech_en_5.1.4_3.4_1698260945506.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertimbau_hate_speech_en_5.1.4_3.4_1698260945506.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bertimbau_hate_speech","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bertimbau_hate_speech","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bertimbau_hate_speech| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/Nelci/bertimbau_hate_speech \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bluebert_sitesentence_diagnosis_classification_en.md b/docs/_posts/ahmedlone127/2023-10-25-bluebert_sitesentence_diagnosis_classification_en.md new file mode 100644 index 00000000000000..a99e900bb220ec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bluebert_sitesentence_diagnosis_classification_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bluebert_sitesentence_diagnosis_classification BertForSequenceClassification from DongHyoungLee +author: John Snow Labs +name: bluebert_sitesentence_diagnosis_classification +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bluebert_sitesentence_diagnosis_classification` is a English model originally trained by DongHyoungLee. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bluebert_sitesentence_diagnosis_classification_en_5.1.4_3.4_1698274392002.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bluebert_sitesentence_diagnosis_classification_en_5.1.4_3.4_1698274392002.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bluebert_sitesentence_diagnosis_classification","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bluebert_sitesentence_diagnosis_classification","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bluebert_sitesentence_diagnosis_classification| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.3 MB| + +## References + +https://huggingface.co/DongHyoungLee/bluebert-sitesentence-diagnosis-classification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-bow_bert_en.md b/docs/_posts/ahmedlone127/2023-10-25-bow_bert_en.md new file mode 100644 index 00000000000000..cfb01eb65765aa --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-bow_bert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bow_bert BertForSequenceClassification from dmrau +author: John Snow Labs +name: bow_bert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bow_bert` is a English model originally trained by dmrau. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bow_bert_en_5.1.4_3.4_1698277505225.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bow_bert_en_5.1.4_3.4_1698277505225.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bow_bert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bow_bert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bow_bert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|408.1 MB| + +## References + +https://huggingface.co/dmrau/bow-bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-emobert_single_binary_en.md b/docs/_posts/ahmedlone127/2023-10-25-emobert_single_binary_en.md new file mode 100644 index 00000000000000..350a5b6314ad2b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-emobert_single_binary_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English emobert_single_binary BertForSequenceClassification from reallycarlaost +author: John Snow Labs +name: emobert_single_binary +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`emobert_single_binary` is a English model originally trained by reallycarlaost. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/emobert_single_binary_en_5.1.4_3.4_1698246867339.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/emobert_single_binary_en_5.1.4_3.4_1698246867339.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("emobert_single_binary","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("emobert_single_binary","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|emobert_single_binary| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/reallycarlaost/emobert-single-binary \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-emobert_valence_5_en.md b/docs/_posts/ahmedlone127/2023-10-25-emobert_valence_5_en.md new file mode 100644 index 00000000000000..118a846cb459a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-emobert_valence_5_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English emobert_valence_5 BertForSequenceClassification from reallycarlaost +author: John Snow Labs +name: emobert_valence_5 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`emobert_valence_5` is a English model originally trained by reallycarlaost. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/emobert_valence_5_en_5.1.4_3.4_1698252491551.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/emobert_valence_5_en_5.1.4_3.4_1698252491551.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("emobert_valence_5","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("emobert_valence_5","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|emobert_valence_5| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/reallycarlaost/emobert-valence-5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-esgbert1_en.md b/docs/_posts/ahmedlone127/2023-10-25-esgbert1_en.md new file mode 100644 index 00000000000000..ddbd10adcb665e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-esgbert1_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English esgbert1 BertForSequenceClassification from owen198 +author: John Snow Labs +name: esgbert1 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`esgbert1` is a English model originally trained by owen198. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/esgbert1_en_5.1.4_3.4_1698270135920.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/esgbert1_en_5.1.4_3.4_1698270135920.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("esgbert1","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("esgbert1","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|esgbert1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/owen198/esgbert1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-finbert_narsil_en.md b/docs/_posts/ahmedlone127/2023-10-25-finbert_narsil_en.md new file mode 100644 index 00000000000000..8fb39c5f320655 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-finbert_narsil_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English finbert_narsil BertForSequenceClassification from Narsil +author: John Snow Labs +name: finbert_narsil +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finbert_narsil` is a English model originally trained by Narsil. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finbert_narsil_en_5.1.4_3.4_1698255147780.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finbert_narsil_en_5.1.4_3.4_1698255147780.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("finbert_narsil","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("finbert_narsil","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finbert_narsil| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Narsil/finbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-fine_tune_bert_combined_en.md b/docs/_posts/ahmedlone127/2023-10-25-fine_tune_bert_combined_en.md new file mode 100644 index 00000000000000..eb6b2eb65cfa90 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-fine_tune_bert_combined_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fine_tune_bert_combined BertForSequenceClassification from nouman-10 +author: John Snow Labs +name: fine_tune_bert_combined +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tune_bert_combined` is a English model originally trained by nouman-10. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tune_bert_combined_en_5.1.4_3.4_1698278330419.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tune_bert_combined_en_5.1.4_3.4_1698278330419.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fine_tune_bert_combined","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fine_tune_bert_combined","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tune_bert_combined| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/nouman-10/fine-tune-bert-combined \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-fine_tune_bert_exist_en.md b/docs/_posts/ahmedlone127/2023-10-25-fine_tune_bert_exist_en.md new file mode 100644 index 00000000000000..cf0ee67577c2ec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-fine_tune_bert_exist_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fine_tune_bert_exist BertForSequenceClassification from nouman-10 +author: John Snow Labs +name: fine_tune_bert_exist +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tune_bert_exist` is a English model originally trained by nouman-10. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tune_bert_exist_en_5.1.4_3.4_1698275174749.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tune_bert_exist_en_5.1.4_3.4_1698275174749.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fine_tune_bert_exist","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fine_tune_bert_exist","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tune_bert_exist| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/nouman-10/fine-tune-bert-exist \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-fine_tune_mbert_exist_en.md b/docs/_posts/ahmedlone127/2023-10-25-fine_tune_mbert_exist_en.md new file mode 100644 index 00000000000000..016cc501da4abd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-fine_tune_mbert_exist_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fine_tune_mbert_exist BertForSequenceClassification from nouman-10 +author: John Snow Labs +name: fine_tune_mbert_exist +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tune_mbert_exist` is a English model originally trained by nouman-10. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tune_mbert_exist_en_5.1.4_3.4_1698277503542.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tune_mbert_exist_en_5.1.4_3.4_1698277503542.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fine_tune_mbert_exist","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fine_tune_mbert_exist","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tune_mbert_exist| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|627.7 MB| + +## References + +https://huggingface.co/nouman-10/fine-tune-mbert-exist \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-finetuned_bert_yelp_v1_en.md b/docs/_posts/ahmedlone127/2023-10-25-finetuned_bert_yelp_v1_en.md new file mode 100644 index 00000000000000..cbed96694bf205 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-finetuned_bert_yelp_v1_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English finetuned_bert_yelp_v1 BertForSequenceClassification from ranraj99 +author: John Snow Labs +name: finetuned_bert_yelp_v1 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finetuned_bert_yelp_v1` is a English model originally trained by ranraj99. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finetuned_bert_yelp_v1_en_5.1.4_3.4_1698269477838.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finetuned_bert_yelp_v1_en_5.1.4_3.4_1698269477838.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_bert_yelp_v1","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_bert_yelp_v1","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finetuned_bert_yelp_v1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/ranraj99/finetuned_bert_yelp_v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-finetuned_iitp_pdt_review_additionalpretrained_bert_base_cased_en.md b/docs/_posts/ahmedlone127/2023-10-25-finetuned_iitp_pdt_review_additionalpretrained_bert_base_cased_en.md new file mode 100644 index 00000000000000..b451842c232238 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-finetuned_iitp_pdt_review_additionalpretrained_bert_base_cased_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English finetuned_iitp_pdt_review_additionalpretrained_bert_base_cased BertForSequenceClassification from aditeyabaral +author: John Snow Labs +name: finetuned_iitp_pdt_review_additionalpretrained_bert_base_cased +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finetuned_iitp_pdt_review_additionalpretrained_bert_base_cased` is a English model originally trained by aditeyabaral. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finetuned_iitp_pdt_review_additionalpretrained_bert_base_cased_en_5.1.4_3.4_1698270716179.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finetuned_iitp_pdt_review_additionalpretrained_bert_base_cased_en_5.1.4_3.4_1698270716179.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_iitp_pdt_review_additionalpretrained_bert_base_cased","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_iitp_pdt_review_additionalpretrained_bert_base_cased","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finetuned_iitp_pdt_review_additionalpretrained_bert_base_cased| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/aditeyabaral/finetuned-iitp_pdt_review-additionalpretrained-bert-base-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-finetuned_iitp_pdt_review_bert_hinglish_big_en.md b/docs/_posts/ahmedlone127/2023-10-25-finetuned_iitp_pdt_review_bert_hinglish_big_en.md new file mode 100644 index 00000000000000..08d1b2ffdb65ec --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-finetuned_iitp_pdt_review_bert_hinglish_big_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English finetuned_iitp_pdt_review_bert_hinglish_big BertForSequenceClassification from aditeyabaral +author: John Snow Labs +name: finetuned_iitp_pdt_review_bert_hinglish_big +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finetuned_iitp_pdt_review_bert_hinglish_big` is a English model originally trained by aditeyabaral. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finetuned_iitp_pdt_review_bert_hinglish_big_en_5.1.4_3.4_1698271343226.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finetuned_iitp_pdt_review_bert_hinglish_big_en_5.1.4_3.4_1698271343226.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_iitp_pdt_review_bert_hinglish_big","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_iitp_pdt_review_bert_hinglish_big","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finetuned_iitp_pdt_review_bert_hinglish_big| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|251.2 MB| + +## References + +https://huggingface.co/aditeyabaral/finetuned-iitp_pdt_review-bert-hinglish-big \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-finetuned_iitp_pdt_review_bert_hinglish_small_en.md b/docs/_posts/ahmedlone127/2023-10-25-finetuned_iitp_pdt_review_bert_hinglish_small_en.md new file mode 100644 index 00000000000000..91b1f3dc23983b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-finetuned_iitp_pdt_review_bert_hinglish_small_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English finetuned_iitp_pdt_review_bert_hinglish_small BertForSequenceClassification from aditeyabaral +author: John Snow Labs +name: finetuned_iitp_pdt_review_bert_hinglish_small +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finetuned_iitp_pdt_review_bert_hinglish_small` is a English model originally trained by aditeyabaral. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finetuned_iitp_pdt_review_bert_hinglish_small_en_5.1.4_3.4_1698272312849.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finetuned_iitp_pdt_review_bert_hinglish_small_en_5.1.4_3.4_1698272312849.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_iitp_pdt_review_bert_hinglish_small","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_iitp_pdt_review_bert_hinglish_small","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finetuned_iitp_pdt_review_bert_hinglish_small| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|251.2 MB| + +## References + +https://huggingface.co/aditeyabaral/finetuned-iitp_pdt_review-bert-hinglish-small \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-finetuned_iitpmovie_additionalpretrained_bert_base_cased_en.md b/docs/_posts/ahmedlone127/2023-10-25-finetuned_iitpmovie_additionalpretrained_bert_base_cased_en.md new file mode 100644 index 00000000000000..e44718c50789b3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-finetuned_iitpmovie_additionalpretrained_bert_base_cased_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English finetuned_iitpmovie_additionalpretrained_bert_base_cased BertForSequenceClassification from aditeyabaral +author: John Snow Labs +name: finetuned_iitpmovie_additionalpretrained_bert_base_cased +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finetuned_iitpmovie_additionalpretrained_bert_base_cased` is a English model originally trained by aditeyabaral. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finetuned_iitpmovie_additionalpretrained_bert_base_cased_en_5.1.4_3.4_1698273021943.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finetuned_iitpmovie_additionalpretrained_bert_base_cased_en_5.1.4_3.4_1698273021943.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_iitpmovie_additionalpretrained_bert_base_cased","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_iitpmovie_additionalpretrained_bert_base_cased","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finetuned_iitpmovie_additionalpretrained_bert_base_cased| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/aditeyabaral/finetuned-iitpmovie-additionalpretrained-bert-base-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-finetuned_sail2017_additionalpretrained_bert_base_cased_en.md b/docs/_posts/ahmedlone127/2023-10-25-finetuned_sail2017_additionalpretrained_bert_base_cased_en.md new file mode 100644 index 00000000000000..2f49078b049b8c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-finetuned_sail2017_additionalpretrained_bert_base_cased_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English finetuned_sail2017_additionalpretrained_bert_base_cased BertForSequenceClassification from aditeyabaral +author: John Snow Labs +name: finetuned_sail2017_additionalpretrained_bert_base_cased +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finetuned_sail2017_additionalpretrained_bert_base_cased` is a English model originally trained by aditeyabaral. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finetuned_sail2017_additionalpretrained_bert_base_cased_en_5.1.4_3.4_1698273843099.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finetuned_sail2017_additionalpretrained_bert_base_cased_en_5.1.4_3.4_1698273843099.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_sail2017_additionalpretrained_bert_base_cased","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_sail2017_additionalpretrained_bert_base_cased","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finetuned_sail2017_additionalpretrained_bert_base_cased| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/aditeyabaral/finetuned-sail2017-additionalpretrained-bert-base-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-finetuned_sail2017_bert_base_cased_en.md b/docs/_posts/ahmedlone127/2023-10-25-finetuned_sail2017_bert_base_cased_en.md new file mode 100644 index 00000000000000..ac2f9a90cdae01 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-finetuned_sail2017_bert_base_cased_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English finetuned_sail2017_bert_base_cased BertForSequenceClassification from aditeyabaral +author: John Snow Labs +name: finetuned_sail2017_bert_base_cased +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finetuned_sail2017_bert_base_cased` is a English model originally trained by aditeyabaral. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finetuned_sail2017_bert_base_cased_en_5.1.4_3.4_1698274459616.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finetuned_sail2017_bert_base_cased_en_5.1.4_3.4_1698274459616.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_sail2017_bert_base_cased","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("finetuned_sail2017_bert_base_cased","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finetuned_sail2017_bert_base_cased| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/aditeyabaral/finetuned-sail2017-bert-base-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-gbert_base_ft_edu_redux_de.md b/docs/_posts/ahmedlone127/2023-10-25-gbert_base_ft_edu_redux_de.md new file mode 100644 index 00000000000000..d5dab38447a7e4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-gbert_base_ft_edu_redux_de.md @@ -0,0 +1,97 @@ +--- +layout: model +title: German gbert_base_ft_edu_redux BertForSequenceClassification from gonzpen +author: John Snow Labs +name: gbert_base_ft_edu_redux +date: 2023-10-25 +tags: [bert, de, open_source, sequence_classification, onnx] +task: Text Classification +language: de +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`gbert_base_ft_edu_redux` is a German model originally trained by gonzpen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gbert_base_ft_edu_redux_de_5.1.4_3.4_1698248491071.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gbert_base_ft_edu_redux_de_5.1.4_3.4_1698248491071.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("gbert_base_ft_edu_redux","de")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("gbert_base_ft_edu_redux","de") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gbert_base_ft_edu_redux| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|de| +|Size:|412.0 MB| + +## References + +https://huggingface.co/gonzpen/gbert-base-ft-edu-redux \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-gbert_base_germeval21_toxic_en.md b/docs/_posts/ahmedlone127/2023-10-25-gbert_base_germeval21_toxic_en.md new file mode 100644 index 00000000000000..9466a85bfc6b69 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-gbert_base_germeval21_toxic_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English gbert_base_germeval21_toxic BertForSequenceClassification from airKlizz +author: John Snow Labs +name: gbert_base_germeval21_toxic +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`gbert_base_germeval21_toxic` is a English model originally trained by airKlizz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gbert_base_germeval21_toxic_en_5.1.4_3.4_1698277581036.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gbert_base_germeval21_toxic_en_5.1.4_3.4_1698277581036.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("gbert_base_germeval21_toxic","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("gbert_base_germeval21_toxic","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gbert_base_germeval21_toxic| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|412.0 MB| + +## References + +https://huggingface.co/airKlizz/gbert-base-germeval21-toxic \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-gbert_base_germeval21_toxic_with_data_augmentation_en.md b/docs/_posts/ahmedlone127/2023-10-25-gbert_base_germeval21_toxic_with_data_augmentation_en.md new file mode 100644 index 00000000000000..ef757c1e4b6f16 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-gbert_base_germeval21_toxic_with_data_augmentation_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English gbert_base_germeval21_toxic_with_data_augmentation BertForSequenceClassification from airKlizz +author: John Snow Labs +name: gbert_base_germeval21_toxic_with_data_augmentation +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`gbert_base_germeval21_toxic_with_data_augmentation` is a English model originally trained by airKlizz. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gbert_base_germeval21_toxic_with_data_augmentation_en_5.1.4_3.4_1698276924848.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gbert_base_germeval21_toxic_with_data_augmentation_en_5.1.4_3.4_1698276924848.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("gbert_base_germeval21_toxic_with_data_augmentation","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("gbert_base_germeval21_toxic_with_data_augmentation","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gbert_base_germeval21_toxic_with_data_augmentation| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|412.0 MB| + +## References + +https://huggingface.co/airKlizz/gbert-base-germeval21-toxic-with-data-augmentation \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-gbert_large_ft_edu_redux_de.md b/docs/_posts/ahmedlone127/2023-10-25-gbert_large_ft_edu_redux_de.md new file mode 100644 index 00000000000000..47a9010f53d233 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-gbert_large_ft_edu_redux_de.md @@ -0,0 +1,97 @@ +--- +layout: model +title: German gbert_large_ft_edu_redux BertForSequenceClassification from gonzpen +author: John Snow Labs +name: gbert_large_ft_edu_redux +date: 2023-10-25 +tags: [bert, de, open_source, sequence_classification, onnx] +task: Text Classification +language: de +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`gbert_large_ft_edu_redux` is a German model originally trained by gonzpen. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/gbert_large_ft_edu_redux_de_5.1.4_3.4_1698251634199.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gbert_large_ft_edu_redux_de_5.1.4_3.4_1698251634199.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("gbert_large_ft_edu_redux","de")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("gbert_large_ft_edu_redux","de") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|gbert_large_ft_edu_redux| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|de| +|Size:|1.3 GB| + +## References + +https://huggingface.co/gonzpen/gbert-large-ft-edu-redux \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-german_tweetstance_bert_uncased_russiaukrainewar_de.md b/docs/_posts/ahmedlone127/2023-10-25-german_tweetstance_bert_uncased_russiaukrainewar_de.md new file mode 100644 index 00000000000000..9d22e58d14280d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-german_tweetstance_bert_uncased_russiaukrainewar_de.md @@ -0,0 +1,97 @@ +--- +layout: model +title: German german_tweetstance_bert_uncased_russiaukrainewar BertForSequenceClassification from joh-ga +author: John Snow Labs +name: german_tweetstance_bert_uncased_russiaukrainewar +date: 2023-10-25 +tags: [bert, de, open_source, sequence_classification, onnx] +task: Text Classification +language: de +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`german_tweetstance_bert_uncased_russiaukrainewar` is a German model originally trained by joh-ga. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/german_tweetstance_bert_uncased_russiaukrainewar_de_5.1.4_3.4_1698263251275.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/german_tweetstance_bert_uncased_russiaukrainewar_de_5.1.4_3.4_1698263251275.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("german_tweetstance_bert_uncased_russiaukrainewar","de")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("german_tweetstance_bert_uncased_russiaukrainewar","de") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|german_tweetstance_bert_uncased_russiaukrainewar| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|de| +|Size:|412.1 MB| + +## References + +https://huggingface.co/joh-ga/german-tweetstance-bert-uncased-russiaukrainewar \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_29_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_29_en.md new file mode 100644 index 00000000000000..5be4e0b72efe6b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_29_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_29 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_29 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_29` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_29_en_5.1.4_3.4_1698261490840.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_29_en_5.1.4_3.4_1698261490840.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_29","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_29","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_29| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-29 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_78_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_78_en.md new file mode 100644 index 00000000000000..35654fca346898 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_78_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_78 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_78 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_78` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_78_en_5.1.4_3.4_1698254237854.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_78_en_5.1.4_3.4_1698254237854.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_78","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_78","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_78| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-78 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_79_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_79_en.md new file mode 100644 index 00000000000000..7bdab1fc191d1d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_79_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_79 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_79 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_79` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_79_en_5.1.4_3.4_1698253479821.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_79_en_5.1.4_3.4_1698253479821.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_79","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_79","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_79| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-79 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_80_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_80_en.md new file mode 100644 index 00000000000000..b9a7a87df6b6f1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_80_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_80 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_80 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_80` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_80_en_5.1.4_3.4_1698255160718.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_80_en_5.1.4_3.4_1698255160718.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_80","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_80","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_80| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-80 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_83_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_83_en.md new file mode 100644 index 00000000000000..a2add8f63a0ebc --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_83_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_83 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_83 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_83` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_83_en_5.1.4_3.4_1698251953324.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_83_en_5.1.4_3.4_1698251953324.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_83","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_83","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_83| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-83 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_84_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_84_en.md new file mode 100644 index 00000000000000..ca21133556868d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_84_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_84 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_84 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_84` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_84_en_5.1.4_3.4_1698251201168.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_84_en_5.1.4_3.4_1698251201168.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_84","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_84","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_84| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-84 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_85_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_85_en.md new file mode 100644 index 00000000000000..281e6b0d2ecaaf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_85_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_85 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_85 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_85` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_85_en_5.1.4_3.4_1698252780778.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_85_en_5.1.4_3.4_1698252780778.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_85","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_85","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_85| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-85 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_86_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_86_en.md new file mode 100644 index 00000000000000..3466fadb0f31c3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_86_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_86 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_86 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_86` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_86_en_5.1.4_3.4_1698248341656.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_86_en_5.1.4_3.4_1698248341656.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_86","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_86","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_86| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-86 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_87_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_87_en.md new file mode 100644 index 00000000000000..7a514aa240f585 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_87_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_87 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_87 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_87` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_87_en_5.1.4_3.4_1698247468504.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_87_en_5.1.4_3.4_1698247468504.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_87","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_87","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_87| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-87 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_88_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_88_en.md new file mode 100644 index 00000000000000..b3a64774ab8e19 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_88_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_88 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_88 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_88` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_88_en_5.1.4_3.4_1698246785005.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_88_en_5.1.4_3.4_1698246785005.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_88","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_88","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_88| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-88 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_92_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_92_en.md new file mode 100644 index 00000000000000..90c44883d204e6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_92_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_92 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_92 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_92` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_92_en_5.1.4_3.4_1698249166450.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_92_en_5.1.4_3.4_1698249166450.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_92","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_92","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_92| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-92 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_93_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_93_en.md new file mode 100644 index 00000000000000..b0c97c30632cb7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_93_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_93 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_93 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_93` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_93_en_5.1.4_3.4_1698250033474.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_93_en_5.1.4_3.4_1698250033474.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_93","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_93","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_93| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-93 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_94_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_94_en.md new file mode 100644 index 00000000000000..8902eaac080cd7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_94_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_94 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_94 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_94` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_94_en_5.1.4_3.4_1698260494787.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_94_en_5.1.4_3.4_1698260494787.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_94","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_94","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_94| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-94 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_95_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_95_en.md new file mode 100644 index 00000000000000..5720bd611dd1bd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_95_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_95 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_95 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_95` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_95_en_5.1.4_3.4_1698257018191.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_95_en_5.1.4_3.4_1698257018191.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_95","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_95","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_95| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-95 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_96_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_96_en.md new file mode 100644 index 00000000000000..cb12b9c5c8cfd3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_96_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_96 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_96 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_96` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_96_en_5.1.4_3.4_1698257719496.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_96_en_5.1.4_3.4_1698257719496.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_96","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_96","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_96| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-96 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_97_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_97_en.md new file mode 100644 index 00000000000000..1aff33221e951d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_97_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_97 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_97 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_97` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_97_en_5.1.4_3.4_1698259651200.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_97_en_5.1.4_3.4_1698259651200.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_97","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_97","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_97| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-97 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_98_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_98_en.md new file mode 100644 index 00000000000000..2093f7c7259197 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_98_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_98 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_98 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_98` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_98_en_5.1.4_3.4_1698258612165.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_98_en_5.1.4_3.4_1698258612165.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_98","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_98","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_98| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-98 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_99_en.md b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_99_en.md new file mode 100644 index 00000000000000..66d2d17970cf0d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-goog_bert_ft_cola_99_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English goog_bert_ft_cola_99 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: goog_bert_ft_cola_99 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`goog_bert_ft_cola_99` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_99_en_5.1.4_3.4_1698256093328.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/goog_bert_ft_cola_99_en_5.1.4_3.4_1698256093328.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_99","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("goog_bert_ft_cola_99","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|goog_bert_ft_cola_99| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/goog_bert_ft_cola-99 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-imdb_bert_base_uncased_kyle1668_en.md b/docs/_posts/ahmedlone127/2023-10-25-imdb_bert_base_uncased_kyle1668_en.md new file mode 100644 index 00000000000000..6d891cd33a40bd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-imdb_bert_base_uncased_kyle1668_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English imdb_bert_base_uncased_kyle1668 BertForSequenceClassification from Kyle1668 +author: John Snow Labs +name: imdb_bert_base_uncased_kyle1668 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`imdb_bert_base_uncased_kyle1668` is a English model originally trained by Kyle1668. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/imdb_bert_base_uncased_kyle1668_en_5.1.4_3.4_1698246411737.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/imdb_bert_base_uncased_kyle1668_en_5.1.4_3.4_1698246411737.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("imdb_bert_base_uncased_kyle1668","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("imdb_bert_base_uncased_kyle1668","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|imdb_bert_base_uncased_kyle1668| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Kyle1668/imdb-bert-base-uncased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-kcbert_en.md b/docs/_posts/ahmedlone127/2023-10-25-kcbert_en.md new file mode 100644 index 00000000000000..11f1e0535b5962 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-kcbert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English kcbert BertForSequenceClassification from jiiyy +author: John Snow Labs +name: kcbert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kcbert` is a English model originally trained by jiiyy. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kcbert_en_5.1.4_3.4_1698257627926.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kcbert_en_5.1.4_3.4_1698257627926.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("kcbert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("kcbert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kcbert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|408.4 MB| + +## References + +https://huggingface.co/jiiyy/kcbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-klue_bert_base_re_ainize_en.md b/docs/_posts/ahmedlone127/2023-10-25-klue_bert_base_re_ainize_en.md new file mode 100644 index 00000000000000..cab0aa0a501d53 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-klue_bert_base_re_ainize_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English klue_bert_base_re_ainize BertForSequenceClassification from ainize +author: John Snow Labs +name: klue_bert_base_re_ainize +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`klue_bert_base_re_ainize` is a English model originally trained by ainize. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/klue_bert_base_re_ainize_en_5.1.4_3.4_1698276026066.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/klue_bert_base_re_ainize_en_5.1.4_3.4_1698276026066.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("klue_bert_base_re_ainize","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("klue_bert_base_re_ainize","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|klue_bert_base_re_ainize| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.7 MB| + +## References + +https://huggingface.co/ainize/klue-bert-base-re \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-legal_bert_based_uncase_en.md b/docs/_posts/ahmedlone127/2023-10-25-legal_bert_based_uncase_en.md new file mode 100644 index 00000000000000..f1abf912760c7c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-legal_bert_based_uncase_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English legal_bert_based_uncase BertForSequenceClassification from amanbawa96 +author: John Snow Labs +name: legal_bert_based_uncase +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`legal_bert_based_uncase` is a English model originally trained by amanbawa96. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/legal_bert_based_uncase_en_5.1.4_3.4_1698264650160.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/legal_bert_based_uncase_en_5.1.4_3.4_1698264650160.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("legal_bert_based_uncase","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("legal_bert_based_uncase","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legal_bert_based_uncase| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.6 MB| + +## References + +https://huggingface.co/amanbawa96/legal-bert-based-uncase \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mascorpus_bert_classifier_en.md b/docs/_posts/ahmedlone127/2023-10-25-mascorpus_bert_classifier_en.md new file mode 100644 index 00000000000000..9279c06b51faff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mascorpus_bert_classifier_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mascorpus_bert_classifier BertForSequenceClassification from fce-m72109 +author: John Snow Labs +name: mascorpus_bert_classifier +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mascorpus_bert_classifier` is a English model originally trained by fce-m72109. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mascorpus_bert_classifier_en_5.1.4_3.4_1698271464648.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mascorpus_bert_classifier_en_5.1.4_3.4_1698271464648.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mascorpus_bert_classifier","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mascorpus_bert_classifier","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mascorpus_bert_classifier| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.9 MB| + +## References + +https://huggingface.co/fce-m72109/mascorpus-bert-classifier \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_10_en.md b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_10_en.md new file mode 100644 index 00000000000000..690f7962a21139 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_10_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_10 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_10 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_10` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_10_en_5.1.4_3.4_1698276807226.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_10_en_5.1.4_3.4_1698276807226.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_10","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_10","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_10| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.1 MB| + +## References + +https://huggingface.co/hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSEC-10 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_1_en.md b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_1_en.md new file mode 100644 index 00000000000000..449cf891785747 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_1_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_1 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_1 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_1` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_1_en_5.1.4_3.4_1698269142584.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_1_en_5.1.4_3.4_1698269142584.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_1","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_1","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.1 MB| + +## References + +https://huggingface.co/hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSEC-1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_2_en.md b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_2_en.md new file mode 100644 index 00000000000000..9898e35ba3109a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_2 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_2 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_2` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_2_en_5.1.4_3.4_1698270054619.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_2_en_5.1.4_3.4_1698270054619.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.1 MB| + +## References + +https://huggingface.co/hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSEC-2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_3_en.md b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_3_en.md new file mode 100644 index 00000000000000..6e8a3de4513985 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_3_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_3 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_3 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_3` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_3_en_5.1.4_3.4_1698270778170.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_3_en_5.1.4_3.4_1698270778170.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_3","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_3","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_3| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.1 MB| + +## References + +https://huggingface.co/hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSEC-3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_4_en.md b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_4_en.md new file mode 100644 index 00000000000000..1a1992e3cd3e7a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_4_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_4 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_4 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_4` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_4_en_5.1.4_3.4_1698271547606.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_4_en_5.1.4_3.4_1698271547606.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_4","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_4","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_4| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.1 MB| + +## References + +https://huggingface.co/hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSEC-4 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_5_en.md b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_5_en.md new file mode 100644 index 00000000000000..cc2d1a49acd000 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_5_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_5 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_5 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_5` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_5_en_5.1.4_3.4_1698272598012.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_5_en_5.1.4_3.4_1698272598012.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_5","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_5","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_5| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.1 MB| + +## References + +https://huggingface.co/hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSEC-5 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_6_en.md b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_6_en.md new file mode 100644 index 00000000000000..9879b1bf039ef9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_6_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_6 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_6 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_6` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_6_en_5.1.4_3.4_1698273428030.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_6_en_5.1.4_3.4_1698273428030.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_6","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_6","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_6| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.1 MB| + +## References + +https://huggingface.co/hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSEC-6 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_7_en.md b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_7_en.md new file mode 100644 index 00000000000000..fbee242a58efe4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_7_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_7 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_7 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_7` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_7_en_5.1.4_3.4_1698274391531.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_7_en_5.1.4_3.4_1698274391531.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_7","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_7","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_7| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.1 MB| + +## References + +https://huggingface.co/hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSEC-7 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_8_en.md b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_8_en.md new file mode 100644 index 00000000000000..c558821e6f6065 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_8 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_8 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_8` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_8_en_5.1.4_3.4_1698274939326.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_8_en_5.1.4_3.4_1698274939326.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.1 MB| + +## References + +https://huggingface.co/hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSEC-8 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_9_en.md b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_9_en.md new file mode 100644 index 00000000000000..12437995f69126 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_9_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_9 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_9 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_9` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_9_en_5.1.4_3.4_1698275862469.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_9_en_5.1.4_3.4_1698275862469.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_9","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_9","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_9| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.1 MB| + +## References + +https://huggingface.co/hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSEC-9 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-model3_marabertv2_t1_en.md b/docs/_posts/ahmedlone127/2023-10-25-model3_marabertv2_t1_en.md new file mode 100644 index 00000000000000..3cb7150d933069 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-model3_marabertv2_t1_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English model3_marabertv2_t1 BertForSequenceClassification from Somah +author: John Snow Labs +name: model3_marabertv2_t1 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`model3_marabertv2_t1` is a English model originally trained by Somah. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/model3_marabertv2_t1_en_5.1.4_3.4_1698268582056.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/model3_marabertv2_t1_en_5.1.4_3.4_1698268582056.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("model3_marabertv2_t1","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("model3_marabertv2_t1","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|model3_marabertv2_t1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|608.8 MB| + +## References + +https://huggingface.co/Somah/Model3_Marabertv2_T1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_12_en.md b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_12_en.md new file mode 100644 index 00000000000000..7e28c81857130f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_12_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English norbert2_sentiment_norec_12 BertForSequenceClassification from NTCAL +author: John Snow Labs +name: norbert2_sentiment_norec_12 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`norbert2_sentiment_norec_12` is a English model originally trained by NTCAL. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_12_en_5.1.4_3.4_1698267978688.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_12_en_5.1.4_3.4_1698267978688.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_12","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_12","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|norbert2_sentiment_norec_12| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/NTCAL/norbert2_sentiment_norec_12 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_13_en.md b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_13_en.md new file mode 100644 index 00000000000000..396647481e0cdd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_13_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English norbert2_sentiment_norec_13 BertForSequenceClassification from NTCAL +author: John Snow Labs +name: norbert2_sentiment_norec_13 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`norbert2_sentiment_norec_13` is a English model originally trained by NTCAL. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_13_en_5.1.4_3.4_1698271367051.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_13_en_5.1.4_3.4_1698271367051.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_13","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_13","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|norbert2_sentiment_norec_13| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/NTCAL/norbert2_sentiment_norec_13 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_14_en.md b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_14_en.md new file mode 100644 index 00000000000000..99a892aa2819c5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_14_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English norbert2_sentiment_norec_14 BertForSequenceClassification from NTCAL +author: John Snow Labs +name: norbert2_sentiment_norec_14 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`norbert2_sentiment_norec_14` is a English model originally trained by NTCAL. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_14_en_5.1.4_3.4_1698273021725.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_14_en_5.1.4_3.4_1698273021725.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_14","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_14","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|norbert2_sentiment_norec_14| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/NTCAL/norbert2_sentiment_norec_14 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_16_en.md b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_16_en.md new file mode 100644 index 00000000000000..6b9f4b229200e7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_16_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English norbert2_sentiment_norec_16 BertForSequenceClassification from NTCAL +author: John Snow Labs +name: norbert2_sentiment_norec_16 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`norbert2_sentiment_norec_16` is a English model originally trained by NTCAL. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_16_en_5.1.4_3.4_1698274392887.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_16_en_5.1.4_3.4_1698274392887.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_16","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_16","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|norbert2_sentiment_norec_16| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/NTCAL/norbert2_sentiment_norec_16 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_tonga_tonga_islands_gpu_3000_rader_en.md b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_tonga_tonga_islands_gpu_3000_rader_en.md new file mode 100644 index 00000000000000..f435fc28bb4ba9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-norbert2_sentiment_norec_tonga_tonga_islands_gpu_3000_rader_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English norbert2_sentiment_norec_tonga_tonga_islands_gpu_3000_rader BertForSequenceClassification from NTCAL +author: John Snow Labs +name: norbert2_sentiment_norec_tonga_tonga_islands_gpu_3000_rader +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`norbert2_sentiment_norec_tonga_tonga_islands_gpu_3000_rader` is a English model originally trained by NTCAL. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_tonga_tonga_islands_gpu_3000_rader_en_5.1.4_3.4_1698276445338.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/norbert2_sentiment_norec_tonga_tonga_islands_gpu_3000_rader_en_5.1.4_3.4_1698276445338.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_tonga_tonga_islands_gpu_3000_rader","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("norbert2_sentiment_norec_tonga_tonga_islands_gpu_3000_rader","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|norbert2_sentiment_norec_tonga_tonga_islands_gpu_3000_rader| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/NTCAL/norbert2_sentiment_norec_to_gpu_3000_rader \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-rubert_base_cased_conversational_paraphrase_v1_ru.md b/docs/_posts/ahmedlone127/2023-10-25-rubert_base_cased_conversational_paraphrase_v1_ru.md new file mode 100644 index 00000000000000..20b94c1925c599 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-rubert_base_cased_conversational_paraphrase_v1_ru.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Russian rubert_base_cased_conversational_paraphrase_v1 BertForSequenceClassification from s-nlp +author: John Snow Labs +name: rubert_base_cased_conversational_paraphrase_v1 +date: 2023-10-25 +tags: [bert, ru, open_source, sequence_classification, onnx] +task: Text Classification +language: ru +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`rubert_base_cased_conversational_paraphrase_v1` is a Russian model originally trained by s-nlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/rubert_base_cased_conversational_paraphrase_v1_ru_5.1.4_3.4_1698270052372.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/rubert_base_cased_conversational_paraphrase_v1_ru_5.1.4_3.4_1698270052372.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("rubert_base_cased_conversational_paraphrase_v1","ru")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("rubert_base_cased_conversational_paraphrase_v1","ru") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rubert_base_cased_conversational_paraphrase_v1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|ru| +|Size:|664.4 MB| + +## References + +https://huggingface.co/s-nlp/rubert-base-cased-conversational-paraphrase-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-rubert_base_cased_sentiment_nepal_bhasa_ru.md b/docs/_posts/ahmedlone127/2023-10-25-rubert_base_cased_sentiment_nepal_bhasa_ru.md new file mode 100644 index 00000000000000..870674ec46887d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-rubert_base_cased_sentiment_nepal_bhasa_ru.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Russian rubert_base_cased_sentiment_nepal_bhasa BertForSequenceClassification from MonoHime +author: John Snow Labs +name: rubert_base_cased_sentiment_nepal_bhasa +date: 2023-10-25 +tags: [bert, ru, open_source, sequence_classification, onnx] +task: Text Classification +language: ru +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`rubert_base_cased_sentiment_nepal_bhasa` is a Russian model originally trained by MonoHime. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/rubert_base_cased_sentiment_nepal_bhasa_ru_5.1.4_3.4_1698251812853.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/rubert_base_cased_sentiment_nepal_bhasa_ru_5.1.4_3.4_1698251812853.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("rubert_base_cased_sentiment_nepal_bhasa","ru")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("rubert_base_cased_sentiment_nepal_bhasa","ru") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rubert_base_cased_sentiment_nepal_bhasa| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|ru| +|Size:|664.4 MB| + +## References + +https://huggingface.co/MonoHime/rubert-base-cased-sentiment-new \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-rubert_base_corruption_detector_ru.md b/docs/_posts/ahmedlone127/2023-10-25-rubert_base_corruption_detector_ru.md new file mode 100644 index 00000000000000..87c0dc0b65bf61 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-rubert_base_corruption_detector_ru.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Russian rubert_base_corruption_detector BertForSequenceClassification from s-nlp +author: John Snow Labs +name: rubert_base_corruption_detector +date: 2023-10-25 +tags: [bert, ru, open_source, sequence_classification, onnx] +task: Text Classification +language: ru +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`rubert_base_corruption_detector` is a Russian model originally trained by s-nlp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/rubert_base_corruption_detector_ru_5.1.4_3.4_1698248343247.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/rubert_base_corruption_detector_ru_5.1.4_3.4_1698248343247.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("rubert_base_corruption_detector","ru")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("rubert_base_corruption_detector","ru") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rubert_base_corruption_detector| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|ru| +|Size:|666.5 MB| + +## References + +https://huggingface.co/s-nlp/rubert-base-corruption-detector \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-rubert_sentence_mixin_en.md b/docs/_posts/ahmedlone127/2023-10-25-rubert_sentence_mixin_en.md new file mode 100644 index 00000000000000..328c4d67e30ce5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-rubert_sentence_mixin_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English rubert_sentence_mixin BertForSequenceClassification from OlegOrwell +author: John Snow Labs +name: rubert_sentence_mixin +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`rubert_sentence_mixin` is a English model originally trained by OlegOrwell. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/rubert_sentence_mixin_en_5.1.4_3.4_1698254334062.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/rubert_sentence_mixin_en_5.1.4_3.4_1698254334062.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("rubert_sentence_mixin","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("rubert_sentence_mixin","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|rubert_sentence_mixin| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|666.5 MB| + +## References + +https://huggingface.co/OlegOrwell/rubert_sentence_mixin \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-scibert_scivocab_uncased_ft_m3_lc_en.md b/docs/_posts/ahmedlone127/2023-10-25-scibert_scivocab_uncased_ft_m3_lc_en.md new file mode 100644 index 00000000000000..44e783f57fd124 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-scibert_scivocab_uncased_ft_m3_lc_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English scibert_scivocab_uncased_ft_m3_lc BertForSequenceClassification from sarahmiller137 +author: John Snow Labs +name: scibert_scivocab_uncased_ft_m3_lc +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scibert_scivocab_uncased_ft_m3_lc` is a English model originally trained by sarahmiller137. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scibert_scivocab_uncased_ft_m3_lc_en_5.1.4_3.4_1698266550136.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scibert_scivocab_uncased_ft_m3_lc_en_5.1.4_3.4_1698266550136.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("scibert_scivocab_uncased_ft_m3_lc","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("scibert_scivocab_uncased_ft_m3_lc","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scibert_scivocab_uncased_ft_m3_lc| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|412.2 MB| + +## References + +https://huggingface.co/sarahmiller137/scibert-scivocab-uncased-ft-m3-lc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-sena_finetuned_bert_en.md b/docs/_posts/ahmedlone127/2023-10-25-sena_finetuned_bert_en.md new file mode 100644 index 00000000000000..7db5bb803c9916 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-sena_finetuned_bert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English sena_finetuned_bert BertForSequenceClassification from snadsdmr +author: John Snow Labs +name: sena_finetuned_bert +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`sena_finetuned_bert` is a English model originally trained by snadsdmr. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/sena_finetuned_bert_en_5.1.4_3.4_1698250033431.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sena_finetuned_bert_en_5.1.4_3.4_1698250033431.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("sena_finetuned_bert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("sena_finetuned_bert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|sena_finetuned_bert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/snadsdmr/sena_finetuned_bert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_mnli_m_distilled_en.md b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_mnli_m_distilled_en.md new file mode 100644 index 00000000000000..06c2eb7b1fe359 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_mnli_m_distilled_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_mnli_m_distilled BertForSequenceClassification from Sayan01 +author: John Snow Labs +name: tiny_bert_mnli_m_distilled +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_mnli_m_distilled` is a English model originally trained by Sayan01. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_mnli_m_distilled_en_5.1.4_3.4_1698265199430.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_mnli_m_distilled_en_5.1.4_3.4_1698265199430.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_mnli_m_distilled","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_mnli_m_distilled","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_mnli_m_distilled| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|54.2 MB| + +## References + +https://huggingface.co/Sayan01/tiny-bert-mnli-m-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_mnli_mm_distilled_en.md b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_mnli_mm_distilled_en.md new file mode 100644 index 00000000000000..3475fe4bb0b79a --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_mnli_mm_distilled_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_mnli_mm_distilled BertForSequenceClassification from Sayan01 +author: John Snow Labs +name: tiny_bert_mnli_mm_distilled +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_mnli_mm_distilled` is a English model originally trained by Sayan01. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_mnli_mm_distilled_en_5.1.4_3.4_1698268786072.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_mnli_mm_distilled_en_5.1.4_3.4_1698268786072.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_mnli_mm_distilled","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_mnli_mm_distilled","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_mnli_mm_distilled| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|54.2 MB| + +## References + +https://huggingface.co/Sayan01/tiny-bert-mnli-mm-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_qqp_128_distilled_en.md b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_qqp_128_distilled_en.md new file mode 100644 index 00000000000000..dddb665505352f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_qqp_128_distilled_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_qqp_128_distilled BertForSequenceClassification from Sayan01 +author: John Snow Labs +name: tiny_bert_qqp_128_distilled +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_qqp_128_distilled` is a English model originally trained by Sayan01. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_qqp_128_distilled_en_5.1.4_3.4_1698273521563.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_qqp_128_distilled_en_5.1.4_3.4_1698273521563.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_qqp_128_distilled","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_qqp_128_distilled","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_qqp_128_distilled| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/Sayan01/tiny-bert-qqp-128-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_kaitlineryan99_en.md b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_kaitlineryan99_en.md new file mode 100644 index 00000000000000..fc7210108e04e5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_kaitlineryan99_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_sst2_distilled_kaitlineryan99 BertForSequenceClassification from kaitlineryan99 +author: John Snow Labs +name: tiny_bert_sst2_distilled_kaitlineryan99 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_sst2_distilled_kaitlineryan99` is a English model originally trained by kaitlineryan99. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_sst2_distilled_kaitlineryan99_en_5.1.4_3.4_1698270563429.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_sst2_distilled_kaitlineryan99_en_5.1.4_3.4_1698270563429.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_sst2_distilled_kaitlineryan99","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_sst2_distilled_kaitlineryan99","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_sst2_distilled_kaitlineryan99| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/kaitlineryan99/tiny-bert-sst2-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_l4_h_512_new_en.md b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_l4_h_512_new_en.md new file mode 100644 index 00000000000000..fa7d7f8643fce7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_sst2_distilled_l4_h_512_new_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_sst2_distilled_l4_h_512_new BertForSequenceClassification from Smith123 +author: John Snow Labs +name: tiny_bert_sst2_distilled_l4_h_512_new +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_sst2_distilled_l4_h_512_new` is a English model originally trained by Smith123. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_sst2_distilled_l4_h_512_new_en_5.1.4_3.4_1698265791333.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_sst2_distilled_l4_h_512_new_en_5.1.4_3.4_1698265791333.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_sst2_distilled_l4_h_512_new","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_sst2_distilled_l4_h_512_new","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_sst2_distilled_l4_h_512_new| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|197.3 MB| + +## References + +https://huggingface.co/Smith123/tiny-bert-sst2-distilled_L4_H_512_New \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_stsb_distilled_en.md b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_stsb_distilled_en.md new file mode 100644 index 00000000000000..460dc400beeca7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tiny_bert_stsb_distilled_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_stsb_distilled BertForSequenceClassification from Sayan01 +author: John Snow Labs +name: tiny_bert_stsb_distilled +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_stsb_distilled` is a English model originally trained by Sayan01. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_stsb_distilled_en_5.1.4_3.4_1698269125608.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_stsb_distilled_en_5.1.4_3.4_1698269125608.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_stsb_distilled","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_stsb_distilled","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_stsb_distilled| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|54.2 MB| + +## References + +https://huggingface.co/Sayan01/tiny-bert-stsb-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tweet_bert_1408_en.md b/docs/_posts/ahmedlone127/2023-10-25-tweet_bert_1408_en.md new file mode 100644 index 00000000000000..c81973e035dab2 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tweet_bert_1408_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tweet_bert_1408 BertForSequenceClassification from dsmsb +author: John Snow Labs +name: tweet_bert_1408 +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tweet_bert_1408` is a English model originally trained by dsmsb. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tweet_bert_1408_en_5.1.4_3.4_1698266582244.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tweet_bert_1408_en_5.1.4_3.4_1698266582244.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tweet_bert_1408","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tweet_bert_1408","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tweet_bert_1408| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|667.3 MB| + +## References + +https://huggingface.co/dsmsb/tweet_bert_1408 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-tyson_bert_base_cased_en.md b/docs/_posts/ahmedlone127/2023-10-25-tyson_bert_base_cased_en.md new file mode 100644 index 00000000000000..564654d2570295 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-tyson_bert_base_cased_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tyson_bert_base_cased BertForSequenceClassification from tyson0420 +author: John Snow Labs +name: tyson_bert_base_cased +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tyson_bert_base_cased` is a English model originally trained by tyson0420. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tyson_bert_base_cased_en_5.1.4_3.4_1698265052918.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tyson_bert_base_cased_en_5.1.4_3.4_1698265052918.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tyson_bert_base_cased","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tyson_bert_base_cased","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tyson_bert_base_cased| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/tyson0420/tyson-bert-base-cased \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-ziwei_bert_imdb_en.md b/docs/_posts/ahmedlone127/2023-10-25-ziwei_bert_imdb_en.md new file mode 100644 index 00000000000000..67179ddbed43e5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-ziwei_bert_imdb_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English ziwei_bert_imdb BertForSequenceClassification from ZiweiG +author: John Snow Labs +name: ziwei_bert_imdb +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ziwei_bert_imdb` is a English model originally trained by ZiweiG. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ziwei_bert_imdb_en_5.1.4_3.4_1698269066935.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ziwei_bert_imdb_en_5.1.4_3.4_1698269066935.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("ziwei_bert_imdb","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("ziwei_bert_imdb","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ziwei_bert_imdb| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/ZiweiG/ziwei-bert-imdb \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-25-ziwei_bertimdb_prob_en.md b/docs/_posts/ahmedlone127/2023-10-25-ziwei_bertimdb_prob_en.md new file mode 100644 index 00000000000000..b3f573a54107c0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-25-ziwei_bertimdb_prob_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English ziwei_bertimdb_prob BertForSequenceClassification from ZiweiG +author: John Snow Labs +name: ziwei_bertimdb_prob +date: 2023-10-25 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ziwei_bertimdb_prob` is a English model originally trained by ZiweiG. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ziwei_bertimdb_prob_en_5.1.4_3.4_1698269802501.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ziwei_bertimdb_prob_en_5.1.4_3.4_1698269802501.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("ziwei_bertimdb_prob","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("ziwei_bertimdb_prob","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|ziwei_bertimdb_prob| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/ZiweiG/ziwei-bertimdb-prob \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_31_en.md b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_31_en.md new file mode 100644 index 00000000000000..be459d228640c9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_31_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_31 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_31 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_31` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_31_en_5.1.4_3.4_1698279222298.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_31_en_5.1.4_3.4_1698279222298.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_31","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_31","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_31| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-31 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_32_en.md b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_32_en.md new file mode 100644 index 00000000000000..28ca593f6fc85c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_32_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_32 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_32 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_32` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_32_en_5.1.4_3.4_1698280115141.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_32_en_5.1.4_3.4_1698280115141.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_32","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_32","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_32| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-32 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_33_en.md b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_33_en.md new file mode 100644 index 00000000000000..d4852066bf9432 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_33_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_33 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_33 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_33` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_33_en_5.1.4_3.4_1698280969297.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_33_en_5.1.4_3.4_1698280969297.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_33","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_33","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_33| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-33 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_34_en.md b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_34_en.md new file mode 100644 index 00000000000000..4f5650c1de1ff7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_34_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_34 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_34 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_34` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_34_en_5.1.4_3.4_1698281806567.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_34_en_5.1.4_3.4_1698281806567.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_34","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_34","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_34| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-34 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_35_en.md b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_35_en.md new file mode 100644 index 00000000000000..9279975d9e2b40 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_35_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_35 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_35 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_35` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_35_en_5.1.4_3.4_1698282610041.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_35_en_5.1.4_3.4_1698282610041.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_35","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_35","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_35| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-35 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_36_en.md b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_36_en.md new file mode 100644 index 00000000000000..63e966ea315dd6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_36_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_36 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_36 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_36` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_36_en_5.1.4_3.4_1698283422017.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_36_en_5.1.4_3.4_1698283422017.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_36","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_36","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_36| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-36 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_37_en.md b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_37_en.md new file mode 100644 index 00000000000000..336e55ac3050a6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_37_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_37 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_37 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_37` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_37_en_5.1.4_3.4_1698284352951.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_37_en_5.1.4_3.4_1698284352951.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_37","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_37","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_37| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-37 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_38_en.md b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_38_en.md new file mode 100644 index 00000000000000..0efa5d1dbe9a86 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_38_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_38 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_38 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_38` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_38_en_5.1.4_3.4_1698285115826.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_38_en_5.1.4_3.4_1698285115826.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_38","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_38","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_38| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-38 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_39_en.md b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_39_en.md new file mode 100644 index 00000000000000..23d04e5dfc9af5 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_39_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_39 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_39 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_39` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_39_en_5.1.4_3.4_1698286112629.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_39_en_5.1.4_3.4_1698286112629.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_39","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_39","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_39| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-39 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_40_en.md b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_40_en.md new file mode 100644 index 00000000000000..3426f7c18c47d4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_40_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_40 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_40 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_40` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_40_en_5.1.4_3.4_1698287281956.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_40_en_5.1.4_3.4_1698287281956.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_40","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_40","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_40| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-40 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_41_en.md b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_41_en.md new file mode 100644 index 00000000000000..93d42590296b2d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_41_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_41 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_41 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_41` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_41_en_5.1.4_3.4_1698288102738.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_41_en_5.1.4_3.4_1698288102738.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_41","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_41","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_41| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-41 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_42_en.md b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_42_en.md new file mode 100644 index 00000000000000..7fe1a22bb1ba44 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_42_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_42 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_42 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_42` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_42_en_5.1.4_3.4_1698288911852.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_42_en_5.1.4_3.4_1698288911852.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_42","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_42","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_42| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-42 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_43_en.md b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_43_en.md new file mode 100644 index 00000000000000..63508b531c145e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_43_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_43 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_43 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_43` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_43_en_5.1.4_3.4_1698289802693.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_43_en_5.1.4_3.4_1698289802693.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_43","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_43","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_43| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-43 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_44_en.md b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_44_en.md new file mode 100644 index 00000000000000..be5bf80e190d33 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_44_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_44 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_44 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_44` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_44_en_5.1.4_3.4_1698290656405.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_44_en_5.1.4_3.4_1698290656405.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_44","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_44","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_44| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-44 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_45_en.md b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_45_en.md new file mode 100644 index 00000000000000..0b3f9bf1fbb430 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_45_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_45 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_45 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_45` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_45_en_5.1.4_3.4_1698291394442.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_45_en_5.1.4_3.4_1698291394442.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_45","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_45","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_45| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-45 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_46_en.md b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_46_en.md new file mode 100644 index 00000000000000..25884f90e3c889 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_46_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_46 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_46 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_46` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_46_en_5.1.4_3.4_1698292222009.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_46_en_5.1.4_3.4_1698292222009.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_46","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_46","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_46| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-46 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_47_en.md b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_47_en.md new file mode 100644 index 00000000000000..486e7412dfba3e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_47_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_47 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_47 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_47` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_47_en_5.1.4_3.4_1698292980207.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_47_en_5.1.4_3.4_1698292980207.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_47","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_47","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_47| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-47 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_48_en.md b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_48_en.md new file mode 100644 index 00000000000000..2ff46a75a01552 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-6ep_bert_ft_cola_48_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English 6ep_bert_ft_cola_48 BertForSequenceClassification from Jeevesh8 +author: John Snow Labs +name: 6ep_bert_ft_cola_48 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`6ep_bert_ft_cola_48` is a English model originally trained by Jeevesh8. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_48_en_5.1.4_3.4_1698294005792.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/6ep_bert_ft_cola_48_en_5.1.4_3.4_1698294005792.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_48","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("6ep_bert_ft_cola_48","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|6ep_bert_ft_cola_48| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-48 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-autonlp_alberti_stanza_names_34318169_en.md b/docs/_posts/ahmedlone127/2023-10-26-autonlp_alberti_stanza_names_34318169_en.md new file mode 100644 index 00000000000000..7d432149903c0b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-autonlp_alberti_stanza_names_34318169_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English autonlp_alberti_stanza_names_34318169 BertForSequenceClassification from alvp +author: John Snow Labs +name: autonlp_alberti_stanza_names_34318169 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`autonlp_alberti_stanza_names_34318169` is a English model originally trained by alvp. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/autonlp_alberti_stanza_names_34318169_en_5.1.4_3.4_1698284856096.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/autonlp_alberti_stanza_names_34318169_en_5.1.4_3.4_1698284856096.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("autonlp_alberti_stanza_names_34318169","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("autonlp_alberti_stanza_names_34318169","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|autonlp_alberti_stanza_names_34318169| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|666.7 MB| + +## References + +https://huggingface.co/alvp/autonlp-alberti-stanza-names-34318169 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_base_emotion_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_base_emotion_en.md new file mode 100644 index 00000000000000..6cf358f69ff907 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_base_emotion_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_emotion BertForSequenceClassification from Anonymous1111 +author: John Snow Labs +name: bert_base_emotion +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_emotion` is a English model originally trained by Anonymous1111. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_emotion_en_5.1.4_3.4_1698294005799.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_emotion_en_5.1.4_3.4_1698294005799.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_emotion","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_emotion","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_emotion| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Anonymous1111/bert-base-emotion \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_base_mnli_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_base_mnli_en.md new file mode 100644 index 00000000000000..b4ca0925e215cf --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_base_mnli_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_mnli BertForSequenceClassification from aloxatel +author: John Snow Labs +name: bert_base_mnli +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_mnli` is a English model originally trained by aloxatel. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_mnli_en_5.1.4_3.4_1698283550584.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_mnli_en_5.1.4_3.4_1698283550584.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_mnli","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_mnli","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_mnli| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/aloxatel/bert-base-mnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_base_nli_mean_tokens_finetuned_polifact_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_base_nli_mean_tokens_finetuned_polifact_en.md new file mode 100644 index 00000000000000..1236e442092002 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_base_nli_mean_tokens_finetuned_polifact_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_nli_mean_tokens_finetuned_polifact BertForSequenceClassification from anuj55 +author: John Snow Labs +name: bert_base_nli_mean_tokens_finetuned_polifact +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_nli_mean_tokens_finetuned_polifact` is a English model originally trained by anuj55. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_nli_mean_tokens_finetuned_polifact_en_5.1.4_3.4_1698279222275.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_nli_mean_tokens_finetuned_polifact_en_5.1.4_3.4_1698279222275.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_nli_mean_tokens_finetuned_polifact","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_nli_mean_tokens_finetuned_polifact","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_nli_mean_tokens_finetuned_polifact| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/anuj55/bert-base-nli-mean-tokens-finetuned-polifact \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_fine_tune_winogrande_ep_8_original_2e_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_fine_tune_winogrande_ep_8_original_2e_en.md new file mode 100644 index 00000000000000..0c4ac9749d6c77 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_fine_tune_winogrande_ep_8_original_2e_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_fine_tune_winogrande_ep_8_original_2e BertForSequenceClassification from Stupendousabhi +author: John Snow Labs +name: bert_base_uncased_fine_tune_winogrande_ep_8_original_2e +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_fine_tune_winogrande_ep_8_original_2e` is a English model originally trained by Stupendousabhi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_fine_tune_winogrande_ep_8_original_2e_en_5.1.4_3.4_1698291041785.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_fine_tune_winogrande_ep_8_original_2e_en_5.1.4_3.4_1698291041785.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_fine_tune_winogrande_ep_8_original_2e","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_fine_tune_winogrande_ep_8_original_2e","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_fine_tune_winogrande_ep_8_original_2e| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Stupendousabhi/bert-base-uncased-fine-tune-winogrande-ep-8_original-2e \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_addresso_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_addresso_en.md new file mode 100644 index 00000000000000..18531b679e74b6 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_addresso_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_addresso BertForSequenceClassification from annafavaro +author: John Snow Labs +name: bert_base_uncased_finetuned_addresso +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_addresso` is a English model originally trained by annafavaro. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_addresso_en_5.1.4_3.4_1698291251858.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_addresso_en_5.1.4_3.4_1698291251858.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_addresso","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_addresso","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_addresso| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/annafavaro/bert-base-uncased-finetuned-addresso \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_coda19_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_coda19_en.md new file mode 100644 index 00000000000000..ecc5a08e47e861 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_coda19_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_coda19 BertForSequenceClassification from appleternity +author: John Snow Labs +name: bert_base_uncased_finetuned_coda19 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_coda19` is a English model originally trained by appleternity. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_coda19_en_5.1.4_3.4_1698292061198.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_coda19_en_5.1.4_3.4_1698292061198.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_coda19","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_coda19","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_coda19| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/appleternity/bert-base-uncased-finetuned-coda19 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_cola_anirudh21_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_cola_anirudh21_en.md new file mode 100644 index 00000000000000..0b53da7de7c37d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_cola_anirudh21_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_cola_anirudh21 BertForSequenceClassification from anirudh21 +author: John Snow Labs +name: bert_base_uncased_finetuned_cola_anirudh21 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_cola_anirudh21` is a English model originally trained by anirudh21. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_cola_anirudh21_en_5.1.4_3.4_1698287133978.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_cola_anirudh21_en_5.1.4_3.4_1698287133978.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_cola_anirudh21","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_cola_anirudh21","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_cola_anirudh21| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/anirudh21/bert-base-uncased-finetuned-cola \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_mrpc_ajrae_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_mrpc_ajrae_en.md new file mode 100644 index 00000000000000..4cd85698bfb711 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_mrpc_ajrae_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_mrpc_ajrae BertForSequenceClassification from ajrae +author: John Snow Labs +name: bert_base_uncased_finetuned_mrpc_ajrae +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_mrpc_ajrae` is a English model originally trained by ajrae. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_mrpc_ajrae_en_5.1.4_3.4_1698279222304.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_mrpc_ajrae_en_5.1.4_3.4_1698279222304.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_mrpc_ajrae","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_mrpc_ajrae","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_mrpc_ajrae| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/ajrae/bert-base-uncased-finetuned-mrpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_mrpc_anirudh21_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_mrpc_anirudh21_en.md new file mode 100644 index 00000000000000..c0489e63661ca9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_mrpc_anirudh21_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_mrpc_anirudh21 BertForSequenceClassification from anirudh21 +author: John Snow Labs +name: bert_base_uncased_finetuned_mrpc_anirudh21 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_mrpc_anirudh21` is a English model originally trained by anirudh21. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_mrpc_anirudh21_en_5.1.4_3.4_1698288102806.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_mrpc_anirudh21_en_5.1.4_3.4_1698288102806.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_mrpc_anirudh21","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_mrpc_anirudh21","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_mrpc_anirudh21| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/anirudh21/bert-base-uncased-finetuned-mrpc \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_qnli_anirudh21_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_qnli_anirudh21_en.md new file mode 100644 index 00000000000000..58aeab0b022353 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_qnli_anirudh21_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_qnli_anirudh21 BertForSequenceClassification from anirudh21 +author: John Snow Labs +name: bert_base_uncased_finetuned_qnli_anirudh21 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_qnli_anirudh21` is a English model originally trained by anirudh21. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_qnli_anirudh21_en_5.1.4_3.4_1698288910566.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_qnli_anirudh21_en_5.1.4_3.4_1698288910566.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_qnli_anirudh21","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_qnli_anirudh21","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_qnli_anirudh21| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/anirudh21/bert-base-uncased-finetuned-qnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_rte_anirudh21_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_rte_anirudh21_en.md new file mode 100644 index 00000000000000..897e06481cb5fd --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_rte_anirudh21_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_rte_anirudh21 BertForSequenceClassification from anirudh21 +author: John Snow Labs +name: bert_base_uncased_finetuned_rte_anirudh21 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_rte_anirudh21` is a English model originally trained by anirudh21. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_rte_anirudh21_en_5.1.4_3.4_1698289621082.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_rte_anirudh21_en_5.1.4_3.4_1698289621082.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_rte_anirudh21","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_rte_anirudh21","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_rte_anirudh21| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/anirudh21/bert-base-uncased-finetuned-rte \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_wnli_anirudh21_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_wnli_anirudh21_en.md new file mode 100644 index 00000000000000..d4309b42d6fdff --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_finetuned_wnli_anirudh21_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_finetuned_wnli_anirudh21 BertForSequenceClassification from anirudh21 +author: John Snow Labs +name: bert_base_uncased_finetuned_wnli_anirudh21 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_finetuned_wnli_anirudh21` is a English model originally trained by anirudh21. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wnli_anirudh21_en_5.1.4_3.4_1698290332780.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_finetuned_wnli_anirudh21_en_5.1.4_3.4_1698290332780.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_wnli_anirudh21","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_finetuned_wnli_anirudh21","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_finetuned_wnli_anirudh21| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/anirudh21/bert-base-uncased-finetuned-wnli \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_itr0_0_0001_all_01_03_2022_14_08_15_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_itr0_0_0001_all_01_03_2022_14_08_15_en.md new file mode 100644 index 00000000000000..bdc208ff24728d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_itr0_0_0001_all_01_03_2022_14_08_15_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_itr0_0_0001_all_01_03_2022_14_08_15 BertForSequenceClassification from ali2066 +author: John Snow Labs +name: bert_base_uncased_itr0_0_0001_all_01_03_2022_14_08_15 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_itr0_0_0001_all_01_03_2022_14_08_15` is a English model originally trained by ali2066. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_itr0_0_0001_all_01_03_2022_14_08_15_en_5.1.4_3.4_1698281981852.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_itr0_0_0001_all_01_03_2022_14_08_15_en_5.1.4_3.4_1698281981852.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_itr0_0_0001_all_01_03_2022_14_08_15","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_itr0_0_0001_all_01_03_2022_14_08_15","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_itr0_0_0001_all_01_03_2022_14_08_15| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/ali2066/bert_base_uncased_itr0_0.0001_all_01_03_2022-14_08_15 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_itr0_0_0001_webdiscourse_01_03_2022_16_08_12_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_itr0_0_0001_webdiscourse_01_03_2022_16_08_12_en.md new file mode 100644 index 00000000000000..06987010647a48 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_base_uncased_itr0_0_0001_webdiscourse_01_03_2022_16_08_12_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_base_uncased_itr0_0_0001_webdiscourse_01_03_2022_16_08_12 BertForSequenceClassification from ali2066 +author: John Snow Labs +name: bert_base_uncased_itr0_0_0001_webdiscourse_01_03_2022_16_08_12 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_uncased_itr0_0_0001_webdiscourse_01_03_2022_16_08_12` is a English model originally trained by ali2066. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_itr0_0_0001_webdiscourse_01_03_2022_16_08_12_en_5.1.4_3.4_1698282705485.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_itr0_0_0001_webdiscourse_01_03_2022_16_08_12_en_5.1.4_3.4_1698282705485.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_itr0_0_0001_webdiscourse_01_03_2022_16_08_12","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_uncased_itr0_0_0001_webdiscourse_01_03_2022_16_08_12","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_base_uncased_itr0_0_0001_webdiscourse_01_03_2022_16_08_12| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/ali2066/bert_base_uncased_itr0_0.0001_webDiscourse_01_03_2022-16_08_12 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_classifier_2e16_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_classifier_2e16_en.md new file mode 100644 index 00000000000000..927b5475aaeaca --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_classifier_2e16_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_classifier_2e16 BertForSequenceClassification from arthurbittencourt +author: John Snow Labs +name: bert_classifier_2e16 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_classifier_2e16` is a English model originally trained by arthurbittencourt. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_2e16_en_5.1.4_3.4_1698283785614.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_2e16_en_5.1.4_3.4_1698283785614.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_classifier_2e16","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_classifier_2e16","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_2e16| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/arthurbittencourt/bert_classifier_2e16 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_classifier_base_cased_chuvash_studio_name_medium_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_classifier_base_cased_chuvash_studio_name_medium_en.md new file mode 100644 index 00000000000000..37212fed67eaa4 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_classifier_base_cased_chuvash_studio_name_medium_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_classifier_base_cased_chuvash_studio_name_medium BertForSequenceClassification from jhonparra18 +author: John Snow Labs +name: bert_classifier_base_cased_chuvash_studio_name_medium +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_classifier_base_cased_chuvash_studio_name_medium` is a English model originally trained by jhonparra18. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_classifier_base_cased_chuvash_studio_name_medium_en_5.1.4_3.4_1698289061093.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_base_cased_chuvash_studio_name_medium_en_5.1.4_3.4_1698289061093.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_classifier_base_cased_chuvash_studio_name_medium","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_classifier_base_cased_chuvash_studio_name_medium","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_classifier_base_cased_chuvash_studio_name_medium| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/jhonparra18/bert-base-cased-cv-studio_name-medium \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_eatable_classification_english_russian_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_eatable_classification_english_russian_en.md new file mode 100644 index 00000000000000..53033e8fceb4b1 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_eatable_classification_english_russian_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_eatable_classification_english_russian BertForSequenceClassification from alexander-karpov +author: John Snow Labs +name: bert_eatable_classification_english_russian +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_eatable_classification_english_russian` is a English model originally trained by alexander-karpov. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_eatable_classification_english_russian_en_5.1.4_3.4_1698281067230.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_eatable_classification_english_russian_en_5.1.4_3.4_1698281067230.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_eatable_classification_english_russian","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_eatable_classification_english_russian","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_eatable_classification_english_russian| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|482.5 MB| + +## References + +https://huggingface.co/alexander-karpov/bert-eatable-classification-en-ru \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_finetuned_char_classification_e81_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_finetuned_char_classification_e81_en.md new file mode 100644 index 00000000000000..2774fad129c405 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_finetuned_char_classification_e81_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetuned_char_classification_e81 BertForSequenceClassification from bhagasra-saurav +author: John Snow Labs +name: bert_finetuned_char_classification_e81 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuned_char_classification_e81` is a English model originally trained by bhagasra-saurav. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuned_char_classification_e81_en_5.1.4_3.4_1698288809765.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuned_char_classification_e81_en_5.1.4_3.4_1698288809765.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_char_classification_e81","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_char_classification_e81","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuned_char_classification_e81| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/bhagasra-saurav/bert-finetuned-char-classification-e81 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_finetuned_char_classification_e8_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_finetuned_char_classification_e8_en.md new file mode 100644 index 00000000000000..8240fc7947993e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_finetuned_char_classification_e8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_finetuned_char_classification_e8 BertForSequenceClassification from bhagasra-saurav +author: John Snow Labs +name: bert_finetuned_char_classification_e8 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuned_char_classification_e8` is a English model originally trained by bhagasra-saurav. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_finetuned_char_classification_e8_en_5.1.4_3.4_1698287074215.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuned_char_classification_e8_en_5.1.4_3.4_1698287074215.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_char_classification_e8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_finetuned_char_classification_e8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_finetuned_char_classification_e8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/bhagasra-saurav/bert-finetuned-char-classification-e8 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_large_uncased_fine_tune_winogrande_ep_8_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_large_uncased_fine_tune_winogrande_ep_8_en.md new file mode 100644 index 00000000000000..bd81daffaf725e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_large_uncased_fine_tune_winogrande_ep_8_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_fine_tune_winogrande_ep_8 BertForSequenceClassification from Stupendousabhi +author: John Snow Labs +name: bert_large_uncased_fine_tune_winogrande_ep_8 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_fine_tune_winogrande_ep_8` is a English model originally trained by Stupendousabhi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_fine_tune_winogrande_ep_8_en_5.1.4_3.4_1698286668181.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_fine_tune_winogrande_ep_8_en_5.1.4_3.4_1698286668181.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_fine_tune_winogrande_ep_8","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_fine_tune_winogrande_ep_8","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_fine_tune_winogrande_ep_8| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/Stupendousabhi/bert-large-uncased-fine-tune-winogrande-ep-8 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_large_uncased_fine_tune_winogrande_ep_8_original_2e_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_large_uncased_fine_tune_winogrande_ep_8_original_2e_en.md new file mode 100644 index 00000000000000..91682889d17276 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_large_uncased_fine_tune_winogrande_ep_8_original_2e_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_fine_tune_winogrande_ep_8_original_2e BertForSequenceClassification from Stupendousabhi +author: John Snow Labs +name: bert_large_uncased_fine_tune_winogrande_ep_8_original_2e +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_fine_tune_winogrande_ep_8_original_2e` is a English model originally trained by Stupendousabhi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_fine_tune_winogrande_ep_8_original_2e_en_5.1.4_3.4_1698290167891.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_fine_tune_winogrande_ep_8_original_2e_en_5.1.4_3.4_1698290167891.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_fine_tune_winogrande_ep_8_original_2e","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_fine_tune_winogrande_ep_8_original_2e","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_fine_tune_winogrande_ep_8_original_2e| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/Stupendousabhi/bert-large-uncased-fine-tune-winogrande-ep-8_original-2e \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_large_uncased_finetuned_winogrande_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_large_uncased_finetuned_winogrande_en.md new file mode 100644 index 00000000000000..b1ab6740671e16 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_large_uncased_finetuned_winogrande_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_finetuned_winogrande BertForSequenceClassification from Sumaia +author: John Snow Labs +name: bert_large_uncased_finetuned_winogrande +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_finetuned_winogrande` is a English model originally trained by Sumaia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_finetuned_winogrande_en_5.1.4_3.4_1698287902297.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_finetuned_winogrande_en_5.1.4_3.4_1698287902297.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_finetuned_winogrande","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_finetuned_winogrande","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_finetuned_winogrande| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/Sumaia/bert-large-uncased-finetuned-winogrande \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_large_uncased_hoax_classifier_fulltext_v1_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_large_uncased_hoax_classifier_fulltext_v1_en.md new file mode 100644 index 00000000000000..636d7838f2a225 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_large_uncased_hoax_classifier_fulltext_v1_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_large_uncased_hoax_classifier_fulltext_v1 BertForSequenceClassification from research-dump +author: John Snow Labs +name: bert_large_uncased_hoax_classifier_fulltext_v1 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_large_uncased_hoax_classifier_fulltext_v1` is a English model originally trained by research-dump. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_large_uncased_hoax_classifier_fulltext_v1_en_5.1.4_3.4_1698282011063.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_large_uncased_hoax_classifier_fulltext_v1_en_5.1.4_3.4_1698282011063.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_hoax_classifier_fulltext_v1","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_large_uncased_hoax_classifier_fulltext_v1","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_large_uncased_hoax_classifier_fulltext_v1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/research-dump/bert-large-uncased_hoax_classifier_fulltext_v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_multilingual_passage_reranking_msmarco_amberoad_xx.md b/docs/_posts/ahmedlone127/2023-10-26-bert_multilingual_passage_reranking_msmarco_amberoad_xx.md new file mode 100644 index 00000000000000..d0299054064397 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_multilingual_passage_reranking_msmarco_amberoad_xx.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Multilingual bert_multilingual_passage_reranking_msmarco_amberoad BertForSequenceClassification from amberoad +author: John Snow Labs +name: bert_multilingual_passage_reranking_msmarco_amberoad +date: 2023-10-26 +tags: [bert, xx, open_source, sequence_classification, onnx] +task: Text Classification +language: xx +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_multilingual_passage_reranking_msmarco_amberoad` is a Multilingual model originally trained by amberoad. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_multilingual_passage_reranking_msmarco_amberoad_xx_5.1.4_3.4_1698285856977.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_multilingual_passage_reranking_msmarco_amberoad_xx_5.1.4_3.4_1698285856977.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_multilingual_passage_reranking_msmarco_amberoad","xx")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_multilingual_passage_reranking_msmarco_amberoad","xx") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_multilingual_passage_reranking_msmarco_amberoad| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|xx| +|Size:|627.7 MB| + +## References + +https://huggingface.co/amberoad/bert-multilingual-passage-reranking-msmarco \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_turkish_text_classification_tr.md b/docs/_posts/ahmedlone127/2023-10-26-bert_turkish_text_classification_tr.md new file mode 100644 index 00000000000000..1e5ef266b3d323 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_turkish_text_classification_tr.md @@ -0,0 +1,97 @@ +--- +layout: model +title: Turkish bert_turkish_text_classification BertForSequenceClassification from akdeniz27 +author: John Snow Labs +name: bert_turkish_text_classification +date: 2023-10-26 +tags: [bert, tr, open_source, sequence_classification, onnx] +task: Text Classification +language: tr +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_turkish_text_classification` is a Turkish model originally trained by akdeniz27. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_turkish_text_classification_tr_5.1.4_3.4_1698280125422.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_turkish_text_classification_tr_5.1.4_3.4_1698280125422.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_turkish_text_classification","tr")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_turkish_text_classification","tr") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_turkish_text_classification| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|tr| +|Size:|414.5 MB| + +## References + +https://huggingface.co/akdeniz27/bert-turkish-text-classification \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_vast_binary_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_vast_binary_en.md new file mode 100644 index 00000000000000..818ab5ca89eed0 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_vast_binary_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_vast_binary BertForSequenceClassification from Babak-Behkamkia +author: John Snow Labs +name: bert_vast_binary +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_vast_binary` is a English model originally trained by Babak-Behkamkia. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_vast_binary_en_5.1.4_3.4_1698287964135.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_vast_binary_en_5.1.4_3.4_1698287964135.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_vast_binary","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_vast_binary","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_vast_binary| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/Babak-Behkamkia/bert_VAST_binary \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-bert_workforce_en.md b/docs/_posts/ahmedlone127/2023-10-26-bert_workforce_en.md new file mode 100644 index 00000000000000..371670d317776c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-bert_workforce_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English bert_workforce BertForSequenceClassification from cduesing +author: John Snow Labs +name: bert_workforce +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_workforce` is a English model originally trained by cduesing. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_workforce_en_5.1.4_3.4_1698291042646.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_workforce_en_5.1.4_3.4_1698291042646.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("bert_workforce","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("bert_workforce","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|bert_workforce| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.6 MB| + +## References + +https://huggingface.co/cduesing/bert_workforce \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-dk_emotion_bert_in_class_ejaalborg2022_en.md b/docs/_posts/ahmedlone127/2023-10-26-dk_emotion_bert_in_class_ejaalborg2022_en.md new file mode 100644 index 00000000000000..47f669b144b055 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-dk_emotion_bert_in_class_ejaalborg2022_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English dk_emotion_bert_in_class_ejaalborg2022 BertForSequenceClassification from EJaalborg2022 +author: John Snow Labs +name: dk_emotion_bert_in_class_ejaalborg2022 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dk_emotion_bert_in_class_ejaalborg2022` is a English model originally trained by EJaalborg2022. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/dk_emotion_bert_in_class_ejaalborg2022_en_5.1.4_3.4_1698281672475.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dk_emotion_bert_in_class_ejaalborg2022_en_5.1.4_3.4_1698281672475.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("dk_emotion_bert_in_class_ejaalborg2022","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("dk_emotion_bert_in_class_ejaalborg2022","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|dk_emotion_bert_in_class_ejaalborg2022| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|414.6 MB| + +## References + +https://huggingface.co/EJaalborg2022/dk_emotion_bert_in_class \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-filbert_en.md b/docs/_posts/ahmedlone127/2023-10-26-filbert_en.md new file mode 100644 index 00000000000000..598a9d571a5101 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-filbert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English filbert BertForSequenceClassification from rajpurkarlab +author: John Snow Labs +name: filbert +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`filbert` is a English model originally trained by rajpurkarlab. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/filbert_en_5.1.4_3.4_1698292222031.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/filbert_en_5.1.4_3.4_1698292222031.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("filbert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("filbert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|filbert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|405.9 MB| + +## References + +https://huggingface.co/rajpurkarlab/filbert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-finbert_pb_en.md b/docs/_posts/ahmedlone127/2023-10-26-finbert_pb_en.md new file mode 100644 index 00000000000000..a1b34e78de3006 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-finbert_pb_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English finbert_pb BertForSequenceClassification from Forturne +author: John Snow Labs +name: finbert_pb +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finbert_pb` is a English model originally trained by Forturne. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finbert_pb_en_5.1.4_3.4_1698279849515.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finbert_pb_en_5.1.4_3.4_1698279849515.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("finbert_pb","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("finbert_pb","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finbert_pb| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|411.6 MB| + +## References + +https://huggingface.co/Forturne/Finbert_PB \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-finbert_slow_en.md b/docs/_posts/ahmedlone127/2023-10-26-finbert_slow_en.md new file mode 100644 index 00000000000000..9aaa19e0aaeeac --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-finbert_slow_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English finbert_slow BertForSequenceClassification from Narsil +author: John Snow Labs +name: finbert_slow +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`finbert_slow` is a English model originally trained by Narsil. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/finbert_slow_en_5.1.4_3.4_1698288809467.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/finbert_slow_en_5.1.4_3.4_1698288809467.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("finbert_slow","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("finbert_slow","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|finbert_slow| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/Narsil/finbert-slow \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-fine_tune_mbert_combined_en.md b/docs/_posts/ahmedlone127/2023-10-26-fine_tune_mbert_combined_en.md new file mode 100644 index 00000000000000..09a52483c2ab2e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-fine_tune_mbert_combined_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English fine_tune_mbert_combined BertForSequenceClassification from nouman-10 +author: John Snow Labs +name: fine_tune_mbert_combined +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`fine_tune_mbert_combined` is a English model originally trained by nouman-10. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/fine_tune_mbert_combined_en_5.1.4_3.4_1698280869859.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/fine_tune_mbert_combined_en_5.1.4_3.4_1698280869859.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("fine_tune_mbert_combined","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("fine_tune_mbert_combined","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|fine_tune_mbert_combined| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|627.7 MB| + +## References + +https://huggingface.co/nouman-10/fine-tune-mbert-combined \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-kd_roberta_1lbert_lambda50_en.md b/docs/_posts/ahmedlone127/2023-10-26-kd_roberta_1lbert_lambda50_en.md new file mode 100644 index 00000000000000..53bd02f4cffb64 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-kd_roberta_1lbert_lambda50_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English kd_roberta_1lbert_lambda50 BertForSequenceClassification from Youssef320 +author: John Snow Labs +name: kd_roberta_1lbert_lambda50 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`kd_roberta_1lbert_lambda50` is a English model originally trained by Youssef320. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/kd_roberta_1lbert_lambda50_en_5.1.4_3.4_1698289193903.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/kd_roberta_1lbert_lambda50_en_5.1.4_3.4_1698289193903.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("kd_roberta_1lbert_lambda50","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("kd_roberta_1lbert_lambda50","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|kd_roberta_1lbert_lambda50| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|43.9 MB| + +## References + +https://huggingface.co/Youssef320/KD_Roberta_1LBERT_lambda50 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v1_en.md b/docs/_posts/ahmedlone127/2023-10-26-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v1_en.md new file mode 100644 index 00000000000000..c45fd4b7eddf16 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v1_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v1 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v1 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v1` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v1_en_5.1.4_3.4_1698291794941.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v1_en_5.1.4_3.4_1698291794941.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v1","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v1","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v1| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.1 MB| + +## References + +https://huggingface.co/hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSEC-v1 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v2_en.md b/docs/_posts/ahmedlone127/2023-10-26-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v2_en.md new file mode 100644 index 00000000000000..d900164f5da98c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v2 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v2 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v2` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v2_en_5.1.4_3.4_1698292580801.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v2_en_5.1.4_3.4_1698292580801.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.1 MB| + +## References + +https://huggingface.co/hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSEC-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v3_en.md b/docs/_posts/ahmedlone127/2023-10-26-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v3_en.md new file mode 100644 index 00000000000000..9ee9f01a503d7d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v3_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v3 BertForSequenceClassification from hw2942 +author: John Snow Labs +name: mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v3 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v3` is a English model originally trained by hw2942. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v3_en_5.1.4_3.4_1698293450177.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v3_en_5.1.4_3.4_1698293450177.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v3","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v3","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|mengzi_bert_base_fin_wallstreetcn_morning_news_market_overview_ssec_v3| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|383.1 MB| + +## References + +https://huggingface.co/hw2942/mengzi-bert-base-fin-wallstreetcn-morning-news-market-overview-SSEC-v3 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-model3_marabertv2_t1_wos_en.md b/docs/_posts/ahmedlone127/2023-10-26-model3_marabertv2_t1_wos_en.md new file mode 100644 index 00000000000000..3977ad58353fa9 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-model3_marabertv2_t1_wos_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English model3_marabertv2_t1_wos BertForSequenceClassification from Somah +author: John Snow Labs +name: model3_marabertv2_t1_wos +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`model3_marabertv2_t1_wos` is a English model originally trained by Somah. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/model3_marabertv2_t1_wos_en_5.1.4_3.4_1698278429002.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/model3_marabertv2_t1_wos_en_5.1.4_3.4_1698278429002.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("model3_marabertv2_t1_wos","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("model3_marabertv2_t1_wos","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|model3_marabertv2_t1_wos| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|608.8 MB| + +## References + +https://huggingface.co/Somah/Model3_Marabertv2_T1_WOS \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-model3_marabertv2_t1_ws_a100_en.md b/docs/_posts/ahmedlone127/2023-10-26-model3_marabertv2_t1_ws_a100_en.md new file mode 100644 index 00000000000000..cf7f086e5bb16b --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-model3_marabertv2_t1_ws_a100_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English model3_marabertv2_t1_ws_a100 BertForSequenceClassification from Somah +author: John Snow Labs +name: model3_marabertv2_t1_ws_a100 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`model3_marabertv2_t1_ws_a100` is a English model originally trained by Somah. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/model3_marabertv2_t1_ws_a100_en_5.1.4_3.4_1698283946931.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/model3_marabertv2_t1_ws_a100_en_5.1.4_3.4_1698283946931.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("model3_marabertv2_t1_ws_a100","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("model3_marabertv2_t1_ws_a100","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|model3_marabertv2_t1_ws_a100| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|608.8 MB| + +## References + +https://huggingface.co/Somah/Model3_Marabertv2_T1_WS_A100 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-model3_marabertv2_t2_wos_en.md b/docs/_posts/ahmedlone127/2023-10-26-model3_marabertv2_t2_wos_en.md new file mode 100644 index 00000000000000..88efa9d9128436 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-model3_marabertv2_t2_wos_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English model3_marabertv2_t2_wos BertForSequenceClassification from Somah +author: John Snow Labs +name: model3_marabertv2_t2_wos +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`model3_marabertv2_t2_wos` is a English model originally trained by Somah. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/model3_marabertv2_t2_wos_en_5.1.4_3.4_1698279331644.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/model3_marabertv2_t2_wos_en_5.1.4_3.4_1698279331644.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("model3_marabertv2_t2_wos","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("model3_marabertv2_t2_wos","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|model3_marabertv2_t2_wos| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|608.8 MB| + +## References + +https://huggingface.co/Somah/Model3_Marabertv2_T2_WOS \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-model3_marabertv2_t2_ws_a100_en.md b/docs/_posts/ahmedlone127/2023-10-26-model3_marabertv2_t2_ws_a100_en.md new file mode 100644 index 00000000000000..44cc3e1265abd7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-model3_marabertv2_t2_ws_a100_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English model3_marabertv2_t2_ws_a100 BertForSequenceClassification from Somah +author: John Snow Labs +name: model3_marabertv2_t2_ws_a100 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`model3_marabertv2_t2_ws_a100` is a English model originally trained by Somah. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/model3_marabertv2_t2_ws_a100_en_5.1.4_3.4_1698286112848.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/model3_marabertv2_t2_ws_a100_en_5.1.4_3.4_1698286112848.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("model3_marabertv2_t2_ws_a100","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("model3_marabertv2_t2_ws_a100","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|model3_marabertv2_t2_ws_a100| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|608.8 MB| + +## References + +https://huggingface.co/Somah/Model3_Marabertv2_T2_WS_A100 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-model4_arabertv2_base_t1_ws_a100_en.md b/docs/_posts/ahmedlone127/2023-10-26-model4_arabertv2_base_t1_ws_a100_en.md new file mode 100644 index 00000000000000..171fb5f7cf6719 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-model4_arabertv2_base_t1_ws_a100_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English model4_arabertv2_base_t1_ws_a100 BertForSequenceClassification from Somah +author: John Snow Labs +name: model4_arabertv2_base_t1_ws_a100 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`model4_arabertv2_base_t1_ws_a100` is a English model originally trained by Somah. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/model4_arabertv2_base_t1_ws_a100_en_5.1.4_3.4_1698282957194.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/model4_arabertv2_base_t1_ws_a100_en_5.1.4_3.4_1698282957194.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("model4_arabertv2_base_t1_ws_a100","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("model4_arabertv2_base_t1_ws_a100","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|model4_arabertv2_base_t1_ws_a100| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|507.3 MB| + +## References + +https://huggingface.co/Somah/Model4_arabertv2_base_T1_WS_A100 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-model4_arabertv2_base_t2_ws_a100_en.md b/docs/_posts/ahmedlone127/2023-10-26-model4_arabertv2_base_t2_ws_a100_en.md new file mode 100644 index 00000000000000..8cd7f7a68df1a7 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-model4_arabertv2_base_t2_ws_a100_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English model4_arabertv2_base_t2_ws_a100 BertForSequenceClassification from Somah +author: John Snow Labs +name: model4_arabertv2_base_t2_ws_a100 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`model4_arabertv2_base_t2_ws_a100` is a English model originally trained by Somah. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/model4_arabertv2_base_t2_ws_a100_en_5.1.4_3.4_1698285029764.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/model4_arabertv2_base_t2_ws_a100_en_5.1.4_3.4_1698285029764.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("model4_arabertv2_base_t2_ws_a100","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("model4_arabertv2_base_t2_ws_a100","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|model4_arabertv2_base_t2_ws_a100| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|507.3 MB| + +## References + +https://huggingface.co/Somah/Model4_arabertv2_base_T2_WS_A100 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-model5_arabertv2_large_t1_wos_en.md b/docs/_posts/ahmedlone127/2023-10-26-model5_arabertv2_large_t1_wos_en.md new file mode 100644 index 00000000000000..1b0a79f3369167 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-model5_arabertv2_large_t1_wos_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English model5_arabertv2_large_t1_wos BertForSequenceClassification from Somah +author: John Snow Labs +name: model5_arabertv2_large_t1_wos +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`model5_arabertv2_large_t1_wos` is a English model originally trained by Somah. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/model5_arabertv2_large_t1_wos_en_5.1.4_3.4_1698280773812.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/model5_arabertv2_large_t1_wos_en_5.1.4_3.4_1698280773812.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("model5_arabertv2_large_t1_wos","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("model5_arabertv2_large_t1_wos","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|model5_arabertv2_large_t1_wos| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.4 GB| + +## References + +https://huggingface.co/Somah/Model5_arabertv2_large_T1_WOS \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-scibert_uncased_finetuned_coda19_en.md b/docs/_posts/ahmedlone127/2023-10-26-scibert_uncased_finetuned_coda19_en.md new file mode 100644 index 00000000000000..cedddcc9d94321 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-scibert_uncased_finetuned_coda19_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English scibert_uncased_finetuned_coda19 BertForSequenceClassification from appleternity +author: John Snow Labs +name: scibert_uncased_finetuned_coda19 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`scibert_uncased_finetuned_coda19` is a English model originally trained by appleternity. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/scibert_uncased_finetuned_coda19_en_5.1.4_3.4_1698292747572.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/scibert_uncased_finetuned_coda19_en_5.1.4_3.4_1698292747572.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("scibert_uncased_finetuned_coda19","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("scibert_uncased_finetuned_coda19","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|scibert_uncased_finetuned_coda19| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|412.2 MB| + +## References + +https://huggingface.co/appleternity/scibert-uncased-finetuned-coda19 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-thext_ai_scibert_en.md b/docs/_posts/ahmedlone127/2023-10-26-thext_ai_scibert_en.md new file mode 100644 index 00000000000000..9bff0b0c5bd90f --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-thext_ai_scibert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English thext_ai_scibert BertForSequenceClassification from morenolq +author: John Snow Labs +name: thext_ai_scibert +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`thext_ai_scibert` is a English model originally trained by morenolq. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/thext_ai_scibert_en_5.1.4_3.4_1698280667934.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/thext_ai_scibert_en_5.1.4_3.4_1698280667934.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("thext_ai_scibert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("thext_ai_scibert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|thext_ai_scibert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|412.1 MB| + +## References + +https://huggingface.co/morenolq/thext-ai-scibert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-thext_bio_scibert_en.md b/docs/_posts/ahmedlone127/2023-10-26-thext_bio_scibert_en.md new file mode 100644 index 00000000000000..0739ffc6a2f76d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-thext_bio_scibert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English thext_bio_scibert BertForSequenceClassification from morenolq +author: John Snow Labs +name: thext_bio_scibert +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`thext_bio_scibert` is a English model originally trained by morenolq. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/thext_bio_scibert_en_5.1.4_3.4_1698281670639.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/thext_bio_scibert_en_5.1.4_3.4_1698281670639.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("thext_bio_scibert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("thext_bio_scibert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|thext_bio_scibert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|412.1 MB| + +## References + +https://huggingface.co/morenolq/thext-bio-scibert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-thext_czech_scibert_en.md b/docs/_posts/ahmedlone127/2023-10-26-thext_czech_scibert_en.md new file mode 100644 index 00000000000000..3b07c997774d9e --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-thext_czech_scibert_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English thext_czech_scibert BertForSequenceClassification from morenolq +author: John Snow Labs +name: thext_czech_scibert +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`thext_czech_scibert` is a English model originally trained by morenolq. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/thext_czech_scibert_en_5.1.4_3.4_1698282612468.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/thext_czech_scibert_en_5.1.4_3.4_1698282612468.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("thext_czech_scibert","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("thext_czech_scibert","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|thext_czech_scibert| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|412.1 MB| + +## References + +https://huggingface.co/morenolq/thext-cs-scibert \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-tiny_bert_cola_128_distilled_en.md b/docs/_posts/ahmedlone127/2023-10-26-tiny_bert_cola_128_distilled_en.md new file mode 100644 index 00000000000000..e90a97e47bd212 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-tiny_bert_cola_128_distilled_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_cola_128_distilled BertForSequenceClassification from Sayan01 +author: John Snow Labs +name: tiny_bert_cola_128_distilled +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_cola_128_distilled` is a English model originally trained by Sayan01. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_cola_128_distilled_en_5.1.4_3.4_1698289915459.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_cola_128_distilled_en_5.1.4_3.4_1698289915459.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_cola_128_distilled","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_cola_128_distilled","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_cola_128_distilled| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/Sayan01/tiny-bert-cola-128-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-tiny_bert_qnli128_distilled_en.md b/docs/_posts/ahmedlone127/2023-10-26-tiny_bert_qnli128_distilled_en.md new file mode 100644 index 00000000000000..6cbdae4e630130 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-tiny_bert_qnli128_distilled_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_qnli128_distilled BertForSequenceClassification from Sayan01 +author: John Snow Labs +name: tiny_bert_qnli128_distilled +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_qnli128_distilled` is a English model originally trained by Sayan01. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_qnli128_distilled_en_5.1.4_3.4_1698290311397.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_qnli128_distilled_en_5.1.4_3.4_1698290311397.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_qnli128_distilled","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_qnli128_distilled","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_qnli128_distilled| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/Sayan01/tiny-bert-qnli128-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-tiny_bert_qnli_128_distilled_en.md b/docs/_posts/ahmedlone127/2023-10-26-tiny_bert_qnli_128_distilled_en.md new file mode 100644 index 00000000000000..c5ab84cec9e0d3 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-tiny_bert_qnli_128_distilled_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_qnli_128_distilled BertForSequenceClassification from Sayan01 +author: John Snow Labs +name: tiny_bert_qnli_128_distilled +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_qnli_128_distilled` is a English model originally trained by Sayan01. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_qnli_128_distilled_en_5.1.4_3.4_1698289506883.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_qnli_128_distilled_en_5.1.4_3.4_1698289506883.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_qnli_128_distilled","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_qnli_128_distilled","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_qnli_128_distilled| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/Sayan01/tiny-bert-qnli-128-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-tiny_bert_sst2_distilled_linxi_en.md b/docs/_posts/ahmedlone127/2023-10-26-tiny_bert_sst2_distilled_linxi_en.md new file mode 100644 index 00000000000000..387c6c377b3f79 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-tiny_bert_sst2_distilled_linxi_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tiny_bert_sst2_distilled_linxi BertForSequenceClassification from linxi +author: John Snow Labs +name: tiny_bert_sst2_distilled_linxi +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tiny_bert_sst2_distilled_linxi` is a English model originally trained by linxi. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tiny_bert_sst2_distilled_linxi_en_5.1.4_3.4_1698279766472.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tiny_bert_sst2_distilled_linxi_en_5.1.4_3.4_1698279766472.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_sst2_distilled_linxi","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tiny_bert_sst2_distilled_linxi","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tiny_bert_sst2_distilled_linxi| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|16.7 MB| + +## References + +https://huggingface.co/linxi/tiny-bert-sst2-distilled \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-tinybertje_v2_en.md b/docs/_posts/ahmedlone127/2023-10-26-tinybertje_v2_en.md new file mode 100644 index 00000000000000..97b5099122006c --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-tinybertje_v2_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English tinybertje_v2 BertForSequenceClassification from GeniusVoice +author: John Snow Labs +name: tinybertje_v2 +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tinybertje_v2` is a English model originally trained by GeniusVoice. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/tinybertje_v2_en_5.1.4_3.4_1698291480102.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tinybertje_v2_en_5.1.4_3.4_1698291480102.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("tinybertje_v2","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("tinybertje_v2","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|tinybertje_v2| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|32.5 MB| + +## References + +https://huggingface.co/GeniusVoice/tinybertje-v2 \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-validate_bert_base_en.md b/docs/_posts/ahmedlone127/2023-10-26-validate_bert_base_en.md new file mode 100644 index 00000000000000..2f09064b031c55 --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-validate_bert_base_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English validate_bert_base BertForSequenceClassification from yashveer11 +author: John Snow Labs +name: validate_bert_base +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`validate_bert_base` is a English model originally trained by yashveer11. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/validate_bert_base_en_5.1.4_3.4_1698283057312.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/validate_bert_base_en_5.1.4_3.4_1698283057312.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("validate_bert_base","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("validate_bert_base","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|validate_bert_base| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|409.4 MB| + +## References + +https://huggingface.co/yashveer11/validate_bert_base \ No newline at end of file diff --git a/docs/_posts/ahmedlone127/2023-10-26-validate_bert_large_en.md b/docs/_posts/ahmedlone127/2023-10-26-validate_bert_large_en.md new file mode 100644 index 00000000000000..789921ec14ea3d --- /dev/null +++ b/docs/_posts/ahmedlone127/2023-10-26-validate_bert_large_en.md @@ -0,0 +1,97 @@ +--- +layout: model +title: English validate_bert_large BertForSequenceClassification from yashveer11 +author: John Snow Labs +name: validate_bert_large +date: 2023-10-26 +tags: [bert, en, open_source, sequence_classification, onnx] +task: Text Classification +language: en +edition: Spark NLP 5.1.4 +spark_version: 3.4 +supported: true +engine: onnx +annotator: BertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`validate_bert_large` is a English model originally trained by yashveer11. + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/validate_bert_large_en_5.1.4_3.4_1698285228683.zip){:.button.button-orange.button-orange-trans.arr.button-icon} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/validate_bert_large_en_5.1.4_3.4_1698285228683.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python + +document_assembler = DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document") + +tokenizer = Tokenizer()\ + .setInputCols("document")\ + .setOutputCol("token") + +sequenceClassifier = BertForSequenceClassification.pretrained("validate_bert_large","en")\ + .setInputCols(["document","token"])\ + .setOutputCol("class") + +pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier]) + +data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text") + +result = pipeline.fit(data).transform(data) + +``` +```scala + +val document_assembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + +val tokenizer = new Tokenizer() + .setInputCols("document") + .setOutputCol("token") + +val sequenceClassifier = BertForSequenceClassification.pretrained("validate_bert_large","en") + .setInputCols(Array("document","token")) + .setOutputCol("class") + +val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier)) + +val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text") + +val result = pipeline.fit(data).transform(data) + + +``` +
+ +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|validate_bert_large| +|Compatibility:|Spark NLP 5.1.4+| +|License:|Open Source| +|Edition:|Official| +|Input Labels:|[documents, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|1.3 GB| + +## References + +https://huggingface.co/yashveer11/validate_bert_large \ No newline at end of file